More stories

  • in

    Revisiting implementation of multiple natural enemies in pest management

    Model equationsOur host-parasite mathematical model involves the following host population components: ‘susceptible’ hosts denoted by (S), and hosts infected by k distinct types of parasites ((k=1,2,…,n)), the corresponding population numbers of infected hosts are denoted by (I_{i_1,i_2,…,i_k}), where each index (i_j) can take a value from 1, …, n (to avoid repeated counting of the same infection configuration, we require throughout the paper that (i_1 More

  • in

    Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen

    Data collectionStudy sitesWe selected four study sites in the Upper Gunnison Basin in Colorado, USA. As we intended for these sites to capture a large amount of variation in environmental conditions, the primary criteria we used for site selection was elevation. Mirroring patterns of mountain weather observed worldwide, elevation is correlated with a multitude of environmental factors in the Rocky Mountains. Predominant trends include a negative correlation between altitude and temperature, a positive correlation between altitude and precipitation34, and transitions in plant community composition (from ‘mountain shrub’ to ‘montane’ to ‘subalpine’ to ‘alpine’) with increasing altitude35.Our sites span a large elevation gradient of approximately 1000 m that captures significant variation in environmental conditions. These sites should not be interpreted as a direct ‘elevation for climate substitution’, especially considering the significant changes in weather at each site that occur over the course of our observation periods, which ran from June to July. The lowest site, ‘cement creek’ (CC; approximately 38.82156° N, 106.86893° W), falls within a sage brush meadow on the boundary between the mountain shrub and montane vegetation zones at 2440 m elevation. The second lowest site, ‘bus turnaround’ (BT; approximately 38.97130° N, 106.99595° W), is situated in an open subalpine meadow at the base of Gothic Mountain at ~ 2940 m. The second highest site, ‘gothic mountain’ (GM; approximately 38.97969° N, 107.01937° W), is also in the subalpine zone on a steep hillside within a clearing in an evergreen forest on the lower slopes of Gothic Mountain at 3220 m. The highest site, ‘high meadow’ (HM; approximately 38.96779° N, 107.02184° W), is on an exposed meadow at the upper fringes of the subalpine zone on the shoulder of Gothic Mountain at 3,410 m.We began our observations at each site by mapping the flax population (see “Population mapping” sub-section below). This occurred on 6/15/2020 for CC, 6/17/2020 for BT, 6/16/2020 for GM, and 6/18/2020 for HM. We concluded all observations at CC on 7/27/2020, at BT on 7/29/2020, at GM on 7/28/2020, and at HM on 7/10/2020 (aside from spore trap collection which concluded on 7/21/2020).Weather monitoringWe deployed environmental sensors to record longitudinal weather data at each of our sites. We recorded temperature, humidity, rainfall, and wind speed and direction (at ~ 1 m above ground) every five minutes (see Supplementary Text 1 for logger specifications). We used temperature and humidity data to calculate absolute humidity (Supplementary Text 2). Due to logistical constraints, rainfall and wind speed/direction loggers were deployed later than temperature and humidity loggers.We extracted weather metrics for a given observation period from this data as follows: We calculated the mean, maximum, and minimum temperature as the mean, maximum, or minimum of temperature readings. Likewise, we calculated mean absolute humidity as the mean of absolute humidity records (calculated from temperature and relative humidity every five minutes). Daily mean rainfall was calculated as of mean rainfall records multiplied by the number of 5 min increments in a day (i.e. 288).Future climate dataTo enable forecasting of the effects of climate change on within and between host transmission processes, we compiled projected future weather data. We extracted downscaled climate and hydrological data generated using the Localized Constructed Analogs (LOCA) method and the CESM1(CAM5) model from the “Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections” archive36,37,38,39,40. Specifically, we extracted values for daily mean temperature, relative humidity, and rainfall from the CESM1(CAM5) hydrology model, and values for daily maximum and minimum temperature from the CESM1(CAM5) climate model for both the RCP4.5 and RCP8.5 emissions scenarios41. Temporally, this data spanned June through July in 2020, 2030, 2040, 2045, 2050, 2060, and 2070. Spatially, this data represents projections for the area spanning (38.9375, 39.0) latitude and (− 107.0, − 106.9375) longitude. This area is the finest grained spatial extent including the BT site for which projections were available.Population mappingIn order to uncover drivers of population level epidemiological dynamics, it is necessary to characterize the ‘landscape of susceptibility’ in the host population. For spatially structured infectious disease systems such as flax rust, this involves documenting not only the infection status of each individual and any likely covariates of contact rate or susceptibility (e.g. size), but also the spatial arrangement of individuals. To accomplish this, we established 10 by 20 m transects at each of our field sites. We recorded the coordinates of each transect corner and the compass bearings associated with the transect axes (so that wind direction could be translated to the transect coordinate system) using a Garmin GPS unit (part #: 010-01735-10). At the beginning of the field season, we mapped the location, height, and infection status (healthy or diseased) of each plant within each transect. Plants with heights less than five centimeters were denoted as seedlings. While other metrics of size such as stem count could be used to form a more complete picture of plant size, we chose to use height alone as a proxy for size as it was the only measure that we could feasibly record for the many hundreds of plants within each transect. At the CC site, we were unable to map all healthy plants in certain areas of the transect due to time constraints, but we did record data for all diseased plants in these regions.Epidemiological surveysAfter documenting the initial epidemiological conditions, we tracked the spatiotemporal spread of flax rust within each transect. Approximately once per week, we visually inspected each flax individual in the transect for signs of disease, recording the location of any newly infected plants so they could be matched to a previously uninfected plant. We also recorded the height of all newly infected plants. We conducted these epidemiological surveys at least seven times at each site. Plants identified as infected in the initial population mapping and in these subsequent epidemiological surveys were marked with flags to ensure that they would not be repeatedly recorded as newly infected. Although we did not map the entirety of the CC transect, we covered the entire 10 by 20 m area in all epidemiological surveys.Diseased focal plantsWithin each transect, we designated a subset of diseased plants as ‘focal diseased plants’ and used them to make detailed measurements of within-host disease spread processes. These processes included changes in infection intensity, which we measured at the plant scale, as well as pustule growth, which we measured at the sub-plant scale using changes in pustule area and as a proxy.All initially infected plants were designated as diseased focal plants, and we continued to give this designation to newly diseased plants (except in some cases, diseased plants with height of approximately 5 cm or less, as these plants were too delicate to be manipulated) until we obtained at least 25 infected focal diseased plants per site. This occurred on 7/20/2020 at CC, on 7/1/2020 at BT, and on 7/9/2020 at GM and HM. We inserted metal tags into the ground at the base of diseased focal plants so they could be quickly and reliably identified. To facilitate longitudinal measurements of disease spread at sub-plant scales, we marked up to three infected stems on each focal diseased plant with pieces of colored flagging tape tied around the base of the stem. These stems were chosen haphazardly. On each of these stems (when possible), we marked the tip of one infected leaf with black ink. We preferentially marked leaves with one or a few pustules. Approximately once every three days, we recorded detailed measurements of plant height and within-host disease spread processes.To measure pustule growth, we photographed each marked leaf with a millimeter ruler in frame using a Cannon EOS Rebel T7i DSLR camera fitted with a Canon EF-S 35 mm f/2.8 Macro IS STM lens and a Cannon MR-14EX II Macro Ring Lite. While achieving consistent photographic angles and lighting conditions is impossible in a field setting, we attempted to keep the leaf and the scale in the same plane while maintaining a perpendicular shooting angle. We also adjusted camera settings so that pustule boundaries could be clearly distinguished in images. We used ImageJ software42 to measure the maximum and minimum diameters of each photographed pustule, and calculated pustule area assuming pustules to be elliptical. To enable the size of individual pustules to be tracked across observations, each measured pustule in each image was labeled so that it could be re-identified in subsequent images based on its location on the leaf and position relative to other pustules. We omitted data for any pustule that could not be confidently identified or whose borders became indistinct from other pustules. We marked a new leaf when we were unable to find the previously marked leaf, when the leaf was accidentally removed during data collection, or when the condition of the leaf deteriorated significantly. We suspect that either the presence of ink or our handling of the plant contributed significantly to the rate at which leaf condition deteriorated, but nevertheless we were able to observe the growth of many pustules over many weeks.To measure infection intensity, we first counted the number of infected stems. Next, for each marked stem, we recorded the length of infected tissue as the length of the stem segment spanning the lowest (relative to the ground) pustule to the highest pustule. Finally, we counted the number of pustules present on a haphazardly selected leaf in the middle of the region of infected tissue on each marked stem. We did not use the same leaf at each observation as the middle of the region of infected tissue changed due to disease spread. Using these measurements, we calculated an infection intensity metric as the product of the number of infected stems, the average length of infected tissue, and the average number of pustules present on a leaf in the middle of the region of infected tissue.Healthy focal plantsAs we wished to investigate patterns of growth in both healthy and infected plants, we designated certain uninfected plants as healthy focal plants, and measured their height whenever we measured the height of focal diseased plants. To control for potential effects of stem marking on the growth of diseased focal plants, we made these same markings on healthy focal plants. Healthy focal plants were selected haphazardly, and we added new healthy focal plants over time to keep the number of diseased and healthy focal plants approximately equal. At some sites, the number of healthy focal plants fell towards the end of the observation period as many healthy focal plants became infected. When this occurred, we stopped recording data for the plant, and in some cases designated it as a diseased focal plant.Spore depositionTo measure the distribution of spore deposition from an infected plant, and how it relates to the infection intensity of the source plant and wind patterns, we deployed spore traps in arrays around a subset of focal diseased plants. We chose focal disease plants that were as removed as possible from other diseased plants to minimize the number of spores originating from other plants that would be caught in the spore traps. These spore traps consisted of a ~ 2 cm2 section of Scotch Permanent Clear Mounting Tape (part #: MT76272-5) affixed to plastic backing with double sided tape. These spore traps were secured into the ground with ~ 5 cm nails. We deployed these traps at distances of either {~ 5 cm} (traps at ~ 5 cm were placed as close to the plant as possible), {~ 5 and 25 cm}, {~ 5, 25, and 50 cm}, or {~ 5, 25, 50, and 100 cm} in each of the four directions corresponding to the axes of the transect in which they were located. The traps were left in the field for approximately one week, and then collected. The sections of mounting tape were transferred to microscope slides and sealed in place using clear packing tape. We then used a light microscope to count the number of spores present in a known area. We counted spores in an area of 0.8 cm2 for most spore traps that contained few or no spores, but we counted a minimum area of 0.1 cm2 for all spore traps, including those with greater than 10,000 spores per cm2. Melampsora lini spores were identified visually using a slide prepared with spores as a reference for spore size, shape, and color. The spores matched descriptions in the literature43. We did not observe any rust fungi other than M. lini in or around our transects, so we can be reasonably certain that our counts represent only M. lini spores from infected focal diseased plants.Statistical analysesPlant growth and within-host disease spreadTo infer how different weather factors affect plant growth, pustule growth, and changes in infection intensity, we fit generalized additive models18. We used longitudinal observations of plant height in healthy and diseased focal plants to fit the model of plant growth, and longitudinal observations of within-host disease spread at various scales in focal diseased plants to fit all other models. Because the observation periods associated with our measurements of these processes varied, we formatted the response variables in change-per-day units: For our analyses of plant growth, pustule growth, and infection intensity progression we used change in plant height per day, change in pustule area per day, and change in infection intensity per day respectively as the response variables.To infer the effects of weather variables, we included smooth terms for these factors as predictors in each model. For each observation of change in plant height, pustule area, or infection intensity, we determined the start and end timepoints of the observation period to extract the corresponding weather data. For observations of plant height, we used 12:00:00 on the day of the observation as the start/end timepoints of the observation period. For observations of pustule area, we used photograph timestamps as the start and end timepoints. For observations of infection intensity, we used the average of the timestamps of photographs taken of pustules on that plant’s leaves as the start and/or end timepoints if such photographs were taken, because all data for an individual diseased focal plant was generally collected at once. If such photographs were not taken, we used the mean timestamp of all photographs taken at that plant’s site on the observation date as the start and/or end timepoint. To focus our analysis on fine-grained relationships between weather and within-host disease spread, we discarded data corresponding to observation windows of eight or more days. According to this criterion, 13 of 609 data points were discarded in the analysis of plant growth, 216 of 3535 data points were discarded in the analysis of pustule growth, and 25 of 338 data points were discarded in the analysis of infection intensity progression. Using the start and end timepoints of the observation period as bounds, we extracted the following variables from the weather data: temperature (mean, maximum, and minimum), absolute humidity, and mean daily rainfall.To account for the nested nature of our data, we included smooth terms for study site and plant identity as random effects (accomplished by setting bs = ”re” in the s() function in mgcv) in each model. In the case of the analyses of pustule growth, plant identity refers to the plant on which the pustules were observed. We also included a term in each model that accounted for the value of the previous observation to capture any allometric effects. In the model of change in plant height per day, this term was a full tensor smooth product of last observed height and infection intensity. In the model of change in pustule area per day, this term was a smooth of last observed pustule area. In the model of change in infection intensity per day, this term was a full tensor smooth product of the base 10 logarithm of the last observed infection intensity and last observed maximum plant height.We fit these models using the gam() function in the mgcv R package and the restricted maximum likelihood parameter estimation method18,44. We used gaussian response distributions with identity link functions. The dimensionality of all basis functions was set to the default value determined by mgcv. The one exception to this involved the marginal basis for log10 infection intensity (a part of the tensor term) in the model of change in infection intensity per day. The dimensionality of this marginal basis was increased to 15 after model diagnostics indicated that the default basis dimension was insufficient. To implement variable selection so that insignificant predictor terms would be effectively removed from the models, we used the double penalty approach of Mara and Wood45 (via the ‘select = TRUE’ option in the mgcv gam() function).The coefficients of the fit models can be used to infer the effects of weather factors on plant growth and within host disease spread. To translate these inferences into predictions about how climate change might affect plant growth and within-host disease spread, we simulated trajectories of plant growth, pustule area, and infection intensity under various climate conditions. For each class of simulation, we started by defining a hypothetical starting state. For simulations of plant growth, this was a 10 cm tall uninfected plant (the plant was assumed to remain uninfected for the entire simulation). For simulations of pustule growth, this was a pustule with area 0.1 cm2. For simulations of infection intensity, this was a 25 cm tall plant with infection intensity 1.0. Next, we simulated 100 trajectories from the model posteriors using observed weather data for each site and a step size of seven days. In these simulations, we restricted plant height, pustule area, and infection intensity to remain greater or equal to 5 cm, 0 mm2, and 0.1, respectively. We included the random effect of site (but not plant identity) when making these predictions. After confirming that these simulation results qualitatively recapitulated observed patterns, we simulated sets of 100 trajectories for future climate data sets. We again excluded the random effect of plant identity when making these projections. We used the same set of 100 random number seeds for each set of 100 trajectory simulations.While performing these simulations, we extracted weather variables as follows: When extracting weather variables from observed data sets, we defined the observation period as 00:00:00 on the first day in a step to 00:00:00 on the last day in a step (e.g. midnight on July 1st to midnight on July 8th). We extracted weather metrics as described in the “Weather data” subsection of the “Data collection” section above. Applying these same methods to the projected weather data is not possible, as these data sets only give only daily mean values. Instead, we calculated mean temperature as the mean of daily mean temperature values, maximum temperature as the maximum of daily maximum temperature values, and minimum temperature as the minimum of daily minimum temperature values. Similarly, we used daily mean temperature and relative humidity to calculate daily mean absolute humidity, and calculated mean absolute humidity as the mean of these values. We applied this same procedure to calculate mean rainfall.Spore dispersalTo infer how spore deposition is related to source plant infection intensity, wind speed, and wind direction, we fit a tilted gaussian plume model22 (TGPM) to our spore deposition data. The equation specifying the TGPM is as follows:$$begin{aligned} & dleft(I,H,s,X,Yright)=frac{I k {W}_{s}}{2 pi s {sigma }^{2} }{e}^{frac{{-Y}^{2}}{2{sigma }^{2}}-frac{{(H-frac{{W}_{s }X}{s})}^{2}}{2{sigma }^{2}}} \ & for quad X >0 0 \ & for quad Xle 0.end{aligned}$$This equation describes the concentration of spores deposited at a given point ((d)) as a function of wind speed ((s)), the infection intensity of the source plant ((I)), a constant relating (I) to the source concentration of spores ((k)), and the coordinates of the point (X,Y), relative to the source. The coordinate system has the source at the origin, the X-axis parallel to the wind direction (with wind flowing in a positive direction), the Y-axis perpendicular to the X-axis on the plane defined by the ground (assumed to be flat), and the vertical Z-axis orthogonal to the X and Y axes. The shape of the three dimensional spore plum emanating from the source is defined by (s), along with constants specifying the falling velocity of spores (({W}_{s})), the height of the source ((H)), and the standard deviation of spore dispersion (({sigma }^{2})) in the horizontal and vertical directions. Following Levine and Okubu22, we calculate ({sigma }^{2}) as a function of a diffusion coefficient, (A), assuming that the variance in spore distribution increases linearly with time. We also assume that spores diffuse along the Y and Z axes at equal rates.$${sigma }^{2}=frac{2AX}{s}.$$We fit the parameters (k, {W}_{s},) and (sigma ) by minimizing the sum of squared differences between model predictions and the spore deposition data we collected from the spore traps arranged around diseased focal plants. We assumed that all spores deposited on a spore trap originated from the associated diseased focal plant, and that spore deposition occurred between noon on the day of deployment and ceased at noon two days post-deployment as the spore traps lost their adhesive properties and became saturated with dust. When predicting spore deposition using the TGPM, we set (I) equal to the infection intensity of the source diseased focal plant on the day of spore trap deployment and (H) equal to half of the maximum height of the source diseased focal plant at the day of spore trap deployment. As wind speed and direction were recorded every 5 min, we calculated total spore deposition as the sum of predicted deposition across five minute time windows. In each individual estimate, we set (s) equal to wind speed and calculated (X) and (Y) from wind direction and the location of the spore trap relative to the focal diseased plant (see Supplementary Text 3).TransmissionUsing the fitted TGPM we next sought to connect patterns of spore dispersal to transmission. The first step in this process was to align data on the spatiotemporal spread of disease with the locations of all healthy and infected plants (including non-focal plants) within each transect so that the relative locations of these could be determined (see Supplementary Text 4). Next, we built a dataset (‘corrected plant height data’) describing the height of all plants on the dates of epidemiological surveys. We preferentially used actual measurements from the initial population survey, epidemiological surveys, or focal plant measurements where they existed. For the majority of plants, height was not recorded on the date of a given epidemiological survey. In these cases, we fore- or hind-casted height from a previous or future record of plant height (whichever was closer) using the fitted generalized additive model of plant growth and observed weather data spanning the period between the date of interest and the closest record. We included the random effect of study site when making predictions, but not the random effect of plant identity as we wished to forecast heights of both focal and non-focal plants and only focal plant data was used to fit the model of plant growth.After completing these steps, we next built a ‘transmission data set’ connecting weather conditions, predicted spore deposition, plant height, and infection occurrence across the time windows between each epidemiological survey. We included separate entries for each healthy plant (focal and non-focal) during each time window. We limited the scope of this data to the periods for each transect in which all infected plants (that were not seedlings) were tracked as diseased focal plants. Using noon on the date of one epidemiological survey as the beginning of the time window, and noon on the date of the next epidemiological survey as the end of the time window, we extracted the same weather variables as described in “Plant growth and within-host disease spread”. Plant height values were extracted from the ‘corrected plant height’ data set. We determined that a plant became infected if a plant with identical coordinates (after data alignment) was recorded as newly infected in the epidemiological survey that took place at the end of the observation period. Total spore deposition for each healthy plant was calculated as the sum of predicted spore deposition originating from each of the plants in the same transect that was infected at the beginning of the time window. To predict the spore deposition experienced by a healthy plant that originated from an individual diseased plant, we used the TGMP(.) We extracted the height of the diseased plant at the beginning of the time window and set (H) to half of this value. Likewise, we extracted the infection intensity of the diseased plant at the beginning of the time window, and set I equal to this value. For diseased seedlings that were not tracked as diseased focal plants, we set I = 0.1, as most were observed to be lightly infected. There were 14 instances of missing infection intensity data for a diseased focal plant on a certain date. In these cases, we fore- or hind-casted infection intensity for that plant using the generalized additive model of infection intensity progression fit in “Plant growth and within-host disease spread” (while including the random effect for study site, but not for plant identity), the closest observation of infection intensity for that plant, and the weather data spanning the time period from the missing observation to the closest measurement. We set the values of (k, {W}_{s},) and (sigma ) to those fit in the “spore deposition” section above. As wind speed and direction were recorded every five minutes, we summed spore deposition over 5 min windows that matched the resolution of the wind speed and direction data. For each of these windows, we predicted spore deposition after setting the values of X and Y based on the relative location of the healthy and diseased plants and the direction of the wind (see Supplementary Information).Using this data set, we modeled infection outcomes in a generalized additive model framework. We assumed a binomial response distribution, coding the outcome of an infection as 1 and the outcome of no infection as 0. To account for variation in the duration of observation period, we used a complementary log–log link function, and included the log time as an offset in the model46. We included smooth terms for all weather variables as predictors, along with smooth terms study site and plant identity as random effects (accomplished by setting bs = ”re” in the s() function in mgcv). To infer the connection between spore deposition and infection, we also included a full tensor smooth product between the base 10 logarithm of mean total spore deposition per day and maximum plant height. When fitting the tensor, we fixed the smoothing parameter for the marginal smooth for log10 total spore deposition to (1times {10}^{10}). This effectively sets the shape of this marginal smooth to be linear, preventing any overfitting involving non-monotonic effects of spore deposition. Height was included as a factor in the tensor for two reasons. First, we calculated spore deposition for a point. In reality, the spore deposition experienced by a plant depends on the size of the ‘target’ that it presents. As such, larger plants are presumably challenged by a greater number of spores than a smaller plant for the same level of predicted spore deposition. Secondly, plant size could be correlated with quantitative resistance, because leaf age can influence pustule development20. To achieve computational feasibility, we fit this model using the bam() function in the mgcv R package and the fast restricted maximum likelihood parameter estimation method (“fREML”). We again implemented variable selection so that insignificant predictor terms would be effectively removed from the models via the double penalty approach.To infer the effects of climate change and to illustrate the relationships between spore deposition, plant height, and infection risk, we predicted odds of infection using the estimated parameters of the fitted GAM (Fig. 5; Supplementary Fig. S14) and various weather data sets. In all of these projections, we included the random effect of site but excluded the random effect of plant identity.Epidemiological modelModel descriptionTo predict how the population level epidemiological dynamics of flax-rust might be affected in various climate change scenarios, we constructed a spatio-temporal epidemic model. This model tracks the infection status, infection intensity, and height of plants over time as infection spreads within the modeled population. There are three components required to simulate the model: (1) initial conditions describing the locations and starting states of all plants, (2) weather data, and (3) models of spore dispersal, transmission, infection intensity progression, and plant growth.The initial conditions for individual simulations of the epidemic model were in all cases constructed to represent either the BT or GM study populations on the date of an early epidemiological survey. For BT, this date was chosen as the first which had complete weather data (spanning 00:000:00 to 24:00:00), which was 6/24/2020. For GM, this date was set to 7/2/2020. Weather data was available for earlier dates, but a sharp increase in prevalence occurred during the prior week, perhaps due to prior missed observations of new infections. The model consistently underestimated observed prevalence when initialized prior to this increase in prevalence. We did not use the HM study population to initialize the model as no epidemiological surveys occurred on a date with complete weather data. Likewise, we do not use the CC study population to initialize the model because not all regions of the transect were mapped (see “Population mapping” sub-section in “Data collection” above).The construction of initial conditions to represent a given site proceeded as follows. The locations of all plants were taken from the ‘corrected plant height’ data set. Plants observed to be infected on the date of the first epidemiological survey were initialized as infected, and all other plants were initialized as uninfected. Following the first epidemiological survey, all infected plants (except for several that were approximately 5 cm or less in height) were designated diseased focal plants and their infection intensity was recorded. We used these measurements as the initial infection intensities of infected plants. We fore- or hind-casted infection intensities for any missing records using the method described in the transmission sub-section of the statistical analyses section above. Plant heights were taken from the ‘corrected plant height’ data set.The weather data component of the model is used to predict spore dispersal, transmission, infection intensity progression, and plant growth in conjunction with the fitted statistical models of these processes. As such, it needs to describe mean weather conditions over the period spanning a simulation step. Additionally, because the TGPM is calibrated to make predictions over 5 min intervals, the weather data must also describe average wind speed and direction over five minute windows.We used the fitted statistical model relating weather to the processes spore dispersal (TGPM), transmission, infection intensity progression, and plant growth to simulate these processes in the epidemiological model. These models contain random effects for both site and plant identity. The random effect of plant identity was excluded when making predictions about infection intensity progression and plant growth as random effects were only fit for focal plants. We included random effects of plant identity when making predictions using the transmission model because random effects were fit for all plants in the modeled populations.Using these components, the model simulates changes in infection status, infection intensity and plant height in time steps of 7 days. The simulation for each time step proceeds as follows:

    (1)

    First, the model predicts the number of spores deposited at the location of each healthy plant over the course of the entire time step. As all infected plants are assumed to be sources of spore deposition, the model predicts total spore deposition as the sum of spore deposition originating from each infected plant. The spore deposition originating from a single infected plant is calculated as the sum of predictions over 5 min windows spanning the time step. Individual predictions were generated using the TGPM with I is set to the infection intensity of the source plant at the beginning of the time step, H set to 0.5 times the height of the source plant at the beginning of the time step, s set to average wind speed, and X and Y set according to locations of the source and target plants and wind direction (see Supplementary Information). The parameters (k, {W}_{s},) and (sigma ) were set to the values fit to spore trap data.

    (2)

    Next, for each healthy plant, the model infers the probability of that plant becoming infected from predicted spore deposition at that plant’s location and mean weather metrics using the transmission model. Following this, a random number in [0,1] is drawn, and if it is equal or lower to the probability of infection that plant becomes infected. All newly infected plants are set to have an infection intensity of 1.0.

    (3)

    After simulating the transmission process, the model next simulates changes in infection intensity. For each plant, we simulated a prediction of the change in infection intensity per day by drawing randomly from the posterior of the infection intensity progression model. We used the infection intensity and height of that plant at the beginning of the time step along with mean weather metrics spanning the time step as the model inputs. We multiplied the predicted change in infection intensity per day by the length of the time step and added it to the plant’s infection intensity at the beginning of the time step to calculate the new infection intensity of the plant.

    (4)

    Finally, we used a similar process to simulate change in plant height. For each plant, we simulated a prediction of the change in height per day by drawing randomly from the posterior of the plant growth model. Again, we used the height of that plant at the beginning of the time step along with mean weather metrics spanning the time step as model inputs. To calculate the new height of the plant, we multiplied the predicted change in height per day by the length of the time step and added this number to the plant’s height at the beginning of the time step.

    There are two sources of stochasticity within the epidemiological model. The first of these sources pertains to the probability of infection. Although the predicted probability of infection for a certain combination of predictor values is always the same, we compare this probability to a random number to determine if infection occurs. The second source of stochasticity pertains to predicted changes in infection intensity and plant height. We predict rates of infection intensity change and plant growth by drawing randomly from the posterior distributions of the corresponding fitted models. These sources of stochasticity allow us to understand the variation in epidemiological model predictions via repeated simulation.SimulationTo validate the epidemiological model, we simulated 10 epidemiological trajectories for the BT and GM sites and compared the results to our observations of flax-rust spread to ensure a qualitative match. For each of these sites, we initialized the model using data corresponding to the site as described above and used observed weather (from the same site) data to run the simulation. When extracting weather variables from observed weather data sets, we defined the observation period as 00:00:00 on the first day in a step to 00:00:00 on the last day in a step (e.g. noon on July 1st to noon on July 8th). We included the random effects of site and plant identity when making predictions from the transmission model, and the random effects of site when making predictions from the infection intensity and plant growth models.To infer the effects of climate change of flax-rust epidemiology, we again initialized the model using data corresponding to the for BT and GM sites, and then simulated 10 epidemiological trajectories for each set of weather data (RCP4.5 and 8.5 emissions scenarios and the years 2020, 2030, 2040, 2045, 2050, 2060 and 2070). We extracted weather metrics as described in the “Weather data” subsection of the “Data collection” section above. We used observed records of wind speed and direction to simulate spore deposition using the TGPM as the projected data sets lack these measures. As before we included the random effects of site and plant identity when making predictions from the transmission model, and the random effects of site when making predictions from the infection intensity and plant growth models. For each set of 10 model simulations corresponding to a unique site and weather data set combination, we used the same 10 random number seeds. More

  • in

    Long-term blast control in high eating quality rice using multilines

    The top-brand nonglutinous rice variety ‘Koshihikari’, which has a high palatability, is extremely susceptible to blast. Therefore, farmers apply fungicides over four times during the rice production season. As Koshihikari is sold by the Niigata brand, it has been traditionally viewed as having a high eating quality in Japan, and because of this, both farmers and consumers have requested that the multiline variety KO-BL be tested to determine if it is equivalent to Koshihikari before its introduction. Trials comparing Koshihikari and KO-BL were carried out in 2003 and 2004 in 594 and 622 fields covering 236 and 315 ha, respectively. These trials evaluated plant homogeneity, eating quality, and blast suppression using fewer fungicidal sprays. Following favorable results, in 2005, all Koshihikari were converted to KO-BL multiline variety covering an area of 94,000 ha. In addition, seed use and cultivation were restricted to the Niigata area to distinguish KO-BL from Koshihikari grown in other prefectures.Seed production and mixture processes are managed with precision by each prefectural official member (Fig. 1a). Original isogenic lines (ILs) were separately produced from the original stock in the original strain fields by the Niigata prefectural government. Using a precise mixture machine, the mixture of four ILs was then blended by weight in 2000 kg volumes, all multiplied by ten (giving a total volume of 20 t). Original production fields and commercial fields all used blended seeds that had been authorized by seed production farmers and commercial farmers in the 2003 and 2004 trials. Thus, it takes two years for seed production at the original strain field followed by the original production field for the preparation of commercial fields; thus, the seed mixture composition needs to be determined at least two years before introduction. Susceptible and resistant (effective) ILs were mixed at a ratio of 3:7 from 2005 to 2019 (Fig. 1b, Supplementary Table S1). Susceptible ILs, possessing Pia and Pii genes, were always mixed at a ratio of 1:2, but the composition of resistant ILs, containing Pita-2, Piz, Pib, Piz-t, and Pit genes, was changed every two to three years to avoid the breakdown of resistance6. These changes were determined by annually monitoring blast race distributions.Figure 1Representative seed production flow from original stock to commercial field and history of Koshihikari BL composition from 2005 to 2019 in Niigata Prefecture. (a) S1–S2, susceptible KO-BL; R1-R2, resistant KO-BL. Seeds obtained from original stock field at the Niigata Agricultural Research Institute. Seeds obtained from the original strain field and the original production field at both designated farmers’ fields. Commercial field (general farmers field) used for KO-BL production. Each field requires a year for seed production. (b) Pia and Pii, susceptible; Pita-2, Piz, Pib, Piz-t, and Pit, resistant. The proportion of susceptible KO-BLs and resistant KO-BLs was consistently 3:7 across years.Full size imageIn Niigata Prefecture, the predominant 5 blast races distributed from 1994 to 2004 were 001.0 (virulent to Koshihikari [Pik-s]), 003.0 (virulent to Pik-s and Pia), 005.0 (virulent to Pik-s and Pii), 007.0 (virulent to Pik-s, Pia, and Pii), and 037.1 (virulent to Pik-s, Pia, Pii, and Pik) (Fig. 2a, Supplementary Table S2). Because all the 5 races were virulent to Koshihikari, which had been widely cultivated in Niigata area during the years, there were no drastic race changes. In addition, genetic variations in blast resistance indicated that Koshihikari also harbored the Pish gene, and that the Pia, Pii, and Pik genes were also dominant in the Hokuriku region, including Niigata Prefecture21. Virulent blast races against the resistance genes Pish, Pia, Pii, Pi3, Pi5(t), Pik, Pik-s, and Pi19(t) were dominantly distributed in Niigata Prefecture22. These reports confirmed that Koshihikari had been susceptible to dominant blast races before KO-BL introduction.Figure 2Blast race change during the 1994–2019 period in Niigata Prefecture and the worst-case simulation of blast race dynamics in KO-BL during the 2005–2019 (years 1–15) period. Races and virulences are shown in Table 1. (a) A red line indicates the year (2005) when KO-BL was introduced. Races 007.0 and 037.1 became dominant after the introduction. (b) Actual races and their rates in 2004 and annual KO-BL compositions from 2005 to 2019 were set in the simulation. Parameters set in the simulation were as follows: maximum lesion number in a year, 10,000,000; weather condition, 10 (favorable); virulent mutation rate, 10–5; overwintering probability, 0.01; number of simulated years, 15; and number of simulation trials, 1000. The 1000 trial results for the lesion number increase in each race were averaged in each year and transformed into rates to show race dynamics. All simulation results are shown in Supplementary Table 6 in Supplementary information 2. The races 007.0 and 037.1 were also dominant until year 15 (correspond to 2019). Both actual and simulated race dynamics showed no outbreaks of the resistant composition of KO-BL.Full size imageIn the 2005 release year of KO-BL, the predominant blast races, 001.0 (virulent to Pik-s) and 003.0 (virulent to Pik-s and Pia), drastically decreased in distribution from 41.8% to 22.3% and 27.6% to 17.3%, respectively (Fig. 2a, Supplementary Table S2). Interestingly, races 001.0 and 003.0 rapidly decreased by 5.4% and 1.3% in 2006, respectively, even though especially Pia, which can be infected by the race 003.0, was used in the KO-BL composition. Because all ILs in the composition of KO-BL were resistant to race 001.0, and race 003.0 was only virulent to Pia, which made up 10% of the annual KO-BL composition (Table 1). In contrast, races 007.0 (virulent to Pik-s, Pia, and Pii) and 037.1 (virulent to Pik-s, Pia, Pii, and Pik) dominated from 2005 to 2019. The higher rate of race 007.0 detection was affected by 30% of the ILs composing the annual KO-BL were susceptible. The second highest rate of race 037.1 detection was affected by a number of factors: the high susceptibility of a minor cultivar that had Pii and Pik, the mosaic configuration of fields typical in Niigata, and the air-borne spread of race 037.1. To maintain consensus on KO-BL cultivation based on total blast suppression in Niigata, rarely detected races virulent to resistant ILs in commercial fields are strictly supervised by the prefectural government to avoid unnecessary confusion in Niigata residents.Table 1 Susceptible or resistant reaction of Koshihikari and KO-BL against blast races.Full size tableIn 2008, to mathematically support KO-BL composition changes, we developed a simulation software to estimate long-term blast race dynamics in multilines using a plant‒pathogen coevolution system23. The model calculated the persistence of resistant ILs to determine the optimal timing of changes to multiline variety compositions. To simulate race dynamics in KO-BL, we set five currently investigated races, 001.0 (virulent to Pik-s), 003.0 (virulent to Pik-s and Pia), 005.0 (virulent to Pik-s and Pii), 007.0 (virulent to Pik-s, Pia, and Pii), and 037.1 (virulent to Pik-s, Pia, Pii, and Pik), and their rates in 2004, as well as five emerging races, 043.0 (virulent to Pik-s, Pia, and Piz), 303.0 (virulent to Pik-s, Pia, and Pita-2), 003.2 (virulent to Pik-s, Pia, and Pib), 403.0 (virulent to Pik-s, Pia, and Piz-t), and 003.4 (virulent to Pik-s, Pia, and Pit) (see Fig. 2b, Supplementary Table S3) against five newly introduced respective resistant KO-BLs (see Fig. 1b, Supplementary Table S1) and the annual KO-BL compositions from 2005 to 2019. The worst case (severe epidemic) simulation result (Fig. 2b, Supplementary Tables S3 and S6) showed that race 007.0 (virulent to susceptible Pik-s, Pia and Pii) became the predominant race (77.4%), and race 037.1 (virulent to Pik-s, Pia, Pii, and Pik) remained at a low frequency (21.6%) until the fifteenth year (corresponding to 2019). In addition, super-race virulent to all KO-BLs did not emerge in this simulation. These suppression of outbreaks of newly emerged virulent races, including super-race on resistant KO-BL was apparently affected by 2–3 years of change in resistant KO-BL composition, and total suppression of blast occurrence decreasing the blast population. These results indicated that almost all the epidemics analyzed reflected actual race dynamics without affecting other minor races from other susceptible cultivars grown in Niigata, especially up to 2011. Thus, our decision support system provides an evaluation of KO-BL persistence and indicates the KO-BL composition changes needed for blast race population control in large areas. In addition, our simulation model may be useful for evaluating future KO-BL composition changes.Blast occurrence drastically decreased after 2005 (Fig. 3a, Supplementary Table S4). The average occurrence of leaf and panicle blast was 46.1% and 52.9% during the 1995–2004 period and 9.5% and 9.6% during the 2005–2019 period, respectively. This resulted in a blast suppression effect by 70% of the resistant composition in KO-BL. Current seed production fields are rarely contaminated with virulent races against resistant KO-BLs. This suggests that seed sanitation contributes to the suppression of virulent pathogen epidemics in multilines. In addition, induced resistance24,25 may have no effect on the practical use of multilines. Rice plants were found to induce a resistance response when inoculated with avirulent races of blast (those that stimulate protective responses to virulent race attacks). As the detection of several races in one area is rare and blast occurrence tends to be low, conditions that induce resistance in field situations do not occur. Fungicide applications to control blast in KO-BL and other minor cultivars decreased by approximately one-third during the 2005–2019 period compared with 2004 (Fig. 3b, Supplementary Table S5). Thus, the commercial scale use of crop diversity is clearly effective for the environmentally friendly control of airborne diseases.Figure 3Leaf and panicle blast occurrence from 1994 to 2019 and blast control area from 2004 to 2019 in Niigata Prefecture. (a) A red line indicates the year (2005) when KO-BL was introduced. (b) Gross fungicide spray area decreased by approximately one-third during the 2005–2019 period compared with 2004.Full size imageThe optimum long-term solution for pathogen population control using genetic diversity includes multilines. Blast occurrence in KO-BL introduced in Niigata, and the theoretical value of blast suppression in KO-BL tested at small scales, were reduced by approximately 10% compared to that of monoculture plots26,27,28. Thirty percent of susceptible ILs in KO-BL have the potential to improve compatible races with susceptible ILs and become predominant in large areas. This would contribute to the suppression of rapid increases in new virulent races emerging in the blast population. To maintain consensus on KO-BL cultivation based on total blast suppression in Niigata, rarely detected races virulent to resistant KO-BLs in commercial fields are strictly monitored by the prefectural government. Educating Niigata farmers ensures the long-term use of KO-BL. In fact, lower blast occurrence has been attributed to careful KO-BL cultivation and seed management.The implementation of genetically diversified homogeneous seed mixtures, rotations with resistant KO-BL, restricted KO-BL cultivation, and pathogen monitoring allowed rice quality to be maintained, diseases to be suppressed, and environmentally sound agriculture to be economically viable in Niigata. Collaboration among prefectural officers, farmers, and consumers in Niigata has resulted in safer rice production with good agricultural practices (GAPs) that meet sustainable development goals (SDGs). In addition, DNA tests differentiate KO-BL from the original Koshihikari for buyers, thereby prohibiting illegal distribution. Multiline varieties have been used in small areas in two different prefectures. For example, in Miyagi pref., Sasanishiki BL consisted of Pik, Pik-m, and Piz at ratios of 4:3:3 and 3:3:4 in 1995 and 1996, respectively. This composition was changed to Pik, Pik-m, Piz, and Piz-t at a ratio of 1:1:4:4 from 1997 to 2007 to prevent an increase in race 037.1 (virulent to the BL: Pik and Pik-m). In addition, an equal mixture of seven BLs (Pib, Pik, Pik-m, Piz, Piz-t, Pita, and Pita-2) was cultivated in 300 ha areas (maximum 4000 ha) from 2008 to 2014 without any outbreaks observed. In Toyama pref., the Koshihikari Toyama BL, which consists of resistant ILs, Pita-2, Pib, and Pik-p at a ratio of 4:4:2, was cultivated in an area of 300 ha and required a 50% reduction in chemical inputs from 2003 up to the present. Our model also calculated a greater than 50-year persistence in terms of the small area effect in both prefectural cases. This result depends on an insufficient pathogen population increase in virulent mutations against resistant ILs (data not shown). In this way, the practical use of a multiline provides control without the need for as much fungicide with or without a periodic change in IL composition. Our results demonstrate that the management of crop and pathogen coevolution can control diseases at large scales and, thereby, contribute to global food security. More

  • in

    Global patterns of vascular plant alpha diversity

    Linder, H. P. Plant diversity and endemism in sub‐Saharan tropical Africa. J. Biogeogr. 28, 169–182 (2001).Article 

    Google Scholar 
    Kier, G. et al. Global patterns of plant diversity and floristic knowledge. J. Biogeogr. 32, 1107–1116 (2005).Article 

    Google Scholar 
    Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Nat. Acad. Sci. 104, 5925–5930 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brummitt, N., Araújo, A. C. & Harris, T. Areas of plant diversity—What do we know? Plants, People, Planet 3, 33–44 (2020).Article 

    Google Scholar 
    Gentry, A. H. Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann. Mo. Bot. Gard. 75, 1–34 (1988).Article 

    Google Scholar 
    Slik, J. F. et al. An estimate of the number of tropical tree species. Proc. Natl Acad. Sci. 112, 7472–7477 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Parmentier, I. et al. The odd man out? Might climate explain the lower tree α‐diversity of African rain forests relative to Amazonian rain forests? J. Ecol. 95, 1058–1071 (2007).Article 

    Google Scholar 
    Weigand, A. et al. Global fern and lycophyte richness explained: How regional and local factors shape plot richness. J. Biogeogr. 47, 59–71 (2020).Article 

    Google Scholar 
    Keil, P. & Chase, J. M. Global patterns and drivers of tree diversity integrated across a continuum of spatial grains. Nat. Ecol. Evol. 3, 390–399 (2019).PubMed 
    Article 

    Google Scholar 
    Lenoir, J. et al. Cross-scale analysis of the region effect on vascular plant species diversity in southern and northern European mountain ranges. PLoS ONE 5, e15734 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

    Google Scholar 
    Bruelheide, H., Jiménez-Alfaro, B., Jandt, U. & Sabatini, F. M. Deriving site-specific species pools from large databases. Ecography 43, 1215–1228 (2020).Article 

    Google Scholar 
    Dengler, J. et al. Species–area relationships in continuous vegetation: Evidence from Palaearctic grasslands. J. Biogeogr. 47, 72–86 (2020).Article 

    Google Scholar 
    Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, And Conservation (Oxford University Press, 2007).Bruelheide, H. et al. sPlot —a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).Article 

    Google Scholar 
    Sabatini, F. M. et al. sPlotOpen—an environmentally balanced, open-access, global dataset of vegetation plots. Glob. Ecol. Biogeogr. 30, 1740–1764 (2021).Article 

    Google Scholar 
    Ricklefs, R. E. Community diversity—relative roles of local and regional processes. Science 235, 167–171 (1987).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Crawley, M. J. & Harral, J. E. Scale dependence in plant biodiversity. Science 291, 864–868 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Antonelli, A. et al. An engine for global plant diversity: highest evolutionary turnover and emigration in the American tropics. Front. Genet. 6, 130 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jiménez-Alfaro, B. et al. History and environment shape species pools and community diversity in European beech forests. Nat. Ecol. Evol. 2, 483–490 (2018).PubMed 
    Article 

    Google Scholar 
    Sabatini, F. M., Jiménez-Alfaro, B., Burrascano, S. & Blasi, C. Drivers of herb-layer species diversity in two unmanaged temperate forests in northern Spain. Community Ecol. 15, 147–157 (2014).Article 

    Google Scholar 
    Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 
    Article 

    Google Scholar 
    Pärtel, M., Bennett, J. A. & Zobel, M. Macroecology of biodiversity: disentangling local and regional effects. N. Phytol. 211, 404–410 (2016).Article 

    Google Scholar 
    Field, R. et al. Spatial species‐richness gradients across scales: a meta‐analysis. J. Biogeogr. 36, 132–147 (2009).Article 

    Google Scholar 
    Biurrun, I. et al. Benchmarking plant diversity of Palaearctic grasslands and other open habitats. J. Veg. Sci. 32, e13050 (2021).Article 

    Google Scholar 
    Da, S. S. et al. Plant biodiversity patterns along a climatic gradient and across protected areas in West Africa. Afr. J. Ecol. 56, 641–652 (2018).Article 

    Google Scholar 
    Gerstner, K., Dormann, C. F., Václavík, T., Kreft, H. & Seppelt, R. Accounting for geographical variation in species–area relationships improves the prediction of plant species richness at the global scale. J. Biogeogr. 41, 261–273 (2014).Article 

    Google Scholar 
    Myers, J. A. et al. Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly. Ecol. Lett. 16, 151–157 (2013).PubMed 
    Article 

    Google Scholar 
    Muñoz Mazón, M. et al. Mechanisms of community assembly explaining beta-diversity patterns across biogeographic regions. J. Veg. Sci. 32, e13032 (2021).Article 

    Google Scholar 
    Sabatini, F. M., Jiménez-Alfaro, B., Burrascano, S., Lora, A. & Chytrý, M. Beta-diversity of central European forests decreases along an elevational gradient due to the variation in local community assembly processes. Ecography 41, 1038–1048 (2018).Article 

    Google Scholar 
    Večeřa, M. et al. Alpha diversity of vascular plants in European forests. J. Biogeogr. 46, 1919–1935 (2019).Article 

    Google Scholar 
    Wüest, R. O. et al. Macroecology in the age of Big Data—Where to go from here? J. Biogeogr. 47, 1–12 (2019).Article 

    Google Scholar 
    Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. blockCV: an r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol. Evol. 10, 225–232 (2019).Article 

    Google Scholar 
    Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Belitz, K. & Stackelberg, P. Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models. Environ. Model. Softw. 139, 105006 (2021).Article 

    Google Scholar 
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Barthlott, W., Mutke, J., Rafiqpoor, D., Kier, G. & Kreft, H. Global centers of vascular plant diversity. Nova Acta Leopoldina NF 92, 61–83 (2005).
    Google Scholar 
    Testolin, R. et al. Global patterns and drivers of alpine plant species richness. Glob. Ecol. Biogeogr. 30, 1218–1231 (2021).Article 

    Google Scholar 
    Wilson, J. B., Peet, R. K., Dengler, J. & Pärtel, M. Plant species richness: the world records. J. Veg. Sci. 23, 796–802 (2012).Article 

    Google Scholar 
    Chytrý, M. et al. The most species-rich plant communities in the Czech Republic and Slovakia (with new world records). Preslia 87, 217–278 (2015).
    Google Scholar 
    Whitmore, T. C., Peralta, R. & Brown, K. Total species count in a Costa Rican tropical rain forest. J. Trop. Ecol. 1, 375–378 (1985).Article 

    Google Scholar 
    Chytrý, M. et al. High species richness in hemiboreal forests of the northern Russian Altai, southern Siberia. J. Veg. Sci. 23, 605–616 (2012).Article 

    Google Scholar 
    Duivenvoorden, J. Vascular plant species counts in the rain forests of the middle Caquetá area, Colombian Amazonia. Biodivers. Conserv. 3, 685–715 (1994).Article 

    Google Scholar 
    Balslev, H., Valencia, R., Paz y Miño, G., Christensen, H. & Nielsen, I. in Forest Biodiversity in North, Central and South America and the Carribean: Research and Monitoring. Man and the Biosphere Series (eds. Dallmeier, F. & Comiskey, J. A.) (Unesco and The Parthenon Publishing Group, 1998).Mendieta‐Leiva, G. et al. EpIG‐DB: a database of vascular epiphyte assemblages in the Neotropics. J. Veg. Sci. 31, 518–528 (2020).Article 

    Google Scholar 
    Spicer, M. E., Mellor, H. & Carson, W. P. Seeing beyond the trees: a comparison of tropical and temperate plant growth forms and their vertical distribution. Ecology 101, e02974 (2020).PubMed 
    Article 

    Google Scholar 
    Royo, A. A. & Carson, W. P. The herb community of a tropical forest in central Panama: dynamics and impact of mammalian herbivores. Oecologia 145, 66–75 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    Sosef, M. S. M. et al. Exploring the floristic diversity of tropical Africa. BMC Biol. 15, 15 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dwomoh, F. K. & Wimberly, M. C. Fire regimes and forest resilience: alternative vegetation states in the West African tropics. Landsc. Ecol. 32, 1849–1865 (2017).Article 

    Google Scholar 
    Condit, R. et al. Beta-diversity in tropical forest trees. Science 295, 666–669 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cao, K. et al. Species packing and the latitudinal gradient in beta-diversity. Proc. R. Soc. B 288, 20203045 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhong, Y. et al. Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide. Nat. Commun. 12, 3137 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Graco-Roza, C. et al. Distance decay 2.0—a global synthesis of taxonomic and functional turnover in ecological communities. Glob. Ecol. Biogeogr. 31, 1399–1421 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Johnson, D. J., Condit, R., Hubbell, S. P. & Comita, L. S. Abiotic niche partitioning and negative density dependence drive tree seedling survival in a tropical forest. Proc. R. Soc. B 284, 20172210 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Naturalist 133, 240–256 (1989).Article 

    Google Scholar 
    Andermann, T., Antonelli, A., Barrett, R. L. & Silvestro, D. Estimating alpha, beta, and gamma diversity through deep learning. Front Plant Sci. 13, 839407 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cardoso, D. et al. Amazon plant diversity revealed by a taxonomically verified species list. Proc. Nat. Acad. Sci. 114, 10695–10700 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cayuela, L. et al. Species distribution modeling in the tropics: problems, potentialities, and the role of biological data for effective species conservation. Trop. Conserv. Sci. 2, 319–352 (2009).Article 

    Google Scholar 
    Lenoir, J. et al. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob. Change Biol. 19, 1470–1481 (2013).ADS 
    Article 

    Google Scholar 
    Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the Anthropocene. PLoS ONE 7, e30535 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kattge, J. et al. TRY plant trait database-enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).ADS 
    Article 

    Google Scholar 
    Dengler, J. et al. The Global Index of Vegetation-Plot Databases (GIVD): a new resource for vegetation science. J. Veg. Sci. 22, 582–597 (2011).Article 

    Google Scholar 
    Lopez‐Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci. 22, 610–613 (2011).Article 

    Google Scholar 
    Chytrý, M. Database of Masaryk University Vegetation Research in Siberia. Biodiver. Ecol. 4, 290 (2012).Article 

    Google Scholar 
    Schmidt, M. et al. The West African Vegetation Database. Biodiv. Ecol. 4, 105–110 (2012).Article 

    Google Scholar 
    Muche, G., Schmiedel, U. & Jürgens, N. BIOTA Southern Africa Biodiversity Observatories Vegetation Database. Biodiver. Ecol. 4, 111–123 (2012).Article 

    Google Scholar 
    Revermann, R. et al. Vegetation database of the Okavango Basin. Phytocoenologia 46, 103–104 (2016).Article 

    Google Scholar 
    N’Guessan, A. E. et al. Drivers of biomass recovery in a secondary forested landscape of West Africa. Ecol. Manag. 433, 325–331 (2019).Article 

    Google Scholar 
    Müller, J. Zur Vegetationsökologie der Savannenlandschaften im Sahel Burkina Fasos (Frankfurt-Main Universität, 2003).Kearsley, E. et al. Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin. Nat. Commun. 4, 2269 (2013).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Djomo Nana, E. et al. Relationship between Survival Rate of Avian Artificial Nests and Forest Vegetation Structure along a Tropical Altitudinal Gradient on Mount Cameroon. Biotropica 47, 758–764 (2015).Article 

    Google Scholar 
    Wana, D. & Beierkuhnlein, C. Responses of plant functional types to environmental gradients in the south‐west Ethiopian highlands. J. Trop. Ecol. 27, 289–304 (2011).Article 

    Google Scholar 
    Finckh, M. Vegetation Database of Southern Morocco. Biodiver. Ecol. 4, 297 (2012).Article 

    Google Scholar 
    Strohbach, B. & Kangombe, F. National Phytosociological Database of Namibia. Biodiver. Ecol. 4, 298–298 (2012).Article 

    Google Scholar 
    Samimi, C. Das Weidepotential im Gutu‐Distrikt (Zimbabwe)—Möglichkeiten und Grenzen der Modellierung unter Verwendung von Landsat TM‐5. Vol. 19 (2003).Černý, T. et al. Classification of Korean forests: patterns along geographic and environmental gradients. Appl. Veg. Sci. 18, 5–22 (2015).Article 

    Google Scholar 
    Nowak, A. et al. Vegetation of Middle Asia: the project state of the art after ten years of survey and future perspectives. Phytocoenologia 47, 395–400 (2017).Article 

    Google Scholar 
    Liu, H., Cui, H., Pott, R. & Speier, M. Vegetation of the woodland‐steppe ecotone in southeastern Inner Mongolia, China. J. Veg. Sci. 11, 525–532 (2000).Article 

    Google Scholar 
    Wang, Y. et al. Combined effects of livestock grazing and abiotic environment on vegetation and soils of grasslands across Tibet. Appl. Veg. Sci. 20, 327–339 (2017).Article 

    Google Scholar 
    Bruelheide, H. et al. Community assembly during secondary forest succession in a Chinese subtropical forest. Ecol. Monogr. 81, 25–41 (2011).Article 

    Google Scholar 
    Cheng, X.-L. et al. Taxonomic and phylogenetic diversity of vascular plants at Ma’anling volcano urban park in tropical Haikou, China: Reponses to soil properties. PLoS ONE 13, e0198517 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hatim, M. Vegetation Database of Sinai in Egypt. Biodiver. Ecol. 4, 303 (2012).Article 

    Google Scholar 
    Drescher, J. et al. Ecological and socio-economic functions across tropical land use systems after rainforest conversion. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 371, 20150275 (2016).Article 

    Google Scholar 
    Dolezal, J., Dvorsky, M. & Kopecky, M. Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya. Sci. Rep. 6, 24881 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Borchardt, P. & Schickhoff, U. Vegetation Database of South‐Western Kyrgyzstan—the walnut‐wildfruit forests and alpine pastures. Biodiver. Ecol. 4, 309 (2012).Article 

    Google Scholar 
    Wagner, V. Eurosiberian meadows at their southern edge: patterns and phytogeography in the NW Tien Shan. J. Veg. Sci. 20, 199–208 (2009).Article 

    Google Scholar 
    von Wehrden, H., Wesche, K. & Miehe, G. Plant communities of the southern Mongolian Gobi. Phytocoenologia 39, 331–376 (2009).Article 

    Google Scholar 
    Chepinoga, V. V. Wetland Vegetation Database of Baikal Siberia (WETBS). Biodiver. Ecol. 4, 311 (2012).Article 

    Google Scholar 
    Korolyuk, A. et al. Database of Siberian Vegetation (DSV). Biodiver. Ecol. 4, 312–312 (2012).Article 

    Google Scholar 
    El-Sheikh, M. A. et al. SaudiVeg ecoinformatics: aims, current status and perspectives. Saudi J. Biol. Sci. 24, 389–398 (2017).PubMed 
    Article 

    Google Scholar 
    Vanselow, K. A. Eastern Pamirs—a vegetation‐plot database for the high mountain pastures of the Pamir Plateau (Tajikistan). Phytocoenologia 46, 105 (2016).Article 

    Google Scholar 
    De Sanctis, M. & Attorre, F. Socotra Vegetation Database. Biodiver. Ecol. 4, 315 (2012).Article 

    Google Scholar 
    Chabbi, A. & Loescher, H. W. Terrestrial Ecosystem Research Infrastructures: Challenges and Opportunities (CRC Press, 2017).Ibanez, T. et al. Structural and floristic diversity of mixed rainforest in New Caledonia: New data from the New Caledonian Plant Inventory and Permanent Plot Network (NC‐PIPPN). Appl. Veg. Sci. 17, 386–397 (2014).Wiser, S. K., Bellingham, P. J. & Burrows, L. E. Managing biodiversity information: development of New Zealand’s National Vegetation Survey databank. N. Z. J. Ecol. 25, 1–17 (2001).
    Google Scholar 
    Whitfeld, T. J. S. et al. Species richness, forest structure, and functional diversity during succession in the New Guinea lowlands. Biotropica 46, 538–548 (2014).Article 

    Google Scholar 
    Dengler, J. & Rūsiņa, S. Database dry grasslands in the Nordic and Baltic Region. Biodiver. Ecol. 4, 319–320 (2012).Article 

    Google Scholar 
    Biurrun, I., García-Mijangos, I., Campos, J. A., Herrera, M. & Loidi, J. Vegetation-plot database of the University of the Basque Country (BIOVEG). Biodiver. Ecol. 4, 328 (2012).Article 

    Google Scholar 
    Vassilev, K., Stevanović, Z. D., Cušterevska, R., Bergmeier, E. & Apostolova, I. Balkan Dry Grasslands Database. Biodiver. Ecol. 4, 330–330 (2012).Article 

    Google Scholar 
    Marcenò, C. & Jiménez‐Alfaro, B. The Mediterranean Ammophiletea Database: a comprehensive dataset of coastal dune vegetation. Phytocoenologia 47, 95–105 (2017).
    Google Scholar 
    Vassilev, K. et al. Balkan Vegetation Database: historical background, current status and future perspectives. Phytocoenologia 46, 89–95 (2016).Article 

    Google Scholar 
    Landucci, F. et al. WetVegEurope: a database of aquatic and wetland vegetation of Europe. Phytocoenologia 45, 187–194 (2015).Article 

    Google Scholar 
    Peterka, T., Jiroušek, M., Hájek, M. & Jiménez‐Alfaro, B. European Mire Vegetation Database: a gap‐oriented database for European fens and bogs. Phytocoenologia 45, 291–297 (2015).Article 

    Google Scholar 
    De Sanctis, M., Fanelli, G., Mullaj, A. & Attorre, F. Vegetation database of Albania. Phytocoenologia 47, 107–108 (2017).Article 

    Google Scholar 
    Willner, W., Berg, C. & Heiselmayer, P. Austrian Vegetation Database. Biodiver. Ecol. 4, 333 (2012).Article 

    Google Scholar 
    Apostolova, I., Sopotlieva, D., Pedashenko, H., Velev, N. & Vasilev, K. Bulgarian Vegetation Database: historic background, current status and future prospects. Biodiver. Ecol. 4, 141–148 (2012).Article 

    Google Scholar 
    Wohlgemuth, T. Swiss Forest Vegetation Database. Biodiver. Ecol. 4, 340 (2012).Article 

    Google Scholar 
    Chytrý, M. & Rafajová, M. Czech National Phytosociological Database: basic statistics of the available vegetation‐plot data. Preslia 75, 1–15 (2003).
    Google Scholar 
    Jansen, F., Dengler, J. & Berg, C. VegMV—the vegetation database of Mecklenburg‐Vorpommern. Biodiver. Ecol. 4, 149–160 (2012).Article 

    Google Scholar 
    Ewald, J., May, R. & Kleikamp, M. VegetWeb—the national online‐repository of vegetation plots from Germany. Biodiver. Ecol. 4, 173–175 (2012).Article 

    Google Scholar 
    Jandt, U. & Bruelheide, H. German vegetation reference database (GVRD). Biodiver. Ecol. 4, 355–355 (2012).Article 

    Google Scholar 
    Garbolino, E., De Ruffray, P., Brisse, H. & Grandjouan, G. The phytosociological database SOPHY as the basis of plant socio-ecology and phytoclimatology in France. Biodiver. Ecol. 4, 177–184 (2012).Article 

    Google Scholar 
    Dimopoulos, P. & Tsiripidis, I. Hellenic Natura 2000 Vegetation Database (HelNAtVeg). Biodiver. Ecol. 4, 388 (2012).Article 

    Google Scholar 
    Fotiadis, G., Tsiripidis, I., Bergmeier, E. & Dimopoulos, P. Hellenic Woodland Database. Biodiver. Ecol. 4, 389 (2012).Article 

    Google Scholar 
    Stančić, Z. Phytosociological Database of Non‐Forest Vegetation in Croatia. Biodiver. Ecol. 4, 391 (2012).Article 

    Google Scholar 
    Lájer, K. et al. Hungarian Phytosociological database (COENODATREF): sampling methodology, nomenclature and its actual stage. Ann. Botanica Nuova Ser. 7, 197–201 (2008).
    Google Scholar 
    Landucci, F. et al. VegItaly: The Italian collaborative project for a national vegetation database. Plant Biosyst. 146, 756–763 (2012).Article 

    Google Scholar 
    Casella, L., Bianco, P. M., Angelini, P. & Morroni, E. Italian National Vegetation Database (BVN/ISPRA). Biodiver. Ecol. 4, 404 (2012).Article 

    Google Scholar 
    Agrillo, E. et al. Nationwide Vegetation Plot Database—Sapienza University of Rome: state of the art, basic figures and future perspectives. Phytocoenologia 47, 221–229 (2017).Article 

    Google Scholar 
    Rūsiņa, S. Semi‐natural Grassland Vegetation Database of Latvia. Biodiver. Ecol. 4, 409 (2012).Article 

    Google Scholar 
    Schaminée, J. H. J. et al. Schatten voor de natuur. Achtergronden, inventaris en toepassingen van de Landelijke Vegetatie Databank (KNNV Uitgeverij, 2006).Kącki, Z. & Śliwiński, M. The Polish Vegetation Database: structure, resources and development. Acta Soc. Bot. Pol. 81, 75–79 (2012).Article 

    Google Scholar 
    Indreica, A., Turtureanu, P. D., Szabó, A. & Irimia, I. Romanian Forest Database: a phytosociological archive of woody vegetation. Phytocoenologia 47, 389–393 (2017).Article 

    Google Scholar 
    Vassilev, K. et al. The Romanian Grassland Database (RGD): historical background, current status and future perspectives. Phytocoenologia 48, 91–100 (2018).Article 

    Google Scholar 
    Aćić, S., Petrović, M., Dajić Stevanović, Z. & Šilc, U. Vegetation database Grassland vegetation in Serbia. Biodiver. Ecol. 4, 418 (2012).Article 

    Google Scholar 
    Golub, V. et al. Lower Volga Valley Phytosociological Database. Biodiver. Ecol. 4, 419 (2012).Article 

    Google Scholar 
    Lysenko, T., Kalmykova, O. & Mitroshenkova, A. Vegetation Database of the Volga and the Ural Rivers Basins. Biodiver. Ecol. 4, 420–421 (2012).Article 

    Google Scholar 
    Prokhorov, V., Rogova, T. & Kozhevnikova, M. Vegetation database of Tatarstan. Phytocoenologia 47, 309–313 (2017).Article 

    Google Scholar 
    Šilc, U. Vegetation Database of Slovenia. Biodiver. Ecol. 4, 428 (2012).Article 

    Google Scholar 
    Šibík, J. Slovak Vegetation Database. Biodiver. Ecol. 4, 429 (2012).Article 

    Google Scholar 
    Kuzemko, A. Ukrainian Grasslands Database. Biodiver. Ecol. 4, 430 (2012).Article 

    Google Scholar 
    Cayuela, L. et al. The Tree Biodiversity Network (BIOTREE-NET): prospects for biodiversity research and conservation in the Neotropics. Biodiver. Ecol. 4, 211–224 (2012).Article 

    Google Scholar 
    Wagner, V., Spribille, T., Abrahamczyk, S. & Bergmeier, E. Timberline meadows along a 1000 km transect in NW North America: species diversity and community patterns. Appl. Veg. Sci. 17, 129–141 (2014).Article 

    Google Scholar 
    Aubin, I., Gachet, S., Messier, C. & Bouchard, A. How resilient are northern hardwood forests to human disturbance? An evaluation using a plant functional group approach. Ecoscience 14, 259–271 (2007).Article 

    Google Scholar 
    Sieg, B., Drees, B. & Daniëls, F. J. A. Vegetation and altitudinal zonation in continental West Greenland. Medd. om. Gr.ønland Biosci. 57, 1–93 (2006).
    Google Scholar 
    Peet, R. K., Lee, M. T., Jennings, M. D. & Faber-Langendoen, D. VegBank—a permanent, open-access archive for vegetation-plot data. Biodiv. Ecol. 4, 233–241 (2012).Article 

    Google Scholar 
    Peet, R. K. et al. Vegetation‐plot database of the Carolina Vegetation Survey. Biodiver. Ecol. 4, 243–253 (2012).Article 

    Google Scholar 
    Walker, D. A. et al. The Alaska Arctic Vegetation Archive (AVA‐AK). Phytocoenologia 46, 221–229 (2016).Peyre, G. et al. VegPáramo, a flora and vegetation database for the Andean páramo. Phytocoenologia 45, 195–201 (2015).Article 

    Google Scholar 
    Vibrans, A. C., Sevgnani, L., Lingner, D. V., Gasper, A. L. & Sabbagh, S. The Floristic and Forest Inventory of Santa Catarina State (IFFSC): methodological and operational aspects. Pesqui. Florest. Brasileira 30, 291–302 (2010).Article 

    Google Scholar 
    Pauchard, A., Fuentes, N., Jiménez, A., Bustamante, R. & Marticorena, A. In Plant Invasions in Protected Areas (eds Foxcroft, L., Pyšek, P., Richardson, D., Genovesi, P.) (Springer, 2013).González-Caro, S., Umaña, M. N., Álvarez, E., Stevenson, P. R. & Swenson, N. G. Phylogenetic alpha and beta diversity in tropical tree assemblages along regional-scale environmental gradients in northwest South America. J. Plant Ecol. 7, 145–153 (2014).Article 

    Google Scholar 
    Bresciano, D., Altesor, A. & Rodríguez, C. The growth form of dominant grasses regulates the invasibility of Uruguayan grasslands. Ecosphere 5, 1–12 (2014).Aiba, S.-i & Kitayama, K. Structure, composition and species diversity in an altitude-substrate matrix of rain forest tree communities on Mount Kinabalu, Borneo. Plant Ecol. 140, 139–157 (1999).Article 

    Google Scholar 
    Armstrong, A. H., Shugart, H. H. & Fatoyinbo, T. E. Characterization of community composition and forest structure in a Madagascar lowland rainforest. Tropical Conserv. Sci. 4, 428–444 (2011).Article 

    Google Scholar 
    Ayyappan, N. & Parthasarathy, N. Biodiversity inventory of trees in a large-scale permanent plot of tropical evergreen forest at Varagalaiar, Anamalais, Western Ghats, India. Biodivers. Conserv 8, 1533–1554 (1999).Article 

    Google Scholar 
    Balslev, H., Valencia, R., Paz y Miño, G., Christensen, H. & Nielsen, I. In Forest biodiversity in North, Central and South America, and the Caribbean: research and monitoring (eds. Dallmeier, F. & Comiskey, J. A.) 585–594 (1998).Bordenave, B. G., Granville, J.-J. D. & Hoff, M. Measurement of species richness of vascular plants in a neotropical rain forest in French Guiana. (1998).Boyle, T. J. B. & Boontawee, B. CIFOR’s Research Programme on Conservation of Tropical Forest Genetic Resources, 395 (Center for International Forestry Research CIFOR, 1995).Bunyavejchewin, S., Baker, P. J., LaFrankie, J. V. & Ashton, P. S. Stand structure of a seasonal dry evergreen forest at Huai Kha Khaeng Wildlife Sanctuary, western Thailand. Nat. Hist. Bull. Siam Soc. 49, 89–106 (2001).
    Google Scholar 
    Cadotte, M. W., Franck, R., Reza, L. & Lovett-Doust, J. Tree and shrub diversity and abundance in fragmented littoral forest of southeastern Madagascar. Biodivers. Conserv. 11, 1417–1436 (2002).Article 

    Google Scholar 
    Cano Ortiz, A. et al. Phytosociological study, diversity and conservation status of the cloud forest in the Dominican Republic. Plants (Basel, Switzerland) 9, 741 (2020).Chisholm, R. A. et al. Scale-dependent relationships between tree species richness and ecosystem function in forests. J. Ecol. 101, 1214–1224 (2013).Article 

    Google Scholar 
    Chu, C. et al. Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees. Ecol. Lett. 22, 245–255 (2019).ADS 
    PubMed 

    Google Scholar 
    Condit, R. S. et al. Tropical Tree a—Diversity: Results From a Worldwide Network of Large Plots (CABI, 2005).D’Amico, C. & Gautier, L. Inventory of a 1-ha lowland rainforest plot in Manongarivo, (NW Madagascar). Candollea 55, 319–340 (2000).
    Google Scholar 
    Davidar, P., Mohandass, D. & Vijayan, L. Floristic inventory of woody plants in a tropical montane (shola) forest in the Palni hills of the Western Ghats, India. Trop. Ecol. 12, 42–58 (2007).
    Google Scholar 
    Davies, S. J. & Becker, P. Floristic composition and stand structure of mixed dipterocarp and heath forests in Brunei Darussalam. J. Trop. Sci. 8, 542–569 (1996).
    Google Scholar 
    Duivenvoorden, J. F. Vascular plant species counts in the rain forests of the middle Caquet area. Colomb. Amazon. Biodivers. Conserv. 3, 685–715 (1994).Article 

    Google Scholar 
    Ek, R. C. Botanical diversity in the tropical rain forest of Guyana: Botanische diversiteit in het tropisch regenwoud van Guyana. (Met een samenvatting in het Nederlands) (Universiteit Utrecht, 1997).Galeano, G., Suárez, S. & Balslev, H. Vascular plant species count in a wet forest in the Chocó area on the Pacific coast of Colombia. Biodivers. Conserv. 7, 1563–1575 (1998).Article 

    Google Scholar 
    Garrigues, J. P. Action anthropique sur la dynamique des formations végétales au sud de l’Inde (Ghâts occidentaux, Etat du Karnataka, District de Shimoga) (University of Claude Bernard, Lyon I, 1999).Gastauer, M., Leyh, W. & Meira-Neto, J. A. A. Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil. Biodiv. Data J. 3, e5425 (2015).Article 

    Google Scholar 
    Helmi, N., Kartawinata, K. & Samsoedin, I. An undescribed lowland natural forest at Bodogol, Gunung Gede Pangrango National Park, Cibodas Biosphere Reserve, West Java, Indonesia. Reinwardtia 13, 33–44 (2009).
    Google Scholar 
    Hernández, L., Dezzeo, N., Sanoja, E., Salazar, L. & Castellanos, H. Changes in structure and composition of evergreen forests on an altitudinal gradient in the Venezuelan Guayana Shield. Rev. de. Biol.ía Tropical 60, 11–33 (2012).
    Google Scholar 
    Ho, B. C. et al. The plant diversity in Bukit Timah Nature Reserve, Singapore. Gardens’ Bull. Singap. 71, 41–144 (2019).Article 

    Google Scholar 
    Hubbel, S. P. & Foster, R. B. In Tropical Rain Forest: Ecology and Management (eds Sutton, S. L., Whitmore, T. C. & Chadwick, S.) 25–41 (Blackwell Scientific Publications,1983).Kartawinata, K., Samsoedin, I., Heriyanto, M. & Afriastini, J. J. A tree species inventory in a one-hectare plot at the Batang Gadis National Park, North Sumatra, Indonesia. Reinwardtia 12, 145 (2013).Article 

    Google Scholar 
    Kiratiprayoon, S. Measuring and monitoring biodiversity in tropical and temperate forests. In: IUFRO Symposium, Chiang Mai (Thailand), 27 Aug-2 (CIFOR, 1994).KuoJung, C., WeiChun, C., KeiMei, C. & ChangFu, H. Vegetation dynamics of a lowland rainforest at the northern border of the paleotropics at Nanjenshan, southern Taiwan. Taiwan J. Sci. 25, 29–40 (2010).
    Google Scholar 
    Lan, G., Zhu, H. & Cao, M. Tree species diversity of a 20-ha plot in a tropical seasonal rainforest in Xishuangbanna, southwest China. J. For. Res. 17, 432–439 (2012).CAS 
    Article 

    Google Scholar 
    Lee, H. S. et al. Floristic and structural diversity of 52 hectares of mixed dipterocarp forest in Lambir Hills National Park, Sarawak, Malaysia. J. Trop. Sci. 14, 379–400 (2002).
    Google Scholar 
    Linares-Palomino, R. et al. Non-woody life-form contribution to vascular plant species richness in a tropical American forest. Plant Ecol. 201, 87–99 (2009).Article 

    Google Scholar 
    Lubini, A. & Mandango, A. Etude phytosociologique et ecologique des forets a Uapaca guineensis dans le nord-est du district forestier central (Zaire). Bull. Jard. Bot. Natl Belg. 51, 231 (1981).Article 

    Google Scholar 
    Makana, J.-R., Hart, T. & Hart, J. Forest structure and diversity of lianas and understory treelets in monodominant and mixed stands in the Ituri Forest, Democratic Republic of the Congo. Liana Article Index 20 (1998).Mansur, M. & Kartawinata, K. Phytosociology of a lower montane forest on Mt. Batulanteh, Sumbawa, Indonesia. Reinwardtia 16, 77 (2017).Article 

    Google Scholar 
    Mikoláš, M. et al. Natural disturbance impacts on trade-offs and co-benefits of forest biodiversity and carbon. Proc. R. Soc. B 288, 20211631 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mohandass, D. & Davidar, P. Floristic structure and diversity of a tropical montane evergreen forest (shola) of the Nilgiri Mountains, southern India. Trop. Ecol. 50, 219–229 (2009).
    Google Scholar 
    Monge González, M. et al. BIOVERA-Tree: tree diversity, community composition, forest structure and functional traits along gradients of forest-use intensity and elevation in Veracruz, Mexico. Biodiv. Data J. 9, e69560 (2021).Ngo, K. M., Davies, S., Nik, H., Faizu, N. & Lum, S. Resilience of a forest fragment exposed to long-term isolation in Singapore. Plant Ecol. Diver. 9, 397–407 (2016).Article 

    Google Scholar 
    Parthasarathy, N. Tree diversity and distribution in undisturbed and human-impacted sites of tropical wet evergreen forest in southern Western Ghats, India. Biodivers. Conserv. 8, 1365–1381 (1999).Article 

    Google Scholar 
    Parthasarathy, N. & Karthikeyan, R. Biodiversity and population density of woody species in a tropical evergreen forest in Courtallum reserve forest, Western Ghats, India. Trop. Ecol. 38 (1997).Pascal, J. P. Wet Evergreen Forests of the Western Ghats of India (Institut français de Pondichéry, 1988).Pascal, J. P. & Pelissier, R. Structure and floristic composition of a tropical evergreen forest in south-west India. J. Trop. Ecol. 12, 191–214 (1996).Article 

    Google Scholar 
    Phillips, O. L. et al. Efficient plot-based floristic assessment of tropical forests. J. Trop. Ecol. 19, 629–645 (2003).Article 

    Google Scholar 
    Proctor, J., Anderson, J. M., Chai, P. & Vallack, H. W. Ecological Studies in Four Contrasting Lowland Rain Forests in Gunung Mulu National Park, Sarawak: I. Forest Environment, Structure and Floristics. J. Ecol. 71, 237 (1983).Article 

    Google Scholar 
    Ramesh, B. R. et al. Forest stand structure and composition in 96 sites along environmental gradients in the central Western Ghats of India. Ecology 91, 3118 (2010).Article 

    Google Scholar 
    Razak, S. A. & Haron, N. W. Phytosociology of Aquilaria Malaccensis Lamk. and its communities from a tropical forest reserve in peninsular Malaysia. Pak. J. Bot. 47, 2143–2150 (2015).
    Google Scholar 
    Romoleroux, K. et al. Especies leñosas (dap= 1 cm) encontradas en dos hectáreas de un bosque de la Amazonía ecuatoriana. Estudios sobre diversidad y ecología de plantas, 189–215 (1997).Sarah, A. R., Nuradnilaila, H., Haron, N. W. & Azani, M. A Phytosociological Study on the Community of Palaquium gutta (Hook. f.) Baill.(Sapotaceae) at Ayer Hitam Forest Reserve, Selangor, Malaysia. Sains Malaysiana 44, 491–496 (2015).Article 

    Google Scholar 
    Schrader, J., Moeljono, S., Tambing, J., Sattler, C. & Kreft, H. A new dataset on plant occurrences on small islands, including species abundances and functional traits across different spatial scales. Biodiv. Data J. 8, e55275 (2020).Article 

    Google Scholar 
    Sheil, D., Kartawinata, K., Samsoedin, I., Priyadi, H. & Afriastini, J. J. The lowland forest tree community in Malinau, Kalimantan (Indonesian Borneo): results from a one-hectare plot. Plant Ecol. Diver. 3, 59–66 (2010).Article 

    Google Scholar 
    Sukumar, R. et al. Long-term monitoring of vegetation in a tropical deciduous forest in Mudumalai, southern India. Curr. Sci. 62, 608–616 (1992).
    Google Scholar 
    van Andel, T. R. Floristic composition and diversity of three swamp forests in northwest Guyana. Plant Ecol. 167, 293–317 (2003).Article 

    Google Scholar 
    Webb, E. L. & Fa’aumu, S. Diversity and structure of tropical rain forest of Tutuila, American Samoa: effects of site age and substrate. Plant Ecol. 144, 257–274 (1999).Article 

    Google Scholar 
    Zimmerman, J. K. et al. Responses of Tree Species to Hurricane Winds in Subtropical Wet Forest in Puerto Rico: Implications for Tropical Tree Life Histories. J. Ecol. 82, 911 (1994).Article 

    Google Scholar 
    Olson, D. M. et al. Terrestrial ecoregions of the worlds: a new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

    Google Scholar 
    Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sandel, B. et al. The influence of Late Quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Schultz, J. The Ecozones of the World (Springer, 2005).Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. Package ‘dismo’. Available online at: http://cran.r-project.org/web/packages/dismo/index.html (2011).Zhou, S. et al. Estimating stock depletion level from patterns of catch history. Fish. Fish. 18, 742–751 (2017).Article 

    Google Scholar 
    Rocchini, D. et al. Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Prog. Phys. Geogr. 35, 211–226 (2011).Article 

    Google Scholar 
    Potapov, P., Laestadius, L. & Minnemeyer, S. Global map of potential forest cover www.wri.org/forest-restoration-atlas (2011).Tuanmu, M. N. & Jetz, W. A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23, 1031–1045 (2014).Article 

    Google Scholar 
    Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).Article 

    Google Scholar 
    Pebesma, E. & Heuvelink, G. Spatio-temporal interpolation using gstat. RFID J. 8, 204–218 (2016).
    Google Scholar 
    R Development Core Team. R: A language and environment for statistical computing v.3.6.1. R Foundation for Statistical Computing http://www.R-project.org/ (2019).South, A. rnaturalearth: World Map Data from Natural Earth v.0.1.0. R package https://CRAN.R-project.org/package=rnaturalearth (2017).Sabatini, F. M. et al. Global patterns of vascular plant alpha-diversity [Dataset]. iDiv Data Repository. https://doi.org/10.25829/idiv.3506-p4c0mo (2022).Sabatini, F. M. fmsabatini/GlobalLocal_PlantRichness: NatComms R3 v.3. Zenodo https://doi.org/10.5281/zenodo.6659837 (2022). More

  • in

    Hotspot in ferruginous rock may have serious implications in Brazilian conservation policy

    Pseudocryptic diversityThe richness was the measure of the subterranean diversity, we surveyed all data about previous records for Brazilian Collembola cave species, ecological status, lithology, and distribution from the literature, and included 11 newly found pseudocryptic species from subterranean habitats in iron and limestone rock. The pseudocryptic species were verified by comparison of chaetotaxy and “micro-morphology” through optic and scanning microscopy of disjunct populations of a widespread morphotype. The imagery was compared under hypotheses of chaetotaxic and morphologic homology, previously defined by different authors. Those populations with consistent discrete chaetotaxic and morphologic patterns were assumed to be independent species, therefore they were taxonomically diagnosed, named, and ordered in a dichotomic identification key with all Brazilian species of the genus.MicroscopySpecimens were preserved in ethanol 70% and mounted on slides following Jordana et al.31, after clearing using Nesbitt’s solution for study under phase contrast microscope, line drawings were made with help of a drawing tube. For scanning electronic microscope (SEM) study, specimens were dehydrated by ethanol, dried in a critical point dryer, and covered in gold.HomologyThe terminology used in the diagnoses for the hypotheses of homology followed: labial chaetotaxy after Gisin32 with additions of Zhang and Pan33, Fjellberg34 for labial palp papillae and maxillary palp; postlabial chaetotaxy after Chen and Christiansen35, with adaptations of Cipola et al.36 for J series; clypeal chaetotaxy after Yoshii and Suhardjono37; labral chaetotaxy after Cipola et al.38; unguiculus lamellae after Hüther39; Anterior dens chaetotaxy after Oliveira et al.40; Mari-Mutt41 for dorsal head chaetotaxy, with additions of Soto-Adames42; Szeptycki43 and Zhang and Deharveng44 for S-chaetotaxy; and Szeptycki45 for dorsal chaetotaxy, with additions and modifications provided by Soto-Adames42 and Zhang et al.46. Symbols used to depict the chaetotaxy are presented in Fig. 4A–C. Codes will be used in italics along the text to replace the morphological description of each chaeta and sensillum type. Additional information about morphology and chaetotaxy of discussed species was obtained from the literature.Abbreviations used in the diagnosesAnt–antennal segment(s); b.c.–basal chaeta(e), t.a.–terminal appendage of the maxillary palp; l.p.–lateral process of labial papilla E, lpc–labial proximal chaeta(e); Th–thoracic segment; Abd–abdominal segment(s); Omt–trochanteral organ; a.e.–antero-external lamella, a.i.–antero-internal lamella, a.t.–unguis apical tooth, b.a.–basal anterior tooth of unguis, b.p.–basal posterior tooth of unguis, m.t.–unguis median tooth, p.i.–postero-internal lamella, p.e.–postero-external lamella; mac–macrochaeta(e), mes–mesochaeta(e), mic–microchaeta(e), ms–specialized microchaeta(e), psp–pseudopore(s), sens–specialized ordinary chaeta(e) (sensillum), MSS–Mesovoid Shallow Substratum.Ecological statusTo avoid subjectivity and ambiguity to determine the ecological status of the species, we assumed to be a troglobite all the species with some degree of troglomorphism exclusively distributed in the subterranean environment, either caves, MSS, or both. Species distributed in the surface and subterranean habitats were assumed to be troglophiles.Identification Key for the known and new species of the genus Trogolaphysa recorded in Brazil
    Taxonomic diagnoses and morphological platesType materials are deposited in the Coleção de Referência de Fauna de Solo, Universidade Estadual da Paraíba (CRFS-UEPB) and Museu Nacional Rio de Janeiro, Universidade Federal do Rio de Janeiro (MNRJ-UFRJ).

    Additional records in Supplementary Material S1, taxonomic references in S2.

    Family Paronellidae Börner, 1906

    Subfamily Paronellinae Börner, 1906

    Tribe Paronellini sensu Zhang et al., 2019

    Genus Trogolaphysa Mills, 1938

    (Figs. 3, 4, 5, 6, 7, 8, 9, 10, 11)

    Figure 3Trogolaphysa sp.: habitus lateral view. (A, B) specimen fixed in ethanol. (C, D) SEM photographs.Full size imageFigure 4Trogolaphysa sp. SEM: general body chaetae. (A) Antennal chaetae, sensilla and scales: 1—macrochaeta with short ciliation, 2—macrochaeta with long ciliation, 3—microchaeta with long ciliation, 4—microchaeta with short ciliation, 5—finger-shaped sens, 6—wrinkly sens, 7—coffee bean shaped sens, 8—rod sens, 9—spine-like sens, 10—Ant IV subapical-organ, 11—lanceolate scale, 12—rounded scales. (B) Head chaetae and scales: 1—strait macrochaeta with long ciliation, 2—blunt macrochaeta, 3—smooth chaeta, 4—blunt chaeta, 5—strait microchaeta with long ciliation, 6—labial r microchaeta, 7—cephalic anterior scale, 8—cephalic posterior scale. (C) Body and appendages chaetae, sens and scales: 1—bothriotrichum, 2—blunt macrochaeta, 3—blunt mesochaeta, 4—dens external ciliate chaeta, 5—smooth microchaeta, 6—blunt microchaeta, 7—fan-shape chaeta, 8—dental spine, 9—‘al’ sens, 10—‘ms’ sens, 11—lanceolate scale, 12—intersegmental scale.Full size imageFigure 5Trogolaphysa sp. SEM: antenna: (A) Ant IV dorsal view. (B) Ant IV apex dorsal view, arrow indicates finger-shaped and wrinkly sens. (C) Ant IV apex ventral view, left arrow indicates Ant IV subapical-organ, right arrow point one sensillum type A8. (D) Ant II dorsal view, dashed line indicates rod sens. (E) Detail of the sensilla of the Ant III apical organ (red). (F) Ant I dorsal view spine like sens (arrows indicate the sensilla in red). (G) Detail of the Ant I basal, arrow indicates psp and antenobasal organ (yellow and red respectively).Full size imageFigure 6Trogolaphysa sp. SEM: head and mouthpart chaetotaxy. (A) clypeus, (B) dorsal head, (C) eyes (red) circled by dashed line, arrow indicates antenobasal organ and psp, (D) ventral head, (E) maxillary palp and sublobal plate (right side), (F) detail of maxillary palp.Full size imageFigure 7Trogolaphysa sp. SEM: thorax and abdomen dorsal chaetotaxy: (A) Th II, (B) Th III, (C) Abd I-II, (D) Abd III.Full size imageFigure 8Trogolaphysa sp. SEM: (A) Abd IV dorsal chaetotaxy, (B) Abd V dorsal chaetotaxy, (C) anal pore and male genital papilla.Full size imageFigure 9Trogolaphysa. sp. SEM: empodial complex III (A) external lamella of unguis with external teeth (pseudonychia, yellow), (B) unguis and unguiculus lateral view, unguis internal lamella with basal, medial and apical teeth (blue, red and yellow respectively), unguiculus with internal and external teeth, tenent hair capitate (white arrow), (C) lateral view, unguiculus lamellae, tenent hair acuminate (white arrow).Full size imageFigure 10Trogolaphysa sp. SEM: appendages (A) Metatrochanteral organ with pseudopores (alveoli marked in yellow, white arrows indicate pseudopores), (B) ventral tube posterior chaetae, (C) ventral tube anterior chaetae, (D) Tenaculum.Full size imageFigure 11Trogolaphysa sp. SEM: furca. (A) manubrial plate pseudopores (yellow), (B) antero-proximal chaetae of dens, (C) dens anterior view, (D) mucro.Full size imageDiagnosisHabitus typical of this genus (Fig. 3A–D), hyaline scales presents on Ant. I–II, head, body, and ventral face of furcula (Figs. 3C–D, 4A–C, 5D, F, 7, 8, 11C), Ant IV smooth or annulated and never subdivided in two (Fig. 5A); eyes 0–8 (ex. Fig. 6C); prelabral and labral formula 4/5,5,4 (prelabral smooth or ciliate, pma smooth chaetae) (Fig. 6A); antennobasal-organ present (Fig. 6C); labial chaetae L1–2 not reduced (Fig. 6E); sublobal plate of maxillary palp with 2 chaetae (Fig. 6E); Th II normally with a5 mac and p3 complex with variable number of mac, and Th III with p3 mac present or abset (Fig. 7A, B), abdominal segments II–IV with 2, 3, 3 bothriotricha (Figs. 7C, D, 8A); unguis with three external lamellae and unguiculus with p.e. lamella serrate or smooth (Fig. 9A–C); trochanteral organ with 2–4 psp (Fig. 10A) collophore anterior side with 2–3 distal mac (Fig. 10C); tenaculum with four teeth on each branch and one anterior chaeta (Fig. 10D); manubrium without spines, manubrial plate with 2–3 psp (Fig. 11A); anterior proximal dens with b.a., b.m. and i5 chaetae (Fig. 11B); dens with 1–2 rows of spines; mucro square or rectangular but relatively short, with 3–5 teeth (Fig. 11D).Trogolaphysa bellinii sp. nov. Oliveira, Lima & ZeppeliniFigures 12, 13 and 14, Tables 1 and 2Figure 12Trogolaphysa bellinii sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 13Trogolaphysa bellinii sp. nov.: Dorsal chaetotaxy: (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 14Trogolaphysa bellinii sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageTable 2 Trogolaphysa species of the Neotropical Region, comparative morphology.Full size tableType material. Holotype female in slide (15,482/CRFS-UEPB): Brazil, Minas Gerais State, Barão de Cocais municipality, cave MDIR-0028, next to “Mina de Brucutu”, 19°52′48.7″S, 43°26′13.6″W, 19–23.viii.2019, Carste team coll. Paratypes in slides (15,468, 15,483/CRFS-UEPB): 2 females, same data as holotype. Paratypes in slides (15,519, 15,576/CRFS-UEPB donated to MNJR): 2 females, same data as holotype. Additional records see S1.Description. Total length (head + trunk) of specimens 1.53–1.75 mm (n = 5), holotype 1.70 mm.Head. Ratio antennae: trunk = 1: 1.29–1.95 (n = 5), holotype = 1: 1.95; Ant III shorter than Ant II; Ant segments ratio as I: II, III, IV = 1: 1.80–2.24, 0.85–2.08, 0.85–2.08, holotype = 1: 1.80, 0.85, 1.34. Antennal chaetotaxy: Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with a longitudinal row with about eight rod sens, ventrally with one subapical-organ and several wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, about three wrinkly sens on external longitudinal row, apical organ with two mic smooth chaetae externally, two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with four sub-apical finger-shaped sens, one wrinkly sens and two subapical rod sens, ventrally with one apical psp, about six wrinkly sens on longitudinal external row (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about five smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0, rarely 2 + 2. Head dorsal chaetotaxy (Fig. 12A) with 12 An (An1a–3), six A (A0–5), five M (M1–5), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pa5 and Pm3 as mes, An1a–3a with 10 mac plus two mes, A0 and A2 as mac; interocular p mes present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 12B). Ventral chaetotaxy with 35–38 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of four to seven mes chaetae distally (Fig. 12B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 12B). Maxillary palp with t.a. smooth and 1.23× larger than b.c.Thorax dorsal chaetotaxy (Fig. 13A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with three mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–7), three mic (m4, m6–6p), three mes (m6e, m7–7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 1.04–1.36: 1 (n = 5), holotype = 1.05: 1.Abdomen dorsal chaetotaxy (Fig. 13B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by five and four fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with six, two and three fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with two mic (A1, A6), two mac (A3, A5), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), three mic (T1, T5–6), one mes (T7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), two mes (Ee10–11), one mac (Ee9), one mic (F1), two mes (F3, F3p), one mac (F2), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by five and two (T3) fan-shaped chaetae respectively; ps and as present, and at least six supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with four psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a, m5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e) two mes (ap6–pp6), four mac (p1, p3–5) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 3.70–4.37 (n = 5), holotype = 1: 4.37.Legs. Trochanteral organ diamond shape with about 20 spine-like chaetae, plus two psp one external and one on distal vertex of Omt (Fig. 14A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with four teeth, basal pair subequal, b.p. not reaching the m.t. apex, m.t. just after the distal half, a.t. present. Unguiculus with lamellae smooth and lanceolate (a.i., a.e., p.i.), except p.e. slightly serrate (Fig. 14B); ratio unguis: unguiculus = 1.56–1.79: 1 (n = 5), holotype = 1.56: 1. Tibiotarsal smooth chaetae about 0.9 × smaller than unguiculus; tenent hair capitate and about 0.55 × smaller than unguis outer lamella.Collophore (Fig. 14C). Anterior side with 12 ciliate, apically acuminate chaetae, five proximal, four subdistal (as mes) and three distal mac; lateral flap with 11 chaetae, five ciliate in the proximal row and six smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 14D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 24 external and 25 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.29 (holotype).Etymology. Species named after Dr. Bruno C. Bellini in recognition of his work on Brazilian Collembola.Remarks. Trogolaphysa bellinii sp. nov. resembles T. bessoni, T. epitychia sp. nov., and T. mariecurieae sp. nov. by 0 + 0 eyes (T. bellinii sp. nov. rarely with 2 + 2 eyes), Th II with 3 + 3 mac, and Th III without mac, but can be distinguished by presenting Abd IV with 4 + 4 central mac (A3, A5, B4–5); T. epitychia sp. nov. with 3 + 3 central mac on Abd IV, T. mariecurieae sp. nov. with 2 + 2 central mac on Abd IV.Trogolaphysa lacerta sp. nov. Lima, Oliveira & ZeppeliniFigures 15, 16 and 17, Tables 1 and 2Figure 15Trogolaphysa lacerta sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 16Trogolaphysa lacerta sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 17Trogolaphysa lacerta sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype male in slide (10,311/CRFS-UEPB): Brazil, Minas Gerais State, Conceição do Rio Acima municipality, cave GAND-115, next to “Lapa do Calango”, 20°04′08.4″S, 43°40′09.9″W, 10.ii–20.iii.2014, Carste team coll. Paratypes in slides (10,312, 10,309/CRFS-UEPB): 2 males, same data as holotype. Paratypes in slides (10,313, 10,314/CRFS-UEPB donated to MNJR): 2 females, same data as holotype. Additional records see S1.Description. Total length (head + trunk) of specimens 1.31–2.43 mm (n = 5), holotype 1.86 mm.Head. Ratio antennae: trunk = 1: 1.33–1.46 (n = 2), holotype = 1: 1.46; Ant III shorter than Ant II; Ant segments ratio, I: II, III, IV = 1: 1.78–2.05: 1.5–1.64: 2.64–2.83, holotype = 1: 1.80: 1.64: 2.64. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with a longitudinal row with about five rod sens, ventrally with one subapical-organ and several wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, one apical wrinkly sens on, apical organ with two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with three sub-apical finger-shaped sens, one wrinkly sens and two apical rod sens, ventrally with one apical psp, one longitudinal external row with two subapical wrinkly sens and two medial finger-shaped sens (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about five smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0, rarely 3 + 3. Head dorsal chaetotaxy (Fig. 15A) with 15 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–2, Pa4–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pm3, Pa5 and Pp7 as mes, An1a–3a with 11 mac plus four meso, A0 and A2 as mac; interocular p mes present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 15B). Ventral chaetotaxy with 36–38 ciliate chaetae and 1 reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of three to five mes chaetae distally (Fig. 15B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 15B). Maxillary palp with t.a. smooth and 1.28× larger than t.a.Thorax dorsal chaetotaxy (Fig. 16A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with six mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–7), three mic (m4, m6–6p), three mes (m6e, m7–7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 1.09–1.46: 1 (n = 5), holotype = 1.09: 1.Abdomen dorsal chaetotaxy (Fig. 16B, C). Abd I m series with six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and two fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), four mic (p6e, p7i–7p), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with seven, two and four fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with four mic (A1, A5–6, Ae1), one mac (A3), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), five mic (T1, T3, T5–7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), two mes (Ee10–11), one mac (Ee9), one mic (F1), two mes (F3–3p), one mac (F2), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and one fan-shaped chaetae respectively; ps and as present, and at least six supernumerary sens with uncertain homology ‘s’(Fig. 8A); Abd. IV posteriorly with five to six psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a, m5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e) two mes (ap6–pp6), four mac (p1, p3–5) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 3.70–4.37 (n = 5), holotype = 1: 4.37.Legs. Trochanteral organ diamond shape with about 24 spine-like chaetae, plus two psp one external and one on distal vertex of Omt (Fig. 17A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with four teeth, basal pair subequal, b.p. not reaching the m.t. apex, m.t. just after the distal half, a.t. present. Unguiculus with all lamellae smooth and lanceolate (a.i., a.e., p.i., p.e.) (Fig. 17B); ratio unguis: unguiculus = 1: 1.50–1.79 (n = 5), holotype = 1: 1.75. Tibiotarsal smooth chaetae about 0.7× smaller than unguiculus; tenent hair slightly acuminate and about 0.44× smaller than unguis outer lamella.Collophore (Fig. 17C). Anterior side with 10 ciliate, apically acuminate chaetae, five proximal (thinner); three subdistal and two distal mac; lateral flap with 11 chaetae, five ciliate in the proximal row and six smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 17D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 50 external and 37 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.31 (n = 5).Etymology. Lacerta from Latin means lizard, in allusion to the name of the cave where this species was found, Lapa do Calango (cave of the Calango), which is a small lizard common in this region.Remarks. Trogolaphysa lacerta sp. nov. The new species resembles T. caripensis, T. ernesti, T. piracurucaensis, T. formosensis and T. dandarae sp. nov. by the number of mac in Th II p3 complex (6 + 6), but is easily distinguished by the head m2 and s5 mic (T. caripensis, T. ernesti, T. formosensis, T. piracurucaensis as mac) and Th III without mac (T. dandarae sp. nov. 3 + 3).Trogolaphysa chapelensis sp. nov. Lima, Oliveira & ZeppeliniFigures 18, 19 and 20, Tables 1 and 2Figure 18Trogolaphysa chapelensis sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 19Trogolaphysa chapelensis sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 20Trogolaphysa chapelensis sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype female in slide (4550/CRFS-UEPB): Brazil, Minas Gerais State, Rio Acima municipality, cave Gruta-2d7, next to “Morro do Chapéu” 20°07′42.1″S, 43°54′26.2″W, 02–10.viii.2011, Andrade et al. coll. Paratypes in slides (4551–4553/CRFS-UEPB): 3 females, Brazil, Minas Gerais State, Rio Acima municipality, cave Gruta-7d7, Qd7, 9d7 respectively, 20°07′42.1″S, 43°54′26.7″W, 29.iii–01.vi.2011, Andrade et al. coll. Paratype in slide (4603/CRFS-UEPB donated to MNJR): 1 female, Brazil, Minas Gerais State, Rio Acima municipality, cave Gruta Qd7, 20°09′46.1″S, 43°49′36.2″W, 925 m, 29.iii–01.vi.2011, Andrade et al. Coll. Additional records see S1.Description. Total length (head + trunk) 1.21–2.22 mm (n = 5), holotype 2.22 mm.Head. Ratio antennae: trunk = 1: 1.31–1.16 (n = 3), holotype = 1: 1.16; Ant III shorter than Ant II; Ant segments ratio, I: II, III, IV = 1: 1.66–1.85, 1.65–1.78, 2.95–3.76, holotype = 1: 1.66, 1.65, 2.95. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with about six rod sens on longitudinal row, ventrally with one subapical-organ and about three subapical wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, one apical wrinkly sens, apical organ with two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with about three sub-apical finger-shaped sens and about three apical rod sens, ventrally with one apical psp, one longitudinal external row with four wrinkly sens (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about three smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 18A) with 15 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pm3 and Pa5 as mes, An1a–3a with 13 mac plus two mes, A0 and A2 as mac; interocular p mic present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 18B). Ventral chaetotaxy with 29 ciliate chaetae; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of six mes chaetae distally (Fig. 18B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 18B). Maxillary palp with t.a. smooth and 1.17× larger than b.c.Thorax dorsal chaetotaxy (Fig. 19A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with four mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–7), two mic (m4–6p), four mes (m6–6e, m7–7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 1.10–1.31: 1 (n = 4), holotype = 1.10: 1.Abdomen dorsal chaetotaxy (Fig. 19B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by five and four fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with six, two and three fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with three mic (A1, A6, Ae1), two mac (A3, A5), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), three mic (T1, T5–6), one mes (T7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), two mes (Ee10–11), one mac (Ee9), one mic (F1), two mes (F3–3p), one mac (F2), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and two (T3) fan-shaped chaetae respectively; ps and as present, and at least six supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with nine psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a, m5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e) two mes (ap6–pp6), four mac (p1, p3–5) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 3.46–5.80 (n = 5), holotype = 1: 5.80.Legs. Trochanteral organ diamond shape with about 23 spine-like chaetae, plus two psp one external and one on distal vertex of Omt (Fig. 20A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with four teeth, basal pair subequal, b.p. not reaching the m.t. apex, m.t. just after the distal half, a.t. present. Unguiculus with lamellae smooth and lanceolate (a.i., a.e., p.i.), except p.e. slightly serrate (Fig. 20B); ratio unguis: unguiculus = 1: 1.63–1.84 (n = 5), holotype = 1: 1.79. Tibiotarsal smooth chaetae about 0.8× smaller than unguiculus; tenent hair capitate and about 0.52× smaller unguis outer lamella.Collophore (Fig. 20C). Anterior side with 13 ciliate, apically acuminate chaetae, seven proximal (thinner); four subdistal and two distal mac; lateral flap with 11 chaetae, five ciliate in the proximal row and six smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 20D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 70 external and 30 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.33 (n = 5).Etymology. Species named after Type locality Morro do Chapeu.Remarks. Trogolaphysa chapelensis sp. nov. resembles T. jacobyi, T. caripensis, T. bessoni, and T. belizeana by te absence of eyes (0 + 0 eyes) but is easily distinguished by presenting 4 + 4 mac in Th II p3 complex (2–3 + 2–3 T. jacobyi; 6 + 6 T. caripensis; 2 + 2 T. bessoni; 2–4 + 2–4 T. belizeana), and 9 + 9 psp posterior Abd IV (4 + 4T. belizeana).Trogolaphysa crystallensis sp. nov. Oliveira, Lima & ZeppeliniFigures 21, 22 and 23, Tables 1 and 2Figure 21Trogolaphysa crystallensis sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 22Trogolaphysa crystallensis sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III; (C) Abd IV–V.Full size imageFigure 23Trogolaphysa crystallensis sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype female in slide (16,252/CRFS-UEPB): Brazil, Minas Gerais State, Mariana municipality, cave LOC-0090, next to “Cachoeira Crystal”, 20°20′20.8″S, 43°23′44.3″W, 11–14.xi.2019, Carste team coll. Paratype in slide (16,251/CRFS-UEPB): female, same data as holotype. Paratype in slide (16,254/CRFS-UEPB donated to MNJR): female, same data as holotype. Additional records see S1.Description. Total length (head + trunk) of specimens 1.40–1.68 mm (n = 3), holotype 1.68 mm.Head. Ratio antennae: trunk = 1: 1.24–2.30 (n = 2), holotype = 1: 1.24; Ant III shorter than Ant II length; Ant segments ratio as I: II, III, IV = 1: 1.72–1.78, 1.58–1.64, 3.11–3.14, holotype = 1: 1.78, 1.64, 3.14. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with about three rod sens on longitudinal row, ventrally with one subapical-organ and several wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, about three wrinkly sens on external longitudinal row, apical organ with two rod sens, and one finger-shaped sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with three sub-apical finger-shaped sens and one wrinkly sens, ventrally with one apical psp (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about three smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 21A) with 12–13 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pa5, Pm3 and Pp7 as mes, An1a–3a, A0 and A2 as mac; interocular p mes present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 21B). Ventral chaetotaxy with 33–35 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of four to six mes chaetae distally (Fig. 21B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 21B). Maxillary palp with t.a. smooth and 1.43 × larger than b.c.Thorax dorsal chaetotaxy (Fig. 22A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with five mac, respectively, al and ms present. Th III a, m, p series with two mic (a1–2), two mes (a6–7), theree mic (m4, m6–6p), three mes (m6e, m7–7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 1.05–1.27: 1 (n = 3), holotype = 1.05: 1.Abdomen dorsal chaetotaxy (Fig. 22B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and two fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae respectively, a5, m2 and m5 bothriotricha with six, two and three fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with three mic (A1, A6, Ae1), two mac (A3, A5), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), three mic (T1, T5–7), five mic (D1–1p, D3–3p, De3), one mes (D2), two mes (E4p–4p2), three mac (E1–3), four mes (Ee9–12), one mic (F1), three mes (F2–3p), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by three and two (T3) fan-shaped chaetae respectively; ps and as present, and at least 14 supernumerary sens with uncertain homology ‘s’(Fig. 8A); Abd. IV posteriorly with three psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a, m5e), three mac (m2–3, m5), five mic (p3a–P6ae), three mes (p6e–pp6), four mac (p1, p3–5) chaetae, respectively; as and acc.p4–5 present. Ratio Abd III: IV = 1: 4.06–4.51 (n = 3), holotype = 1: 4.51.Legs. Trochanteral organ diamond shape with about 18 spine-like chaetae, plus two psp one external and one on distal vertex of Omt (Fig. 23A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with three teeth, basal pair subequal, b.p. little larger, but not reaching the m.t. apex, m.t. just after the distal half, a.t. absent. Unguiculus with all lamellae smooth and lanceolate (a.i., a.e., p.i., p.e.) (Fig. 23B); ratio unguis: unguiculus = 1.48–1.79: 1 (n = 3), holotype = 1.48: 1. Tibiotarsal smooth chaetae about 0.8× smaller than unguiculus; tenent hair acuminate and about 0.5× smaller than unguis outer lamella.Collophore (Fig. 23C). Anterior side with 10 ciliate, apically acuminate chaetae, six proximal, two subdistal (as mes) and two distal mac; lateral flap with 11 chaetae, five ciliate in the proximal row and six smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 23D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 60 external and 28 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.31 (holotype).Etymology. Species named after Type locality Cachoeira Crystal (Portuguese for Crystal falls).Remarks. Trogolaphysa crystallensis sp. nov. resembles T. barroca sp. nov., T. gisbertae sp. nov., T. sotoadamesi sp. nov., T. triocelata and T. zampauloi sp. nov. by the absence of eyes (0 + 0 eyes) (T. triocelata 3 + 3 and T. zampauloi sp. nov. eventually 4 + 4), Th II with 5 + 5 mac, and Th III without mac. Can be distinguished from T. barroca sp. nov., T. gisbertae sp. nov., and T. sotoadamesi sp. nov. by the Abd IV with 4 + 4 central mac (A3, A5, B4–5); T. barroca sp. nov., T. gisbertae sp. nov., and T. triocelata, with 3 + 3 and T. sotoadamesi sp. nov. 2 + 2 central mac on Abd IV. Finally, the new species differentiates from T. zampauloi sp. nov. by unpaired lamella of unguis with one tooth, Omt with about 18 spine-like chaetae, dens external row with about 58 spines on T. crystallensis sp. nov. and unpaired lamella of unguis with two teeth, Omt with about 26 spine-like chaetae, dens external row with about 30 spines on T. zampauloi sp. nov.Trogolaphysa sotoadamesi sp. nov. Ferreira, Oliveira & ZeppeliniFigures 24, 25 and 26, Tables 1 and 2Figure 24Trogolaphysa sotoadamesi sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore, Gray cut circle pseudopore at the under surface.Full size imageFigure 25Trogolaphysa sotoadamesi sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 26Trogolaphysa sotoadamesi sp. nov.: (A) Trochanteral organ, ((B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype male in slide (13,162/CRFS-UEPB): Brazil, Minas Gerais State, Mariana municipality, cave ALEA 0003, next to “Mina de Alegria”, 20°09′6.81″S, 43°29′13.6″W, 07.ii.2018, Bioespeloeo team coll. Paratypes in slides (13,146, 13,153/CRFS-UEPB): 2 females, same data as holotype, except 12.vi.2017. Paratype in slide (13,173, 13,186/CRFS-UEPB donated to MNJR): 2 females, same data as holotype, except 09.vi.2017. Additional records see S1.Description. Total length (head + trunk) of specimens1.50–1.81 mm (n = 5), holotype 1.50 mm.Head. Ratio antennae: trunk = 1: 1.26–1.45 (n = 3), holotype = 1: 1.38; Ant III shorter than Ant II; Ant segments ratio, I: II, III, IV = 1: 1.78–2.76, 1.52–2.22, 2.61–3.90, holotype = 1: 2.04, 1.68, 3.16. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with a longitudinal row with about three rod sens, ventrally with one subapical-organ and with several wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, several wrinkly sens, apical organ with two coffee bean-like sens, one rod sens and one finger-shaped sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with two sub-apical rod sens and two finger-shaped sens, ventrally with one apical psp and several finger-shaped sens (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with about seven basal spine-like sens, about three smooth mic and several finger-shaped sens (Fig. 3A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 24A) with 16 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pm3 as mes (rarely mic), Pa5 as mes, An1a–3a, A0 and A2 as mac; interocular p mes present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 24B). Ventral chaetotaxy with 37 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of six mes chaetae distally (Fig. 24B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–p3) and subequal in length (Fig. 24B). Maxillary palp with t.a. smooth and 1.28× larger than b.c.Thorax dorsal chaetotaxy (Fig. 25A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with five mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3, a6), one mes (a7), four mic (m4, m6–7, m6p), two mes (m6e, m7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 1.17–1.52: 1 (n = 5), holotype = 1.03: 1.Abdomen dorsal chaetotaxy (Fig. 25B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by five and three fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with five, two and three fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with five mic (A1, A3, A5–6, Ae1), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), two mic (T1, T6), two mes (T5, T7), three mic (D1–2), two mes (D3p, De3), two mes (E4p–p2), three mac (E1–3), one mic (Ee12), three mes (Ee9–11), one mic (F1), two mes (F3–3p), one mac (F2), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and three (T3) fan-shaped chaetae respectively; ps and as present, and at least five supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with four psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a, m5e), three mac (m2–3, m5), five mic (p3a–p6ae), one mic (P6e) two mes (ap6–pp6), four mac (p1, p3–5) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 5.03–4.42 (n = 5), holotype = 1: 4.42.Legs. Trochanteral organ triangular shape with about 19–21 spine-like chaetae, plus two psp one external and one on distal vertex of Omt (Fig. 26A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with two teeth, basal pair unequal, b.p. larger than b.a.; m.t. and a.t. absent. Unguiculus with all lamellae smooth and lanceolate (a.i., a.e., p.i., p.e.) (Fig. 26B); ratio unguis: unguiculus = 1: 1.46–1.91 (n = 5), holotype = 1: 1.91. Tibiotarsal smooth chaetae about 0.8 × smaller unguiculus; tenent hair acuminate and about 0.4 × smaller than unguis outer lamella.Collophore (Fig. 26C). Anterior side with seven ciliate, apically acuminate chaetae, three proximal, two subdistal and two distal mac; lateral flap with nine chaetae, four ciliate in the proximal row and five smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with five ciliate chaetae (two inner mac) and three psp (Fig. 26D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 35 external and 26 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.39 (n = 5).Etymology. Species named after Dr. Felipe N. Soto-Adames for his contribution on Collembola taxonomy and systematics.Remarks. Trogolaphysa sotoadamesi sp. nov. resembles T. barroca sp. nov., T. crystallensis sp. nov., T. gisbertae sp. nov., T. zampauloi sp. nov. by 0 + 0 eyes (T. zampauloi sp. nov. rarely with 4 + 4 eyes), Th II p3 complex with five mac, Th III without mac, manubrial plate with five ciliate chaetae and mucro with four teeth. The new species T. sotoadamesi sp. nov. with 2 + 2 central mac on Abd IV differentiates from T. barroca sp. nov., T. gisbertae sp. nov. with 3 + 3, and T. crystallensis sp. nov., T. zampauloi sp. nov. with 4 + 4 central mac.Trogolaphysa mariecurieae sp. nov. Ferreira, Oliveira & ZeppeliniFigures 27, 28 and 29, Tables 1 and 2Figure 27Trogolaphysa mariecurieae sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore, Gray cut circle pseudopore at the under surface.Full size imageFigure 28Trogolaphysa mariecurieae sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 29Trogolaphysa mariecurieae sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype female in slide (9109/CRFS-UEPB): Brazil, Minas Gerais State, Conceição do Mato Dentro municipality, MSS 10/11, next to “Pico do Soldado” 19°00′23.86″S, 43°23′41.266″W, 11–10.ix.2015, Carste team coll. Paratypes in slides (5888, 5857/CRFS-UEPB): 2 females, same data as holotype, except,19–23.v.2014, Soares et al. coll.Paratype in slide (9222, 10,760/CRFS-UEPB donated to MNJR): 2 females, same data as holotype, except 19°00′18.72″S, 43°23′30.031″W, 14.x.2015 and 18–20.iv.2016. Additional records see S1.Description. Total length (head + trunk) of specimens 1.07–1.49 mm (n = 5), holotype 1.49 mm.Head. Ratio antennae: trunk = 1: 1.69–1.91 (n = 2), holotype = 1: 1.69; Ant III shorter than Ant II length; Ant segments ratio, I: II, III, IV = 1: 2.00–2.75, 1.69–2.55, 4.02–5.29, holotype = 1: 2.75, 1.69, 4.02. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short less ciliate mic and mac, and finger-shaped sens, dorsally with one longitudinal row with about four rod sens, ventrally with one subapical-organ and several wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short less ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, apical organ with two rod sens (Fig. 4A); Ant II dorsally and ventrally with several short less ciliate mic and mac, dorsally with five apical rod sens, ventrally with one apical psp, about five wrinkly sens on longitudinal external row (Fig. 4A); and Ant I dorsally and ventrally with several short less ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with seven basal spine-like sens, about five smooth mic, and several finger-shaped sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 27A) with 12 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–3, Pa5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pm3 and Pa5 as mes, An1a–3a, A0 and A2 as mac; interocular p mic present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 27B). Ventral chaetotaxy with 34 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of six mes chaetae distally (Fig. 27B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 27B). Maxillary palp with t.a. smooth and 1.13× larger than b.c.Thorax dorsal chaetotaxy (Fig. 28A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with three mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–7), three mic (m4–m6p), three mes (m6e, m7–7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 0.85–1.02: 1 (n = 4), holotype = 0.89: 1. Abdomen dorsal chaetotaxy (Fig. 28B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and two fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), two mic (p6e, p7i), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with five, two and two fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with three mic (A1, A6, Ae1), one mac (A4), two mic (B1–2), one mes (B6), one mac (B5), four mic (C1–4), three mic (T1, T5, T7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), two mes (Ee10–11), one mac (Ee9), one mic (F1), three mes (F2–3p), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and three (T3) fan-shaped chaetae respectively; ps and as present, and at least five supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with four psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), five mac (m2–3, m5–5e), five mic (p3a–p6ae), two mes (ap6–pp6), four mac (p1, p3–5) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 4.27–5.91 (n = 5), holotype = 1: 5.02.Legs. Trochanteral organ diamond shape with about 15 spine-like chaetae, plus 2–3 psp one external, one on distal vertex and another (present or absent) on top of posterior spines row of Omt (Fig. 29A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with two teeth, basal pair subequal, b.p. larger than b.a., inner lamella with unpaired small m.t. between b.a. and b.p. and a.t. absent. Unguiculus with all lamellae smooth and truncate (a.i., a.e., p.i., p.e.) (Fig. 29B); ratio unguis: unguiculus = 1.50–1.95: 1 (n = 5), holotype = 1.95: 1. Tibiotarsal smooth chaetae about 0.9× smaller than unguiculus; tenent hair slightly capitate and about 0.6× smaller than unguis outer lamella.Collophore (Fig. 29C). Anterior side with eight ciliate, apically acuminate chaetae, six proximal and two distal mac; lateral flap with 13 chaetae, five ciliate in the proximal row and eight smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 29D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 40 external and 22 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.23 (holotype).Etymology. Species named after Dr. Marie Skłodowska-Curie for her enormous contribution to science.Remarks. Trogolaphysa mariecurieae sp. nov. resembles T. bellinii sp. nov. T. jacobyia and T. epitychia sp. nov. by the absence of eyes (T. bellinii sp. nov. rarely with 2 + 2 eyes), Th II p3 complex with three mac and with one unpaired tooth on inner lamella of unguis. The new species T. mariecurieae sp. nov. (Abd IV with 2 + 2 mac) differs from T. jacobyia, T. epitychia sp. nov. both with Abd IV 3 + 3, and T. bellinii sp. nov. with 4 + 4 central mac. T. mariecurieae sp. nov. and T. bellinii sp. nov. with capitate tenent hair, in contrast with T. jacobyia and T. epitychia sp. nov. with acuminated tenant hair.Trogolaphysa barroca sp. nov. Brito & ZeppeliniFigures 30, 31 and 32, Tables 1 and 2Figure 30Trogolaphysa barroca sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 31Trogolaphysa barroca sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 32Trogolaphysa barroca sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype female in slide (13,167/CRFS-UEPB): Brazil, Minas Gerais State, Mariana municipality, ALFA-0003 cave, 20°09′06.8″S, 43°29′13.6″W, 07–27.ii.2018, Bioespeleo team coll. Paratype in slide (13,150/CRFS-UEPB): 1 female, same data as holotype, except 12.vi.2017. Paratype in slide (13,158/CRFS-UEPB donated to MNJR): 1 female, same data as holotype. Paratype in slide (13,197/CRFS-UEPB): 1 female, Brazil, Minas Gerais State, Mariana municipality, ALEA-0004 cave, 20°09′00.0″S, 43°29′11.8″W, 07.ii.2018, Bioespeleo team coll. Paratype in slide (13,203/CRFS-UEPB): 1 female, Brazil, Minas Gerais State, Mariana municipality, ALEA-0002 cave, 20°08′56.5″S, 43°29′09.8″W, 27.ii.2018, Bioespeleo team coll. Additional records see S1.Description. Total length (head + trunk) of specimens 1.70–2.13 mm (n = 5), holotype 1.81 mm.Head. Ratio antennae: trunk = 1: 1.27–1.60 (n = 3), holotype = 1: 1.27; Ant III shorter than Ant II; Ant segments ratio as, I: II, III, IV = 1: 1.90–2.41, 1.64–2.02, 2.69–3.64, holotype = 1: 1.90, 1.67, 2.69. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short less ciliate mic and mac, and finger-shaped sens, dorsally with about four rod sens on longitudinal row, ventrally with one subapical-organ and several wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short less ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, about nine wrinkly sens on external longitudinal row, apical organ with one finger-shaped sens, two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short less ciliate mic and mac, dorsally with two sub-apical finger-shaped sens and two subapical rod sens, ventrally with one apical psp, and several wrinkly sens on longitudinal external row (Fig. 4A); and Ant I dorsally and ventrally with several short less ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with about five basal spine-like sens, about five smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 30A) with 14–15 An (An1a–3), six A (A0–5), five M (M1–5), six S (S1–6), two Ps (Ps2, Ps5), four Pa (Pa1–3, Pa5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pm3 as mic, A3 as mes, An1a–3, A0, A2 and Pa5 as mac; interocular p mic present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 30B). Ventral chaetotaxy with 33 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of five to six mes chaetae distally (Fig. 30B). Prelabral chaetae weakly ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and subequal the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3), and subequal in length (Fig. 30B). Maxillary palp with t.a. smooth and 1.14 × larger than b.c.Thorax dorsal chaetotaxy (Fig. 31A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with five mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–7), two mic (m4, m6p), four mes (m6–6e, m7–7e), and four mic (p1–3, p6), respectively. Ratio Th II: III = 1.11–1.35: 1 (n = 5), holotype = 1.29: 1.Abdomen dorsal chaetotaxy (Fig. 31B, C). Abd I a, m series with one (a5) and six (m2–6e) mic, respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and three fan-shaped chaetae, respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae, respectively; a5, m2 and m5 bothriotricha with six, two and three fan-shaped chaetae, respectively; as sens elongated, ms present. Abd IV A–Fe series with four mic (A1, A5–6, Ae1), one mac (A3), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), three mic (T1, T5–6), one mes (T7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), three mes (Ee9–11), one mic (F1), three mes (F2–3p), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and two (T3) fan-shaped chaetae, respectively; ps and as present, and at least seven supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with four to six psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a–5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e), two mes (ap6, pp6), four mac (p1, p3–5) chaetae, respectively; as and acc.p4–5 present. Ratio Abd III: IV = 1: 3.38–5.55 (n = 5), holotype = 1: 5.27.Legs. Trochanteral organ diamond shape with about 16–21 spine-like chaetae, plus 2–3 psp one external, and two (one of them present or absent) on top of posterior spines row of Omt (Fig. 32A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with two teeth, basal pair subequal; b.p. little larger than b.a., m.t. and a.t. absent. Unguiculus with all lamellae smooth and lanceolate (a.i., a.e., p.i., p.e.) (Fig. 32B); ratio unguis: unguiculus = 1.53–1.67: 1 (n = 5), holotype = 1.61: 1. Tibiotarsal smooth chaetae about 0.61 × smaller than unguiculus; tenent hair acuminate and about 0.4 × smaller than unguis outer lamella.Collophore (Fig. 32C). Anterior side with eight ciliate, apically acuminate chaetae, four proximal (thinner), one subdistal and three distal mac; lateral flap with 10 chaetae, five ciliate in the proximal row and five smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with five ciliate chaetae (three inner mac) and three psp (Fig. 32D). Dens posterior face with two or more longitudinal rows of spines-like chaetae about 22 external and 37–39 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.33 (holotype).Etymology. Refers to the Baroque art (which is “barroco” noun, in Portuguese) of Mariana, Minas Gerais, type locality.Remarks. Trogolaphysa barroca sp. nov. resembles T. formosensis by head Pm3 mic (mac in T. piracurucaensis, T. gisbertae sp. nov. and T. dandarae sp. nov.; mes in T. ernesti, T. sotoadamesi sp. nov. and T. mariecurieae sp. nov.); 3 + 3 head dorsal mac like T. ernesti, although in the new species it is as A0, A2 and Pa5, and in T. ernesti is A0, A2–3; unguis m.t. and a.t. teeth absent like T. sotoadamesi sp. nov. and T. dandarae sp. nov. (present in T. bellini sp. nov., T. lacerta sp. nov. and T. chapelensis sp. nov.).Trogolaphysa epitychia sp. nov. Oliveira, Lima & ZeppeliniFigures 33, 34 and 35, Tables 1 and 2Figure 33Trogolaphysa epitychia sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 34Trogolaphysa epitychia sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 35Trogolaphysa epitychia sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype male in slide (10,578/CRFS-UEPB): Brazil, Minas Gerais State, Conceição do Mato Dentro municipality, cave CSS-0118, next to “São Sebastião do Bom Sucesso”, 18°56′14.1″S, 43°24′43.8″W, 21.xi–15.xii.2016, Carste team coll. Paratypes in slides (10,580, 10,585/CRFS-UEPB): 2 females, same data as holotype. Paratypes in slides (10,653, 10,692/CRFS-UEPB donated to MNJR): 1 female and 1 male, same data as holotype, except 22.xi–15.xii.2016 and 31.v–12.vi.2016, respectively. Additional records see S1.Description. Total length (head + trunk) 1.13–1.35 mm (n = 5), holotype 1.13 mm.Head. Ratio antennae: trunk = 1: 1.29–1.95 (n = 5), holotype = 1: 1.95; Ant III shorter than Ant II; Ant segments ratio as I: II, III, IV = 1: 1.69–2.20, 1.14–1.86, 2.37–3.52, holotype = 1: 1.71, 1.14, 2.37. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with one longitudinal row with about six rod sens, ventrally with one subapical-organ and one longitudinal row with about four wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, about three wrinkly sens on external longitudinal row, apical organ with two coffee bean-like sens, one rod sens and one smooth mic (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with about six sub-apical finger-shaped sens and one wrinkly sens, ventrally with one apical psp, about three wrinkly sens on longitudinal external row (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, three basal spine-like sens, ventrally with four basal spine-like sens, about three smooth mic, several finger-shaped sens, and two wrinkly sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 33A) with 12 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pm3 and Pa5 as mes, An1a–3a, A0 and A2 as mac; interocular p mes present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 33B). Ventral chaetotaxy with 31–32 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of five to six mes chaetae distally (Fig. 33B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 33B). Maxillary palp with t.a. smooth and 1.26× larger than b.c.Thorax dorsal chaetotaxy (Fig. 34A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with three mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–a7), three mic (m4, m6–6p), three mes (m6e, m7–7e), four mic (p1–3, p6) respectively. Ratio Th II: III = 1.05–1.21: 1 (n = 5), holotype = 1.18: 1.Abdomen dorsal chaetotaxy (Fig. 34B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and two fan-shaped chaetae respectively. Abd III a, m, p series with two mic (a7i–7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae, respectively; a5, m2 and m5 bothriotricha with five, two and one fan-shaped chaetae, respectively; as sens elongated, ms present. Abd IV A–Fe series with three mic (A1, A6, Ae1), two mac (A3, A5), two mic (B1, B4), one mes (B6), one mac (B5), four mic (C1–4), four mic (T1, T3, T5–6), one mac (T7), six mic (D1–3p, De3), two mic (E4p–4p2), three mac (E1–3), one mic (Ee11), three mes (Ee9–10, Ee12), one mic (F1), three mes (F2–3p), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by five and two fan-shaped chaetae, respectively; ps and as present, and at least seven supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with three psp. Abd V a, m, p series with three mic (a1, a3, a6), one mac (a5), two mic (m3, me5), three mac (m2, m5–5a), two mic (p3a–4a), one mes (p5a) two mac (p6ai–6ae), four mes (p5–pp6), three mac (p1, p3–4) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 4.69–5.55 (n = 5), holotype = 1: 4.88.Legs. Trochanteral organ in V–shape with about 15 spine-like chaetae, plus 4 psp one external, one on distal vertex and another two on top of posterior spines row of Omt (Fig. 35A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with four teeth, basal pair subequal, b.p. little larger, not reaching the m.t. apex, m.t. just after the distal half, a.t. absent. Unguiculus with all lamellae smooth and slightly truncate (a.i., a.e., p.i., p.e.) (Fig. 35B); ratio unguis: unguiculus = 1.17–1.98: 1 (n = 5), holotype = 1.17: 1. Tibiotarsal smooth chaetae about 0.8× smaller than unguiculus; tenent hair acuminate and about 0.53× smaller than unguis outer lamella.Collophore (Fig. 35C). Anterior side with nine ciliate, apically acuminate chaetae, five proximal, two subdistal and two distal mac; lateral flap with 10 chaetae, five ciliate in the proximal row and five smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with five ciliate chaetae (two inner mac) and three psp (Fig. 35D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 60 external and 34 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.30 (holotype).Etymology. Epitychia from Greek means success, in allusion to the collection site where the species was found São Sebastião do Bom Sucesso.Remarks. Trogolaphysa epitychia sp. nov. resembles T. bellinii sp. nov., T. bessoni, and T. mariecurieae sp. nov. by the absence of eyes (T. bellinii sp. nov. rarely with 2 + 2 eyes), Th II with 3 + 3 mac, and Th III without mac. Differentiates from T. bellinii sp. nov. and T. mariecurieae sp. nov. by Abd IV with 3 + 3 (A3, A5, B5), 4 + 4, and 2 + 2 mac on Abd IV respectively; on T. bellinii sp. nov. and can be distinguished from T. bessoni by the absence of unpaired tooth on inner lamella of unguis, external row of dens with 25 spines, inner row of dens with 20 spines (T. epitychia sp. nov. with one unpaired tooth m.t. on inner lamella of unguis, external row of dens with about 60 spines and inner row of dens with about 34 spines).Trogolaphysa zampauloi sp. nov. Lima, Oliveira & ZeppeliniFigures 36, 37 and 38, Tables 1 and 2Figure 36Trogolaphysa zampauloi sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 37Trogolaphysa zampauloi sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 38Trogolaphysa zampauloi sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype female in slide (11,851/CRFS-UEPB): Brazil, São Paulo State, Ribeira municipality, cave MTD-13, nexto to “Serra Pontalhão”, 24°38′47.4″S, 48°57′52.6″W, 08–20.iii.2016, Carste team coll. Paratypes in slides (11,875–11,878/CRFS-UEPB): 2 males and 2 females, Brazil, São Paulo State, Ribeira municipality, cave MTD-02, 24°37′27.3″S, 48°57′35.8″W, 08–20.iii.2016. Paratype in slide (11,876/CRFS-UEPB donated to MNJR). Additional records see S1.Description. Total length (head + trunk) of specimens 1.35–1.91 mm (n = 5), holotype 1.35 mm.Head. Ratio antennae: trunk = 1: 1.35–1.55 (n = 2), holotype = 1: 1.55; Ant III smaller than Ant II length; Ant segments ratio as I: II, III, IV = 1: 1.71–2.38, 1.60–1.88, 2.85–3.61, holotype = 1: 2.38, 1.88, 3.61. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with about three rod sens on longitudinal row, ventrally with one subapical-organ, and about three wrinkly sens (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, one apical wrinkly sens, apical organ with two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with about three sub-apical finger-shaped sens and two apical rod sens, ventrally with one apical psp, one longitudinal external row with two subapical finger-shaped sens and two medial wrinkly sens (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about four smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0 to 4 + 4. Head dorsal chaetotaxy (Fig. 36A) with 14 An (An1a–3), six A (A0–5), four M (M1–4), five S (S2–6), two Ps (Ps2, Ps5), four Pa (Pa1–3, Pa5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; Pe4, Pe6, Pm3 and Pa5 as mes, An1a–3a as mac, A0 and A2 as mac, A3–5 as mes; interocular p mes present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 36B). Ventral chaetotaxy with about 37 ciliate chaetae, plus one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of eight mes chaetae distally (Fig. 36B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 36B). Maxillary palp with t.a. smooth and 1.17 × smaller than b.a.Thorax dorsal chaetotaxy (Fig. 37A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with five mac, respectively, al and ms present. Th III a, m, p series with three mic (a1–3), two mes (a6–a7), three mic (m4, m6–6p), three mes (m6e, m7–7e), four mic (p1–3, p6), respectively. Ratio Th II: III = 1.02–1.48: 1 (n = 5), holotype = 1.21: 1Abdomen dorsal chaetotaxy (Fig. 37B, C). Abd I a, m series with one (a5) and six (m2–6e) mic respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el as mic and as present; a5 and m2 bothriotricha surrounded by three and two fan-shaped chaetae respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with five, two and three fan-shaped chaetae respectively, as sens elongated, ms present. Abd IV A–Fe series with three mic (A1, A6, Ae1), two mac (A3, A5), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), three mic (T1, T5–6), one mes (T7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), one mes (Ee11), two mac (Ee9–10), one mic (F1), two mes (F3–3p), one mac (F2), one mic (Fe2), two mes (Fe3, Fe5), one mac (Fe4) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and four (T3) fan-shaped chaetae respectively; ps and as present, and at least six supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with three psp. Abd V a, m, p series with two mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a, m5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e) two mes (ap6–pp6), four mac (p1, p3–5) chaetae, respectively; as, acc.p4–5 present. Ratio Abd III: IV = 1: 3.29–4.28 (n = 5), holotype = 1: 4.10.Legs. Trochanteral organ diamond shape with about 27 spine-like chaetae, plus 3–4 psp one external, one on distal vertex and another two (one of them present or absent) on top of posterior spines row of Omt (Fig. 38A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with four teeth, basal pair subequal, b.p. not reaching the m.t. apex, m.t. just after the distal half, a.t. present. Unguiculus with all lamellae smooth and lanceolate (a.i., a.e., p.i., p.e.) (Fig. 38B); ratio unguis: unguiculus = 1.63–1.84 (n = 5), holotype = 1: 1.79. Tibiotarsal smooth chaetae about 0.8× smaller than unguiculus; tenent hair acuminate and about 0.39× smaller than unguis outer edge.Collophore (Fig. 38C). Anterior side with five ciliate, apically acuminate chaetae, two proximal (thinner); one subdistal and two distal mac; lateral flap with 11 chaetae, five ciliate in the proximal row and six smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 38D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 30 external and 23 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.29 (n = 5).Etymology. Species named after the field biologist MSc. Robson de Almeida Zampaulo for his contribution to Brazilian biospeleology.Remarks. Trogolaphysa zampauloi sp. nov. resembles T. caripensis; T. ernesti; T. piracurucaensis by Th III without mac, and 4 + 4 central mac (A3, A5, B4–5) in Abd IV, but is easily distinguished from these species by the presence of Th II with 4 + 4 mac in p3 complex (6 + 6T. caripensis, T. ernesti, T. piracurucaensis). For more comparisons see remarks in T. crystallensis sp. nov.Trogolaphysa gisbertae sp. nov. Brito & ZeppeliniFigures 39, 40 and 41, Tables 1 and 2Figure 39Trogolaphysa gisbertae sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 40Trogolaphysa gisbertae sp. nov.: Dorsal chaetotaxy: (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 41Trogolaphysa gisbertae sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy.Full size imageType material. Holotype female in slide (6668/CRFS-UEPB): Brazil, Pará State, Parauapebas municipality, cave N1N8-N8-017, next to “Serra Norte”, 06°10′05.9″S, 50°09′25.6″W, 02–29.iv.2015, Carste team coll. Paratype in slide (6669/CRFS-UEPB donated to MNJR): 1 female, same data as holotype, except 04.ix–06.x.2014. Paratype in slide (6973/CRFS-UEPB): 1 female, same data as holotype, except 04.ix–06.x.2014. Paratypes in slides (6657, 7138/CRFS-UEPB): 2 females, Brazil, Pará State, Parauapebas municipality, N1N8-N8-020 cave, 06°10′07.8″S, 50°09′25.4″W, 17.vii–04.viii.2014, Carste team coll. Additional records see S1.Description. Total length (head + trunk) of specimens 1.10–1.23 mm (n = 5), holotype 1.15 mm.Head. Ratio antennae: trunk = 1: 1.44–1.55 (n = 3); Ant segments ratio as I: II, III, IV = 1: 1.67–2.43, 1.58–2.63, 2.91–5.46, holotype = 1: 2.03, – , 3.90. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with about five rod sens in row, ventrally with one subapical-organ and several wrinkly sens row (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, about four wrinkly sens on external longitudinal row, apical organ with one finger-shaped sens, two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with four finger-shapedd sens in row and two subapical rod sens, ventrally with one apical psp, and about five wrinkly sens on longitudinal external row (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about five smooth mic and several fniger-shaped sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 39A) with 11 An (An1a–3), six A (A0–5), four M (M1–4), five S (S1–5), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; An1a–3a, A0, A2–3, Pa5 and Pm3 as mac; interocular p absent. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 39B). Ventral chaetotaxy with 20 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of three to four mes chaetae distally (Fig. 39B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and surpassing the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 39B). Maxillary palp with t.a. smooth and 1.32 × larger than b.c.Thorax dorsal chaetotaxy (Fig. 40A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with five mac, respectively, al and ms presents. Th III a, m, p series with three mic (a1–3), two mes (a6–7), three mic (m4, m6–6p), three mes (m6e, m7–7e), and four mic (p1–3, p6), respectively. Ratio Th II: III = 1.00–2.60: 1 (n = 5), holotype = 1.28: 1.Abdomen dorsal chaetotaxy (Fig. 40B, C). Abd I a, m series with one (a5) and six (m2–6e) mic, respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and two fan-shaped chaetae, respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6), three mic (p6e, p7i–7), one mac (p6) chaetae, respectively; a5, m2 and m5 bothriotricha with six, two and three fan-shaped chaetae, respectively, as sens elongated, ms present. Abd IV A–Fe series with four mic (A1, A5–6, Ae1), one mac (A3), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), four mic (T1, T5–7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), three mes (Ee9–11), one mic (F1), three mes (F2–3p), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and two (T3) fan-shaped chaetae, respectively; ps and as present, and at least six supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with one to three psp. Abd V a, m, p series with three mic (a1, a3), one mes (a6), one mac (a5), two mes (m5a–5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e), two mes (ap6, pp6), four mac (p1, p3–5) chaetae, respectively; as and acc.p4–5 present. Ratio Abd III: IV = 1: 3.29–4.90 (n = 5), holotype = 1: 3.29.Legs. Trochanteral organ diamond shape with about 25 spine-like chaetae, plus two psp one external, and one on distal vertex of Omt (Fig. 41A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with three teeth, basal pair subequal, b.p. not reaching the m.t. apex, m.t. just after the distal half, a.t. absent. Unguiculus with lamellae smooth and slightly truncate (a.i., a.e., p.i.), except p.e. slightly serrate (Fig. 41B); ratio unguis: unguiculus = 1.59–2.05: 1 (n = 5), holotype = 1.62: 1. Tibiotarsal smooth chaetae about 0.9× smaller than unguiculus; tenent hair acuminate and about 0.53× smaller than unguis outer lamella.Collophore (Fig. 41C). Anterior side with five ciliate, apically acuminate chaetae, one proximal (thinner); two subdistal and two distal mac; lateral flap with 10 chaetae, five ciliate in the proximal row and five smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with five ciliate chaetae (three inner mac) and three psp (Fig. 41D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 18 external and 24 internal, external spines larger and thinner than internal ones. Mucro with four teeth, ratio width: length = 0.26 (holotype).Etymology. Honor to Gisberta Salce Júnior, Brazilian woman, murdered in 2006 (Porto, Portugal) in a transphobia crime.Remarks. Trogolaphysa gisbertae sp. nov. differs from T. ernesti and T. formosensis (with 0 + 0 head dorsal mac), T. piracurucaensis, and T. barroca sp. nov. (1+1 head dorsal mac); and resembles T. dandarae sp. nov. (with 5+5 head dorsal mac), but it is easily distinguishable by Th II p3 complex and Th III mac (5 + 5 and 0 + 0, 6 + 6 and 3 + 3, respectively); and unguis with m.t. present (absent in T. sotoadamesi sp. nov., T. barroca sp. nov.).Trogolaphysa dandarae sp. nov. Brito & ZeppeliniFigures 42, 43 and 44, Tables 1 and 2Figure 42Trogolaphysa dandarae sp. nov.: (A) Head dorsal chaetotaxy, (B) labial proximal chaetae, basomedial and basolateral labial fields and postlabial chaetotaxy. Black cut circle, pseudopore; Gray cut circle pseudopore at the under surface.Full size imageFigure 43Trogolaphysa dandarae sp. nov.: Dorsal chaetotaxy. (A) Th II–III, (B) Abd I–III, (C) Abd IV–V.Full size imageFigure 44Trogolaphysa dandarae sp. nov.: (A) Trochanteral organ, (B) Distal tibiotarsus and empodial complex III (anterior view), (C) Manubrial plate, (D) Antero-lateral view of collophore chaetotaxy, (E) Mucro.Full size imageType material. Holotype female in slide (12,775/CRFS-UEPB): Brazil, Pará State, Parauapebas municipality, cave N4WS-0018/48, next to “Serra Norte”, 06°04′34.5″S, 50°11′37.7″W, 21–30.vii.2018, Brandt Meio Ambiente team coll. Paratype in slide (12,776/CRFS-UEPB donated to MNJR): 1 female, same data as holotype. Paratypes in slides (12,777, 12,778/CRFS-UEPB): 2 females, same data as holotype. Paratypes in slides (12,772, 12,773/CRFS-UEPB): 2 females, Brazil, Pará State, Parauapebas municipality, N4WS-0016 cave, 06°04′35.5″S, 50°11′37.1″W, 21–30.vii.2018, Brandt Meio Ambiente team coll. Additional records see S1.Description. Total length (head + trunk) of specimens 1.43–1.75 mm (n = 5), holotype 1.58 mm.Head. Ratio antennae: trunk = 1: 0.83–0.98 (n = 4), holotype = 1: 0.83; Ant III larger than Ant II; Ant segments ratio as I: II: III: IV = 1: 1.36–1.77: 1.65–2.03: 2.84–3.27, holotype = 1: 1.72: 1.99: 3.21. Antennal chaetotaxy (no represented): Ant IV dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally with about two rod sens sub-apical on longitudinal row, ventrally with one subapical-organ and about three wrinkly sens on longitudinal row (Fig. 4A); Ant III dorsally and ventrally with several short ciliate mic and mac, and finger-shaped sens, dorsally without modified sens, ventrally with one apical psp, about three wrinkly sens and three smooth mic on external longitudinal row, apical organ with one finger-shaped sens, two coffee bean-like sens, and one rod sens (Fig. 4A); Ant II dorsally and ventrally with several short ciliate mic and mac, dorsally with about four sub-apical finger-shaped sens and two subapical rod sens, ventrally with one apical psp, and several wrinkly sens on longitudinal external row (Fig. 4A); and Ant I dorsally and ventrally with several short ciliate mic and mac, dorsally with three basal spine-like sens, ventrally with four basal spine-like sens, about five smooth mic and several finger-shaped sens (Fig. 4A). Eyes 0 + 0. Head dorsal chaetotaxy (Fig. 42A) with 12 An (An1a–3), six A (A0–5), four M (M1–4), six S (S1–6), two Ps (Ps2, Ps5), four Pa (Pa1–5), two Pm (Pm1, Pm3), seven Pp (Pp1–7), and two Pe (Pe4, Pe6) chaetae; A1 as mes, An1a–3, A0, A2, S5, Pa5 and Pm3 as mac; interocular p mic present. Basomedian and basolateral labial fields with a1–5 smooth, M, Me, E and L1–2 ciliate, r reduced (Fig. 42B). Ventral chaetotaxy with 28 ciliate chaetae and one reduced lateral spine; postlabial G1–4; X, X4; H1–4; J1–2, chaetae b.c. present and a collar row of five chaetae distally (Fig. 42B). Prelabral chaetae ciliate. Labral chaetae smooth, no modifications. Labial papilla E with l.p. finger-shaped and subequal the base of apical appendage. Labial proximal chaetae smooth (an1–3, p2–3) and subequal in length (Fig. 42B). Maxillary palp with t.a. smooth and 1.58 × larger than b.c.Thorax dorsal chaetotaxy (Fig. 43A). Th II a, m, p series with two mic (a1–2), one mac (a5), three mic (m1–2, m4) and four mic (p4–6e), p3 complex with six mac, respectively, al and ms presents. Th III a, m, p series with three mic (a1–3), two mes (a6–7), two mic (m6–6p), three mes (m6e, m7–7e), and one mic (p6), respectively. Ratio Th II: III = 0.82–1.13: 1 (n = 6), holotype = 1.13: 1.Abdomen dorsal chaetotaxy (Fig. 43B, C). Abd I a, m series with one (a5) and six (m2–6e) mic, respectively, ms present. Abd II a, m, p series with two mic (a6–7), two mac (m3, m5), three mic (p5–7) respectively, el mic and as present; a5 and m2 bothriotricha surrounded by four and four fan-shaped chaetae, respectively. Abd III a, m, p series with one mic (a7), three fan-shaped chaetae (a2–3, a6), two mic (m7i–7), three mac (m3, am6, pm6) and three mic (p6e–7), one mac (p6) chaetae respectively; a5, m2 and m5 bothriotricha with five, two and two fan-shaped chaetae, respectively, as sens elongated, ms present. Abd IV A–Fe series with four mic (A1, A5–6, Ae1), one mac (A3), one mic (B1), one mes (B6), two mac (B4–5), four mic (C1–4), three mic (T1, T5–6), one mes (T7), five mic (D1–3, De3), one mes (D3p), one mic (E4p2), one mes (E4p), three mac (E1–3), one mic (Ee12), three mes (Ee9–11), one mic (F1), three mes (F2–3p), one mic (Fe2), three mes (Fe3–5) chaetae, respectively; T2, T4 and E4 bothriotricha surrounded by four and two (T3) fan-shaped chaetae, respectively; ps and as present, and at least six supernumerary sens with uncertain homology ‘s’ (Fig. 8A); Abd. IV posteriorly with three psp. Abd V a, m, p series with three mic (a1, a3), one mes (a6), one mac (a5), two mic (m5a–5e), three mac (m2–3, m5), five mic (p3a–6ae), one mic (p6e), two mes (ap6, pp6), four mac (p1, p3–5) chaetae, respectively; as and acc.p4–5 present. Ratio Abd III: IV = 1: 2.98–4.82 (n = 6), holotype = 1: 3.81.Legs. Trochanteral organ diamond shape with about 19 spine-like chaetae, plus 2–3 psp one external, one on distal vertex and another (present or absent) on top of posterior spines row of Omt (Fig. 44A). Unguis outer side with one paired tooth straight and not developed on proximal third; inner lamella wide with two teeth, basal pair subequal, m.t. and a.t. absent. Unguiculus with all lamellae smooth and lanceolate (a.i., a.e., p.i., p.e.) (Fig. 44B); ratio unguis: unguiculus = 1.49–1.80: 1 (n = 6), holotype = 1.80: 1. Tibiotarsal smooth chaetae about 1.25× smaller than unguiculus; tenent hair slightly capitate and about 0.54× smaller than unguis outer lamella.Collophore (Fig. 44C). Anterior side with 11 ciliate, apically acuminate chaetae, six proximal (thinner); two subdistal and three distal mac; lateral flap with 11 chaetae, five ciliate in the proximal row and six smooth in the distal row.Furcula. Covered with ciliate chaetae, spine-like chaetae and scales. Manubrial plate with four ciliate chaetae (two inner mac) and three psp (Fig. 44D). Dens posterior face with two or more longitudinal rows of spine-like chaetae about 31–39 external and 18–21 internal, external spines larger and thinner than internal ones. Mucro with three teeth (Fig. 44E), ratio width: length = 0,28 (holotype).Etymology. Honor to Dandara Kettley, Brazilian man, transvestite, murdered in 2017 (Ceará, Brazil) in a homophobia crime.Remarks. Trogolaphysa dandarae sp. nov. resembles T. ernesti, T. formosensis and T. piracurucaensis by chaetae head S5 mac (all other Brazilian cave species with S5 mic); head Pm3 mac as in T. gisbertae sp. nov., but they are different in terms of head ventral proximal collar mac, unguiculus, tenent hair and collophore anterior distal chaetae (5 + 5, smooth pe, capitate, 3 + 3 and 4 + 4, serrate pe, acuminate, 2 + 2, respectively); Th II P3 complex with 6 + 6 and Th III with 3 + 3 mac (6 + 6 and 0 + 0 in T. lacerta sp. nov., T. piracurucaensis, T. ernesti and T. caripensis); T. dandarae sp. nov., T. belizeana and T. jacobyi are the only cave species with 3 + 3 teeth in the mucro. See the comparison among them in remarks of the late species. More

  • in

    Ecological and human health risk assessment of heavy metal(loid)s in agricultural soil in hotbed chives hometown of Tangchang, Southwest China

    Soil physical–chemical properties and HMs concentrationsThe soil physical–chemical properties and HMs concentrations are summarized in Table 1. The soil mean pH value was 6.17 and ranged from 4.16 to 9.04 in different sites. The samples sites of level for pH ≤ 6.0(acidic soil), 6.0  7.5(alkaline soil) were 51.5%, 36.4% and 12.1%, respectively. The average content of TN, TP and TK were 1.33 g kg−1, 1.16 g kg−1 and 23.6 g kg−1, and ranged from 0.7 g kg−1 to 2.4 g kg−1, 0.22 g kg−1 to 20.8 g kg−1 and 10.3 g k g−1 to 28 g kg−1, respectively.The mean concentration of Cd, Hg, As, Pb, Cr, Cu, Ni and Zn were 0.221, 0.155, 9.76, 32.2, 91.9, 35.2, 37.1 and 108.8 mg kg−1. Except Cd, the average concentration of Hg, As, Pb, Cr, Cu, Ni and Zn exceeded 93.8%, 7.1%, 6.3%, 17.8%, 25.3%, 10.7%, and 32.4% the soil background values for Chengdu, respectively, which indicates that HMs are enriched to a certain extent in soil. The CV of the HMs in the agricultural soils increased in the order Ni(10.1%), Cr(10.9%), Pb(15.4%), As(21.4%), Cd(31.1%), Zn(57.8%), Hg(58.1%) and Cu(59.9%). The exceptionally high variability of Cu, Hg and Zn indicates that these metals differed greatly with respect to different sites, and the existence of abnormally high values is the main reason that the CV was high. It further indicates that Cu, Hg and Zn may be affected by external interference factors. The mean concentration of all HMs in soil were below the risk screening values for soil contamination (GB 15618-2018) (MEEC, 2018), however, the results showed that in 76, 1, 1, 6 and 2 of sample sites the level of Cd, Pb, Cr, Cu and Zn exceeded the risk screening values.Assessment of heavy metal(loid)s pollutionIndex of geo-accumulationThe index of geo-accumulation of HMs in the soil in the study area are shown in Fig. 2. In a descending order of magnitude of Igeo mean value, the eight elements were as follows: Hg(0.18)  > Zn(-0.22)  > Cu(-0.30)  > Cr(-0.36)  > Ni(-0.45)  > Pb(-0.51)  > As(-0.52)  > Cd(-0.82), indicating that the soil HMs Zn, Cu, Cr, Ni, Pb, As and Cd in the study area were generally in a no contamination according to the defined classes, while Hg was in uncontaminated to moderately contaminated.Figure 2Indexes of geo-accumulation, pollution indexes and potential ecological risk indexes of HMs in study aera. Circles at the top and bottom of box plots correspond to the maximum and minimum values, respectively. The square in the box plot is the average value. Horizontal lines at the top, middle, and bottom of the box plot correspond to 75% percentile, median, and 25% percentile, respectively.Full size imageThe Igeo values for Zn, Cu, Cr, Ni, Pb, As and Cd in more than 90% of samples were less than zero, only a few outliers in the soil were classified as moderately contaminated or worse. Which meaning point pollution at those sample sites. However, the Igeo values for Hg in 47.34%, 40.61%,10.66% and 1.40% of samples were belonged to  As  > Pb  > Ni  > Cu  > Hg  > Zn  > Cd. Cr, As and Pb were the largest contributors for both adults and children, accounting for 47.33% and 42.37%, 32.68% and 39.64%, 15.95% and 13.26%, respectively. Indicating that attention should be pain to the Cr, As and Pb elements due to their noncarcinogenic risk. Which was basically consistent with the research results of Bo et al.1 and Bao et al.32. Overall, The HMs of Cr, As and Pb were the main non-carcinogenic factors in soil in the study area, and the risk control of these elements should be strengthened.Carcinogenic risk assessmentThe CR of the HMs are shown in Table 5. Three exposure pathways were considered for Cd and Hg in our study. But As and Pb were considered carcinogenic by ingestion and inhalation, and Cr was considered carcinogenic through inhalation. The TCR mean values were 3.68 × 10−5 for adults and 6.27 × 10−5 for children, obviously were in the range 1 × 10−4 from 1 × 10−6, suggesting the TCR caused by HMs in the study area was acceptable on the whole, but it still exceeds the soil treatment threshold value 10−6. The CR average values through the three exposure pathways were CRing 3.65 × 10−5, CRinh 2.53 × 10−7 and CRderm 1.07 × 10−7 for adults, CRing 6.25 × 10−5, CRinh 1.12 × 10−7 and CRderm 4.50 × 10−8 for adults. Clearly the CRing was much larger than CRinh and CRderm for both adults and children, which indicates that oral ingestion is the major exposure pathway for CR. Which was consistent with the research results of Song et al.31 and Bo et al.1. For single HM, the CR value of Pb and Hg for adults were 2.72 × 10−5 and 8.6 × 10−6, and Pb, Hg and Cd for children were 4.63 × 10−5, 1.48 × 10−5 and 1.36 × 10−6, respectively, within acceptable criterion. Overall, the longterm health effects for adults and children are not serious at current single HM level.In this study, the total contents of HMs in the soils were used to assess health risk, the bioavailability of HMs were not considered, which may have caused the assessment results to be higher than the actual local situation1,31,42. In addition, because the parameters of health risk evaluation for children were set to be more sensitive than those for adults, the non-carcinogenic risk and carcinogenic risk for children were higher than those for adults2,43,44. However, those risks were at acceptable or negligible levels. Therefore, the study area is suitable for safe and clean production of hotbed chives. More

  • in

    Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use

    Fisher, M. C. et al. Threats posed by the fungal kingdom to humans, wildlife, and agriculture. MBio 11, e00449–20 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fisher, M. C., Hawkins, N. J., Sanglard, D. & Gurr, S. J. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science 360, 739–742 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nash, A. et al. MARDy: Mycology Antifungal Resistance Database. Bioinformatics 34, 3233–3234 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ksiezopolska, E. et al. Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata. Curr. Biol. 4, 5314–5326.e10 (2021).Article 
    CAS 

    Google Scholar 
    Bryce Taylor, M. et al. yEvo: Experimental evolution in high school classrooms selects for novel mutations and epistatic interactions that impact clotrimazole resistance in S. cerevisiae. Preprint at bioRxiv https://doi.org/10.1101/2021.05.02.442375 (2021).Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–271 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gerstein, A. C., Lo, D. S. & Otto, S. P. Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast. Genetics 192, 241–252 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yang, F. et al. The fitness costs and benefits of trisomy of each Candida albicans chromosome. Genetics 218, iyab056 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kanafani, Z. A. & Perfect, J. R. Antimicrobial resistance: resistance to antifungal agents: mechanisms and clinical impact. Clin. Infect. Dis. 46, 120–128 (2008).PubMed 
    Article 

    Google Scholar 
    Iyer, K. R., Revie, N. M., Fu, C., Robbins, N. & Cowen, L. E. Treatment strategies for cryptococcal infection: challenges, advances and future outlook. Nat. Rev. Microbiol. 19, 454–466 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Longley, D. B., Harkin, D. P. & Johnston, P. G. 5-fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer 3, 330–338 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Erbs, P., Exinger, F. & Jund, R. Characterization of the Saccharomyces cerevisiae FCY1 gene encoding cytosine deaminase and its homologue FCA1 of Candida albicans. Curr. Genet. 31, 1–6 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wrenbeck, E. E., Azouz, L. R. & Whitehead, T. A. Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded. Nat. Commun. 8, 15695 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chen, J. Z., Fowler, D. M. & Tokuriki, N. Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase. eLife 9, e56707 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, A., Acevedo-Rocha, C. G. & Reetz, M. T. Boosting the efficiency of site-saturation mutagenesis for a difficult-to-randomize gene by a two-step PCR strategy. Appl. Microbiol. Biotechnol. 102, 6095–6103 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Biot-Pelletier, D. & Martin, V. J. J. Seamless site-directed mutagenesis of the Saccharomyces cerevisiae genome using CRISPR-Cas9. J. Biol. Eng. 10, 6 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dionne, U. et al. Protein context shapes the specificity of SH3 domain-mediated interactions in vivo. Nat. Commun. 12, 1597 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eddy, A. A. Expulsion of uracil and thymine from the yeast Saccharomyces cerevisiae: contrasting responses to changes in the proton electrochemical gradient. Microbiology 143, 219–229 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kurtz, J. E., Exinger, F., Erbs, P. & Jund, R. New insights into the pyrimidine salvage pathway of Saccharomyces cerevisiae: requirement of six genes for cytidine metabolism. Curr. Genet. 36, 130–136 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fujimura, H. Growth inhibition of Saccharomyces cerevisiae by the immunosuppressant leflunomide is due to the inhibition of uracil uptake via Fur4p. Mol. Gen. Genet. 260, 102–107 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Després, P. C., Dubé, A. K., Nielly-Thibault, L., Yachie, N. & Landry, C. R. Double selection enhances the efficiency of Target-AID and Cas9-based genome editing in yeast. G3 8, 3163–3171 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Wang, J. et al. Role of glutamate 64 in the activation of the prodrug 5-fluorocytosine by yeast cytosine deaminase. Biochemistry 51, 475–486 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ivankov, D. N., Finkelstein, A. V. & Kondrashov, F. A. A structural perspective of compensatory evolution. Curr. Opin. Struct. Biol. 26, 104–112 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mayrose, I., Graur, D., Ben-Tal, N. & Pupko, T. Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. Mol. Biol. Evol. 21, 1781–1791 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tarassov, K. An in vivo map of the yeast protein interactome. Science 320, 1465–1470 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Freschi, L., Torres-Quiroz, F., Dubé, A. K. & Landry, C. R. qPCA: a scalable assay to measure the perturbation of protein–protein interactions in living cells. Mol. Biosyst. 9, 36–43 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mirdita, M. et al. ColabFold – Making protein folding accessible to all. Preprint at bioRxiv https://doi.org/10.1101/2021.08.15.456425 (2022).Pokusaeva, V. O. et al. An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape. PLoS Genet. 15, e1008079 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oliver, J. D. et al. F901318 represents a novel class of antifungal drug that inhibits dihydroorotate dehydrogenase. Proc. Natl Acad. Sci. USA 113, 12809–12814 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hoenigl, M. et al. The antifungal pipeline: fosmanogepix, ibrexafungerp, olorofim, opelconazole, and rezafungin. Drugs https://doi.org/10.1007/s40265-021-01611-0 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Verweij, P. E., Te Dorsthorst, D. T. A., Janssen, W. H. P., Meis, J. F. G. M. & Mouton, J. W. In vitro activities at pH 5.0 and pH 7.0 and in vivo efficacy of flucytosine against Aspergillus fumigatus. Antimicrob. Agents Chemother. 52, 4483–4485 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gsaller, F. et al. Mechanistic basis of pH-dependent 5-flucytosine resistance in Aspergillus fumigatus. Antimicrob. Agents Chemother. https://doi.org/10.1128/AAC.02593-17 (2018).Garland, T. Jr. Trade-offs. Curr. Biol. 24, R60–R61 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chang, Y. C. et al. Moderate levels of 5-fluorocytosine cause the emergence of high frequency resistance in cryptococci. Nat. Commun. 12, 3418 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Billmyre, R. B., Applen Clancey, S., Li, L. X., Doering, T. L. & Heitman, J. 5-fluorocytosine resistance is associated with hypermutation and alterations in capsule biosynthesis in Cryptococcus. Nat. Commun. 11, 127 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brachmann, C. B. et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14, 115–132 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Janke, C. et al. A versatile toolbox for PCR-based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes. Yeast 21, 947–962 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Goldstein, A. L. & McCusker, J. H. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast 15, 1541–1553 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    DeLuna, A., Springer, M., Kirschner, M. W. & Kishony, R. Need-based up-regulation of protein levels in response to deletion of their duplicate genes. PLoS Biol. 8, e1000347 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Casadaban, M. J. & Cohen, S. N. Analysis of gene control signals by DNA fusion and cloning in Escherichia coli. J. Mol. Biol. 138, 179–207 (1980).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yachie, N. et al. Pooled-matrix protein interaction screens using barcode fusion genetics. Mol. Syst. Biol. 12, 863 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Andrews, S. FastQC: A quality control analysis tool for high throughput sequencing data (Babraham Bioinformatics, 2016); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).Article 

    Google Scholar 
    Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reback, J. et al. pandas-dev/pandas: Pandas 1.3.4. Zenodo https://doi.org/10.5281/zenodo.5574486 (2021).Waskom, M. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).Article 

    Google Scholar 
    Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform. https://doi.org/10.1186/1471-2105-13-31 (2012).Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rice, P., Longden, L. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. https://doi.org/10.1016/S0168-9525(00)02024-2 (2000).Article 
    PubMed 

    Google Scholar 
    Ryan, O. W., Poddar, S. & Cate, J. H. D. Crispr–cas9 genome engineering in Saccharomyces cerevisiae cells. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.prot086827 (2016).Amberg, D. C., Burke, D. J. & Strathern, J. N. Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual (CSHL Press, 2005).Ireton, G. C., Black, M. E. & Stoddard, B. L. The 1.14 A crystal structure of yeast cytosine deaminase: evolution of nucleotide salvage enzymes and implications for genetic chemotherapy. Structure 11, 961–972 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Marchant, A. et al. The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs. eLife 8, e46754 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Usmanova, D. R. et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics 34, 3653–3658 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chorostecki, U., Molina, M., Pryszcz, L. P. & Gabaldón, T. MetaPhOrs 2.0: integrative, phylogeny-based inference of orthology and paralogy across the tree of life. Nucleic Acids Res. 48, W553–W557 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Byrne, K. P. & Wolfe, K. H. The Yeast Gene Order Browser: combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res. 15, 1456–1461 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).Article 
    CAS 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lõoke, M., Kristjuhan, K. & Kristjuhan, A. Extraction of genomic DNA from yeasts for PCR-based applications. Biotechniques 50, 325–328 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schlecht, U., Miranda, M., Suresh, S., Davis, R. W. & St Onge, R. P. Multiplex assay for condition-dependent changes in protein-protein interactions. Proc. Natl Acad. Sci. USA 109, 9213–9218 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Diss, G. & Lehner, B. The genetic landscape of a physical interaction. eLife 7, e32472 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Observing and modeling long-term persistence of P. noctiluca in coupled complementary marine systems (Southern Tyrrhenian Sea and Messina Strait)

    Lucas, C. H. et al. Gelatinous zooplankton biomass in the global oceans: Geographic variation and environmental drivers. Glob. Ecol. Biogeogr. 23, 701–714. https://doi.org/10.1111/Geb.12169 (2014).Article 

    Google Scholar 
    Condon, R. H. et al. Recurrent jellyfish blooms are a consequence of global oscillations. Proc. Natl. Acad. Sci. USA 110, 1000–1005. https://doi.org/10.1073/pnas.1210920110 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    Graham, W. M. et al. Linking human well-being and jellyfish: Ecosystem services, impacts, and societal responses. Front. Ecol. Environ. 12, 515–523. https://doi.org/10.1890/130298 (2014).Article 

    Google Scholar 
    Lucas, C. H., Gelcich, S. & Uye, S. I. Living with jellyfish: Management and adaptation strategies. In Jellyfish Blooms (eds Pitt, K. A. & Lucas, C. H.) 129–150 (Springer, 2014).Chapter 

    Google Scholar 
    De Donno, A. et al. Impact of stinging jellyfish proliferations along south Italian coasts: Human health hazards, treatment and social costs. Int. J. Environ. Res. Public Health 11, 2488–2503 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bosch-Belmar, M. et al. Consequences of stinging plankton blooms on finfish mariculture in the Mediterranean Sea. Front. Mar. Sci. 4, 240. https://doi.org/10.3389/fmars.2017.0024 (2017).Article 

    Google Scholar 
    Mayer, A. G. Medusae of the World: The Hydromedusae 132–498 (Carnegie institution of Washington, 1910).Book 

    Google Scholar 
    Kramp, P. L. Synopsis of the medusae of the world. J. Mar. Biol. Assoc. UK 40, 1–469 (1961).
    Google Scholar 
    Canepa, A. et al. Pelagia noctiluca in the Mediterranean Sea. In Jellyfish Blooms (eds Pitt, K. A. & Lucas, C. H.) 237–266 (Springer, 2014).Chapter 

    Google Scholar 
    Marambio, M. et al. Unfolding jellyfish bloom dynamics along the Mediterranean basin by transnational citizen science initiatives. Diversity 13, 274. https://doi.org/10.3390/d13060274 (2021).Article 

    Google Scholar 
    Mamish, S., Durgham, H. & Ikhtiyar, S. The first Pelagia noctiluca outbreak off the Syrian coast (the eastern Mediterranean Sea), five years after its first appearance. SSRG Int. J. Agric. Environ. Sci. 6, 72–75 (2019).
    Google Scholar 
    Daly Yahia, M. N. et al. Are outbreaks of Pelagia noctiluca (Forsskäl, 1775) more frequent in the Mediterranean Basin?. ICES Coop. Res. Rep. 300, 8–14 (2010).
    Google Scholar 
    Aissi, M., Touzri, C., Gueroun, S. K. M., Kefi-Daly Yahia, O. & Daly Yahia, M. N. Persistent occurrence and life cycle of Pelagia noctiluca in the channel of Bizerte (Northern Tunisia). Ecol. Environ. Conserv. 20, 1453–1460 (2014).
    Google Scholar 
    Kogovsĕk, T., Bogunović, B. & Malej, A. Recurrence of bloom forming scyphomedusae: Wavelet analysis of a 200-year time series. Hydrobiologia 645, 81–96 (2010).Article 
    CAS 

    Google Scholar 
    Pestoric, B. et al. Scyphomedusae and ctenophora of the eastern adriatic: Historical overview and new data. Diversity 13, 186. https://doi.org/10.3390/d13050186 (2021).CAS 
    Article 

    Google Scholar 
    UNEP (United Nations Environmental Programme). Workshop on Jellyfish Blooms in the Mediterranean, Athens (1984).UNEP (United Nations Environmental Programme). Jellyfish blooms in the Mediterranean Sea. Proceedings of II Workshop on Jellyfish in the Mediterranean Sea, Athens (1991).Goy, J., Morand, P. & Etienne, M. Long term fluctuations of Pelagia noctiluca (Cnidaria, Scyphomedusa) in the western Mediterranean. Sea Prediction by climatic variables. Deep-Sea Res. A 36, 269–279 (1989).ADS 
    Article 

    Google Scholar 
    Bernard, P., Berline, L. & Gorsky, G. Long term (1981–2008) monitoring of the jellyfish Pelagia noctiluca (Cnidaria, Scyphozoa) on the French Mediterranean Coasts. J. Oceanogr. Res. Data 4, 1–10 (2011).
    Google Scholar 
    Brotz, L., Cheung, W. W. L., Kleisner, K., Pakhomov, E. & Pauly, D. Increasing jellyfish population: Trends in large marine ecosystems. Hydrobiologia 690, 3–20 (2012).Article 

    Google Scholar 
    Rosa, S., Pansera, M., Granata, A. & Guglielmo, L. Interannual variability, growth, reproduction and feeding of Pelagia noctiluca (Cnidaria: Scyphozoa) in the Straits of Messina (Central Mediterranean Sea): Linkages with temperature and diet. J. Mar. Syst. 111–112, 97–107 (2013).Article 

    Google Scholar 
    Aoutien, M., Bekkali, R., Nachit, D., Luan, K. & Mrhraoui, M. Predicting jellyfish strandings in the Moroccan North-West Mediterranean coastline. Eur. Sci. J. 15, 72–84. https://doi.org/10.19044/esj.2019.v15n2p72 (2019).Article 

    Google Scholar 
    Lynam, C. P., Hay, S. J. & Brierley, A. S. Interannual variability in abundance of North Sea jellyfish and links to the North Atlantic Oscillation. Limnol. Oceanogr. 49, 637–643 (2004).ADS 
    Article 

    Google Scholar 
    Lynam, C. P. et al. Have jellyfish in the Irish Sea benefited from climate change and overfishing?. Glob. Change Biol. 17, 767–782 (2011).ADS 
    Article 

    Google Scholar 
    Brodeur, R. D. et al. Rise and fall of jellyfish in the eastern Bering Sea in relation to climate regime shifts. Prog. Oceanogr. 77, 103–111 (2008).ADS 
    Article 

    Google Scholar 
    Molinero, J. C. et al. Climate control on the longterm anomalous changes of zooplankton communities in the Northwestern Mediterranean. Glob. Change Biol. 14, 11–26 (2008).ADS 
    Article 

    Google Scholar 
    Licandro, P. et al. A blooming jellyfish in the northeast Atlantic and Mediterranean. Biol. Let. 6, 688–691 (2010).CAS 
    Article 

    Google Scholar 
    Ferraris, M. et al. Distribution of Pelagia noctiluca (Cnidaria, Scyphozoa) in the Ligurian Sea (NW Mediterranean Sea). J. Plankton Res. 34, 874–885 (2012).Article 

    Google Scholar 
    Malačič, V., Petelin, B. & Malej, A. Advection of the jellyfish Pelagia noctiluca (Scyphozoa) studied by the Lagrangian tracking of water mass in the climatic circulation of the Adriatic Sea. Geophys. Res. Abstr. 9, 02802 (2007).
    Google Scholar 
    Rubio, P. & Muñoz, J. M. Predicción estival del riesgo de blooms de Pelagia noctiluca (litoral central catalán). In Situaciones de riesgo climático en España (ed. Novau, J. C.) 281–287 (Instituto Pirenaico de Ecología, 1997).
    Google Scholar 
    Berline, L., Zakardjian, B., Molcard, A., Ourmieres, Y. & Guihou, K. Modeling jellyfish Pelagia noctiluca transport and stranding in the Ligurian Sea. Mar. Pollut. Bull. 70, 90–99 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Olds, A. D. et al. Quantifying the conservation value of seascape connectivity: A global synthesis. Glob. Ecol. Biogeogr. 25, 3–15 (2016).Article 

    Google Scholar 
    Vodopivec, M., Peliz, A. J. & Malej, A. Offshore marine constructions as propagators of moon jellyfish dispersal. Environ. Res. Lett. 12, 084003 (2017).ADS 
    Article 

    Google Scholar 
    Chen, J. Z., Huang, S. L. & Han, Y. S. Impact of long-term habitat loss on the Japanese eel Anguilla japonica. Estuar. Coast. Shelf Sci. 151, 361–369 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Fernandez-Arcaya, U. et al. Ecological role of submarine canyons and need for canyon conservation: A review. Front. Mar. Sci. 4, 5. https://doi.org/10.3389/fmars.2017.00005 (2017).Article 

    Google Scholar 
    Würtz, M. Towards a Mediterranean canyon inventory. Workshop (EBSAs), 7 to 11 April 2014, Málaga, Spain, 1–4 (2014).Sacchetti, F. Il ritorno di MeteoMedusa. Focus (Madison) 237, 92–94 (2012).
    Google Scholar 
    Benedetti-Cecchi, L. et al. Deterministic factors overwhelm stochastic environmental fluctuations as drivers of jellyfish outbreaks. PLoS ONE 10, e0141060. https://doi.org/10.1371/journal.pone.0141060 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Malej, A. & Malej, M. Population dynamics of the jellyfish Pelagia noctiluca (Forsskäl, 1775). In Proceedings of the 25th EMBS, Marine Eutrophication and Population Dynamics (ed. Colombo, G.A.) 215–219 (Olsen & Olsen, 1992).Rottini-Sandrini, L., Avian, M., Axiak, V. & Malej, A. The breeding period of Pelagia noctiluca (Scyphozoa, Semaeostomeae) in the Adriatic and central Mediterranean Sea. Nova Thalass. 6, 65–75 (1983).
    Google Scholar 
    Milisenda, G. et al. Reproductive and bloom patterns of Pelagia noctiluca in the Strait of Messina, Italy. Estuar. Coast. Shelf Sci. 201, 29–39. https://doi.org/10.1016/j.ecss.2016.01.002 (2018).ADS 
    Article 

    Google Scholar 
    Magazzù, G. et al. Picoplankton: Contribution to phytoplankton production in the Strait of Messina. Mar. Ecol. 8, 21–31 (1987).ADS 
    Article 

    Google Scholar 
    Guglielmo, L., Crescenti, N., Costanzo, G. & Zagami, G. Zooplankton and micronekton communities in the Straits of Messina. In The Straits of Messina ecosystem, present knowledge for an ecohydrodynamical approach. Proceedings of Symposium held in Messina, 4–6 April 1991, Messina (eds. Guglielmo, L., Manganaro, A. & De Domenico, E.) 247–270 (Dipartimento di Biologia Animale ed Ecologia, 1995).Guglielmo, L. et al. The Strait of Messina: A key area for Pelagia noctiluca (Cnidaria, Scyphozoa). In Jellyfish: Ecology, Distribution Patterns and Human Interactions (ed. Mariottini, G. L.) 71–90 (Nova Science Publishers Inc., 2017).
    Google Scholar 
    Astraldi, M. & Gasparini, G. P. The seasonal characteristics of the circulation in the Tyrrhenian Sea. In: Seasonal and Interannual Variability of the Western Mediterranean Sea, Coast. Estuar. Studies, Vol. 46, 115–134 (American Geophysical Union, 1994).Krivosheya, V. G. Water circulation and structure in the Tyrrhenian Sea. Oceanology 23, 166–171 (1983).
    Google Scholar 
    Millot, C. Circulation in the Western Mediterranean Sea. J. Mar. Syst. 20, 423–442. https://doi.org/10.1016/S0924-7963(98)00078-5 (1999).Article 

    Google Scholar 
    Vetrano, A., Napolitano, E., Iacono, R., Schroeder, K. & Gasparini, G. P. Tyrrhenian Sea circulation and water mass fluxes in spring 2004: Observations and model results. J. Geophys. Res. 115, C06023 (2010).ADS 

    Google Scholar 
    Iacono, R., Napolitano, E., Marullo, S., Artale, V. & Vetrano, A. Seasonal variability of the Tyrrhenian Sea surface geostrophic circulation as assessed by altimeter data. J. Phys. Oceanogr. 43, 1710–1732. https://doi.org/10.1175/JPO-D-12-0112.1 (2013).ADS 
    Article 

    Google Scholar 
    Boero, F. et al. CoCoNet: Towards coast to coast networks of Marine Protected Areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential. Sci. Res. Inf. Technol. 6(Suppl.), 1–95 (2016).
    Google Scholar 
    Rio, M. H. et al. A mean dynamic topography of the Mediterranean Sea computed from altimetric data, in-situ measurements and a general circulation model. J. Mar. Syst. 65, 484–508 (2007).Article 

    Google Scholar 
    Cucco, A. et al. Hydrodynamic modelling of coastal seas: The role of tidal dynamics in the Messina Strait, Western Mediterranean Sea. Nat. Hazards Earth Syst. Sci. 16, 1553–1569 (2016).ADS 
    Article 

    Google Scholar 
    Hopkins, T. S., Salusti, E. & Settimi, D. Tidal forcing of the water mass interface in the Straits of Messina. J. Geophys. Res. 89, 2013–2024 (1984).ADS 
    Article 

    Google Scholar 
    Bignami, F. & Salusti, E. Tidal currents and transient phenomena in the Strait of Messina: A review. In: The Physical Oceanography of Sea Straits (ed. Pratt, L. J.) 95–124 (Kluwer Academic, 1990).Azzaro, F., Decembrini, F., Raffa, F. & Crisafi, E. Seasonal variability of phytoplankton fluorescence in relation to the Straits of Messina (Sicily) tidal upwelling. Ocean Sci. Discuss. 4, 415–440 (2007).ADS 

    Google Scholar 
    De Domenico, E., Cortese, G. & Pulicanò, G. Chemical characteristics of the waters in the Straits of Messina. In The Straits of Messina ecosystem, present knowledge for an ecohydrodynamical approach. Proceedings of Symposium held in Messina, 4–6 April 1991, Messina (eds. Guglielmo, L., Manganaro, A., & De Domenico, E.) 155–167 (Dipartimento di Biologia Animale ed Ecologia Marina, 1995).Guglielmo, L. Distribuzione di Chetognati nell’area idrografica dello Stretto di Messina. Pubbl. Staz. Zool. Napoli 40, 34–72 (1976).
    Google Scholar 
    Sitran, R., Bergamasco, A., Decembrini, F. & Guglielmo, L. Temporal succession of tintinnids in the northern Ionian Sea, Central Mediterranean. J. Plankton Res. 29, 495–508 (2007).Article 

    Google Scholar 
    AA.VV. Final Scientific Report of the Project Cluster 10—SAM “Realizzazione ed attivazione di una rete integrata di piattaforme costiere e mezzo mobile attrezzati per Sistemi Avanzati di Monitoraggio delle acque (SAM)”, funded by the Italian Ministry of University and Scientifical and Technological Research (MURST), Internal Data File, Istituto Sperimentale Talassografico, National Research Council, Messina, Italy (2005).Sitran, R. Caratterizzazione dei popolamenti microzooplanctonici nell’area idrografica dello Stretto di Messina, University of Messina, Ph.D. Thesis XVII cycle (2006) (in Italian).Bergamasco, A. et al. A laboratory for the observation of a highly-energetic coastal marine system: The Straits of Messina. In Volume DTA/06–2011, “Marine Research at CNR” 2185–2202 (Department of Earth and Environment of National Research Council, 2011).Doyle, T. K. et al. Widespread occurrence of the jellyfish Pelagia noctiluca in Irish coastal and shelf waters. J. Plankton Res. 30, 963–968 (2008).Article 

    Google Scholar 
    Guglielmo, L. Spiaggiamenti di eufausiacei lungo la costa messinese dello Stretto dal dicembre 1968 al dicembre 1969. Boll. Pesca Piscic. Idrobiol. 24, 71–77 (1969).
    Google Scholar 
    Guglielmo, L., Costanzo, G. & Berdar, A. Ulteriore contributo alla conoscenza dei crostacei spiaggiati lungo il litorale messinese dello Stretto. Atti Soc. Pelorit. 19, 129–156 (1973).
    Google Scholar 
    Scotto Di Carlo, B., Costanzo, G., Fresi, E., Guglielmo, L. & Ianora, A. Feeding ecology and stranding mechanisms in two lanternfishes, Hygophum benoiti and Myctophum punctatum. Mar. Ecol. Prog. Ser 9, 13–24 (1982).ADS 
    Article 

    Google Scholar 
    Battaglia, P., Ammendolia, G., Cavallaro, M., Consoli, P. & Esposito, V. Influence of lunar phases, winds and seasonality on the stranding of mesopelagic fish in the Strait of Messina (Central Mediterranean Sea). Mar. Ecol. 38, e12459. https://doi.org/10.1111/maec.12459 (2017).Article 

    Google Scholar 
    Umgiesser, G., Canu, D. M., Cucco, A. & Solidoro, C. A finite element model for the Venice Lagoon. Development, set up, calibration and validation. J. Mar. Syst. 51, 123–145 (2004).Article 

    Google Scholar 
    Ferrarin, C., Bergamasco, A., Umgiesser, G. & Cucco, A. Hydrodynamics and spatial zonation of the Capo Peloro coastal system (Sicily) through 3-D numerical modeling. J. Mar. Syst. 117, 96–107 (2013).Article 

    Google Scholar 
    Umgiesser, G., Ferrarin, C., Cucco, A., De Pascalis, F. & Bellafiore, D. Comparative hydrodynamics of 10 Mediterranean lagoons by means of numerical modeling. J. Geophys. Res. Oceans 119, 2212–2226 (2014).ADS 
    Article 

    Google Scholar 
    Cucco, A., Quattrocchi, G., Satta, A., Antognarelli, F. & De Biasio, F. Predictability of wind-induced sea surface transport in coastal areas. J. Geophys. Res. Oceans 121, 5847–5871. https://doi.org/10.1002/2016JC011643 (2016).ADS 
    Article 

    Google Scholar 
    Cucco, A., Quattrocchi, G. & Zecchetto, S. The role of temporal resolution in modeling the wind induced sea surface transport in coastal seas. J. Mar. Syst. 193, 46–58. https://doi.org/10.1016/j.jmarsys.2019.01.004 (2019).Article 

    Google Scholar 
    Quattrocchi, G. et al. An operational numerical system for oil stranding risk assessment in a high-density vessel traffic area. Front. Mar. Sci. 8, 585396. https://doi.org/10.3389/fmars.2021.585396 (2021).Article 

    Google Scholar 
    Cucco, A. et al. A high-resolution real-time forecasting system for predicting the fate of oil spills in the Strait of Bonifacio (western Mediterranean Sea). Mar. Pollut. Bull. 64, 1186–1200 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cucco, A. & Umgiesser, G. The Trapping Index: How to integrate the Eulerian and the Lagrangian approach for the computation of the transport time scales of semi-enclosed basins. Mar. Pollut. Bull. 98, 210–220 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Quattrocchi, G. et al. Optimal design of a Lagrangian observing system for hydrodynamic surveys. J. Oper. Oceanogr. 9(suppl.), s77–s88. https://doi.org/10.1080/1755876X.2015.1114805 (2016).Article 

    Google Scholar 
    Quattrocchi, G. et al. Hydrodynamic controls on connectivity of the high commercial value shrimp Parapenaeus longirostris (Lucas, 1846) in the Mediterranean Sea. Sci. Rep. 9, 16935. https://doi.org/10.1038/s41598-019-53245-8 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pastor-Prieto, M. et al. Spatial heterogeneity of Pelagia noctiluca ephyrae linked to water masses in the Western Mediterranean. PLoS ONE 16, e0249756. https://doi.org/10.1371/journal.pone.0249756 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Haeckel, E. Das system der medusen. Monographie der Medusen 499–510 (Gustav Fischer Verlag, 1880).
    Google Scholar 
    Avian, M. Temperature influence on in vitro reproduction and development of Pelagia noctiluca (Forsskäl, 1775). Boll. Zool. 53, 385–391 (1986).Article 

    Google Scholar 
    Fossette, S. et al. Current-oriented swimming by jellyfish and its role in bloom maintenance. Curr. Biol. 25, 342–347. https://doi.org/10.1016/j.cub.2014.11.050 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pinardi, N. et al. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis. Prog. Oceanogr. 132, 318–332 (2015).ADS 
    Article 

    Google Scholar 
    Demirov, E. & Pinardi, N. Simulation of the Mediterranean Sea circulation from 1979 to 1993: Part I. The interannual variability. J. Mar. Syst. 33–34, 23–50 (2002).Article 

    Google Scholar 
    Menna, M. et al. New insights of the Sicily channel and Southern Tyrrhenian sea variability. Water 11, 1355 (2019).Article 

    Google Scholar 
    Avian, M. & Rottini Sandrini, L. Oocyte development in four species of scyphomedusa in the northern Adriatic Sea. Hydrobiologia 216/217, 189–195 (1991).Article 

    Google Scholar 
    Malej, A. Behaviour and trophic ecology of the jellyfish Pelagia noctiluca (Forsskäl, 1775). J. Exp. Mar. Biol. Ecol. 126, 259–270 (1989).Article 

    Google Scholar 
    Lo Bianco, S. Notizie biologiche riguardanti specialmente il periodo di maturità sessuale degli animali del golfo di Napoli. Mitt. Zool. Stn. Neapel 19, 513–761 (1909).
    Google Scholar 
    Purcell, J. E., Malej, A. & Benović, A. Potential links of jellyfish to eutrophication and fisheries. In Coastal and Estuarine Studies, Ecosystem at the Land-Sea Margin Drainage Basin to Coastal Sea (eds Malone, T. C. et al.) 241–263 (American Geophysical Union, 1999).Chapter 

    Google Scholar 
    Spezie, G. C., Sansone, E., Budillon, G. & Gallarato, A. Caratterizzazione idrodinamica del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia. Campagna oceanografica 1994. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM94). In Data Rep., (eds. Faranda, F. M. & Povero, P.) 1–82 (1995).Spezie, G. C. et al. Rilievi idrodinamici nel sistema Eolie e nei bacini limitrofi di Cefalù e Gioia. Campagna oceanografiche 1995. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 1–98 (1996).Carrada, G. C., Ribera D’Alcalà, M. & Saggiomo, V. The pelagic system of the Southern Tyrrhenian Sea. Some comments and working hypotheses. In Proceedings IX Proceedings XII Italian Association of Oceanography and Limnology Congress 151–166 (1992).Povero, P., Misic, C., Acconero, A. & Fabiano M. Distribuzione e caratterizzazione biochimica della sostanza organica particellata nelle acque del Tirreno Sud Orientale. In Acts 12 Congress of the Italian Association of Oceanology and Limnology 227–237 (1998).Brancato, G., Minutoli, R., Granata, A., Sidoti, O. & Guglielmo L. Diversity and vertical migration of euphausiids across the Straits of Messina area. In: Mediterranean Ecosystem: Structures and Processes (eds. Faranda, F. M., Guglielmo, L. & Spezie, G.) 131–141 (Springer, 2001).Sitran, R., Bergamasco, A., Decembrini, F. & Guglielmo, L. Microzooplankton (tintinnid ciliates) diversity: Coastal community structure and driving mechanisms in the Southern Tyrrhenian Sea (Western Mediterranean). J. Plankton Res. 31, 153–170 (2009).Article 

    Google Scholar 
    Fonda Umani, S., Monti, M., Minutoli, R. & Guglielmo, L. Recent advances in the Mediterranean researches on zooplankton: from spatial–temporal patterns of distribution to processes oriented studies. Adv. Oceanogr. Limnol. 1, 295–356 (2010).Article 

    Google Scholar 
    Giordano, D. et al. Summer larval fish assemblages in the Southern Tyrrhenian Sea (Western Mediterranean Sea). Mar. Ecol. 36, 104–117. https://doi.org/10.1111/maec.12123 (2015).ADS 
    Article 

    Google Scholar 
    Fonda Umani, S., Milani, L. & Martecchini, E. Distribuzione dei popolamenti microzooplanctonici durante la campagna oceanografica Eolie 1994. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 199–222 (1995).Carrada, G. C., Mangoni, O. & Sgrosso, S. Distribuzione spaziale di clorofilla a e di feopigmenti in diverse frazioni dimensionali del fitoplancton. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 197–216 (1996).Guglielmo, L. et al. Distribuzione verticale e migrazione giornaliera dello zooplancton e del micronecton nel Tirreno meridionale (Isole Eolie). Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 217–246 (1996).Innamorati, M., Lazzara, L., Massi, L., Biondi, N. & Nuccio, C. Fitoplancton, luce e produzione primaria nella’Arcipelago delle Isole Eolie, in estate. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 161–196 (1996).Zunini Sertorio, T., Licandro, P., Giallain, M. & Bernat, P. Distribuzione verticale della biomassa zooplanctonica su una stazione delle Isole Eolie (Luglio 1995). Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 247–254 (1996).Sabates, A. et al. Pathways for Pelagia noctiluca jellyfish intrusions onto the Catalan shelf and their interactions with early life fish stages. J. Mar. Syst. 187, 52–61 (2018).Article 

    Google Scholar 
    Mosetti, F. Currents in the Straits of Messina. In The Straits of Messina ecosystem (eds Guglielmo, L. et al.) 13–29 (University of Messina, Department of Marine Biology and Ecology, 1995).
    Google Scholar 
    Zavodnik, D. Spatial aggregations of the swarming jellyfish Pelagia noctiluca (Scyphozoa). Mar. Biol. 94, 265–269 (1987).Article 

    Google Scholar 
    El Rahi, J., Weeber, M. P. & El Serafy, G. Modelling the effect of behavior on the distribution of the jellyfish Mauve stinger (Pelagia noctiluca) in the Balearic Sea using an individual-based model. Ecol. Model. 433, 109230 (2020).Article 

    Google Scholar 
    Axiak, V. & Civili, F. S. Jellyfish blooms in the Mediterranean: causes, mechanisms, impact on man and the environment. A programme review. In: UNEP: Jellyfish blooms in the Mediterranean. Proceedings of the II Workshop on Jellyfish in the Mediterranean Sea. MAP Tech. Rep. Ser. Vol. 47, 1–10 (UNEP, 1991).Keesing, J. K. et al. Role of winds and tides in timing of beach strandings, occurrence, and significance of swarms of the jellyfish Crambione mastigophora Mass 1903 (Scyphozoa: Rhizostomeae: Catostylidae) in north-western Australia. Hydrobiologia 768, 19–36. https://doi.org/10.1007/s10750-015-2525-5 (2016).CAS 
    Article 

    Google Scholar 
    Aglieri, G. et al. First evidence of inbreeding, relatedness and chaotic genetic patchiness in the holoplanktonic jellyfish Pelagia noctiluca (Scyphozoa, Cnidaria). PLoS ONE 9, e99647. https://doi.org/10.1371/journal.pone.0099647 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alpers, W., Brandt, P. & Rubino, A. Internal waves generated in the Strait of Gibraltar and Messina: Observations from space. In Remote Sensing of the European Seas (eds. Barale, V. & Gade, M.) 319–330 (Springer, 2008). https://doi.org/10.1007/978-1-4020-6772.Droghei, R. et al. The role of Internal Solitary Waves on deep-water sedimentary processes: The case of up-slope migrating sediment waves off the Messina Strait. Sci. Rep. 6, 36376. https://doi.org/10.1038/srep36376 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    La Forgia, G. et al. Sediment resuspension and bedform generation induced by internal solitary waves. Geophys. Res. Abs. Vol. 21, EGU2019-9121, EGU General Assembly (2019).Lohmann, H. Die Stromunger in der Strasse von Messina und die verteilung des planktons in derselben. Int. Rev. Ges. Hydrobiol. 2, 505–556 (1909).Article 

    Google Scholar 
    Magazzù, G. & Andreoli, C. Trasferimenti fitoplanctonici attraverso lo Stretto di Messina in relazione alle condizioni idrologiche. Boll. Pesca Piscic. Idrobiol. 26, 125–193 (1971).
    Google Scholar 
    Palanques, A. et al. General patterns of circulation, sediment fluxes and ecology of the Palamòs (La Fonera) submarine canyon, northwestern Mediterranean. Progr. Oceanogr. 66, 89–119 (2005).ADS 
    Article 

    Google Scholar 
    Granata, A. et al. Vertical distribution and diel migration of zooplankton and micronekton in Polcevera submarine canyon of the Ligurian mesopelagic zone (NW Mediterranean Sea). Progr. Oceanogr. 183, 102298. https://doi.org/10.1016/j.pocean (2020).Article 

    Google Scholar 
    Zagami, G. et al. Spring copepod vertical zonation pattern and diel migration in the open Ligurian Sea (north-western Mediterranean). Progr. Oceanogr. 183, 102297. https://doi.org/10.1016/j.pocean (2020).Article 

    Google Scholar 
    Danovaro, R. & Boero, F. Italian seas. In: World Seas: An Environmental Evaluation. Vol. I Europe, The Americas and West Africa. (ed. Sheppard, C.) 283–306 (Elsevier Ltd., 2019). https://doi.org/10.1016/B978-0-12-805068-2.00044-9Lo Iacono, C., Sulli, A. & Agate, M. Submarine canyons of north-western Sicily (Southern Tyrrhenian Sea): Variability in morphology, sedimentary processes and evolution on a tectonically active margin. Deep-Sea Res. 104, 93–105 (2014).
    Google Scholar  More