More stories

  • in

    Mechanical weeding enhances ecosystem multifunctionality and profit in industrial oil palm

    EthicsNo ethics approval was required for this study. Our study was conducted in a state-owned industrial oil palm plantation where we established a cooperation with the estate owner to access the site and collect data. No endangered or protected species were sampled. Research permits were obtained from the Ministry of Research, Technology and Higher Education, and sample collection and sample export permits were obtained from the Ministry of Environment and Forestry of the Republic of Indonesia.Study area and experimental designOur study was conducted in a state-owned industrial oil palm plantation (PTPN VI) located in Jambi, Indonesia (1.719° S, 103.398° E, 73 m above sea level). Initial planting of oil palms within the 2,025 ha plantation area started in 1998 and ended in 2002; planting density was 142 palms ha−1, spaced 8 m apart in each row and between rows, and palms were ≥16 years old during our study period of 2016–2020. The study sites have a mean annual temperature of 27.0 ± 0.2 °C and a mean annual precipitation of 2,103 ± 445 mm (2008–2017, Sultan Thaha Airport, Jambi). The management practices in large-scale oil palm plantations typically result in three contrasting management zones: (1) a 2 m radius around the base of the palm that was weeded (four times a year) and raked before fertilizer application, hereafter called the ‘palm circle’; (2) an area occurring every second inter-row, where pruned senesced palm fronds were piled up, hereafter called ‘frond piles’; and (3) the remaining area of the plantation where less weeding (two times a year) and no fertilizer were applied, hereafter called ‘inter-rows’.Within this oil palm plantation, we established a management experiment in November 2016 with full factorial treatments of two fertilization rates × two weeding practices: conventional fertilization rates at PTPN VI and other large-scale plantations (260 kg N–50 kg P–220 kg K ha−1 yr−1), reduced fertilization rates based on quantified nutrient export by harvest (136 kg N–17 kg P ha−1 yr−1–187 kg K ha−1 yr−1), herbicide and mechanical weeding15. The reduced fertilization treatment was based on quantified nutrient export from fruit harvest, calculated by multiplying the nutrient content of fruit bunches with the long-term yield data of the plantation. Fertilizers were applied yearly in April and October following weeding and raking of the palm circle. The common practice at PTPN VI and other large-scale plantations on acidic Acrisol soils is to apply lime and micronutrients, and these were unchanged in our management experiment. Before each N–P–K fertilizer application, dolomite and micronutrients were applied to the palm circle in all treatment plots using the common rates)52: 426 kg ha−1 yr−1 dolomite and 142 kg Micro-Mag ha−1 yr−1 (containing 0.5% B2O3, 0.5% CuO, 0.25% Fe2O3, 0.15% ZnO, 0.1% MnO and 18% MgO). Herbicide treatment was carried out using glyphosate in the palm circle (1.50 l ha−1 yr−1, split into four applications per year) and in the inter-rows (0.75 l ha−1 yr−1, split into two applications per year). Mechanical weeding was done using a brush cutter in the same management zones and at the same frequency as the herbicide treatment.The 22 factorial design resulted in four treatment combinations: conventional fertilization with herbicide treatment, reduced fertilization with herbicide treatment, conventional fertilization with mechanical weeding and reduced fertilization with mechanical weeding. The four treatments were randomly assigned on 50 m × 50 m plots replicated in four blocks, totalling 16 plots. The effective measurement area was the inner 30 m × 30 m area within each replicate plot to avoid any possible edge effects. For indicators (below) that were measured within subplots, these subplots were distributed randomly within the inner 30 m × 30 m of a plot. All replicate plots were located on flat terrain and on an Acrisol soil with a sandy clay loam texture.Ecosystem functions and multifunctionalityOur study included multiple indicators for each of the eight ecosystem functions23, described in details below (Supplementary Tables 1 and 2). All the parameters were expressed at the plot level by taking the means of the subplots (that is, biological parameters) or the area-weighted average of the three management zones per plot (that is, soil parameters). (1) Greenhouse gas (GHG) regulation was indicated by NEP, soil organic C (SOC) and soil GHG fluxes. (2) Erosion prevention was signified by the understory vegetation cover during the four-year measurements. (3) Organic matter decomposition was indicated by leaf litter decomposition and soil animal decomposer activity. (4) Soil fertility was signified by gross N mineralization rate, effective cation exchange capacity (ECEC), base saturation and microbial biomass N. (5) Pollination potential was designated by pan-trapped arthropod abundance and nectar-feeding bird activity. As such, it does not quantify the pollination potential for oil palm, which is mainly pollinated by a single weevil species, but rather as a proxy for a general pollination potential for other co-occurring plants. (6) Water filtration (the capacity to provide clean water) was indicated by leaching losses of the major elements. (7) Plant refugium (the capacity to provide a suitable habitat for plants) as signified by the percentage ground cover of invasive plants to the total ground cover of understory vegetation during the four-year measurements. (8) Biological control (the regulation of herbivores via predation) was indicated by insectivorous bird and bat activities and the soil arthropod predator activity.All the ecosystem functions were merged into a multifunctionality index using the established average and threshold approaches12. For average multifunctionality, we first averaged the z-standardized values (Statistics) of indicators for each ecosystem function and calculated the mean of the eight ecosystem functions for each plot. For threshold multifunctionality, this was calculated from the number of functions that exceeds a set threshold, which is a percentage of the maximum performance level of each function12; we investigated the range of thresholds from 10% to 90% to have a complete overview. The maximum performance was taken as the average of the three highest values for each indicator per ecosystem function across all plots to reduce effect of potential outliers. For each plot, we counted the number of indicators that exceeded a given threshold for each function and divided by the number of indicators for each function12.Indicators of GHG regulationWe calculated annual NEP for each plot as: net ecosystem C exchange – harvested fruit biomass C (ref. 16), whereby net ecosystem C exchange = Cout (or heterotrophic respiration) – Cin (or net primary productivity)53. The net primary productivity of oil palms in each plot was the sum of aboveground biomass production (aboveground biomass C + frond litter biomass C input + fruit biomass C) and belowground biomass production. Aboveground biomass production was estimated using allometric equations developed for oil palm plantations in Indonesia54, using the height of palms measured yearly from 2019 to 2020. Annual frond litter biomass input was calculated from the number and dry mass of fronds pruned during harvesting events of an entire year in each plot and was averaged for 2019 and 2020. Aboveground biomass production was converted to C based on C concentrations in wood and leaf litter55. Annual fruit biomass C production (which is also the harvest export) was calculated from the average annual yield in 2019 and 2020 and the measured C concentrations of fruit bunches. Belowground root biomass and litter C production were taken from previous work in our study area55, and it was assumed constant for each plot. Heterotrophic respiration was estimated for each plot as: annual soil CO2 C emission (below) × 0.7 (based on 30% root respiration contribution to soil respiration from a tropical forest in Sulawesi, Indonesia56) + annual frond litter biomass C input × 0.8 (~80% of frond litter is decomposed within a year in this oil palm plantation8). SOC was measured in March 2018 from composite samples collected from two subplots in each of the three management zones per plot down to 50 cm depth. Soil samples were air dried, finely ground and analysed for SOC using a CN analyser (Vario EL Cube, Elementar Analysis Systems). SOC stocks were calculated using the measured bulk density in each management zone, and values for each plot were the area-weighted average of the three management zones (18% for palm circle, 15% for frond piles and 67% for inter-rows)15,22.From July 2019 to June 2020, we conducted monthly measurements of soil CO2, CH4 and N2O fluxes using vented, static chambers permanently installed in the three management zones within two subplots per plot11,57. Annual soil CO2, CH4 and N2O fluxes were trapezoidal interpolations between measurement periods for the whole year, and values for each plot were the area-weighted average of the three management zones (above).Indicators of erosion preventionDiversity and abundance of vascular plants were assessed once a year from 2016 to 2020 before weeding in September–November. In five subplots per plot, we recorded the occurrence of all vascular plant species and estimated the percent cover of the understory vegetation. The percentage cover and plant species richness of each measurement year were expressed in ratio to that of 2016 to account for initial differences among the plots before the start of the experiment. For example, percentage cover in 2017 was:$$mathrm{Cover}_{2017} = frac{{left( {mathrm{Cover}_{2017} – mathrm{Cover}_{2016}} right)}}{{mathrm{Cover}_{2016}}}$$The values from five subplots were averaged to represent each plot.Indicators of organic matter decompositionLeaf litter decomposition was determined using litter bags (20 cm × 20 cm with 4 mm mesh size) containing 10 g of dry oil palm leaf litter8. Three litter bags per plot were placed on the edge of the frond piles in December 2016. After eight months of incubation in the field, we calculated leaf litter decomposition as the difference between initial litter dry mass and litter dry mass following incubation. Soil animal decomposer activity is described below (Soil arthropods).Indicators of soil fertilityAll these indicators were measured in February–March 2018 in the three management zones within two subplots per plot22. Gross N mineralization rate in the soil was measured in the top 5 cm depth on intact soil cores incubated in situ using the 15N pool dilution technique58. ECEC and base saturation were measured in the top 5 cm depth as this is the depth that reacts fast to changes in management22. The exchangeable cation concentrations (Ca, Mg, K, Na, Al, Fe, Mn) were determined by percolating the soil with 1 mol l−1 of unbuffered NH4Cl, followed by analysis of the percolates using an inductively coupled plasma-atomic emission spectrometer (ICP-AES; iCAP 6300 Duo view ICP Spectrometer, Thermo Fisher Scientific). Base saturation was calculated as the percentage exchangeable bases (Mg, Ca, K and Na) on ECEC. Microbial biomass N was measured from fresh soil samples using the fumigation-extraction method59. The values for each plot were the mean of the two subplots that were the area-weighted average of the three management zones (above)15,22.Indicators of general pollination potentialFluorescent yellow pan traps were used to sample aboveground arthropods (to determine pollinator communities60) in November 2016, September 2017 and June 2018. The traps were attached to a platform at the height of the surrounding vegetation within a 2 × 3 grid centred in the inter-rows of each plot in six clusters of three traps, totalling 18 traps per plot. Traps were exposed in the field for 48 h. We stored all trapped arthropods in 70% ethanol and later counted and identified to order and species level. The abundance of trapped arthropods in 2017 and 2018 were calculated as the ratio to the abundance in 2016 to account for initial differences among the plots before the start of the experiment. The activity of nectar-feeding birds is described below (Birds and bats).Indicators of water filtrationElement leaching losses were determined from analyses of soil-pore water sampled monthly at 1.5 m depth using suction cup lysimeters (P80 ceramic, maximum pore size 1 μm; CeramTec) over the course of one year (2017–2018)15. Lysimeters were installed in the three management zones within two subplots per plot. Dissolved N was analysed using continuous flow injection colorimetry (SEAL Analytical AA3, SEAL Analytical), whereas these other elements were determined using ICP-AES. The values for each plot were the mean of the two subplots that were the area-weighted average of the three management zones15,22.Indicators of plant refugiumIn five subplots per plot, the percentage cover and species richness of invasive understory plant species were assessed once a year from 2016 to 2020 before weeding in September–November. We defined invasive species as those plants non-native to Sumatra61 and among the ten dominant species (excluding oil palm) in the plantation for each year. The percentage cover of invasive understory plant species of each measurement year was expressed in a ratio to that of 2016 to account for initial differences among the plots before the start of the experiment. The values for each plot were represented by the average of five subplots.Indicators of biological controlThe activities of insectivorous birds and bats are described below (Birds and bats). In five subplots per plot, soil invertebrates were collected (Soil arthropods), counted, identified to taxonomic order level and subsequently classified according to their trophic groups that include predators60. The values from five subplots were average to represent each plot.BiodiversityBiodiversity was measured by the taxonomic richness of seven multitrophic groups, described in details below (Supplementary Tables 1 and 2).Understory plant species richnessThe method is described above (Indicators of erosion prevention), using the number of species as an indicator (Supplementary Table 2).Soil microorganism richnessThis was determined in May 2017 by co-extracting RNA and DNA from three soil cores (5 cm diameter, 7 cm depth) in five subplots per plot62. While DNA extraction describes the entire microbial community, RNA represents the active community. The v3–v4 region of the 16S rRNA gene was amplified and sequenced with a MiSeq sequencer (Illumina). Taxonomic classification was done by mapping curated sequences against the SILVA small subunit (SSU) 138 non-redundant (NR) database63 with the Basic Local Alignment Search Tool (BLASTN)64.Soil arthropod order richnessFor determination of soil arthropods, we collected soil samples (16 cm × 16 cm, 5 cm depth) in five subplots per plot in October–November 2017. We extracted the animals from the soil using a heat-gradient extractor65, collected them in dimethyleneglycol-water solution (1:1) and stored in 80% ethanol. The extracted animals were counted and identified to taxonomic order level61. They were also assigned to the trophic groups decomposers, herbivores and predators based on the predominant food resources recorded in previous reviews and a local study66,67. Orders with diverse feeding habits were divided into several feeding groups, for example, Coleoptera were divided into mostly predatory families (Staphylinidae, Carabidae), herbivorous families (for example, Curculionidae) and decomposer families (for example, Tenebrionidae). The total number of individuals per taxonomic group in each subplot was multiplied by the group-specific metabolic rate, which were summed to calculate soil animal decomposer activity. The values from five subplots were average to represent each plot.Aboveground arthropod order and insect family richnessIn addition to the fluorescent yellow pan traps described above (Indicators of general pollination potential), sweep net and Malaise trap samplings were conducted in June 2018, which targeted the general flying and understory dwelling arthropod communities. Sweep net sampling was conducted within the understory vegetation along two 10 m long transects per plot, with ten sweeping strokes performed per transect. In each plot, we installed a single Malaise trap between two randomly chosen palms and exposed it for 24 h. Arthropods were counted, identified to taxonomic order level and the insects to taxonomic family level and values from the three methods were summed to represent each plot.Birds and batsBirds and bats passing at each replicate plot were sampled in September 2017 using SM2Bat + sound recorders (Wildlife Acoustics) with two microphones (SMX-II and SMX-US) placed at a height of 1.5 m in the middle of each plot68. We assigned the bird vocalization to species with Xeno-Canto69 and the Macaulay library70. Insectivorous bat species richness was computed by dividing them into morphospecies based on the characteristics of their call (call frequency, duration, shape). In addition, we gathered information on proportional diet preferences of the bird species using the EltonTrait database71. We defined birds feeding on invertebrates (potential biocontrol agents) as the species with a diet of at least 80% invertebrates and feeding on nectar (potential pollinators), if the diet included at least 20% of nectar.Economic indicatorsWe used six indicators linked to the level and stability of yield and profit: yield, lower fifth quantile of the yield per palm per plot, shortfall probability, management costs, profit and relative gross margin. We assessed fruit yield by weighing the harvested fruit bunches from each palm within the inner 30 m × 30 m area of each plot. The harvest followed the schedule and standard practices of the plantation company: each palm was harvested approximately every ten days and the lower fronds were pruned. For each plot, we calculated the average fruit yield per palm and scaled up to a hectare, considering the planting density of 142 palms per ha. Because the palms in each plot have different fruiting cycles and were harvested continuously, the calculation of an annual yield may lead to misleading differences between treatments. Therefore, we calculated the cumulative yield from the beginning of the experiment to four years (2017–2020), which should account for the inter- and intra-annual variations in fruit production of the palms in the plots and thus allowing for comparison among treatments. As effects of management practices on yield may be delayed46, we also calculated the cumulative yield during two consecutive years (2017–2018 and 2019–2020) and checked for treatment effects on yield and profit indicators separately for these two periods.We computed risk indicators on the cumulative yield and on the yield between the two periods. We used the lowest fifth quantile of the yield per palm per plot (left side of the distribution) to indicate the production of the palms with lowest performance. Also, we determined the yield shortfall probability (lower partial moment 0th order), defined as the share of palms that fell below a predefined threshold of yield; the thresholds chosen were 630 kg−1 per palm for cumulative yield and 300 kg−1 per palm per year for the two-year yield, which corresponded to 75% of the average yield.Revenues and costs were calculated as cumulative values during four years of the experiment (2017–2020) using the same prices and costs for all the years. This was because we were interested in assessing the economic consequences of different management treatments, and they might be difficult to interpret when changes in prices and costs between calendar years are included, which are driven by external market powers rather than the field-management practices. For the same reason, we abstained from discounting profits. Given the usually high discount rates applied to the study area, slight differences in harvesting activities between calendar years or months might lead to high systematic differences between the management treatments, which are associated with the variation in work schedule within the plantation rather than the actual difference among management treatments. Revenues were calculated from the yield and the average price of the fruit bunches in 2016 and 201761. Material costs were the sum of the costs of fertilizers, herbicide and gasoline for the brush cutter. Labour costs were calculated from the minimum wage in Jambi and the time (in labour hours) needed for the harvesting, fertilizing and weeding operations, which were recorded in 2017 for each plot. The weeding labour included the labour for raking the palm circle before fertilization, which was equal in all treatments, and the weeding in the palm circle and inter-rows either with herbicide or brush cutter. In addition, we included the time to remove C. hirta, which must be removed mechanically from all plots once a year, calculated from the average weed-removal time in the palm circle and the percentage cover of C. hirta in each plot for each year. We then calculated the profit as the difference between revenues and the total management costs and the relative gross margin as the gross profit proportion of the revenues.StatisticsTo test for differences among management treatments for each ecosystem function and across indicators of biodiversity, the plot-level value of each indicator was first z standardized (z = (actual value − mean value across plots) / standard deviation)4. This prevents the dominance of one or few indicators over the others, and z standardization allows several distinct indicators to best characterize an ecosystem function or biodiversity4. Standardized values were inverted (multiplied by −1) for indicators of which high values signify undesirable effect (that is, NEP, soil N2O and CH4 fluxes, element leaching losses, invasive plant cover, yield shortfall, management costs) for intuitive interpretations. For a specific ecosystem function (Supplementary Figs. 1 and 2) and across indicators of biodiversity (Fig. 2), linear mixed-effects (LME) models were used to assess differences among management treatments (fertilization, weeding and their interaction) as fixed effects with replicate plots and indicators (Supplementary Tables 2 and 3) as random effects. The significance of the fixed effects was evaluated using ANOVA72. The LME model performance was assessed using diagnostic residual plots73. As indicator variables may systematically differ in their responses to management treatments, we also tested the interaction between indicator and treatment (Table 1). For testing the differences among management treatments across ecosystem functions (that is, multifunctionality; Fig. 1), we used for each replicate plot the average of z-standardized indicators of each ecosystem function and ranges of thresholds (that is, number of functions that exceeds a set percentage of the maximum performance of each function12; Supplementary Fig. 3). The LME models had management treatments (fertilization, weeding and their interaction) as fixed effects and replicate plots and ecosystem functions as random effects; the interaction between ecosystem function and treatment were also tested to assess if there were systematic differences in their responses to management treatments (Table 1). As we expected that the type of weeding will influence ground vegetation, we tested for differences in ground cover of understory vegetation, measured from 2016 to 2020, using LME with management treatments as fixed effect and replicate plots and year as random effects. Differences among management treatments (fertilization, weeding and their interaction) in yield and profit indicators, which were cumulative values over four years (Fig. 3) or for two separate periods (2017–2018 and 2019–2020; Supplementary Fig. 4), were assessed using linear model ANOVA (Table 1). For clear visual comparison among management treatments across ecosystem functions, multitrophic groups for biodiversity, and yield and profit indicators, the fifth and 95th percentiles of their z-standardized values were presented in a petal diagram (Fig. 4 and Supplementary Fig. 5). Data were analysed using R (version 4.0.4), using the R packages ‘nlme’ and ‘influence.ME’73.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

  • in

    New machine learning-based automatic high-throughput video tracking system for assessing water toxicity using Daphnia Magna locomotory responses

    Test organisms and exposuresIn this study, we used test organisms and reagents according to the Acute Toxicity Test Method of Daphnia magna Straus(Cladocera, Crustacea); ES 04704.1b29. Daphnia magna were fostered at the National Institute of Environmental Research and were adopted. During the test, adult female Daphnia magna over two weeks of age, cultured over several generations, were transferred to a freshly prepared container the day before the test. Daphnia magna are neonates for less than 24 h after birth29. To maintain the sensitivity of the organism, young individuals less than 24 h old that reproduced the following day were used. Individuals of a similar size were selected for the test. Daphnia magna was fed YCT, which is a mixture of green algae in Chlorella sp., yeast, Cerophy II(R), and trout chow. Sufficient amounts of prey were supplied 2 h before the test to minimize the effects of prey during the test. The test medium was prepared by dissolving KCl (8 mg/L), (text {MgSO}_4) (120 mg/L), (text {CaSO}_4 cdot 2 text {H}_2 text {O} ) (120 mg/L), and (text {NaHCO}_3) (192 mg/L) in deionized water.Automatic high-throughput Daphnia magna tracking systemTo build an automatic high-throughput Daphnia magna tracking system, we equipped the system with a video analysis algorithm as well as flow cells (Fig. 1). In the tracking system, six flow cells filled with culture medium were installed in the device. Each flow cell contained 10 Daphnia magna. Subsequently, to automatically measure the state of Daphnia magna, the six flow cells were photographed at 15 frames per second using a camera (Industrial Development Systems imaging) equipped with a CMOSIS sensor capable of infrared imaging. A red light close to the infrared spectrum was placed at the back of the flow cells for uniform illumination and to minimize stress on Daphnia magna. To capture the size and movement of the Daphnia magna as accurately as possible, the camera was set to a frame rate of 15 fps and a resolution of 2048 (times ) 1088 (2.23 MB), using a 12 mm lens. The distance between the flow cell and the camera was set to 16 cm. To measure the number of mobile Daphnia magna, their lethality, and swimming inhibition automatically and simultaneously, one camera for every two cells was used to collect the status data of Daphnia magna. For assessing ecotoxicity, the video analysis system used images obtained from the six flow cells to track each Daphnia magna and estimate key statistics such as the number of mobile individuals, average distance, and radius of activity.Figure 1New automatic high-throughput video tracking system for behavioral analysis using Daphnia magna as a model organismFull size imageThe automatic high-throughput video tracking system in the ecotoxicity measuring device was designed to continuously measure the ecotoxicity of Daphnia magna (Fig. 2). Daphnia magna moves faster at high temperatures and is less active at low temperatures. Thus, a constant temperature module that can be set to an appropriate Daphnia magna habitat temperature (20 ± 2 (^{circ })C) was added to create a suitable culture environment for Daphnia magna29. Natural pseudo-light ((lambda >590) nm, 3000 k) was installed on the upper part of the detector for proper habitat light intensity (500 Lux–1000 Lux). The size of the flow cell was set as small as possible while observing the movement of the Daphnia magna. An automatic feeding system was installed so that food could be injected during the replacement cycle. The six independent multi-flow cells were designed with an automatic dilution injection module; therefore, these flow cells were diluted to six different concentrations (100%, 50%, 25%, 12.5%, 6.25%, and 0%).Figure 2Schematic representation of the automatic high-throughput video tracking systemFull size imageAutomatic tracking algorithmThe CPU used for Daphnia magna tracking was Intel i5-9300H @ 2.40 GHz, with 8 GB of memory and Windows 10 Pro 64-bit operating system. In this experiment, the algorithms were trained using 12 Daphnia magna videos and tested using an additional four Daphnia magna videos. Subsequently, the detection and tracking methods were compared. The videos, each of which had a duration of 30 s, were captured at a rate of 15 frames per second. Generally, for long-time or real-time videos, the following factors must be considered in tracking Daphnia magna: automatic binarization between the object and background, effective classification of Daphnia magna or noise, and the speed of the algorithm. Therefore, to develop an efficient tracking algorithm, we propose the following tracking process (Fig. 3A). In this process, each frame is initially converted into an image and the background is identified from the obtained video (Fig. 3B). The background is the average of the frames over the previous 20 s, and the tracking system takes 20 s to capture the first background image. The background is subtracted from the image for object detection (Fig. 3C). The objects include Daphnia magna and noise such as droplets and sediment. The difference between the background and frame images is binarized, and each area of the binarized values is regarded as an object. Conventionally, the binarized values are manually generated using specific thresholds. In this study, the images are automatically binarized using k-means clustering to select the threshold value. After binarization, several machine learning methods are used to classify the objects as Daphnia magna or noise (Fig. 3D). For a faster tracking algorithm, we use simple machine learning methods such as random forest (RF) and support vector machine (SVM). The predicted Daphnia magna are tracked using SORT24, which is a fast and highly accurate tracking algorithm (Fig. 3E). Finally, based on the tracked results, statistics for assessing ecotoxicity, such as the number of mobile individuals, average distance, and radius of activity, are estimated to evaluate the toxicity of the aquatic environment.Figure 3Automatic Daphnia magna tracking algorithm process. (A) Overview of automatic tracking algorithm process. (B) Image extraction step. (C) Background subtraction step. (D) Daphnia magna detection step. (E) Daphnia magna tracking step.Full size imagek-means clustering for automatic background subtractionMany tracking algorithms assume that the background is fixed. With fixed backgrounds, the difference between the frame and background can be used to identify objects. However, automatically selecting the precise threshold value for image pixel binarization becomes one of the key problems in identifying objects. The proposed method applies k-means clustering to the pixel values of the subtracted image30, and the center value of each calculated cluster mean is selected as the threshold value (Fig. 4). In the k-means clustering method, grouping is repeatedly performed using the distance between data points31. For binarization, two groups are formed. Let (mu _1 (t)) be the mean of pixels less than the threshold and (mu _2(t)) be the mean of pixels greater than the threshold. At first, (mu _1(t), mu _2(t)) are randomly initialized. Subsequently, each pixel is grouped into a closer mean of each group. The above steps are repeated several times until the group experiences a few changes. Finally, the threshold is calculated as an average of the two means.Figure 4Example of automatic threshold value setting for binarization between objects and background using k-means clusteringFull size imageClassification methodsObject detection based solely on the subtraction between the background and frame images may have low accuracy. As the background in the proposed process is the average value of the frame images, noise may occur. Although this noise is removed by threshold selection in binarization, using only the threshold selection is not efficient for long or real-time videos. Therefore, additional noise must be classified and removed using machine learning models, requiring the construction of a database. In the database, the obtained objects are manually labeled as noise or Daphnia magna and are called ground truth. For classification, the resized 8 (times ) 8 image of each object is stored in the database. The resized image is transformed into a feature using the Sobel edge detection algorithm32 and entered as inputs to the classification models. In this study, classification models such as RF33 SVM34 were used.RF is a model that integrates several decision tree models35. All training data are sampled with a replacement for training each decision tree model. The decision tree model is trained to split intervals of each independent variable by minimizing the gini index (Eq. 1) or entropy index (Eq. 2). The gini index and entropy index denote the impurity within the intervals.$$begin{aligned} G= & {} 1- sum _{i=1}^{c} p_i ^2 end{aligned}$$
    (1)
    $$begin{aligned} E= & {} – sum _{i=1}^{c} p_i log_2 p_i end{aligned}$$
    (2)
    where (p_i) is a probability within i-th interval, and c is the number of intervals. For better performance, the RF selects independent variables of training data randomly. This step serves to reduce the correlation of each model. If predictions of each decision tree are uncorrelated, then the variance of an integrated prediction of models is smaller than the variance of each model. RF integrates several model predictions using the voting method. An advantage of the RF method is that it avoids overfitting because the model uses the average of many predictions.SVM is a model designed to search for a hyperplane to maximize the distance, or margin, between support vectors. The hyperplane refers to the plane that divides two different groups, and the support vector represents the closest vector to the hyperplane. Let (D=({textbf{x}}_i, y_i), i=1, ldots , n, {textbf{x}}_i in {mathbb {R}}^p, y_n in { -1,1 }) be training data. Suppose that the training data are completely separated linearly by a hyperplane; then, the hyperplane is expressed as Eq. 3.$$begin{aligned} {textbf{w}}^T {textbf{x}} + b = 0, end{aligned}$$
    (3)
    where ({textbf{w}}) is a weight vector of the hyperplane, and b is a bias. The weight vector is updated by minimizing Eq. 4.$$begin{aligned} L = {1 over 2} {textbf{w}}^T {textbf{w}} text { subject to } y_i ({textbf{w}}^T {textbf{x}} + b) ge 1 end{aligned}$$
    (4)
    We can transform Eqs. 4 to  5 by using the Lagrange multiplier method.$$begin{aligned} L^* = {1 over 2} {textbf{w}}^T {textbf{w}} – sum _{i=1}^n a_i { y_i ({textbf{w}}^T x_i + {-}) – 1 }, end{aligned}$$
    (5)
    where (a_i) is the Lagrange multiplier. We can efficiently solve Eq. 5 using a dual form. Furthermore, Eq. 5 can be solved in a case where it is not completely separated using a slack variable and a kernel trick can be used to estimate the nonlinear hyperplane.SORT trackerSORT, one of the frameworks for solving the multiple object tracking (MOT) problem, aims to achieve efficient real-time tracking24. The SORT method framework is created by combining the estimation step and the association step. The estimation step forecasts the next position of each predicted Daphnia magna. The association step matches the forecasting position and next true position of each predicted Daphnia magna. In the estimation step, the SORT framework uses the Kalman filter to forecast the position of the predicted Daphnia magna in the next frame. The position of each predicted Daphnia magna is expressed as Eq. 6.$$begin{aligned} {textbf{x}} = [u,v,s,r,{dot{u}}, {dot{v}}, {dot{s}}]^T end{aligned}$$
    (6)
    where u and v are the center positions of each predicted Daphnia magna, s is the scale size of the bounding box, and r is the aspect ratio of the bounding box. ({dot{u}}), ({dot{v}}), and ({dot{s}}) are the amounts of change in each variable. In the association step, to associate the forecasting position and true position, the framework adopts the intersection-over-union (IOU)36 as the association metric. The Hungarian algorithm is loaded into the SORT framework to perform fast and efficient Daphnia magna association prediction. In this study, a mixed metric of IOU36 and Euclidean distance37 was used instead of only the IOU that is used in SORT (Eq. 7) for more efficient association.$$begin{aligned} C_{ij} = (1-lambda ) {max_d – d_{ij} over max_d} + lambda cdot IOU_{ij} end{aligned}$$
    (7)
    where (d_{ij}) is the Euclidean distance between the i-th predicted Daphnia magna in the before frame and the j-th predicted Daphnia magna in the next frame, and (lambda ) is the weight of (IOU_{ij}). (IOU_{ij}) is the IOU between the i-th predicted Daphnia magna in the before-frame and the j-th predicted Daphnia magna in the next frame.MetricsThe binary confusion matrix consists of true positive (TP), true negative (TN), false positive (FP), and false negative (FN)38. TP is the number of cases where the predicted Daphnia magna matches the actual Daphnia magna, TN is the number of cases where the objects predicted as noise are actual noise, FP is the number of cases where the predicted Daphnia magna differs from the actual Daphnia magna, and FN is the number of cases where the objects predicted as noise are not actual noise. In this study, accuracy, recall, precision, and F1 scores (Eq. 8) were used as the metrics for comparing the machine learning methods.$$begin{aligned} begin{aligned} Accuracy&= {TP + FP over TP + TN + FP + FN} \ Recall&= {TP over TP + TN} \ Precision&= {TP over TP + FP} \ F1 score&= 2 times {Precision times Recall over Precision + Recall} end{aligned} end{aligned}$$
    (8)
    Standard MOT metrics to evaluate tracking performance include multi-object tracking accuracy (MOTA) and multi-object tracking precision (MOTP). An important task of MOT is to identify and track the same object across two frames. Identification (ID) precision (IDP), ID recall (IDR), ID F1 measure (IDF1), and ID switches (IDs) may be used as measures for evaluating the identification and tracking of the same objects39,40.Data analysisThe toxicity test using Daphnia magna was performed following the Korean official Acute Toxicity Test Method29. The test medium was prepared by dissolving KCl (8 mg/L), (text {MgSO}_4) (120 mg/L), (text {CaSO}_4 cdot 2 text {H}_2 text {O} ) (120 mg/L), and (text {NaHCO}_3) (192 mg/L) in deionized water. Considering that Daphnia magna are neonates for less than 24 h after birth29, five neonates were exposed to 50 mL of different concentrations of heavy metals such as Potassium dichromate, Copper(II) sulfate pentahydrate, and Lead(II) sulfate (6.25, 12.5, 25, 50, and 100%) and 50 mL of culture media. Potassium dichromate is a common inorganic reagent used as an oxidizing agent in chemical industries. Copper(II) sulfate pentahydrate is a trace material widely used in industrial processes and agriculture. A significant amount of copper is emitted in semiconductor manufacturing processes, which adversely impacts the aquatic ecosystem. When present as an ion in water, copper can be acutely toxic to aquatic organisms such as Daphnia magna. Lead(II) sulfate is another nonessential and nonbiodegradable heavy metal. It is highly toxic to numerous organisms even at low concentrations and can accumulate in aquatic ecosystems41. Twenty Daphnia magna (four replicates of five each) were exposed to each test solution for 24 h. The term “immobility” means that the Daphnia magna remains stationary after exposure to chemicals such as Potassium dichromate, Copper(II) sulfate pentahydrate, and Lead(II) sulfate. In this study, immobility was used as an endpoint identifier, and the number of mobile Daphnia magna were counted to evaluate the EC50 values for the samples using the ToxCalc 5.0 program (Tidepoll Software, USA).The locomotory responses of Daphnia magna were tested after 0, 12, 18, and 24 h of exposure at different concentrations. Potassium dichromate ((text {K}_2text {Cr}_2text {O}_7)) at 2 mg/L was connected to the Daphnia magna tracking system, and standard toxic substances were automatically diluted to 100%, 50%, 25%, 12.5%, and 6.25%. The automatic high-throughput Daphnia magna tracking system automatically measured the tracking results of a 1-minute-long video at hourly intervals. The average moving distance for 20 s of each Daphnia magna in each chamber was analyzed using a repeated measures ANOVA (RMANOVA). RMANOVA was used for the analysis of data obtained by repeatedly measuring the same Daphnia magna42. It analyzes the concentration effect excluding the time effect at each hour. The time effect means the change in average distance per 20 s. RMANOVA was implemented using the agricolae package of the R 4.0.4 program43. To remove the noise affecting RMANOVA, the Daphnia magna that remained stationary for 20 s or more were removed from the observations. In this study, we used the significance level at 5%. More

  • in

    Disentangling the mixed effects of soil management on microbial diversity and soil functions: A case study in vineyards

    Ritz, K. & Young, I. M. Interactions between soil structure and fungi. Mycologist 18, 52–59 (2004).Article 

    Google Scholar 
    Schimel, J. P. & Schaeffer, S. M. Microbial control over carbon cycling in soil. Front. Microbiol. 3, 348 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

    Google Scholar 
    van der Heijden, M. G. A. & Wagg, C. Soil microbial diversity and agro-ecosystem functioning. Plant Soil 363, 1–5 (2013).Article 
    CAS 

    Google Scholar 
    Winter, S. et al. Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis. J. Appl. Ecol. 55, 2484–2495 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Belmonte, S. A. et al. Effect of long-term soil management on the mutual interaction among soil organic matter, microbial activity and aggregate stability in a vineyard. Pedosphere 28, 288–298 (2018).Article 
    CAS 

    Google Scholar 
    Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Kratschmer, S. et al. Enhancing flowering plant functional richness improves wild bee diversity in vineyard inter-rows in different floral kingdoms. Ecol. Evol. 11, 7927–7945 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Constancias, F. et al. Microscale evidence for a high decrease of soil bacterial density and diversity by cropping. Agron. Sustain. Dev. 34, 831–840 (2014).Article 
    CAS 

    Google Scholar 
    Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J. & Scow, K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS One 13, e0192953 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vink, S. N., Chrysargyris, A., Tzortzakis, N. & Salles, J. F. Bacterial community dynamics varies with soil management and irrigation practices in grapevines (Vitis vinifera L.). Appl. Soil Ecol. 158, 103807 (2021).Article 

    Google Scholar 
    Pingel, M., Reineke, A. & Leyer, I. A 30-years vineyard trial: plant communities, soil microbial communities and litter decomposition respond more to soil treatment than to N fertilization. Agr. Ecosyst. Environ. 272, 114–125 (2019).Article 
    CAS 

    Google Scholar 
    Sharma-Poudyal, D., Schlatter, D., Yin, C., Hulbert, S. & Paulitz, T. Long-term no-till: a major driver of fungal communities in dryland wheat cropping systems. PLoS One 12, e0184611 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hungria, M., Franchini, J. C., Brandão-Junior, O., Kaschuk, G. & Souza, R. A. Soil microbial activity and crop sustainability in a long-term experiment with three soil-tillage and two crop-rotation systems. Appl. Soil. Ecol. 42, 288–296 (2009).Article 

    Google Scholar 
    Pascault, N. et al. In situ dynamics of microbial communities during decomposition of wheat, rape, and alfalfa residues. Microb. Ecol. 60, 816–828 (2010).Article 
    PubMed 

    Google Scholar 
    Tresch, S. et al. Litter decomposition driven by soil fauna, plant diversity and soil management in urban gardens. Sci. Total Environ. 658, 1614–1629 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Faust, S., Koch, H.-J., Dyckmans, J. & Joergensen, R. G. Response of maize leaf decomposition in litterbags and soil bags to different tillage intensities in a long-term field trial. Appl. Soil. Ecol. 141, 38–44 (2019).Article 

    Google Scholar 
    Liu, Y.-R. et al. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol. Biochem. 118, 35–41 (2018).Article 
    CAS 

    Google Scholar 
    Yang, C., Liu, N. & Zhang, Y. Soil aggregates regulate the impact of soil bacterial and fungal communities on soil respiration. Geoderma 337, 444–452 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bruggisser, O. T., Schmidt-Entling, M. H. & Bacher, S. Effects of vineyard management on biodiversity at three trophic levels. Biol. Cons. 143, 1521–1528 (2010).Article 

    Google Scholar 
    Lienhard, P. et al. Pyrosequencing evidences the impact of cropping on soil bacterial and fungal diversity in Laos tropical grassland. Agron. Sustain. Dev. 34, 525–533 (2014).Article 

    Google Scholar 
    Schnoor, T. K., Lekberg, Y., Rosendahl, S. & Olsson, P. A. Mechanical soil disturbance as a determinant of arbuscular mycorrhizal fungal communities in semi-natural grassland. Mycorrhiza 21, 211–220 (2011).Article 
    PubMed 

    Google Scholar 
    Kazakou, E. et al. A plant trait-based response-and-effect framework to assess vineyard inter-row soil management. Bot. Lett. 163, 373–388 (2016).Article 

    Google Scholar 
    Svensson, J. R., Lindegarth, M., Jonsson, P. R. & Pavia, H. Disturbance-diversity models: What do they really predict and how are they tested?. Proc. Biol. Sci. 279, 2163–2170 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Bao, T. et al. Moderate disturbance increases the PLFA diversity and biomass of the microbial community in biocrusts in the Loess Plateau region of China. Plant Soil 451, 499–513 (2020).Article 
    CAS 

    Google Scholar 
    Liu, J. et al. Soil carbon content drives the biogeographical distribution of fungal communities in the black soil zone of northeast China. Soil Biol. Biochem. 83, 29–39 (2015).Article 
    CAS 

    Google Scholar 
    Cotton, J. & Acosta-Martínez, V. Intensive tillage converting grassland to cropland immediately reduces soil microbial community size and organic carbon. Agric. Environ. Lett. 3, 180047 (2018).Article 

    Google Scholar 
    Poeplau, C. et al. Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Change Biol. 17, 2415–2427 (2011).Article 
    ADS 

    Google Scholar 
    Burns, K. N. et al. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: differentiation by vineyard management. Soil Biol. Biochem. 103, 337–348 (2016).Article 
    CAS 

    Google Scholar 
    Steiner, M. et al. Local conditions matter: minimal and variable effects of soil disturbance on microbial communities and functions in European vineyards. PLoS One 18, e0280516 (2023).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).Article 
    CAS 

    Google Scholar 
    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626–631 (2006).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Eisenhauer, N. Plant diversity effects on soil microorganisms: spatial and temporal heterogeneity of plant inputs increase soil biodiversity. Pedobiologia 59, 175–177 (2016).Article 

    Google Scholar 
    Porazinska, D. L. et al. Plant diversity and density predict belowground diversity and function in an early successional alpine ecosystem. Ecology 99, 1942–1952 (2018).Article 
    PubMed 

    Google Scholar 
    Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).Article 
    PubMed 

    Google Scholar 
    Sun, Y.-Q., Wang, J., Shen, C., He, J.-Z. & Ge, Y. Plant evenness modulates the effect of plant richness on soil bacterial diversity. Sci. Total Environ. 662, 8–14 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Kuzyakov, Y. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).Article 
    CAS 

    Google Scholar 
    Huo, C., Luo, Y. & Cheng, W. Rhizosphere priming effect: a meta-analysis. Soil Biol. Biochem. 111, 78–84 (2017).Article 
    CAS 

    Google Scholar 
    Dimassi, B. et al. Effect of nutrients availability and long-term tillage on priming effect and soil C mineralization. Soil Biol. Biochem. 78, 332–339 (2014).Article 
    CAS 

    Google Scholar 
    Prescott, C. E. Litter decomposition: What controls it and how can we alter it to sequester more carbon in forest soils?. Biogeochemistry 101, 133–149 (2010).Article 
    CAS 

    Google Scholar 
    Petraglia, A. et al. Litter decomposition: effects of temperature driven by soil moisture and vegetation type. Plant Soil 435, 187–200 (2019).Article 
    CAS 

    Google Scholar 
    Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. (2016).Bani, A. et al. The role of microbial community in the decomposition of leaf litter and deadwood. Appl. Soil. Ecol. 126, 75–84 (2018).Article 

    Google Scholar 
    Bonanomi, G., Capodilupo, M., Incerti, G., Mazzoleni, S. & Scala, F. Litter quality and temperature modulate microbial diversity effects on decomposition in model experiments. Community Ecol. 16, 167–177 (2015).Article 

    Google Scholar 
    Daebeler, A. et al. Pairing litter decomposition with microbial community structures using the Tea Bag Index (TBI). SOIL Discuss. [preprint]; 10.5194/soil-2021-110 (2021).Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).Article 

    Google Scholar 
    Schaller, K. Praktikum zur Bodenkunde und Pflanzenernährung. Hochschule Geisenheim, (2000).Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schoch, C. L. et al. SI: Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl. Acad. Sci. U.S.A. 109, 6241–6246 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Available at https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Joshi, N. A. & Fass, J. N. sickle – A Windowed Adaptive Trimming Tool for FASTQ Files Using Quality. Available at https://github.com/najoshi/sickle (2011).Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Westcott, S. L. & Schloss, P. D. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere 2, e00073 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gweon, H. S. et al. PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform. Methods Ecol. Evol. 6, 973–980 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).Article 
    PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. Available at https://www.R-project.org/ (2019).McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Haegeman, B. et al. Robust estimation of microbial diversity in theory and in practice. ISME J. 7, 1092–1101 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Scheu, S. Automated measurement of the respiratory response of soil microcompartments: Active microbial biomass in earthworm faeces. Soil Biol. Biochem. 24, 1113–1118 (1992).Article 

    Google Scholar 
    Mori, T. Validation of the Tea Bag Index as a standard approach for assessing organic matter decomposition: a laboratory incubation experiment. Ecol. Ind. 141, 109077 (2022).Article 
    CAS 

    Google Scholar 
    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–142. Available at https://CRAN.R-project.org/package=nlme (2019).Lenth, R. Emmeans: Estimated Marginal Means, Aka Least-Squares Means. R package version 1.4.4. Available at https://CRAN.R-project.org/package=emmeans (2020).Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar 
    Grace, J. B., Anderson, T. M., Olff, H. & Scheiner, S. M. On the specification of structural equation models for ecological systems. Ecol. Monogr. 80, 67–87 (2010).Article 

    Google Scholar 
    Shipley, B. A new inferential test for path models based on directed acyclic graphs. Struct. Equ. Model. 7, 206–218 (2000).Article 
    MathSciNet 

    Google Scholar  More

  • in

    Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation

    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

    Google Scholar 
    Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).Article 

    Google Scholar 
    Peng, S.-S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).Article 

    Google Scholar 
    Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).Article 

    Google Scholar 
    Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850-2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).Article 

    Google Scholar 
    Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).Article 

    Google Scholar 
    Longo, M. et al. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 30, 1639–1660 (2016).Article 

    Google Scholar 
    Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).Article 

    Google Scholar 
    Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K. & Thompson, J. R. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Front. Ecol. Environ. 16, 213–221 (2018).Article 

    Google Scholar 
    Franklin, C. M. A., Harper, K. A. & Clarke, M. J. Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space. Can. J. For. Res. 51, 274–282 (2020).Article 

    Google Scholar 
    Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).Article 

    Google Scholar 
    Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 

    Google Scholar 
    Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).Article 

    Google Scholar 
    Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

    Google Scholar 
    Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Article 

    Google Scholar 
    Schoene, D., Killmann, W., Lüpke, H. V. & LoycheWilkie, M. Forests and Climate Change Working Paper 5: Definitional Issues Related to Reducing Emissions from Deforestation in Developing Countries (FAO, 2007).Goetz, S. J. et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environ. Res. Lett. 10, 123001 (2015).Article 

    Google Scholar 
    Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).Article 

    Google Scholar 
    Cadenasso, M. L., Traynor, M. M. & Pickett, S. T. Functional location of forest edges: gradients of multiple physical factors. Can. J. For. Res. 27, 774–782 (1997).Article 

    Google Scholar 
    Schmidt, M., Jochheim, H., Kersebaum, K.-C., Lischeid, G. & Nendel, C. Gradients of microclimate, carbon and nitrogen in transition zones of fragmented landscapes – a review. Agric. For. Meteorol. 232, 659–671 (2017).Article 

    Google Scholar 
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 

    Google Scholar 
    Silva Junior, C. H. L. et al. Amazonian forest degradation must be incorporated into the COP26 agenda. Nat. Geosci. 14, 634–635 (2021).Article 

    Google Scholar 
    Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).Article 

    Google Scholar 
    Windisch, M. G., Davin, E. L. & Seneviratne, S. I. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).Article 

    Google Scholar 
    Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Article 

    Google Scholar 
    Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 10, 2015–2031 (2018).Article 

    Google Scholar 
    Zhao, Z. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. https://doi.org/10.1038/s41561-021-00763-8 (2021).Cook, M., Schott, J. R., Mandel, J. & Raqueno, N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens. https://doi.org/10.3390/rs61111244 (2014).Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).Article 

    Google Scholar 
    Broadbent, E. N. et al. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757 (2008).Article 

    Google Scholar 
    Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).Article 

    Google Scholar 
    Silva Junior, C. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).Article 

    Google Scholar 
    Laurance, W. F. et al. Biomass collapse in Amazonian forest fragments. Science 278, 1117–1118 (1997).Article 

    Google Scholar 
    Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).Article 

    Google Scholar 
    Zheng, C., Jia, L. & Hu, G. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite Earth observations. J. Hydrol. 613, 128444 (2022).Article 

    Google Scholar 
    Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).Article 

    Google Scholar 
    Laurance, W. F. et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

    Google Scholar 
    de Paula, M. D., Costa, C. P. A. & Tabarelli, M. Carbon storage in a fragmented landscape of Atlantic forest: the role played by edge-affected habitats and emergent trees. Trop. Conserv. Sci. 4, 349–358 (2011).Article 

    Google Scholar 
    van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

    Google Scholar 
    Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).Article 

    Google Scholar 
    Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).Article 

    Google Scholar 
    Kozlowski, T. T. Responses of woody plants to flooding and salinity. Tree Physiol. 17, 490–490 (1997).Article 

    Google Scholar 
    Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).Article 

    Google Scholar 
    Sze, J. S., Carrasco, L. R., Childs, D. & Edwards, D. P. Reduced deforestation and degradation in Indigenous lands pan-tropically. Nat. Sustain. 5, 123–130 (2022).Article 

    Google Scholar 
    Masson-Delmotte, V. et al. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds) (Cambridge Univ. Press, 2021).Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-Ground Biomass for the Years 2010, 2017 and 2018, v3 (NERC EDS Centre for Environmental Data Analysis, 2021); https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).Article 

    Google Scholar 
    Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022).Article 

    Google Scholar 
    Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).Article 

    Google Scholar 
    Matthews, H. D., Gillett, N. P., Stott, P. A. & Zickfeld, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).Article 

    Google Scholar 
    Li, W. et al. Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14, 5053–5067 (2017).Article 

    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

    Google Scholar  More

  • in

    Evaluation of the growth, adaption, and ecosystem services of two potentially-introduced urban tree species in Guangzhou under drought stress

    Study site, tree selections, and drought-simulation experimentThis research was performed in Guangzhou (22°26′-23°56′N, 112°57′-114°03′E), which is a core city located in subtropical zones. With an area of 7434.4 km2 and a population of 18.87 million, Guangzhou’s urbanization rate has reached 86.46%. To cope with multiple environmental challenges, several urban-forest nurseries were established to cultivate and introduce various tree species. Among them, we selected the one in Tianhe District as our study site. This nursery was not only abundant with native and exotic tree species but also equipped with similar edatope in cities, which was ideal for our research.Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt), originating from the west of Britain and southeast of Europe, were common urban tree species planted in European cities. Based on their performance in providing ecological and landscape functions, these two tree species were considered to be introduced for urban greening. Therefore, Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt) were selected as our objectives, which were investigated for their growth and ecosystem services to evaluate their adaption in Guangzhou. In addition, a native tree species Tilia miqueliana Maxim (Tm) was also implemented concurrent measurement as a comparison.For each of the three surveyed tree species, ten trees with a diameter at breast height (DBH) around 5.5 cm and tree height around 2.5 m were chosen for our experiment, which were thought to possess similar initial statuses. To investigate the impact of drought on the growth and ecosystem services of the three selected tree species, a controlled experiment was launched from January to December in 2020. For each tree species, five trees were planted in the common environment as the controlled group, while the other five trees were under the precipitation-exclusion installation (PEI) as the drought-simulation group. Consisting of several water-proof tents, PEI was adequately large and could completely prevent trees from obtaining rainfalls, which created a precipitation-exclusive environment to simulate an enduring drought event within the whole research period (Fig. 1).Figure 1Schematic diagram of the drought simulation experiment for the three surveyed tree species.Full size imageEnvironmental monitoring systemsClimatic data were sampled every 10 min with a weather station (WP3103 mesoscale automatic weather station, China) located at an unshaded site in the nursery. The data were stored in the logger and copied to our laboratory to produce daily or monthly data. All the climatic variables, including photosynthetically active radiation (PAR, µmol m-2 s-1), wind speed (m s-1), precipitation (mm), and air temperature (°C) were calculated from January to December in 2020.For volumetric soil water content (%; VWC), the HOBO MX2307 system (Onetemp, Adelaide, Australia), placed in a shaded box in the nursery, was applied for all the three tree species from both the controlled and drought-simulation groups. For each individual tree, the sensing probe was inserted horizontally at the depths of 30 cm and located 20 cm in the northern direction from the tree stems. Based on the daily readings, monthly means were calculated from January to December in 2020.Measurement of above-ground growthTo investigate the above-ground growth of the three tree species from both the controlled and drought-simulation groups, their DBH (diameter at breast height, cm), tree height (m), and LAI (leaf area index) were measured at the beginning of each month in 2020. DBH was measured with the help of a caliper (Altraco Inc., Sausalito, California, USA), and their tree heights were measured using a standard tape. The crown analytical instrument CI-110 (Camas, Washington State, USA) was used to capture an accurate image of tree crowns and calculate LAI. Sufficient numbers of points were measured and recorded to describe each tree’s average crown shape. The software FV2200 (LICOR Biosciences, Lincoln, NE) helped compute each tree’s crown width and crown area.Measurement of below-ground growthFine root coring campaigns were launched for all the trees of the three tree species from both the controlled and drought treatment groups every three to four months, i.e., in February, May, September, and December. Although the coring campaign might damage part of the roots, the fine roots obtained each time were a mere portion of the whole root system, not affecting the general development of trees’ underground processes. For every individual tree, two 30-cm soil cores were applied in each direction of north, south, east, and west, of which one was located at 20 cm to the trunk (paracentral roots) and the other one was located at 40 cm (outer roots). In addition, the soil samples were evenly divided into three horizons which were 0–10 cm (shallow layer), 10–20 cm (middle layer), and 20–30 cm (deep layer). Then a sieve with 2-mm mesh size was used to filter all the fine roots. The fine roots were washed carefully to remove the adherent soils and dried in an oven at 65 ℃ for 72 h. Finally, all the samples were weighed using a balance with an accuracy of four decimal places to obtain the dry weight. The fine root biomass at different depths was calculated using the dry weight divided by the cross-sectional area of the auger20.Model’s simulation of ecosystem servicesThe process-based model City-Tree was used to predict the ecosystem services of the three tree species from both the controlled and drought-simulation groups23. The model required the data of tree growth parameters including tree height, DBH, and crown area together with environmental conditions such as edaphic and climatic data24. In this research, cooling, evapotranspiration and CO2 fixation of the three surveyed tree species in the controlled and drought-treatment groups were simulated at the end of 2020.The actual evapotranspiration eta was calculated from the potential evapotranspiration using fetp[t], Tilia’s factors fetp[t], and the reduction factor fred:$${mathrm{et}}_{mathrm{a}}={mathrm{f}}_{mathrm{red}}*{mathrm{f}}_{mathrm{etp}}left[mathrm{t}right]*{mathrm{et}}_{mathrm{p}}$$The process of tree’s evapotranspiration (etp) was calculated on the basis of SVAT algorithm together with Penman formula in the module on water balance as below:$${mathrm{et}}_{mathrm{p}}=left[mathrm{s }/ left(mathrm{s}+upgamma right)right]*left({mathrm{r}}_{mathrm{s}}-{mathrm{r}}_{mathrm{L}}right) /mathrm{ L}+left[1-mathrm{s }/ left(mathrm{s}+upgamma right)right]*{mathrm{e}}_{mathrm{s}}*mathrm{f }left({mathrm{v}}_{mathrm{u}}right)$$with γ: psychrometric constant in hPa K−1; s: the slope of the saturation vapour pressure curve in hPa K−1; rs: short wave radiation balance in W m−2; rL: long-wave radiation balance in W m−2; L: specific evaporation heat in W m−2 mm−1 d; es: saturation deficit in hPa; f (vu): ventilation function with vu being the daily average wind speed in m s−1.Within the module cooling, the energy needed for the transition of water from liquid to gaseous phase was calculated based on the crown area (CA) and the transpiration eta sum:$${mathrm{E}}_{mathrm{A}}= {mathrm{et}}_{mathrm{a}}*mathrm{CA}-left({mathrm{L}}_{mathrm{O}}* -0.00242*mathrm{temp}right) / {mathrm{f}}_{mathrm{con}}$$with EA: energy released by a tree through transpiration (kWh tree-1), LO: energy needed for the transition of the 1 kg of water from the liquid to gaseous phase = 2.498 MJ (kgH2O)-1 and temp = temperature in ℃, fcon: 0.5.The calculation of new assimilation in the module of photosynthesis and respiration was on the basis of the approach of Haxeltine and Prenticem25. The model assumed that 50% of the incoming short-wave radiation is photosynthetic active radiation (PAR). Using the LAI and a light extinction factor of 0.5, the radiation amount of 1 m2 leaf area can be estimated based on an exponential function according to the Lambert–Beer law. This way, the gross assimilation per m2 leaf area as the daily mean of the month can be derived from:$${text{A}} = {text{d}}*{{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} mathord{left/ {vphantom {{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} {left( {2*uptheta } right)}}} right. kern-0pt} {left( {2*uptheta } right)}}$$with A: gross assimilation [g C m−2 d−1]; d: mean day length of the month [h]; Jp: reaction of photosynthesis on absorbed photosynthetic radiation [g C m−2 h−1]; Jr: rubisco limited rate of photosynthesis [g C m−2 h−1]; θ: form factor = 0.7.Jp was defined as a function of the photosynthetic active radiation PAR in mol m−2 h−1 and the efficiency of carbon fixation per absorbed PAR [g C mol−1].$${text{J}}_{{text{p}}} = {text{c}}_{{text{p}}} {text{*PAR}}$$$${text{c}}_{{text{p}}} = alpha *left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right){ /}left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right)*gamma *{text{m}}_{{{text{co}}_{2} }} *{text{i}}left[ {text{t}} right]$$with α: intrinsic quantum efficiency for CO2 uptake = 0.08; pci: partial pressure of the internal CO2 [Pa]; r: CO2 compensation point [Pa]; ϒ: species dependent adjustment function for tree age; m CO2: molecular mass of C = 12.0 g mol−1; i[t]: influence of temperature on efficiency.Net assimilation AN [g C m−2 d−1] was then derived from the gross assimilation A and the dark respiration Rd by:$${text{A}}_{{text{N}}} = {text{A}} – {text{R}}_{{text{d}}}$$$${text{R}}_{{text{d}}} =upbeta *{text{V}}_{{text{m}}}$$where Vm was calculated as:$${text{V}}_{{text{m}}} = {1 mathord{left/ {vphantom {1 upbeta }} right. kern-0pt} upbeta } * {{{text{c}}_{{text{p}}} } mathord{left/ {vphantom {{{text{c}}_{{text{p}}} } {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2uptheta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2uptheta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}} right. kern-0pt} {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2theta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2theta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}$$By multiplying AN, the number of days and the total leaf area, the entire monthly net assimilation of the tree can be obtained. In this study, we assumed a fixed share of 50% as respiration based on the gross primary production that the resulting net primary production NPP was transformed in the content of fixed carbon by multiplying the value with the carbon conversion factor 0.524.$${mathrm{Carbon}}_{mathrm{fix}}=0.5*mathrm{NPP}$$Statistical analysesThe software package R was used for statistical analysis. To investigate the differences between means, two-sampled t-test and analysis of variance (ANOVA) with Tukey’s HSD (honestly significant difference) test were used. All the cases, the means were reported as significant when P  More

  • in

    Spatial ecology of the invasive Asian common toad in Madagascar and its implications for invasion dynamics

    Hui, C. & Richardson, D. M. Invasion Dynamics (Oxford University Press, 2017).Book 
    MATH 

    Google Scholar 
    Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).Book 

    Google Scholar 
    Shigesada, N., Kawasaki, K. & Takeda, Y. Modeling stratified diffusion in biological invasions. Am. Nat. 146, 229–251 (1995).Article 

    Google Scholar 
    Chuang, A. & Peterson, C. R. Expanding population edges: Theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512 (2016).Article 
    ADS 

    Google Scholar 
    Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: A review of pond-breeding amphibians. Q. Rev. Biol. 95, 36 (2020).Article 

    Google Scholar 
    Measey, G. J. et al. A global assessment of alien amphibian impacts in a formal framework. Divers. Distrib. 22, 970–981 (2016).Article 

    Google Scholar 
    Antonelli, A., Smith, R. J., Perrigo, A. L. & Crottini, A. Madagascar’s extraordinary biodiversity: Evolution, distribution, and use. Science 378, eabf0869 (2022).
    Article 
    CAS 
    PubMed 

    Google Scholar 
    Marshall, B. M. et al. Widespread vulnerability of Malagasy predators to the toxins of an introduced toad. Curr. Biol. 28, R654–R655 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Licata, F. et al. Toad invasion of Malagasy forests triggers severe mortality of a predatory snake. Biol. Inv. 24, 1189–1198 (2022).Article 

    Google Scholar 
    Licata, F. et al. Abundance, distribution and spread of the invasive Asian toad Duttaphrynus melanostictus in eastern Madagascar. Biol. Inv. 21, 1615–1626 (2019).Article 

    Google Scholar 
    McClelland, P., Reardon, J. T., Kraus, F., Raxworthy, C. J. & Randrianantoandro, C. Asian toad eradication feasibility report for Madagascar (Te Anau, 2015).Smith, M. A. & Green, D. M. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations?. Ecography 28, 110–128 (2005).Article 

    Google Scholar 
    Shine, R. et al. Increased rates of dispersal of free-ranging cane toads (Rhinella marina) during their global invasion. Sci. Rep. 11, 23574 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: Boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).Article 

    Google Scholar 
    Van Petegem, K. H. P. et al. Empirically simulated spatial sorting points at fast epigenetic changes in dispersal behaviour. Evol. Ecol. 29, 299–310 (2015).Article 

    Google Scholar 
    Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Licata, F., Andreone, F., Crottini, A., Harison, R. F. & Ficetola, G. F. Does spatial sorting occur in the invasive Asian toad in Madagascar? Insights into the invasion unveiled by morphological analyses. JZSER 2021, 1–9 (2021).
    Google Scholar 
    Schwarzkopf, L. & Alford, R. A. Nomadic movement in tropical toads. Oikos 96, 492–506 (2002).Article 

    Google Scholar 
    Brown, G. P., Kelehear, C. & Shine, R. Effects of seasonal aridity on the ecology and behaviour of invasive cane toads in the Australian wet–dry tropics. Funct. Ecol. 25, 1339–1347 (2011).Article 

    Google Scholar 
    Duellman, W. E. & Trueb, L. Biology of Amphibians (JHU Press, 1994).Book 

    Google Scholar 
    Wells, K. D. The Ecology and Behavior of Amphibians (University of Chicago Press, 2010). https://doi.org/10.7208/9780226893334.Book 

    Google Scholar 
    Shaw, A. K., Kokko, H. & Neubert, M. G. Sex difference and Allee effects shape the dynamics of sex-structured invasions. J. Anim. Ecol. 87, 36–46 (2018).Article 
    PubMed 

    Google Scholar 
    Schwarzkopf, L. & Alford, R. A. Desiccation and shelter-site use in a tropical amphibian: Comparing toads with physical models. Funct. Ecol. 10, 193–200 (1996).Article 

    Google Scholar 
    Wogan, G. O. U., Stuart, B. L., Iskandar, D. T. & McGuire, J. A. Deep genetic structure and ecological divergence in a widespread human commensal toad. Biol. Lett. 12, 20150807 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Licata, F. Exploring the invasion dynamics and impacts of the invasive Asian common toad in Madagascar (University of Porto, 2022).
    Google Scholar 
    Reilly, S. B. et al. Toxic toad invasion of Wallacea: A biodiversity hotspot characterized by extraordinary endemism. Glob. Change Biol. 23, 5029–5031 (2017).Article 
    ADS 

    Google Scholar 
    Jørgensen, C. B., Shakuntala, K. & Vijayakumar, S. Body size, reproduction and growth in a tropical toad, Bufo melanostictus, with a comparison of ovarian cycles in tropical and temperate zone anurans. Oikos 46, 379 (1986).Article 

    Google Scholar 
    Vences, M. et al. Tracing a toad invasion: Lack of mitochondrial DNA variation, haplotype origins, and potential distribution of introduced Duttaphrynus melanostictus in Madagascar. Amphib. Reptilia 38, 197–207 (2017).Article 

    Google Scholar 
    Ngo, B. V. & Ngo, C. D. Reproductive activity and advertisement calls of the Asian common toad Duttaphrynus melanostictus (Amphibia, Anura, Bufonidae) from Bach Ma National Park, Vietnam. Zool. Stud. 52, 12 (2013).Article 

    Google Scholar 
    Licata, F. et al. The Asian toad (Duttaphrynus melanostictus) in Madagascar: A report of an ongoing invasion. In Problematic Wildlife II: New Conservation and Management Challenges in the Human-Wildlife Interactions (eds Angelici, F. M. & Rossi, L.) 617–638 (Springer, 2020). https://doi.org/10.1007/978-3-030-42335-3_21.Chapter 

    Google Scholar 
    Moore, M., Solofo Niaina Fidy, J. F. & Edmonds, D. The new toad in town: Distribution of the Asian toad, Duttaphrynus melanostictus, in the Toamasina area of eastern Madagascar. Trop. Conserv. Sci. 8, 440–455 (2015).Article 

    Google Scholar 
    Licata, F. et al. Using public surveys to rapidly profile biological invasions in hard-to-monitor areas. Anim. Conserv. https://doi.org/10.1111/acv.12835 (2023).Article 

    Google Scholar 
    Zhang, M. et al. Automatic high-resolution land cover production in madagascar using sentinel-2 time series, tile-based image classification and google earth engine. Remote Sensing 12, 3663 (2020).Article 
    ADS 

    Google Scholar 
    Peel, M. C., Finlayson, B. L. & Mcmahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 4, 439–473 (2007).
    Google Scholar 
    Merkel, A. Toamasina Climate (Madagascar). Accessed 20 July 2022. https://en.climate-data.org/africa/madagascar/toamasina/toamasina-4029/
    (2021).Gordon, A. Secondary sexual characters of Bufo melanostictus schneider. Copeia 1933, 204–207 (1933).Article 

    Google Scholar 
    Alford, R. & Rowley, J. Techniques for tracking amphibians: The effects of tag attachment, and harmonic direction finding versus radio telemetry. Amphib. Reptilia 28, 367–376 (2007).Article 

    Google Scholar 
    Lassueur, T., Joost, S. & Randin, C. F. Very high resolution digital elevation models: Do they improve models of plant species distribution?. Ecol. Modell. 198, 139–153 (2006).Article 

    Google Scholar 
    Abrams, M., Crippen, R. & Fujisada, H. ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sensing 12, 1156 (2020).Article 
    ADS 

    Google Scholar 
    Brown, G. P., Phillips, B. L., Webb, J. K. & Shine, R. Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia. Biol. Conserv. 133, 88–94 (2006).Article 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
    MATH 

    Google Scholar 
    Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster (2021).Yagi, K. T. & Green, D. M. Performance and movement in relation to postmetamorphic body size in a pond-breeding amphibian. J. Herpetol. 51, 482–489 (2017).Article 

    Google Scholar 
    Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119 (2014).Article 

    Google Scholar 
    Tingley, R. & Shine, R. Desiccation risk drives the spatial ecology of an invasive anuran (Rhinella marina) in the australian semi-desert. PLoS ONE 6, e25979 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richards, S. J., Sinsch, U. & Alford, R. A. Radio Tracking. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds Heyer, R. et al.) 155–158 (Smithsonian Institution, 1994).
    Google Scholar 
    Altobelli, J. T., Dickinson, K. J. M., Godfrey, S. S. & Bishop, P. J. Methods in amphibian biotelemetry: Two decades in review. Austral. Ecol. 47, 1382–1395 (2022).Article 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002). https://doi.org/10.1007/978-1-4757-2917-7_3.Book 
    MATH 

    Google Scholar 
    Richards, S. A., Whittingham, M. J. & Stephens, P. A. Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 65, 77–89 (2011).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (2021).Bates, D. et al. lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. (2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    Barton, K. MuMIn: Multi-Model Inference. (2022).Hodges, C. W., Marshall, B. M., Hill, J. G. & Strine, C. T. Malayan kraits (Bungarus candidus) show affinity to anthropogenic structures in a human dominated landscape. bioRxiv https://doi.org/10.1101/2021.09.08.459477 (2021).Article 

    Google Scholar 
    Muller, B. J., Cade, B. S. & Schwarzkopf, L. Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts. Ecosphere 9, e02067 (2018).Article 

    Google Scholar 
    Linsenmair, K. E. & Spieler, M. Migration patterns and diurnal use of shelter in a ranid frog of a West African savannah: A telemetric study. Amphib. Reptilia 19, 43–64 (1998).Article 

    Google Scholar 
    Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).Article 
    PubMed 

    Google Scholar 
    Ward-Fear, G., Greenlees, M. J. & Shine, R. Toads on lava: spatial ecology and habitat use of invasive cane yoads (Rhinella marina) in Hawai’i. PLoS ONE 11, e0151700 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huang, W.-S., Lin, J.-Y. & Yu, J.Y.-L. Male reproductive cycle of the toad Bufo melanostictus in Taiwan. Zool. Sci. 14, 497–503 (1997).Article 

    Google Scholar 
    Brown, G. P., Phillips, B. L. & Shine, R. The straight and narrow path: the evolution of straight-line dispersal at a cane toad invasion front. Proc. R. Soc. B 281, 20141385 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Perkins, T. A., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).Article 
    PubMed 

    Google Scholar 
    Ochocki, B. M. & Miller, T. E. X. Rapid evolution of dispersal ability makes biological invasions faster and more variable. Nat. Commun. 8, 14315 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, B. L., Brown, G. P., Travis, J. M. J. & Shine, R. Reid’s paradox revisited: The evolution of dispersal kernels during range expansion. Am. Nat. 172, S34–S48 (2008).Article 
    PubMed 

    Google Scholar 
    Kot, M., Lewis, M. A. & van den Driessche, P. Dispersal data and the spread of invading organisms. Ecology 77, 2027–2042 (1996).Article 

    Google Scholar 
    Deguise, I. & Richardson, J. S. Movement behaviour of adult western toads in a fragmented, forest landscape. Can. J. Zool. 87, 1184–1194 (2009).Article 

    Google Scholar 
    Mitrovich, M. J., Gallegos, E. A., Lyren, L. M., Lovich, R. E. & Fisher, R. N. Habitat use and movement of the endangered Arroyo toad (Anaxyrus californicus) in coastal southern California. J. Herpetol. 45, 319–328 (2011).Article 

    Google Scholar 
    Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).Article 
    PubMed 

    Google Scholar 
    Enriquez-Urzelai, U., Montori, A., Llorente, G. A. & Kaliontzopoulou, A. Locomotor mode and the evolution of the hindlimb in western mediterranean anurans. Evol. Biol. 42, 199–209 (2015).Article 

    Google Scholar 
    Junior, B. T. & Gomes, F. R. Relation between water balance and climatic variables associated with the geographical distribution of anurans. PLoS ONE 10, e0140761 (2015).Article 

    Google Scholar 
    Klockmann, M., Günter, F. & Fischer, K. Heat resistance throughout ontogeny: Body size constrains thermal tolerance. Glob. Change Biol. 23, 686–696 (2017).Article 
    ADS 

    Google Scholar 
    Petrovskii, S., Mashanova, A. & Jansen, V. A. A. Variation in individual walking behavior creates the impression of a Lévy flight. PNAS 108, 8704–8707 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. PNAS 110, 13452–13456 (2013).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tingley, R. et al. New weapons in the toad toolkit: A review of methods to control and mitigate the biodiversity impacts of invasive Cane toads (Rhinella marina). Q. Rev. Biol. 92, 123–149 (2017).Article 
    PubMed 

    Google Scholar 
    Novoa, A. et al. Invasion syndromes: A systematic approach for predicting biological invasions and facilitating effective management. Biol. Invasions 22, 1801–1820 (2020).Article 

    Google Scholar 
    DeVore, J. L., Crossland, M. R., Shine, R. & Ducatez, S. The evolution of targeted cannibalism and cannibal-induced defenses in invasive populations of cane toads. Proc. Natl. Acad. Sci. 118, e2100765118 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muller, B. J. & Schwarzkopf, L. Relative effectiveness of trapping and hand-capture for controlling invasive cane toads (Rhinella marina). Int. J. Pest Manag. 64, 185–192 (2018).Article 
    CAS 

    Google Scholar  More

  • in

    Photodegradation of a bacterial pigment and resulting hydrogen peroxide release enable coral settlement

    Knowlton, N. The future of coral reefs. Proc. Natl. Acad. Sci. 98, 5419–5425 (2001).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 1979(301), 929–933 (2003).Article 
    ADS 

    Google Scholar 
    Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 1979(318), 1737–1742 (2007).Article 
    ADS 

    Google Scholar 
    Eakin, C. M. et al. Monitoring coral reefs from space. Oceanography 23, 118–133 (2010).Article 

    Google Scholar 
    Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Baker, D. M., Freeman, C. J., Wong, J. C. Y., Fogel, M. L. & Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J. 12, 921–930 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Berkelmans, R. & van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change. Proc. R. Soc. B Biol. Sci. 273, 2305–2312 (2006).Article 

    Google Scholar 
    Byler, K. A., Carmi-Veal, M., Fine, M. & Goulet, T. L. Multiple symbiont acquisition strategies as an adaptive mechanism in the coral Stylophora pistillata. PLoS One 8, e59596 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cumbo, V., van Oppen, M. & Baird, A. Temperature and Symbiodinium physiology affect the establishment and development of symbiosis in corals. Mar. Ecol. Prog. Ser. 587, 117–127 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Mundy, C. N. & Babcock, R. C. Role of light intensity and spectral quality in coral settlement: Implications for depth-dependent settlement?. J. Exp. Mar Biol. Ecol. 223, 235–255 (1998).Article 

    Google Scholar 
    Gleason, D. F., Edmunds, P. J. & Gates, R. D. Ultraviolet radiation effects on the behavior and recruitment of larvae from the reef coral Porites astreoides. Mar. Biol. 148, 503–512 (2006).Article 

    Google Scholar 
    Yusuf, S., Zamani, N. P., Jompa, J. & Junior, M. Z. Larvae of the coral Acropora tenuis (Dana 1846) settle under controlled light intensity. IOP Conf. Ser. Earth Environ. Sci. 253, 012023 (2019).Article 

    Google Scholar 
    Vermeij, M. J. A., Marhaver, K. L., Huijbers, C. M., Nagelkerken, I. & Simpson, S. D. Coral larvae move toward reef sounds. PLoS One 5, e10660 (2010).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Doropoulos, C. et al. Characterizing the ecological trade-offs throughout the early ontogeny of coral recruitment. Ecol. Monogr. 86, 20–44 (2016).Article 

    Google Scholar 
    Morse, D. E., Hooker, N., Morse, A. N. C. & Jensen, R. A. Control of larval metamorphosis and recruitment in sympatric agariciid corals. J. Exp. Mar. Biol. Ecol. 116, 193–217 (1988).Article 

    Google Scholar 
    Price, N. Habitat selection, facilitation, and biotic settlement cues affect distribution and performance of coral recruits in French Polynesia. Oecologia 163, 747–758 (2010).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ritson-Williams, R., Arnold, S. N., Paul, V. J. & Steneck, R. S. Larval settlement preferences of Acropora palmata and Montastraea faveolata in response to diverse red algae. Coral Reefs 33, 59–66 (2014).Article 
    ADS 

    Google Scholar 
    Negri, A., Webster, N., Hill, R. & Heyward, A. Metamorphosis of broadcast spawning corals in response to bacteria isolated from crustose algae. Mar. Ecol. Prog. Ser. 223, 121–131 (2001).Article 
    ADS 

    Google Scholar 
    Webster, N. S. et al. Metamorphosis of a scleractinian coral in response to microbial biofilms. Appl. Environ. Microbiol. 70, 1213–1221 (2004).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Erwin, P. M., Song, B. & Szmant, A. M. Settlement behavior of Acropora palmata planulae: effects of biofilm age and crustose coralline algal cover. In Proceedings of 11th International Coral Reef Symposium 24, (2008).Siboni, N. et al. Crustose coralline algae that promote coral larval settlement harbor distinct surface bacterial communities. Coral Reefs 39, 1703–1713 (2020).Article 

    Google Scholar 
    Petersen, L.-E. et al. Mono- and multispecies biofilms from a crustose coralline alga induce settlement in the scleractinian coral Leptastrea purpurea. Coral Reefs 40, 381–394 (2021).Article 

    Google Scholar 
    Jorissen, H. et al. Coral larval settlement preferences linked to crustose coralline algae with distinct chemical and microbial signatures. Sci. Rep. 11, 14610 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tebben, J. et al. Induction of larval metamorphosis of the coral Acropora millepora by tetrabromopyrrole isolated from a pseudoalteromonas bacterium. PLoS One 6, e19082 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tebben, J. et al. Chemical mediation of coral larval settlement by crustose coralline algae. Sci. Rep. 5, 10803 (2015).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tran, C. & Hadfield, M. Larvae of Pocillopora damicornis (Anthozoa) settle and metamorphose in response to surface-biofilm bacteria. Mar. Ecol. Prog. Ser. 433, 85–96 (2011).Article 
    ADS 

    Google Scholar 
    Sneed, J. M., Sharp, K. H., Ritchie, K. B. & Paul, V. J. The chemical cue tetrabromopyrrole from a biofilm bacterium induces settlement of multiple Caribbean corals. Proc. R. Soc. B Biol. Sci. 281, 20133086 (2014).Article 

    Google Scholar 
    Petersen, L.-E., Kellermann, M. Y., Nietzer, S. & Schupp, P. J. Photosensitivity of the bacterial pigment cycloprodigiosin enables settlement in coral larvae—light as an understudied environmental factor. Front. Mar. Sci. 8, 749070 (2021).Article 

    Google Scholar 
    Heyward, A. J. & Negri, A. P. Natural inducers for coral larval metamorphosis. Coral Reefs 18, 273–279 (1999).Article 

    Google Scholar 
    Harrington, L., Fabricius, K., Death, G. & Negri, A. Recognition and selection of settlement substrata determine post-settlement survival in corals. Ecology 85, 3428–3437 (2004).Article 

    Google Scholar 
    Da-Anoy, J. P., Villanueva, R. D., Cabaitan, P. C. & Conaco, C. Effects of coral extracts on survivorship, swimming behavior, and settlement of Pocillopora damicornis larvae. J. Exp. Mar. Biol. Ecol. 486, 93–97 (2017).Article 

    Google Scholar 
    Morse, D. E. & Morse, A. N. C. Enzymatic characterization of the morphogen recognized by Agaricia humilis (Scleractinian Coral) larvae. Biol. Bull. 181, 104–122 (1991).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kitamura, M., Koyama, T., Nakano, Y. & Uemura, D. Characterization of a natural inducer of coral larval metamorphosis. J. Exp. Mar. Biol. Ecol. 340, 96–102 (2007).Article 

    Google Scholar 
    Kitamura, M., Schupp, P. J., Nakano, Y. & Uemura, D. Luminaolide, a novel metamorphosis-enhancing macrodiolide for scleractinian coral larvae from crustose coralline algae. Tetrahedron Lett. 50, 6606–6609 (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maru, N. et al. Relative configuration of luminaolide. Tetrahedron Lett. 54, 4385–4387 (2013).Article 
    CAS 

    Google Scholar 
    Nietzer, S., Moeller, M., Kitamura, M. & Schupp, P. J. Coral larvae every day: Leptastrea purpurea, a brooding species that could accelerate coral research. Front. Mar. Sci. 5, 466 (2018).Article 

    Google Scholar 
    Moeller, M., Nietzer, S. & Schupp, P. J. Neuroactive compounds induce larval settlement in the scleractinian coral Leptastrea purpurea. Sci. Rep. 9, 2291 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Petersen, L.-E., Kellermann, M. Y. & Schupp, P. J. Secondary metabolites of marine microbes: from natural products chemistry to chemical ecology. In YOUMARES 9 – The Oceans: Our Research, Our Future: Proceedings of the 2018 Conference for Young Marine Researcher in Oldenburg, Germany (eds Jungblut, S. et al.) 159–180 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-20389-4_8.Chapter 

    Google Scholar 
    Fiegel, L. J. et al. A detailed visualization of the early development stages of Leptastrea purpurea reveals distinct bio-optical features. Front. Mar. Sci. 10, 1–10 (2023).
    Google Scholar 
    Strader, M. E., Aglyamova, G. V. & Matz, M. V. Molecular characterization of larval development from fertilization to metamorphosis in a reef-building coral. BMC Genom. 19, 17 (2018).Article 

    Google Scholar 
    Puisay, A. et al. Parental bleaching susceptibility leads to differences in larval fluorescence and dispersal potential in Pocillopora acuta corals. Mar. Environ. Res. 163, 105200 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Perez-Tomas, R. & Vinas, M. New insights on the antitumoral properties of prodiginines. Curr. Med. Chem. 17, 2222–2231 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    You, Z. et al. Insights into the anti-infective properties of prodiginines. Appl. Microbiol. Biotechnol. 103, 2873–2887 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kellermann, M. Y., Yoshinaga, M. Y., Valentine, R. C., Wörmer, L. & Valentine, D. L. Important roles for membrane lipids in haloarchaeal bioenergetics. Biochim. Biophys. Acta (BBA) Biomembr. 1858, 2940–2956 (2016).Article 
    CAS 

    Google Scholar 
    Hirose, M., Yamamoto, H. & Nonaka, M. Metamorphosis and acquisition of symbiotic algae in planula larvae and primary polyps of Acropora spp.. Coral Reefs 27, 247–254 (2008).Article 
    ADS 

    Google Scholar 
    Bollati, E. et al. Green fluorescent protein-like pigments optimize the internal light environment in symbiotic reef building corals. Elife 11, e73521 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Palmer, C. V., Modi, C. K. & Mydlarz, L. D. Coral fluorescent proteins as antioxidants. PLoS One 4, e7298 (2009).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alegado, R. A. et al. A bacterial sulfonolipid triggers multicellular development in the closest living relatives of animals. Elife 1, e00013 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Woznica, A. et al. Bacterial lipids activate, synergize, and inhibit a developmental switch in choanoflagellates. Proc. Natl. Acad. Sci. 113, 7894–7899 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    He, J. et al. Bacterial nucleobases synergistically induce larval settlement and metamorphosis in the invasive mussel Mytilopsis sallei. Appl. Environ. Microbiol. 85, e01039 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Guo, H., Rischer, M., Westermann, M. & Beemelmanns, C. Two distinct bacterial biofilm components trigger metamorphosis in the colonial hydrozoan Hydractinia echinata. MBio 12, e00401 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ross, C., Fogarty, N. D., Ritson-Williams, R. & Paul, V. J. Interspecific variation in coral settlement and fertilization success in response to hydrogen peroxide exposure. Biol. Bull. 233, 206–218 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Boettcher, A. A., Dyer, C., Casey, J. & Targett, N. M. Hydrogen peroxide induced metamorphosis of queen conch, Strombus gigas: tests at the commercial scale. Aquaculture 148, 247–258 (1997).Article 
    CAS 

    Google Scholar 
    Covarrubias, L., Hernández-García, D., Schnabel, D., Salas-Vidal, E. & Castro-Obregón, S. Function of reactive oxygen species during animal development: Passive or active?. Dev. Biol. 320, 1–11 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gauron, C. et al. Hydrogen peroxide (H2O2) controls axon pathfinding during zebrafish development. Dev. Biol. 414, 133–141 (2016).Article 
    CAS 
    PubMed 

    Google Scholar  More

  • in

    Interspecific interactions alter the metabolic costs of climate warming

    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).Article 
    CAS 

    Google Scholar 
    Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

    Google Scholar 
    Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

    Google Scholar 
    Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
    CAS 

    Google Scholar 
    White, C. R., Alton, L. A., Bywater, C. L., Lombardi, E. J. & Marshall, D. J. Metabolic scaling is the product of life history optimization. Science 377, 834–839 (2022).Article 
    CAS 

    Google Scholar 
    Savage, V. M., Gilloly, J. F., Brown, J. H. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).Article 

    Google Scholar 
    Bernhardt, J. R., Sunday, J. M. & O’Connor, M. I. Metabolic theory and the temperature–size rule explain the temperature dependence of population carrying capacity. Am. Nat. 192, 687–697 (2018).Article 

    Google Scholar 
    Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).Article 

    Google Scholar 
    Schuster, L., Cameron, H., White, C. R. & Marshall, D. J. Metabolism drives demography in an experimental field test. Proc. Natl Acad. Sci. USA 118, e2104942118 (2021).Article 
    CAS 

    Google Scholar 
    Amarasekare, P. & Coutinho, R. M. The intrinsic growth rate as a predictor of population viability under climate warming. J. Anim. Ecol. 82, 1240–1253 (2013).Article 

    Google Scholar 
    Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. Am. Nat. 179, 178–191 (2012).Article 

    Google Scholar 
    Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).Article 

    Google Scholar 
    Comeault, A. A. & Matute, D. R. Temperature-dependent competitive outcomes between the fruit flies Drosophila santomea and Drosophila yakuba. Am. Nat. 197, 312–323 (2021).Article 

    Google Scholar 
    Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).Article 
    CAS 

    Google Scholar 
    Davis, A. J., Lawton, J. H., Shorrocks, B. & Jenkinson, L. S. Individualistic species responses invalidate simple physiological models of community dynamics under global environmental change. J. Anim. Ecol. 67, 600–612 (1998).Article 

    Google Scholar 
    Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).Article 

    Google Scholar 
    Janča, M. & Gvoždík, L. Costly neighbours: heterospecific competitive interactions increase metabolic rates in dominant species. Sci. Rep. 7, 5177 (2017).Article 

    Google Scholar 
    Pettersen, A. K., Hall, M. D., White, C. R. & Marshall, D. J. Metabolic rate, context-dependent selection, and the competition–colonization trade-off. Evol. Lett. 4, 333–344 (2020).Article 

    Google Scholar 
    DeLong, J. P., Hanley, T. C. & Vasseur, D. A. Competition and the density dependence of metabolic rates. J. Anim. Ecol. 83, 51–58 (2014).Article 

    Google Scholar 
    Reid, D., Armstrong, J. D. & Metcalfe, N. B. Estimated standard metabolic rate interacts with territory quality and density to determine the growth rates of juvenile Atlantic salmon. Funct. Ecol. 25, 1360–1367 (2011).Article 

    Google Scholar 
    Ayala, F. J. in Essays in Evolution and Genetics in Honor of Theodosius Dobzhansky (eds Hecht, M. K. & Steere, W. C.) 121–158 (Springer, 1970).Atkinson, W. D. & Shorrocks, B. Aggregation of larval Diptera over discrete and ephemeral breeding sites: the implications for coexistence. Am. Nat. 124, 336–351 (1984).Article 

    Google Scholar 
    McKenzie, J. A. & McKechnie, S. W. A comparative study of resource utilization in natural populations of Drosophila melanogaster and D. simulans. Oecologia 40, 299–309 (1979).Article 
    CAS 

    Google Scholar 
    Alton, L. A. et al. Developmental nutrition modulates metabolic responses to projected climate change. Funct. Ecol. 34, 2488–2502 (2020).Article 

    Google Scholar 
    Mitchell, K. A. & Hoffmann, A. A. Thermal ramping rate influences evolutionary potential and species differences for upper thermal limits in Drosophila. Funct. Ecol. 24, 694–700 (2010).Article 

    Google Scholar 
    Overgaard, J., Kristensen, T. N., Mitchell, K. A. & Hoffmann, A. A. Thermal tolerance in widespread and tropical Drosophila species: does phenotypic plasticity increase with latitude? Am. Nat. 178, S80–S96 (2011).Article 

    Google Scholar 
    Kellermann, V. et al. Comparing thermal performance curves across traits: how consistent are they? J. Exp. Biol. 222, jeb193433 (2019).Article 

    Google Scholar 
    Terblanche, J. S., Clusella-Trullas, S. & Chown, S. L. Phenotypic plasticity of gas exchange pattern and water loss in Scarabaeus spretus (Coleoptera: Scarabaeidae): deconstructing the basis for metabolic rate variation. J. Exp. Biol. 213, 2940–2949 (2010).Article 

    Google Scholar 
    Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
    CAS 

    Google Scholar 
    Bos, M., Burnet, B., Farrow, R. & Woods, R. A. Mutual facilitation between larvae of the sibling species Drosophila melanogaster and D. simulans. Evolution 31, 824–828 (1977).Article 
    CAS 

    Google Scholar 
    Arthur, W. On the complexity of a simple environment: competition, resource partitioning and facilitation in a two-species Drosophila system. Phil. Trans. R. Soc. B 313, 471–508 (1986).
    Google Scholar 
    Hodge, S., Mitchell, P. & Arthur, W. Factors affecting the occurrence of facilitative effects in interspecific interactions: an experiment using two species of Drosophila and Aspergillus niger. Oikos 87, 166–174 (1999).Article 

    Google Scholar 
    Bath, E., Morimoto, J. & Wigby, S. The developmental environment modulates mating-induced aggression and fighting success in adult female Drosophila. Funct. Ecol. 32, 2542–2552 (2018).Article 

    Google Scholar 
    Thibert, J., Farine, J. P., Cortot, J. & Ferveur, J. F. Drosophila food-associated pheromones: effect of experience, genotype and antibiotics on larval behavior. PLoS ONE 11, e0151451 (2016).Article 

    Google Scholar 
    Chown, S. L. et al. Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct. Ecol. 21, 282–290 (2007).Article 

    Google Scholar 
    Becker, R. A., Wilks, A. R. & Brownrigg, R. mapdata: extra map databases. R version 2.3.0 https://CRAN.R-project.org/package=mapdata (2018).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Bolker, B. & R Development Core Team bbmle: tools for general maximum likelihood estimation. R version 1.0.25 https://CRAN.R-project.org/package=bbmle (2022).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn (Sage, 2019).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R version 0.4.6 https://CRAN.R-project.org/package=DHARMa (2022).Messamah, B., Kellermann, V., Malte, H., Loeschcke, V. & Overgaard, J. Metabolic cold adaptation contributes little to the interspecific variation in metabolic rates of 65 species of Drosophilidae. J. Insect Physiol. 98, 309–316 (2017).Article 
    CAS 

    Google Scholar 
    Chamberlain, S. et al. rgbif: interface to the global biodiversity information facility API. R version 3.7.3 https://CRAN.R-project.org/package=rgbif (2022).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    Hijmans, R. J. raster: geographic data analysis and modeling. R version 3.6-3 https://CRAN.R-project.org/package=raster (2022).Alton, L. A. & Kellermann, V. Data for “Interspecific interactions alter the metabolic costs of climate warming”. Zenodo https://doi.org/10.5281/zenodo.7475922 (2023).White, C. R. et al. Geographical bias in physiological data limits predictions of global change impacts. Funct. Ecol. 35, 1572–1578 (2021).Article 

    Google Scholar  More