More stories

  • in

    Detritivore conversion of litter into faeces accelerates organic matter turnover

    Detritivore and leaf litter collection
    We collected six phylogenetically diverse species of detritivores in various areas of the Scottish Lowlands in May and June 2018, including three millipede species (Diplopoda), two woodlouse species (Crustacea) and one snail species (Gastropoda). Millipede species include the common pill millipede (Glomeris marginata (Villers, 1789)) collected near Peebles, UK (55°38′45.8″N, 3°07′55.4″W), the striped millipede (Ommatoiulus sabulosus (Linnaeus, 1758)) collected near Dunfermline, UK (56°02′23.7″N, 3°19′49.2″W) and the white-legged millipede (Tachypodoiulus niger (Leach, 1815)) collected near Dundee, UK (56°32′08.5″N, 3°01′51.9″W). Woodlouse species include the common pill woodlouse (Armadillidium vulgare (Latreille, 1804)) collected near Dunfermline, UK (56°01′35.3″N 3°23′14.1″W) and the common rough woodlouse (Porcellio scaber (Latreille, 1804) collected in Stirling, UK (56°07′26.7″N, 3°55′51.2″W). The snail species was the brown-lipped snail (Cepaea nemoralis (Linnaeus, 1758)) collected in Stirling, UK (56°08′07.3″N, 3°55′16.3″W). These species are common in diverse ecosystems across Mediterranean and temperate ecosystems in Europe, where they feed on decomposing litter and produce large amounts of faeces16,38,39,40. Detritivores were kept in plastic boxes and fed with moist litter from various tree species from their respective collection sites before the start of the experiment.
    To obtain a gradient of leaf litter quality, we collected leaf litter from six deciduous broadleaf tree species in the Scottish Lowlands. These species include sycamore maple (Acer pseudoplatanus, L.), horse chestnut (Aesculus hippocastanum, L.), common hazel (Corylus avellana, L.), European beech (Fagus sylvatica, L.), English oak (Quercus robur, L.) from a woodland near Dundee, UK (56°32′08.5″N, 3°01′51.9″W) and lime (Tilia platyphyllos, L.) from a woodland in Stirling, UK (56°08′29.5″N, 3°55′14.2″W). Because detritivores are most active in spring and summer in these ecosystems, they feed on partially decomposed litter, which they prefer over freshly fallen litter (David and Gillon8). We thus collected leaf litter from the forest floor in May 2018, air-dried it and stored it in cardboard boxes until use.
    Faeces production
    To compare the quality and decomposability of leaf litter with faeces derived from the same litter and produced by diverse detritivore species, we set up two series of boxes for the production of the needed material. In the first of these series, we placed each detritivore species together with each litter species to produce the 36 different faeces types (Fig. 1; 6 litter species × 6 detritivore species = 36 faeces types). The second of these series contained the litter species only without any detritivores to produce intact litter from each tree species (6 litter species) under the same conditions for the same amount of time. In total, 42 different substrates were generated. To do so, we placed ca. 30 g of air-dry leaf litter from each species separately in plastic boxes (30 cm × 22 cm × 5.5 cm) to which we added ca. 50 individuals from each detritivore species separately or no detritivore for the intact litter treatment. We sprayed the litter with water to optimise litter moisture for detritivore consumption while avoiding water accumulation at the bottom of the boxes. We kept the boxes at room temperature (ca. 20 °C) for 4 weeks and collected the produced faeces/intact litter twice a week. For the faeces, we placed the content of each box in a large bucket and gently agitated to let detritivores and faeces fall to the bottom of the bucket. After collecting the faeces, we placed all the leaf litter and detritivores back into their boxes and sprayed the litter with water to keep moisture conditions constant. For the intact litter treatment, we followed the same procedure but collected just three random leaves out of the buckets. After each collection step, the combination-specific pools of leaf litter and faeces were dried at 30 °C. At the end of the faeces production period, we manually removed small leaf litter fragments from all combination-specific pools of faeces. Additionally, because detritivores feed on leaf lamina and leave leaf veins mostly uneaten6, we cut out the veins from the species-specific pools of intact leaf litter. This was done to ensure the comparability of quality and decomposability between faeces and intact litter.
    Litter and faeces quality
    To evaluate the effect of litter conversion into detritivore faeces on organic matter quality, we compared the quality of faeces to that of intact litter by measuring a series of physical and chemical quality parameters on all 42 substrates (6 litter species + 36 faeces types). Chemical characteristics included total carbon (C) and nitrogen (N) concentrations, DOC and TDN concentrations, total tannin concentrations, and 13C solid-state NMR spectra. Physical characteristics included WHC and specific area (surface area per unit of mass). Prior to these measurements, we drew three subsamples from each pool of substrate type. A part of each subsample was ground using a ball mill (TissueLyser II, Qiagen) to measure total C, N and tannin concentration and generate NMR spectra. The other part of each subsample was kept intact and used for all other measurements. All measurements were thus done on these three subsamples per substrate type, except for NMR spectra that were measured once per substrate type on a sample made by pooling all three ground subsamples. This pooling was necessary to obtain a sample large enough for the NMR analyses. Total C and N concentrations were measured with a flash CHN elemental analyser (Flash Smart, ThermoScientific). To measure DOC and TDN, we extracted leachates by placing ca. 30 mg of air-dried material with 25 ml of deionised water in 50 ml Falcon tubes and agitating the tubes horizontally on a reciprocal shaker for 1 h. Water extracts were then filtered through 0.45-μm cellulose nitrate filters to isolate the leachate fraction. Concentrations of DOC and TDN in leachates were measured with a TOC analyser (Shimadzu, Kyoto, Japan) equipped with a supplementary module for N. Tannin concentrations were measured with the protein-precipitable phenolics microplate assay, a microplate protocol adapted from Hagerman and Butler41. We obtained 13C-NMR spectra by applying 13C cross-polarisation magic angle spinning NMR spectroscopy using a 200 MHz spectrometer (Bruker, Billerica, USA). The samples were spun in 7 mm zirconium dioxide rotors at 6.8 kHz with an acquisition time of 0.01024 s. To avoid Hartmann–Hahn mismatches, a ramped 1H impulse was applied during a contact time of 1 ms. We applied a delay time of 2.0 s and the number of scans was set to 1500, yet some of the samples required longer measurements due to the low amount of sample material; in this case, we multiplied the number of scans to 3000, 6000 or 15000. As reference for the chemical shift, tetramethylsilane was used (0 ppm). We used the following chemical shift regions to integrate the spectra: −10–45 ppm alkyl C, 45–110 ppm O/N alkyl C, 110–160 ppm aromatic C, and 160–220 ppm carboxylic C. We measured the WHC by placing ca. 15 mg of air-dried intact material with 1.5 ml of deionised water in 2 ml Eppendorf tubes, agitating the tubes horizontally on a reciprocal shaker for 2 h, retrieving the material and placing it on a Whatman filter to remove excess water, weighing the wet material and reweighing it after drying at 65 °C for 48 h. We measured the specific area of leaf litter, faecal pellets and faeces particles from photographs using a stereomicroscope (ZEISS STEMI 508). For leaf litter and faecal pellets, we took photographs of ca. 20 mg of air-dried intact material. To visualise faeces particles, we weighed ca. 1 mg of air-dried faecal pellets and placed them in a beaker with 20 ml of deionised water for 2 h, allowing complete dissolution of the faecal pellets. We then filtered the faeces particles and photographed the filters under a stereomicroscope. Dimensions of each litter pieces and faecal pellets/faeces particles were measured using the image analysis software (ImageJ, version 1.46r). For all substrate types, we divided the calculated surface area by the dry mass of the sample to obtain the specific area.
    Faeces and litter decomposition parameters
    To evaluate the effect of litter conversion into detritivore faeces on C and N cycling, we compared the C and N loss of faeces to that of intact litter by incubating all 42 substrates in microcosms under controlled conditions for 6 months (180 days). Microcosms consisted of 250-ml plastic containers filled with 90 mg of air-dry soil collected from a temperate grassland (56°8′40.1″N, 3°54′50.9″W). We chose this soil to avoid any home-field advantage effect as this soil did not receive litter input from any of the studied tree species and none of the selected soil animals were present at this site. About 120 mg of each substrate were placed separately within a small polyvinyl chloride tube (30 mm diameter × 30 mm height) closed in the bottom with a 100-µm mesh and left open on the top. Each tube was then placed on top of the soil within the microcosm. Five replicates per substrate were prepared, resulting in a total of 210 microcosms (42 substrates × 5 replicates). Microcosms were watered by adding water directly over the tube containing faeces/litter so as to reach 70% of soil WHC and incubated at 22 °C and 70% relative humidity in a controlled environment chamber. To limit desiccation while ensuring gas exchange, we drilled four 3-mm holes in each microcosm cap. These microcosms were then weighed weekly and watered to their initial weight at 70% soil WHC. We placed replicates on separated shelves according to a randomised complete block design. Both block positions within the controlled environment chamber and microcosm positions within blocks were randomised weekly. After 180 days, remaining intact litter and faeces in microcosms were collected, dried at 30 °C for 48 h, weighed and ground with a ball mill (TissueLyser II, Qiagen). We measured C and N concentrations in all samples with a flash CHN Elemental Analyser (Flash Smart, ThermoScientific). The percentage of C and N lost after the incubation was calculated as:

    $$frac{{M_{rm{i}} times {rm{CN}}_{rm{i}} – M_{rm{f}} times {rm{CN}}_{rm{f}}}}{{M_{rm{i}} times {rm{CN}}_{rm{i}}}} times 100,$$

    where Mi and Mf are the initial and final 30 °C dry masses, respectively, and CNi and CNf are the initial and final C or N concentrations, respectively.
    Statistics and reproducibility
    To visualise how the 11 physicochemical characteristics were related and how their values differed between all substrates, we used a PCA, with all variables centred and standardised prior to ordination. Because NMR spectra were measured on a composite sample combining the three replicates of each substrate, a unique value was attributed to all replicates for each NMR region.
    To test our first hypothesis, we tested the overall effect of substrate form (faeces vs. intact litter) on quality (scores on PC1 and PC2) and decomposition (C and N losses) of all substrates using Student’s t tests. To identify the faeces types with significantly different quality (scores on PC1 and PC2) and decomposition (C and N losses) compared to that of the intact litter from which the faeces were derived, we tested the effect of substrate identity (all 42 substrates included as individual levels) on quality (scores on PC1 and PC2) and decomposition (C and N losses) using one-way ANOVAs. We then used Tukey’s honestly significant difference tests to determine significant differences between each faeces type and the corresponding intact litter.
    To test our second hypothesis, we expressed the changes in quality and decomposition following litter conversion into detritivore faeces as net differences in quality (scores on PC1 and PC2) and decomposition (C and N losses) between faeces and the litter from which faeces were derived. We then compared the hypothesised role of intact litter quality/decomposition (PC1 and PC2 scores, C and N losses) and the role of detritivore species on changes in quality/decomposition (net differences in PC1 and PC2 scores, C and N losses) by performing ANCOVAs with intact litter quality/decomposition as the continuous variable and detritivore species as categorical variable (all six detritivore species as individual levels). For all ANVOCAs, the variance associated with each term (intact litter quality/decomposition; detritivore species; interaction) was computed by dividing the sum of squares by the total sum of squares.
    To evaluate the relation between quality parameters (PC1 and PC2 scores) and C and N losses from intact litter and faeces separately, we determined the relations between intact litter and faeces C and N losses and their scores on PC1 and PC2 with simple linear regressions and visualised these relations by fitting these variables as supplementary variables on the PCA.
    For all statistical tests on C and N losses, block was included in the model as a random variable. All data were checked for normal distribution and homoscedasticity of residuals. All analyses were performed using the R software (version 3.5.3).
    Reporting summary
    Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    An annotated dataset of bioacoustic sensing and features of mosquitoes

    We conducted a laboratory study in the facilities provided by the Natural History Museum of Funchal (Mosquito Lab). Three species of mosquitoes were recorded to determine their dominant frequencies and spectral behaviors. The species used for this collection and study were A. Aegypti, C. Quinquefasciatus & Pipiens and Culiseta, which came from a lab colony established from captures collected in Funchal city in 2019.
    The mosquitoes were kept in an environmental room simulating natural conditions, with 60 ± 10% relative humidity and temperature of 20–25 °C. Mosquitoes were housed individuals in boxes (25 × 25 × 25 cm) covered with a mesh cap. They were fed with 20% sucrose solution supplemented with 1 g aquarium fish food mixed daily from the brand “Sera Guppy Gran”. The duration of the study was approximately 48 days. All mosquitoes used in these experiments were 7–25 days old. For the recording process, sensors were incorporated into the boxes and the tests conducted on 12–18 specimens for Aedes Aegypti, 7–12 specimens for Culex and 4 specimens for Culiseta. The duration of the extracted sequences ranged from 0 to 300 ms. To generate samples closer to real-world acquisition conditions we added environmental noise in some mosquito samples.
    Uncompressed audio of real sound waves was converted to digital format without any further processing. This means that recordings are exact copies of the source audio, recorded in WAV files.
    The acoustic sensor uses a low-noise omnidirectional microphone capsule2. The microphone converts sound into electrical signals with a specific signal to noise ratio (80 dB), self-noise, and residual noise. All these parameters influence the quality of the acquired sound.
    Noise can be a significant problem when acquiring physical signals as voltages. Signal smoothing attempts to capture the essential information in the signal while leaving out the noise. This is done by interpolating the raw signal to estimate the original one17.
    To collect samples, we used three devices: one of them was our prototype comprising a Teensy 3.2 audio board, microphone and environmental sensor for 44.1 kHz sampling rate. The other two were general-purpose smartphones (Huawei P20 Lite and IPhone 4) used to record samples with a 8 and 48 kHz sampling rate, respectively.
    To start a colony for our experience, we installed traps and buckets of water to catch eggs and adult mosquitoes. The female Aedes mosquitoes require a blood meal before each egg-laying18. The eggs are deposited individually on the inner walls of any container capable of storing water. This work was conducted jointly with the Natural History Museum of Madeira and IASaude (the regional health authority of Madeira islands) as part of a plan to control the spread of mosquitoes in the city of Funchal (Fig. 1).
    Fig. 1

    Location and number of traps in the city of Funchal, Madeira, Portugal.

    Full size image

    A. Aegypti mosquitoes, lay the most eggs in the velcro tape, while Culex and Culiseta prefer to lay directly in rafts on still water or in other substances19. Traps with a ventilation system were also used to capture adult mosquitoes, especially Culex and Culiseta.
    Figure 2 shows the procedure from egg collection to mosquito germination, and also the boxes that are used for further acquisition of sound samples. It is noteworthy that after 25–30 days the mosquitoes die due to the conditions imposed in the study.
    Fig. 2

    Procedure for collecting audio samples for different species.

    Full size image

    Step A comprises the gathering of eggs and mosquitoes. The figures show a bucket inside which mosquitoes lay eggs on a velcro tape, and also a trap. These traditional methods allow a fine assessment of the distribution of mosquito populations over time and space (periodically summarized in epidemiological bulletins). In step B the collected eggs are germinated to create a colony. Then, (step C) mosquitoes are placed in boxes and fed with a sugar solution and fish food20. Finally, in step D, audio samples are collected by the devices: mobile phones and low-cost IoT. This procedure is repeated when the colony dies after 25 days, starting from step B.
    Audio was recorded inside boxes (25 × 25 × 25 cm) where the mosquitoes were located at a maximum distance of 27 cm from the microphone placed in the center of the box. The signal amplitude fluctuates significantly over time as the mosquitoes in free flight approach the microphone or move away.
    Continuous recordings were then split into 300 millisecond (ms) snippets. Since mosquitoes have a very short flight, it was necessary to apply a slight stimulus on the wall of the boxes (covered by a net) to force them to fly.
    To analyze each mosquito recording, 34 features were extracted taking into account several parameters of the signal belonging to three different domains: time (1–3), frequency (4–8, 22–34) and cepstrum (9–21), analyzed below in the Technical Validation section21,22.
    These features are often used for speech signal classification, but are useful when handling non-speech signals as well. They enable a comprehensive analysis of the mosquito sounds in terms of amplitude, energy, zero crossing rate, power, frequency variation in the audio file, tonality, loudness, etc. The features are included in the dataset23 and their computation is demonstrated in the Code Availability section. More

  • in

    A high-resolution map of reactive nitrogen inputs to China

    Database structure
    The Coupled Human And Natural Systems Nitrogen Cycling Model Spatial Distribution (CHANS-SD) 1.0 database consists of three files (Fig. 1). The ‘data file’ provides N inputs of 6 land use types, including cropland, forest, grassland, water, built-up area and unused land. The ‘readme file’ explains the abbreviations used in the ‘data file’ and ‘source file’, and provides the units of all variables (Fig. 1). The ‘source file’ includes the full references and input data used in the database (Fig. 1).
    Fig. 1

    A general view of N inputs to 6 land-use types in China and the framework of The Coupled Human And Natural Systems Nitrogen Cycling Model Spatial Distribution (CHANS-SD) 1.0 database. N input to cropland comprises of N fertilizer, N deposition, irrigation, livestock manure, human excretion, cropland BNF (Biological N Fixation), and straw recycle; N input to forest comprises of N fertilizer, N deposition and forest BNF; N input to grassland comprises of N fertilizer, N deposition and grassland BNF; N input to water comprises of cropland runoff, livestock runoff, forest runoff, human wastewater discharged, industrial wastewater, WTP (Water Treatment Plant) effluent and N deposition; N input to built-up area and unused land is N deposition. The data of Taiwan is absent. The data consists of three files: the ‘data file’ (CHANS-SD 1.0 Data File) is the main file, includes N inputs in all 6 land-use types. The ‘readme file’ (CHANS-SD 1.0 Read Me) explains the abbreviations and units, and the ‘source file’ includes the full references used in the database. Base map is applied without endorsement from GADM data (https://gadm.org/).

    Full size image

    Data compilation
    We applied the CHANS model to calculate N inputs by land use type. The CHANS model calculates all nitrogen (N) fluxes that can be identified, together with the linkages among subsystems, within a country, state (province), city or watershed, with a mass balance principle. The system is divided into 14 subsystems: cropland, grassland, forest, livestock, aquaculture, industry, human, pet, urban green land, wastewater treatment, garbage treatment, atmosphere, surface water, and groundwater. N cycling starts from the entry of reactive N (Nr) that activated from N2 into the system or from Nr direct input to the system from outside, and terminates when Nr is transformed to N2 or lost to outside the system.
    N input to cropland subsystem is the largest component of N input to China, including N fertilizer, atmospheric N deposition, N from irrigation, N from livestock manure, N from human excretion, cropland biological N fixation (BNF) and straw recycle. N input to forest subsystem, including forest BNF and N deposition, is the second largest component of N input to China given the large area of forest. N input into surface water subsystem, including Nr runoff from cropland, livestock and forest, human wastewater discharged, industrial waste water, WTP (Water Treatment Plant) effluent and N deposition, is the third largest component of N input in China (Fig. 2). The details of the CHANS model including all the code and parameters of N cycling, and the protocols for the calculation of all related N fluxes can be found from https://person.zju.edu.cn/en/bjgu#930811.
    Fig. 2

    N input to China and proportion of its six components. (a) A spatial distribution map of N input to China, N input in east of China is much higher than that in west of China; North Plain, Sichuan Basin and the North East Plain currently receive the highest N loads (The data of Taiwan is absent). (b) Proportion of N input to cropland, forest, grassland, built-up area, unused land and water, respectively; proportion of N input to cropland is highest among all land-use types. Base map is applied without endorsement from GADM data (https://gadm.org/).

    Full size image

    The National Bureau of Statistics of China1 provides data of cropland N fertilizer use, planting area, livestock and population in the statistical yearbook of each province. Taiwan, Hong Kong, and Macao were not included owing to data limitations. Gridded datasets of land use and GDP is derived from the Resource and Environment Data Cloud Platform12, and data on China’s hydro-basin distribution is collected from the FAO website GeoNetwork13.
    In the cropland subsystem, livestock manure input to cropland (MANUREan, kg/yr) was calculated based on animal population (POPan, head), excretion factor (EXCREan, kg N/head/yr) and rate of livestock manure applied to cropland (REan, %) according to Eq. (1) (Fig. 3a),

    $${MANURE}_{an}={POP}_{an}times {EXCRE}_{an}times {RE}_{an}$$
    (1)

    Fig. 3

    Important components of N inputs to cropland. (a), N input from livestock manure to cropland, high values ( >100 kg/ha) concentrate in the North Plain; (b), N input from N fertilizer to cropland, compared to other items, high values ( >100 kg/ha) are widely distributed across China; (c), N input from human excretion to cropland; (d), N input from cropland BNF (biological N fixation) to cropland. The data of Taiwan is absent. Base map is applied without endorsement from GADM data (https://gadm.org/).

    Full size image

    Amounts of chemical N fertilizer consumption at county scale were obtained from the statistical yearbook of counties (Fig. 3b). Human manure to cropland (MANUREhu, kg /yr) was calculated by urban population (POPur, person), rural population (POPru, person), excretion factor (EXCREhu, kg N/person/yr), rate of urban excretion return to cropland (REur, %) and rate of rural excretion return to cropland (REru, %) according to Eq. (2) (Fig. 3c).

    $${MANURE}_{hu}=({POP}_{ur}times {RE}_{ur}+{POP}_{ru}times {RE}_{ru})times {EXCRE}_{hu}$$
    (2)

    Cropland BNF (CBNF, kg/yr) was calculated by planting area (area, ha) and N fixation rate (rfix, kg N/ha/yr) according to Eq. (3) (Fig. 3d),

    $$CBNF=areatimes {r}_{fix}$$
    (3)

    Straw recycling and N input from irrigation are calculated by using nationally uniform values (Table 1).
    Table 1 A summary of N inputs to six land-use types in 2017 in China.
    Full size table

    N input to the grassland subsystem include N fertilizer, manure recycle, N deposition, grassland BNF and irrigation for artificial grassland. N input in forest subsystem include N fertilizer to artificial forests, N deposition and forest BNF.
    In the surface water system, N inputs to each watershed include runoff from cropland, livestock, and forest areas, human wastewater discharge, industrial wastewater, WTP effluent and atmospheric N deposition. Cropland runoff was calculated by spatial distribution of cropland input (Fig. 4a). Livestock runoff using the spatial distribution of livestock manure (Fig. 4b). Finally, forest runoff was calculated by applying the spatial distribution of forest areas. Human wastewater discharge was calculated based on the spatial distribution of the human population (including urban and rural populations) (Fig. 4c). Industrial wastewater was calculated by using the spatial distribution of GDP since industrial output is highly correlated with GDP on regional scale (Fig. 4d). Similarly, the distribution of WTP and its effluent Nr are highly correlated with urban population on regional scale, therefore, WTP effluent was calculated by spatial distribution of urban population.
    Fig. 4

    Important components of N inputs to water. (a) N input from cropland runoff to water, shows similar distribution with N input to cropland; (b) N input from livestock runoff to water, shows similar distribution with N input from livestock manure to cropland; (c) N input from human wastewater to water, shows similar distribution with population; (d) N input from industrial wastewater to water, shows similar distribution with GDP. The data of Taiwan is absent. Base map is applied without endorsement from GADM data (https://gadm.org/).

    Full size image

    For N deposition, the satellite observations on NO2 columns were derived from GOME-2. GOME-2 overpass times provided global coverage of NO2 with a variable ground spatial resolution of 80 km × 40 km (every day). We used the monthly TEMIS NO2 product at a spatial resolution of 0.25° latitude × 0.25° longitude downloaded from the website of Tropospheric Emission Monitoring Internet Service14. The satellite observations on NH3 columns were derived from IASI onboard the meteorological platforms MetOp-A and MetOp-B15 with an elliptical footprint of 12 × 12 km up to 20 × 39 km depending on the satellite-viewing angle. The daily NH3 columns were downloaded from IASI Portal16. We processed the daily data into the monthly NH3 columns averaged by daily observations at a horizontal resolution of 0.25° latitude × 0.25° longitude using the arithmetic mean method17,18. N deposition of every subsystem was obtained from the national N deposition spatial distribution map by ‘spatial join’ tool of ArcGIS 10.6. More

  • in

    Managing incursions of Vespa velutina nigrithorax in the UK: an emerging threat to apiculture

    Nests
    A summary of the nest results can be found in Tables 1 (physical description of nests) and 2 (ploidy). All adult females that were weighed were classed as workers or founder queens using the information found in Rome et al.8, which defined a limit of 593 mg wet weight or 250 mg dry weight to discriminate between workers and founder queens. The average wet weight of founder queens in September was 624 mg (N = 5). All nests recovered had fewer adults present than expected, this was presumed to be due to the loss of adult hornets during the destruction process and subsequent removal of the nest from its original location.
    Table 1 Summary of observations from all nests discovered in the UK.
    Full size table

    Table 2 Ploidy of nests.
    Full size table

    Tetbury
    The findings from the examination of the Tetbury nest have been described previously10 but are briefly summarised again here. This nest was discovered on 28th September 2016. In total, 70 adult hornets were found in the nest. The wet weight of 57 adult female hornets ranged from 202 to 322 mg with a mean of 256 mg (N = 19), whilst that of 13 adult male hornets ranged from 248 to 326 mg with a mean of 290 mg (N = 7). The nest diameter was 23 cm and the nest contained five combs, four of which contained all life stages (eggs, larvae, pupae, teneral adults, adults) of the Asian hornet. All life stages examined were diploid. The nest was likely derived from a single queen mated to a single drone.
    Woolacombe
    A nest was discovered in Woolacombe on 27 September 2017. In total, 166 adult hornets were found in the nest, all female. The wet weight of adult female hornets ranged from 172 to 508 mg with a mean of 333 ± 5 mg (N = 166). Based on the information in Rome et al.8, described above, none of the females found in the Woolacombe nest were founder queens. The nest was 27 cm in diameter and is the largest nest discovered in England to date. The nest contained seven combs and all life stages were present, although the larval samples were too degraded for DNA analysis. Haploid individuals were present at the egg and pupal stages, the remainder of the individuals examined were diploid. Based on life stages present and the ploidy, the queen began laying haploid eggs on approximately 2nd September. The genetic analysis (results from COLONY2, verified manually) showed that the offspring sampled were likely to be the product of a single queen and three drones.
    Fowey nests 1 + 2
    Two nests were discovered in Fowey, Cornwall in 2018 on 3rd and 20th September, 40 m apart. The first nest contained three combs and had a diameter of 15 cm. No eggs or early instar larvae were present but late instar larvae, teneral adults and three adults were present. All individuals sampled were diploid. From the absence of eggs and early instar larvae, it was concluded that the queen was absent/missing in the 2–3 weeks prior to nest discovery. The second nest had a diameter of 19 cm and contained four combs with brood in all stages. Seven adult males and eight females were found in the nest; the males were diploid. All eggs and six of the ten larvae genotyped were haploid, while the pupae, teneral adults and adults were diploid. No queen was found. From the genetic analysis of the two nests, it was shown that both were highly likely to be offspring of a single queen and drone, with the first nest discovered presumably a primary nest and the other nest the secondary nest. From the ploidy of the life-stages present, it was inferred that the queen began laying haploid eggs around the 30th August.
    New Alresford nest
    The first nest found in Hampshire was discovered in New Alresford on 24th September 2018. The nest was 18 cm diameter and contained four combs with all life stages present. Twenty-eight males and 94 females were found. All the adults, teneral adults and pupae sampled were diploid, while within the larvae, five out of 10 were haploid, and within the eggs, two out of seven were haploid. The queen began laying haploid eggs around the 3rd September. The individuals from this nest were highly likely the offspring of a single queen and two drones.
    Brockenhurst nest
    The second nest found in Hampshire was discovered in Brockenhurst (approximately 30 miles away from New Alresford) and was destroyed on 04th October 2018. The nest was 18.5 cm diameter and there were three combs present with brood from the larvae stage onwards; no eggs were present indicating a recent loss of the queen or cessation of laying. All larvae and pupae were haploid and adult males were also haploid. The only diploid individuals present were worker females. The queen ceased laying before any diploid (future gyne) eggs were laid. The nest was consistent with being the offspring of a single queen mated with two drones.
    Drayton Bassett nest
    The first nest of 2019 was discovered on September 2nd at Drayton Bassett, Staffordshire. On arrival at Fera Science Ltd, the nest was too damaged to determine its size. Five adult female hornets were found in the nest. The wet weight of adult female hornets ranged from 197 to 312 mg with a mean of 271.6 (n = 5). The average wet weight of founder queens in September in the study by Rome et al.8 was 624 mg (n = 5). Based on this, it would appear that none of the females found in the nest were founder queens. All life stages were present in the nest, and all individuals genotyped were diploid. The nest was consistent with being the offspring of a single queen mated to a single drone.
    Christchurch nests 1 + 2
    On 01st October, 2019, a nest 13 cm diameter was discovered in Christchurch, Dorset. Two combs were present in the nest. One adult female hornet was found in the nest. The wet weight of this adult female hornet was 545 mg and the mesoscutum width was 4.6 mm. In a study by Pérez de Heredia et al.16 individuals taken from nests with a unimodal population had one individual per nest that had a mesoscutum width above 4.5 mm; no other individuals in these nests reached a mesoscutum width of 4.5 mm. It is therefore likely that the individual found in this nest was the queen. The combs contained eggs and larvae and had genotypes consistent with being the offspring of the queen that was present. Two eggs were haploid, the remainder of the eggs and larvae genotyped were diploid. On October 10th, a second nest was discovered in Christchurch, 10 m from the first nest, but could not be measured as it was intertwined with vegetation and fragmented upon removal. No adult hornets were found in the nest. Two combs were present, with capped and uncapped cells. Larvae, pupae and teneral adults were found, all of which were diploid. No eggs were found. Both nests from Christchurch were consistent with being the offspring of the same queen, mated to a single drone. The first nest found was likely to be the secondary nest, the second nest found likely to be the primary nest.
    Information on all nests is found in Table 1. Map locations for each nest are shown in Fig. 1 and images of each nest are shown in Fig. 2.
    Figure 2

    Images of UK nests: (a) Tetbury, (b) Woolacombe, (c) Fowey nest 1, (d) Fowey nest 2, (e) Brockenhurst, (f) New Alresford, (g) Drayton Bassett, (h) Christchurch nest 1 and (i) Christchurch nest 2. Where shown, scale bar represents 5 cm.

    Full size image

    Genetic relatedness
    Overall, the genetic diversity in the UK is relatively low for all locations, for all three measures used (mean number of alleles per locus, observed and expected heterozygosity; Table 3). However, it should be taken into consideration that the data for each UK nest are from individuals that were all closely related to each other (full, half siblings). A single combined figure for the UK was not calculated as it seems unlikely there is a UK population. Compared to the Asian hornet diversity data from Arca et al. (2015)11, the UK diversity is lower than France, which itself is lower than the diversity found in Asia (Table 2). This trend reflects the likely colonisation history of the hornet, which colonised France from Asia, and the UK incursions are likely to derive from populations on the European mainland.
    Table 3 Genetic diversity measure (average number of alleles per locus) and the observed and expected heterozygosity for Asian hornet populations sampled in the British Isles, and from France (data from Arca et al. 201511).
    Full size table

    The occurrence of microsatellite alleles in the UK nests and France and Asia (from Arca et al.11) are given in supplementary material 1. In comparison with the Asian and French data in Arca et al.11, the UK samples had a restricted subset of alleles (35 alleles in total) that were all found in the French populations (60 alleles). In turn, all French alleles were a subset of those found in Asia (178 alleles). Similarly, the majority of private alleles were found in Asia (114), a small number in France (n = 3) and none in the UK (supplementary material 1).
    In all cases, individuals recovered near a nest (within 2 km) were offspring of the recovered nearby nest (or nests, where there were primary and secondary nests). The majority of these individuals were found within 500 m of the nest. Most individuals caught in isolated locations away from nests (over 15 km) were not offspring of the recovered nests, with the exception of the individual recovered in Liskeard, which had a genotype compatible with being the offspring of the Fowey nest, some 17 km distant.
    To exclude the possibility that any of the founding queens that escaped the destruction of the nest went on to produce viable nests that gave rise to nests caught in the subsequent year, we considered whether the inferred parental genotypes from a nest in year one could be the parents of the inferred parental genotypes in year two. For example, whether the queen from the Tetbury nest in 2016 could have formed a second nest and the offspring from that nest been the parents to the Woolacombe nest in 2017. In no case were the inferred parental genotypes compatible with this scenario. Additionally, where more than one nest was found in a single year (i.e. New Alresford/ Brockenhurst/ Fowey in 2018, Christchurch / Drayton Bassett in 2019), we considered whether the foundress queens and founder drones were full siblings to each other. Again, in no case was this possible (although they could have been half siblings to each other). Genotype data are provided in Supplementary material 2. More

  • in

    Phenotypic variation, functional traits repeatability and core collection inference in Synsepalum dulcificum (Schumach & Thonn.) Daniell reveals the Dahomey Gap as a centre of diversity

    1.
    Kurihara, K. & Beidler, L. M. Taste-modifying protein from miracle fruit. Science 161, 1241–1243 (1968).
    ADS  CAS  Article  PubMed  Google Scholar 
    2.
    Achigan-Dako, E. G., Tchokponhoué, D. A., N’Danikou, S., Gebauer, J. & Vodouhè, R. S. Current knowledge and breeding perspectives for the miracle plant Synsepalum dulcificum (Schum. et Thonn.) Daniell. Genet. Resour. Crop. Evol. 62, 465–476 (2015).

    3.
    Fandohan, A. B. et al. Usages traditionnels et valeur économique de Synsepalum dulcificum au Sud-Bénin. Bois For. Trop. 332, 17–30 (2017).
    Article  Google Scholar 

    4.
    Oumorou, M., Dah-Dovonon, J., Aboh, B., Hounsoukaka, M. & Sinsin, B. Contribution á la conservation de Synsepalum dulcificum: régénération et importance socio-économique dans le département de l’ouémé (Bénin). Ann. Sci. Agron. 14, 101–120 (2010).
    Google Scholar 

    5.
    Rodrigues, J. F., da Silva Andrade, R., Bastos, S. C., Coelho, S. B. & Pinheiro, A. C. M. Miracle fruit: An alternative sugar substitute in sour beverages. Appetite 107, 645–653 (2016).
    Article  PubMed  Google Scholar 

    6.
    Andrade, A. C. et al. Effect of different quantities of miracle fruit on sour and bitter beverages. LWT 99, 89–97 (2019).
    CAS  Article  Google Scholar 

    7.
    Swamy, K. B., Hadi, S. A., Sekaran, M. & Pichika, M. R. The clinical effects of Synsepalum dulcificum: a review. J. Med. Food. 17, 1165–1169 (2014).
    CAS  Article  PubMed  Google Scholar 

    8.
    Chen, C. C., Liu, I. M. & Cheng, J. T. Improvement of insulin resistance by miracle fruit (Synsepalum dulcificum) in fructose-rich chow-fed rats. Phytother. Res. 20, 987–992 (2006).
    Article  PubMed  Google Scholar 

    9.
    Han, Y. C., Wu, J. Y. & Wang, C. K. Modulatory effects of miracle fruit ethanolic extracts on glucose uptake through the insulin signaling pathway in C2C12 mouse myotubes cells. Food Sci. Nutr. 7, 1035–1042 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    10.
    Obafemi, T. O., Akinmoladun, A. C., Olaleye, M. T., Agboade, S. O. & Onasanya, A. A. Antidiabetic potential of methanolic and flavonoid-rich leaf extracts of Synsepalum dulcificum in type 2 diabetic rats. J. Ayurveda Integr. Med. 8, 238–246. https://doi.org/10.1016/j.jaim.2017.01.008 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    11.
    Buckmire, R. & Francis, F. Pigments of miracle fruit, Synsepalum dulcificum, Schum, as potential food colorants. J. Food Sci. 43, 908–911 (1978).
    CAS  Article  Google Scholar 

    12.
    Del Campo, R., Zhang, Y. & Wakeford, C. Effect of miracle fruit (Synsepalum dulcificum) seed oil (MFSO®) on the measurable improvement of hair breakage in women with damaged hair: a randomized, double-blind, placebo-controlled, eight-month trial. J. Clin. Aesthet. Dermat. 10, 39–48 (2017).
    Google Scholar 

    13.
    Gorin, S. et al. Beneficial effects of an investigational wristband containing Synsepalum dulcificum (miracle fruit) seed oil on the performance of hand and finger motor skills in healthy subjects: a randomized controlled preliminary study. Phytother. Res. 32, 321–332 (2018).
    CAS  Article  PubMed  Google Scholar 

    14.
    Adomou, A. Vegetation patterns and environmental gradients in Benin PhD thesis, University of Wageningen, (2005).

    15.
    Tchokponhoué, D. et al. Regeneration ability and seedling growth in the miracle plant Synsepalum dulcificum (Schumach. & Thonn.) Daniell. Fruits 73, 13–21 (2018).

    16.
    Tchokponhoué, D. A., N’Danikou, S., Houéto, J. S. & Achigan-Dako, E. G. Shade and nutrient-mediated phenotypic plasticity in the miracle plant Synsepalum dulcificum (Schumach. & Thonn.) Daniell. Sci. Rep. 9(5137), 1–11 (2019).

    17.
    Tchokponhoué, D. A., N’Danikou, S., Hale, I., Van Deynze, A. & Achigan-Dako, E. G. Early fruiting in Synsepalum dulcificum (Schumach. & Thonn.) Daniell juveniles induced by water and inorganic nutrient management. F1000Res. 6, 1–17 (2017).

    18.
    Choi, S. E. & Garza, J. Effects of different miracle fruit products on the sensory characteristics of different types of sour foods by descriptive analysis. J. Food Sci. 85, 36–49 (2020).
    CAS  Article  PubMed  Google Scholar 

    19.
    Tafazoli, S. et al. Safety assessment of miraculin using in silico and in vitro digestibility analyses. Food Chem. Toxicol. 133(110762), 1–10 (2019).
    Google Scholar 

    20.
    Chibuzor, I. A., Bukola, O., Adejoke, A. O. & Chidozie, O. P. Genetic assessment of the shrub Synsepalum dulcificum (Schumach & Thonn.) Daniell in Nigeria using the Randomly Amplified Polymorphic DNA (RAPD). Int. J. Genet. Genom. 4, 45–50 (2017).

    21.
    Sogbohossou, E. D. et al. Association between vitamin content, plant morphology and geographical origin in a worldwide collection of the orphan crop Gynandropsis gynandra (Cleomaceae). Planta 250, 933–947 (2019).
    CAS  Article  PubMed  Google Scholar 

    22.
    Singh, K., Sharma, Y. P. & Gairola, S. Morphological characterization of wild Rosa L. germplasm from the Western Himalaya, India. Euphytica 216(41), 1–24. https://doi.org/https://doi.org/10.1007/s10681-020-2567-2 (2020).

    23.
    Sun, W. et al. Multivariate analysis reveals phenotypic diversity of Euscaphis japonica population. PLoS ONE 14, 1. https://doi.org/10.1371/journal.pone.0219046 (2019).
    CAS  Article  Google Scholar 

    24.
    Yazdanpour, F., Khadivi, A. & Etemadi-Khah, A. Phenotypic characterization of black raspberry to select the promising genotypes. Sci. Hortic. Amsterdam 235, 95–105. https://doi.org/10.1016/j.scienta.2018.02.071 (2018).
    CAS  Article  Google Scholar 

    25.
    Fereidoonfar, H., Salehi-Arjmand, H., Khadivi, A. & Akramian, M. Morphological variability of sumac (Rhus coriaria L.) germplasm using multivariate analysis. Ind. Crops Prod. 120, 162–170, https://doi.org/https://doi.org/10.1016/j.indcrop.2018.04.034 (2018).

    26.
    Norouzi, E., Erfani-Moghadam, J., Fazeli, A. & Khadivi, A. Morphological variability within and among three species of Ziziphus genus using multivariate analysis. Sci. Hortic. Amsterdam 222, 180–186. https://doi.org/10.1016/j.scienta.2017.05.016 (2017).
    Article  Google Scholar 

    27.
    Khadivi-Khub, A. & Anjam, K. Morphological characterization of Prunus scoparia using multivariate analysis. Plant Syst. Evol. 300, 1361–1372 (2014).
    Article  Google Scholar 

    28.
    Vihotogbé, R., van den Berg, R. G. & Sosef, M. S. Morphological characterization of African bush mango trees (Irvingia species) in West Africa. Genet. Resour. Crop. Evol. 60, 1597–1614 (2013).
    Article  Google Scholar 

    29.
    Ouborg, N. J. Integrating population genetics and conservation biology in the era of genomics. Biol. Lett. 6, 3–6 (2010).
    Article  PubMed  Google Scholar 

    30.
    Martínez-García, P. J. et al. Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia). Tree Genet. Genomes 13, 109 (2017).
    Article  Google Scholar 

    31.
    Falconer, D. S. Introduction to quantitative genetics. (Oliver And Boyd; Edinburgh; London, 1960).

    32.
    Fonseca, C. E. L. d., Morais, F. M. d., Gonçalves, H. M., Aquino, F. d. G. & Rocha, F. S. Repeatability of fruit traits from two Hancornia speciosa populations from the core region of the Brazilian Cerrado. Pesqui. Agropecu Bras. 53, 710–716 (2018).

    33.
    Zou, S., Yao, X., Zhong, C., Zhao, T. & Huang, H. Genetic analysis of fruit traits and selection of superior clonal lines in Akebia trifoliate (Lardizabalaceae). Euphytica 214(111), 1–9. https://doi.org/10.1007/s10681-018-2198-z (2018).
    ADS  CAS  Article  Google Scholar 

    34.
    Sanou, H. et al. Phenotypic variation of agromorphological traits of the shea tree, Vitellaria paradoxa CF Gaertn., in Mali. Genet. Resour. Crop. Evol. 53, 145–161 (2006).

    35.
    Albuquerque, A. S., Bruckner, C. H., Cruz, C. D., Salomão, L. C. C. & Neves, J. C. L. Repeatability and correlations among peach physical traits. Crop Breed. Appl. Biot. 4, 441–445 (2004).
    Article  Google Scholar 

    36.
    Belaj, A. et al. Developing a core collection of olive (Olea europaea L.) based on molecular markers (DArTs, SSRs, SNPs) and agronomic traits. Tree Genet. Genomes 8, 365–378 (2012).

    37.
    Le Cunff, L. et al. Construction of nested genetic core collections to optimize the exploitation of natural diversity in Vitis vinifera L. subsp. sativa. BMC Plt. Biol. 8:31, 1–12 (2008).

    38.
    Mahmoodi, R. et al. Development of a core collection in Iranian walnut (Juglans regia L.) germplasm using the phenotypic diversity. Sci. Hortic. Amsterdam 249, 439–448 (2019).

    39.
    Tchokponhoué, D. A., N’Danikou, S. & Achigan-Dako, E. G. A combination of approaches evidenced seed storage behaviour in the miracle berry Synsepalum dulcificum (Schumach. et Thonn.) Daniell. BMC Plt. Biol. 19:117, 1–13 (2019).

    40.
    Edesi, J., Tolonen, J., Ruotsalainen, A. L., Aspi, J. & Häggman, H. Cryopreservation enables long-term conservation of critically endangered species Rubus humulifolius. Biodivers. Conserv. 29, 303–314. https://doi.org/10.1007/s10531-019-01883-9 (2020).
    Article  Google Scholar 

    41.
    Bharuth, V., Naidoo, C., Pammenter, N. W., Lamb, J. M. & Moodley, T. Responses to chilling of recalcitrant seeds of Ekebergia capensis from different provenances. S. Afr. J. Bot. 130, 8–24 (2020).
    Article  Google Scholar 

    42.
    Leal, M. E. The African rain forest during the Last Glacial Maximum an archipelago of forests in a sea of grass. (2004).

    43.
    Swenson, U., Richardson, J. E. & Bartish, I. V. Multi-gene phylogeny of the pantropical subfamily Chrysophylloideae (Sapotaceae): evidence of generic polyphyly and extensive morphological homoplasy. Cladistics 24, 1006–1031 (2008).
    Article  Google Scholar 

    44.
    Juhé-Beaulaton, D. “Fèves”, “pois” et “grains” dans le golfe de Guinée : problèmes d’identification des plantes dans les sources historiques. in Plantes et paysages d’Afrique, une histoire à explorer (ed Chastanet M) 45–68 (1998).

    45.
    Inglett, G. E. & May, J. F. Tropical plants with unusual taste properties. Econ. Bot. 22, 326–331. https://doi.org/10.1007/BF02908127 (1968).
    Article  Google Scholar 

    46.
    Salzmann, U. & Hoelzmann, P. The Dahomey Gap: an abrupt climatically induced rain forest fragmentation in West Africa during the late Holocene. Holocene 15, 190–199. https://doi.org/10.1191/0959683605hl799rp (2005).
    ADS  Article  Google Scholar 

    47.
    Booth, A. The Niger, the Volta and the Dahomey Gap as geographic barriers. Evolution 12, 48–62 (1958).
    Article  Google Scholar 

    48.
    White, F. The vegetation of Africa: a descriptive memoir to accompany the UNESCO/AETFAT/UNSO vegetation map of Africa. (1983).

    49.
    Niñez, V. Household gardens: theoretical and policy considerations. Agr. Syst. 23, 167–186. https://doi.org/10.1016/0308-521X(87)90064-3 (1987).
    Article  Google Scholar 

    50.
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing,Vienna, Austria. https://www.R-project.org/. (2019).

    51.
    Peterson, B. G. et al. ‘Performance analytics’: econometric tools for performance and risk analysis. R Team Cooperation. (2018).

    52.
    Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3–an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259 (2012).
    Article  Google Scholar 

    53.
    Moon, K.-W. Interactive plot. in Learn ggplot2 Using Shiny App (ed Keon-Woong Moon) 295–347 (Springer, 2016).

    54.
    Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).
    Article  Google Scholar 

    55.
    YiLan, L. & RuTong, Z. clustertend: Check the Clustering tendency. R package version 1 (2015).

    56.
    Kassambara, A. & Mundt, F. Factoextra: extract and visualize the results of multivariate data analyses. R package version 1, 4 (2017).
    Google Scholar 

    57.
    Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
    Article  Google Scholar 

    58.
    De Beukelaer, H., Davenport, G. F. & Fack, V. Core Hunter 3: flexible core subset selection. BMC Bioinformatics 19(203), 1–12 (2018).
    Google Scholar 

    59.
    Kim, K.-W. et al. PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics 23, 2155–2162 (2007).
    CAS  Article  PubMed  Google Scholar 

    60.
    Hu, J., Zhu, J. & Xu, H. Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theor. Appl. Genet. 101, 264–268 (2000).
    CAS  Article  Google Scholar 

    61.
    61Wickham, H. ggplot2: elegant graphics for data analysis. (Springer, 2016).

    62.
    Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. 10, 439–446 (2018).
    Article  Google Scholar 

    63.
    South, A. Rworldxtra: Country boundaries at high resolution. R package version 1 (2012).

    64.
    South, A. rnaturalearth: World map data from Natural Earth. R package version 0.1. 0 (2017).

    65.
    Salako, V. K. et al. Home gardens: an assessment of their biodiversity and potential contribution to conservation of threatened species and crop wild relatives in Benin. Genet. Resour. Crop. Evol. 61, 313 (2014).
    Article  Google Scholar 

    66.
    van Rompaey, R. S. Forest gradients in West Africa: a spatial gradient analysis, Wageningen, (1993).

    67.
    Gwali, S. et al. Morphological variation among shea tree (Vitellaria paradoxa subsp. nilotica)‘ethnovarieties’ in Uganda. Genet. Resour. Crop. Evol. 59, 1883–1898 (2012).

    68.
    Metougui, M. L., Mokhtari, M., Maughan, P. J., Jellen, E. N. & Benlhabib, O. Morphological variability, heritability and correlation studies within an argan tree population (Argania spinosa (L.) Skeels) preserved in situ. Int. J. Agr. For. 7, 42–51 (2017).

    69.
    Tsobeng, A. et al. Tree-to-tree variation in fruit of three populations of Trichoscypha acuminata in Cameroon. Sci. Afr. 7, 1–12 (2020).
    Google Scholar 

    70.
    Omondi, M. et al. Fruit morphological diversity and productivity of baobab (Adansonia digitata L.) in coastal and lower eastern Kenya. For. Trees Livelihoods 28, 266–280 (2019).
    Article  Google Scholar 

    71.
    Abdulai, I., Krutovsky, K. V. & Finkeldey, R. Morphological and genetic diversity of shea trea (Vitellaria paradoxa) in the savannah regions of Ghana. Genet Res Crop Evol 64, 1253–1268 (2017).
    Article  Google Scholar 

    72.
    Karambiri, M., Elias, M., Vinceti, B. & Grosse, A. Exploring local knowledge and preferences for shea (Vitellaria pradoxa) ethnovarieties in Soutwest Burkina-Faso through a gender and ethnic lens. For. Trees Livelihoods 26, 13–28 (2016).
    Article  Google Scholar 

    73.
    Ayensu, E. S. Morphology and anatomy of Synsepalum dulcificum (Sapotaceae). Bot. J. Linn. Soc. 65, 179–187 (1972).
    Article  Google Scholar 

    74.
    Lim, T. K. in Edible medicinal and non-medicinal plants Vol. 6 (ed T.K. Lim) 146–150 (Springer, Dordrecht, 2013).

    75.
    Huang, W., Chung, H. Y., Xuan, W., Wang, G. & Li, Y. The cholesterol-lowering activity of miracle fruit (Synsepalum dulcificum). J. Food Biochem. 1, e13185. https://doi.org/10.1111/jfbc.13185 (2020).
    Article  Google Scholar 

    76.
    Ahmed, A. A. O. et al. Tree-to-tree variability in fruits and kernels of a Balanites aegyptiaca (L.) Del. population grown in Sudan. Trees 34, 111–119 (2020).

    77.
    Zou, S., Yao, X., Zhong, C., Zhao, T. & Huang, H. Effectiveness of recurrent selection in Akebia trifoliata (Lardizabalaceae) breeding. Sci. Hortic. Amsterdam 246, 79–85 (2019).
    Article  Google Scholar 

    78.
    Houehanou, T. D. et al. Morphological trait variation and relationships of Afzelia africana Sm. caused by climatic conditions and anthropogenic disturbance in Benin (West Africa). Genet. Resour. Crop. Evol. 66, 1091–1105 (2019).

    79.
    Gouwakinnou, G. N., Assogbadjo, A. E., Lykke, A. M. & Sinsin, B. Phenotypic variations in fruits and selection potential in Sclerocarya birrea subsp. birrea. Sci. Hortic. Amsterdam 129, 777–783 (2011).

    80.
    Cotterill, P. P. & Dean, C. A. Successful tree breeding with index selection (CSIRO, Division of Forestry and Forest Products, 1990).
    Google Scholar 

    81.
    Zobel, B. & Talbert, J. Applied forest tree improvement. (John Wiley & Sons, 1984).

    82.
    Atangana, A. R. et al. Tree-to-tree variation in stearic and oleic acid content in seed fat from Allanblackia floribunda from wild stands: potential for tree breeding. Food Chem. 126, 1579–1585 (2011).
    CAS  Article  PubMed  Google Scholar 

    83.
    Shelbourne, C. Genetic gains from different kinds of breeding population and seed or plant production population. S. Afr. For. J. 160, 49–65 (1992).
    Google Scholar 

    84.
    Leakey, R. & Page, T. The ‘ideotype concept’and its application to the selection of cultivars of trees providing agroforestry tree products. For. Trees Livelihoods 16, 5–16 (2006).
    Article  Google Scholar 

    85.
    Bhattacharjee, R., Khairwal, I., Bramel, P. J. & Reddy, K. Establishment of a pearl millet [Pennisetum glaucum (L.) R. Br.] core collection based on geographical distribution and quantitative traits. Euphytica 155, 35–45 (2007).

    86.
    Escribano, P., Viruel, M. & Hormaza, J. in XII EUCARPIA Symposium on Fruit Breeding and Genetics 814. 67–70.

    87.
    Duan, H. et al. Genetic characterization of Chinese fir from six provinces in southern China and construction of a core collection. Sci. Rep. 7, 1–10 (2017).
    Article  CAS  Google Scholar  More

  • in

    Co-activation of Akt, Nrf2, and NF-κB signals under UPRER in torpid Myotis ricketti bats for survival

    1.
    Carey, H. V., Andrews, M. T. & Martin, S. L. Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiological Rev. 83, 1153–1181 (2003).
    CAS  Article  Google Scholar 
    2.
    Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274 (2004).
    CAS  Article  Google Scholar 

    3.
    Lindell, S. L. et al. Natural resistance to liver cold ischemia-reperfusion injury associated with the hibernation phenotype. Am. J. Physiol.-Gastrointest. Liver Physiol. 288, G473–G480 (2005).
    CAS  Article  Google Scholar 

    4.
    Dave, K. R., Christian, S. L., Perez-Pinzon, M. A. & Drew, K. L. Neuroprotection: lessons from hibernators. Comp. Biochem. Physiol. Part B: Biochem. Mol. Biol. 162, 1–9 (2012).
    CAS  Article  Google Scholar 

    5.
    Hofmann, S., Cherkasova, V., Bankhead, P., Bukau, B. & Stoecklin, G. Translation suppression promotes stress granule formation and cell survival in response to cold shock. Mol. Biol. Cell 23, 3786–3800 (2012).
    CAS  Article  Google Scholar 

    6.
    Pluquet, O., Pourtier, A. & Abbadie, C. The unfolded protein response and cellular senescence. A review in the theme: cellular mechanisms of endoplasmic reticulum stress signaling in health and disease. Am. J. Physiol.-Cell Physiol. 308, C415–C425 (2015).
    CAS  Article  Google Scholar 

    7.
    Hetz, C. The unfolded protein response: controlling cell fate decisions under ER stress and beyond. Nat. Rev. Mol. Cell Biol. 13, 89 (2012).
    CAS  Article  Google Scholar 

    8.
    Guo, F.-J. et al. XBP1S protects cells from ER stress-induced apoptosis through Erk1/2 signaling pathway involving CHOP. Histochemistry Cell Biol. 138, 447–460 (2012).
    CAS  Article  Google Scholar 

    9.
    Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. Science 334, 1081–1086 (2011).
    CAS  Article  Google Scholar 

    10.
    Kramer, G. Two phosphorylation sites on eIF-2α. FEBS Lett. 267, 181–182 (1990).
    CAS  Article  Google Scholar 

    11.
    Wek, R., Jiang, H.-Y. & Anthony, T. Coping with stress: eIF2 kinases and translational control. (Portland Press Limited, 2006).

    12.
    Palam, L. R., Baird, T. D. & Wek, R. C. Phosphorylation of eIF2 facilitates ribosomal bypass of an inhibitory upstream ORF to enhance CHOP translation. J. Biol. Chem. 286, 10939–10949 (2011).

    13.
    Roller, C. & Maddalo, D. The molecular chaperone GRP78/BiP in the development of chemoresistance: mechanism and possible treatment. Front. Pharmacol. 4, 10 (2013).
    Article  CAS  Google Scholar 

    14.
    Lee, A. S. The ER chaperone and signaling regulator GRP78/BiP as a monitor of endoplasmic reticulum stress. Methods 35, 373–381 (2005).
    CAS  Article  Google Scholar 

    15.
    Jiang, H.-Y. et al. Phosphorylation of the α subunit of eukaryotic initiation factor 2 is required for activation of NF-κB in response to diverse cellular stresses. Mol. Cell. Biol. 23, 5651–5663 (2003).
    CAS  Article  Google Scholar 

    16.
    Prell, T. et al. Endoplasmic reticulum stress is accompanied by activation of NF-κB in amyotrophic lateral sclerosis. J. Neuroimmunol. 270, 29–36 (2014).
    CAS  Article  Google Scholar 

    17.
    Rajesh, K. et al. Phosphorylation of the translation initiation factor eIF2α at serine 51 determines the cell fate decisions of Akt in response to oxidative stress. Cell Death Dis. 6, e1591 (2015).
    CAS  Article  Google Scholar 

    18.
    Nivon, M. et al. NFκB is a central regulator of protein quality control in response to protein aggregation stresses via autophagy modulation. Mol. Biol. Cell 27, 1712–1727 (2016).
    CAS  Article  Google Scholar 

    19.
    Lemasters, J. J. In Molecular Pathology (second edn) 1–24 (Elsevier, 2018).

    20.
    Karin, M. & Lin, A. NF-κB at the crossroads of life and death. Nat. Immunol. 3, 221 (2002).
    CAS  Article  Google Scholar 

    21.
    Schmidlin, C. J., Dodson, M. B., Madhavan, L. & Zhang, D. D. Redox regulation by NRF2 in aging and disease. Free Rad. Biol. Med. 134, 702–707 (2019).

    22.
    Cullinan, S. B. & Diehl, J. A. Coordination of ER and oxidative stress signaling: the PERK/Nrf2 signaling pathway. Int. J. Biochem. Cell Biol. 38, 317–332 (2006).
    CAS  Article  Google Scholar 

    23.
    Wiersma, M. et al. Torpor-arousal cycles in Syrian hamster heart are associated with transient activation of the protein quality control system. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 223, 23–28 (2018).

    24.
    Mamady, H. & Storey, K. B. Up-regulation of the endoplasmic reticulum molecular chaperone GRP78 during hibernation in thirteen-lined ground squirrels. Mol. Cell. Biochem. 292, 89–98 (2006).
    CAS  Article  Google Scholar 

    25.
    Mamady, H. & Storey, K. B. Coping with the stress: expression of ATF4, ATF6, and downstream targets in organs of hibernating ground squirrels. Arch. Biochem. Biophys. 477, 77–85 (2008).
    CAS  Article  Google Scholar 

    26.
    Zhang, J. et al. Prosurvival roles mediated by the PERK signaling pathway effectively prevent excessive endoplasmic reticulum stress-induced skeletal muscle loss during high-stress conditions of hibernation. J. Cell. Physiol. 234, 19728–19739 (2019).

    27.
    Carey, H., Frank, C. & Seifert, J. Hibernation induces oxidative stress and activation of NF-κB in ground squirrel intestine. J. Comp. Physiol. B 170, 551–559 (2000).
    CAS  Article  Google Scholar 

    28.
    Fleck, C. C. & Carey, H. V. Modulation of apoptotic pathways in intestinal mucosa during hibernation. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 289, R586–R595 (2005).
    CAS  Article  Google Scholar 

    29.
    Zhang, Y. et al. Critical roles of mitochondria in brain activities of torpid Myotis ricketti bats revealed by a proteomic approach. J. Proteom. 105, 266–284 (2014).
    CAS  Article  Google Scholar 

    30.
    Cui, X. A., Zhang, H. & Palazzo, A. F. p180 promotes the ribosome-independent localization of a subset of mRNA to the endoplasmic reticulum. PLoS Biol. 10, e1001336 (2012).
    CAS  Article  Google Scholar 

    31.
    Wen, W.-L. et al. Vgl1, a multi-KH domain protein, is a novel component of the fission yeast stress granules required for cell survival under thermal stress. Nucleic Acids Res. 38, 6555–6566 (2010).
    CAS  Article  Google Scholar 

    32.
    Tsuchiya, N. et al. SND1, a component of RNA-induced silencing complex, is up-regulated in human colon cancers and implicated in early stage colon carcinogenesis. Cancer Res. 67, 9568–9576 (2007).
    CAS  Article  Google Scholar 

    33.
    Yoo, B. K. et al. Increased RNA-induced silencing complex (RISC) activity contributes to hepatocellular carcinoma. Hepatology 53, 1538–1548 (2011).
    CAS  Article  Google Scholar 

    34.
    Halperin, L., Jung, J. & Michalak, M. The many functions of the endoplasmic reticulum chaperones and folding enzymes. IUBMB Life 66, 318–326 (2014).
    CAS  Article  Google Scholar 

    35.
    Obchoei, S. et al. Cyclophilin A: potential functions and therapeutic target for human cancer. Med. Sci. Monit. 15, RA221–RA232 (2009).
    CAS  Google Scholar 

    36.
    Wei, Y. et al. Antiapoptotic and proapoptotic signaling of cyclophilin A in endothelial cells. Inflammation 36, 567–572 (2013).
    Article  CAS  Google Scholar 

    37.
    Kelleher, D. J. & Gilmore, R. DAD1, the defender against apoptotic cell death, is a subunit of the mammalian oligosaccharyltransferase. Proc. Natl Acad. Sci. USA 94, 4994–4999 (1997).
    CAS  Article  Google Scholar 

    38.
    Zhou, L. et al. DsbA-L alleviates endoplasmic reticulum stress-induced adiponectin downregulation. Diabetes 59, 2809–2816 (2010).
    CAS  Article  Google Scholar 

    39.
    Liu, M. et al. Endoplasmic reticulum (ER) localization is critical for DsbA-L protein to suppress ER stress and adiponectin down-regulation in adipocytes. J. Biol. Chem. 290, 10143–10148 (2015).
    CAS  Article  Google Scholar 

    40.
    Santhekadur, P. K. et al. Multifunction protein staphylococcal nuclease domain containing 1 (SND1) promotes tumor angiogenesis in human hepatocellular carcinoma through novel pathway that involves nuclear factor κB and miR-221. J. Biol. Chem. 287, 13952–13958 (2012).
    CAS  Article  Google Scholar 

    41.
    Pan, Y. H. et al. Adaptation of phenylalanine and tyrosine catabolic pathway to hibernation in bats. PLoS ONE 8, e62039 (2013).
    CAS  Article  Google Scholar 

    42.
    Vattem, K. M. & Wek, R. C. Reinitiation involving upstream ORFs regulates ATF4 mRNA translation in mammalian cells. Proc. Natl Acad. Sci. USA 101, 11269–11274 (2004).
    CAS  Article  Google Scholar 

    43.
    Lu, Z. & Xu, S. ERK1/2 MAP kinases in cell survival and apoptosis. IUBMB Life 58, 621–631 (2006).
    CAS  Article  Google Scholar 

    44.
    Sun, F.-C. et al. Localization of GRP78 to mitochondria under the unfolded protein response. Biochem. J. 396, 31–39 (2006).
    CAS  Article  Google Scholar 

    45.
    van Breukelen, F. & Martin, S. L. Translational initiation is uncoupled from elongation at 18 C during mammalian hibernation. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 281, R1374–R1379 (2001).
    Article  Google Scholar 

    46.
    van Breukelen, F., Sonenberg, N. & Martin, S. L. Seasonal and state-dependent changes of eIF4E and 4E-BP1 during mammalian hibernation: implications for the control of translation during torpor. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 287, R349–R353 (2004).
    Article  Google Scholar 

    47.
    Pan, P. & van Breukelen, F. Preference of IRES-mediated initiation of translation during hibernation in golden-mantled ground squirrels, Spermophilus lateralis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, R370–R377 (2011).
    CAS  Article  Google Scholar 

    48.
    Liu, L. et al. Hypoxia-induced energy stress regulates mRNA translation and cell growth. Mol. Cell 21, 521–531 (2006).
    Article  CAS  Google Scholar 

    49.
    Harvey, R., Dezi, V., Pizzinga, M. & Willis, A. E. Post-transcriptional control of gene expression following stress: the role of RNA-binding proteins. Biochem. Soc. Trans. 45, 1007–1014 (2017).
    CAS  Article  Google Scholar 

    50.
    Mobin, M. B. et al. The RNA-binding protein vigilin regulates VLDL secretion through modulation of Apob mRNA translation. Nat. Commun. 7, 12848 (2016).
    CAS  Article  Google Scholar 

    51.
    Srere, H. K., Wang, L. & Martin, S. L. Central role for differential gene expression in mammalian hibernation. Proc. Natl Acad. Sci. USA 89, 7119–7123 (1992).
    CAS  Article  Google Scholar 

    52.
    Han, Y. et al. Adaptation of peroxisome proliferator-activated receptor alpha to hibernation in bats. BMC Evolut. Biol. 15, 88 (2015).
    Article  CAS  Google Scholar 

    53.
    Lee, M., Choi, I. & Park, K. Activation of stress signaling molecules in bat brain during arousal from hibernation. J. Neurochem. 82, 867–873 (2002).
    CAS  Article  Google Scholar 

    54.
    Storey, K. B. Out cold: biochemical regulation of mammalian hibernation-a mini-review. Gerontology 56, 220–230 (2010).
    Article  Google Scholar 

    55.
    Lei, M., Dong, D., Mu, S., Pan, Y.-H. & Zhang, S. Comparison of brain transcriptome of the greater horseshoe bats (Rhinolophus ferrumequinum) in active and torpid episodes. PLoS ONE 9, e107746 (2014).
    Article  CAS  Google Scholar 

    56.
    Wortel, I. M., van der Meer, L. T., Kilberg, M. S. & van Leeuwen, F. N. Surviving stress: modulation of ATF4-mediated stress responses in normal and malignant cells. Trends Endocrinol. Metab. 28, 794–806 (2017).
    CAS  Article  Google Scholar 

    57.
    Cullinan, S. B. et al. Nrf2 is a direct PERK substrate and effector of PERK-dependent cell survival. Mol. Cell. Biol. 23, 7198–7209 (2003).
    CAS  Article  Google Scholar 

    58.
    Ni, M., Zhang, Y. & Lee, A. S. Beyond the endoplasmic reticulum: atypical GRP78 in cell viability, signalling and therapeutic targeting. Biochem. J. 434, 181–188 (2011).
    CAS  Article  Google Scholar 

    59.
    Yin, Q. et al. Antioxidant defenses in the brains of bats during hibernation. PLoS ONE 11, e0152135 (2016).
    Article  CAS  Google Scholar 

    60.
    Allan, M. E. & Storey, K. B. Expression of NF-κB and downstream antioxidant genes in skeletal muscle of hibernating ground squirrels, Spermophilus tridecemlineatus. Cell Biochem. Funct. 30, 166–174 (2012).
    CAS  Article  Google Scholar 

    61.
    Ni, Z., McMullen, D. C. & Storey, K. B. Expression of Nrf2 and its downstream gene targets in hibernating 13-lined ground squirrels, Spermophilus tridecemlineatus. Mol. Cell. Biochem. 312, 121–129 (2008).
    Article  CAS  Google Scholar 

    62.
    Németh, J. et al. S100A8 and S100A9 are novel nuclear factor kappa B target genes during malignant progression of murine and human liver carcinogenesis. Hepatology 50, 1251–1262 (2009).
    Article  CAS  Google Scholar 

    63.
    Sun, S. et al. Cyclophilin A (CypA) interacts with NF-κB subunit, p65/RelA, and contributes to NF-κB activation signaling. PLoS ONE 9, e96211 (2014).
    Article  CAS  Google Scholar 

    64.
    Bolignano, D. et al. Neutrophil gelatinase-associated lipocalin (NGAL) in human neoplasias: a new protein enters the scene. Cancer Lett. 288, 10–16 (2010).
    CAS  Article  Google Scholar 

    65.
    Drew, K. L., Rice, M. E., Kuhn, T. B. & Smith, M. A. Neuroprotective adaptations in hibernation: therapeutic implications for ischemia-reperfusion, traumatic brain injury and neurodegenerative diseases. Free Radic. Biol. Med. 31, 563–573 (2001).
    CAS  Article  Google Scholar 

    66.
    Bouma, H. R. et al. Induction of torpor: mimicking natural metabolic suppression for biomedical applications. J. Cell Physiol. 227, 1285–1290 (2012).
    CAS  Article  Google Scholar 

    67.
    Cerri, M. et al. Hibernation for space travel: Impact on radioprotection. Life Sci. Space Res. 11, 1–9 (2016).
    Article  Google Scholar 

    68.
    Uchida, Y., Tokizawa, K. & Nagashima, K. Characteristics of activated neurons in the suprachiasmatic nucleus when mice become hypothermic during fasting and cold exposure. Neurosci. Lett. 579, 177–182 (2014).
    CAS  Article  Google Scholar 

    69.
    Sato, N., Marui, S., Ozaki, M. & Nagashima, K. Cold exposure and/or fasting modulate the relationship between sleep and body temperature rhythms in mice. Physiol. Behav. 149, 69–75 (2015).
    CAS  Article  Google Scholar 

    70.
    Tokizawa, K., Uchida, Y. & Nagashima, K. Thermoregulation in the cold changes depending on the time of day and feeding condition: physiological and anatomical analyses of involved circadian mechanisms. Neuroscience 164, 1377–1386 (2009).
    CAS  Article  Google Scholar 

    71.
    Van Breukelen, F. & Martin, S. L. Invited review: molecular adaptations in mammalian hibernators: unique adaptations or generalized responses? J. Appl. Physiol. 92, 2640–2647 (2002).
    Article  Google Scholar 

    72.
    Piersma, S. R. et al. Workflow comparison for label-free, quantitative secretome proteomics for cancer biomarker discovery: method evaluation, differential analysis, and verification in serum. J. Proteome Res. 9, 1913–1922 (2010).
    CAS  Article  Google Scholar 

    73.
    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).
    Article  CAS  Google Scholar 

    74.
    Yu, D. et al. EGPS 1.0: Comprehensive software for multi-omic and evolutionary analyses. Natl Sci. Rev. 6, 867–869 (2019).

    75.
    Yin, Q. et al. Maintenance of neural activities in torpid Rhinolophus ferrumequinum bats revealed by 2D gel-based proteome analysis. Biochim. Biophys. Acta Proteins Proteom. 1865, 1004–1019 (2017).
    CAS  Article  Google Scholar 

    76.
    Romero-Calvo, I. et al. Reversible Ponceau staining as a loading control alternative to actin in Western blots. Anal. Biochem. 401, 318–320 (2010).
    CAS  Article  Google Scholar 

    77.
    Deutsch, E. W. et al. The ProteomeXchange consortium in 2020: enabling ‘big data’approaches in proteomics. Nucleic Acids Res. 48, D1145–D1152 (2020).
    CAS  PubMed  Google Scholar  More

  • in

    Toxicity of the herbicides diuron, propazine, tebuthiuron, and haloxyfop to the diatom Chaetoceros muelleri

    1.
    Carbery, K., Owen, R., Frickers, T., Otero, E. & Readman, J. Contamination of Caribbean coastal waters by the antifouling herbicide Irgarol 1051. Mar. Pollut. Bull. 52, 635–644. https://doi.org/10.1016/j.marpolbul.2005.10.013 (2006).
    CAS  Article  Google Scholar 
    2.
    Hernández-Romero, A. H., Tovilla-Hernández, C., Malo, E. A. & Bello-Mendoza, R. Water quality and presence of pesticides in a tropical coastal wetland in southern Mexico. Mar. Pollut. Bull. 48, 1130–1141. https://doi.org/10.1016/j.marpolbul.2004.01.003 (2004).
    CAS  Article  Google Scholar 

    3.
    Castillo, L. E., de la Cruz, E. & Ruepert, C. Ecotoxicology and pesticides in tropical aquatic ecosystems of Central America. Environ. Toxicol. Chem. 16, 41–51. https://doi.org/10.1002/etc.5620160104 (1997).
    CAS  Article  Google Scholar 

    4.
    Basheer, C., Obbard, J. P. & Lee, H. K. Persistent organic pollutants in Singapore’s coastal marine environment: part I, seawater. Water Air Soil Pollut. 149, 295–313. https://doi.org/10.1023/A:1025689600993 (2003).
    ADS  CAS  Article  Google Scholar 

    5.
    Ali, H. R. et al. Contamination of diuron in coastal waters around Malaysian Peninsular. Mar. Pollut. Bull. 85, 287–291. https://doi.org/10.1016/j.marpolbul.2014.05.049 (2014).
    CAS  Article  Google Scholar 

    6.
    Okamura, H., Aoyama, I., Ono, Y. & Nishida, T. Antifouling herbicides in the coastal waters of western Japan. Mar. Pollut. Bull. 47, 59–67. https://doi.org/10.1016/S0025-326X(02)00418-6 (2003).
    CAS  Article  Google Scholar 

    7.
    Roche, H., Salvat, B. & Ramade, F. Assessment of the pesticides pollution of coral reefs communities from French Polynesia. Rev. Ecol. https://hdl.handle.net/2042/55860 (2011).

    8.
    Sarkar, S. K. et al. Occurrence, distribution and possible sources of organochlorine pesticide residues in tropical coastal environment of India: an overview. Environ. Int. 34, 1062–1071. https://doi.org/10.1016/j.envint.2008.02.010 (2008).
    CAS  Article  Google Scholar 

    9.
    Devlin, M. M. et al. Advancing our understanding of the source, management, transport and impacts of pesticides on the Great Barrier Reef 2011–2015. Report for the Queensland Department of Environment and Heritage Protection. Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication, James Cook University, Cairns, Australia (2015).

    10.
    GBR. Great Barrier Reef Marine Park Authority 2019, Great Barrier Reef Outlook Report 2019, GBRMPA, Townsville. https://www.gbrmpa.gov.au/our-work/outlook-report-2019 (2019).

    11.
    Brodie, J. et al. Terrestrial pollutant runoff to the Great Barrier Reef: an update of issues, priorities and management responses. Mar. Pollut. Bull. 65, 81–100. https://doi.org/10.1016/j.marpolbul.2011.12.012 (2012).
    CAS  Article  Google Scholar 

    12.
    Lewis, S. E. et al. Herbicides: a new threat to the Great Barrier Reef. Environ. Pollut. 157, 2470–2484. https://doi.org/10.1016/j.envpol.2009.03.006 (2009).
    CAS  Article  Google Scholar 

    13.
    RWQIP. Reef 2050 Water Quality Improvement Plan 2017–2022. Australian and Queensland Government. https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0017/46115/reef-2050-water-quality-improvement-plan-2017-22.pdf (2018).

    14.
    Shaw, M. et al. Monitoring pesticides in the Great Barrier Reef. Mar. Pollut. Bull. 60, 113–122. https://doi.org/10.1016/j.marpolbul.2009.08.026 (2010).
    CAS  Article  Google Scholar 

    15.
    Grant, S. et al. Marine Monitoring Program: Annual report for inshore pesticide monitoring 2015–2016 (Report for the Great Barrier Reef Marine Park Authority, Great Barrier Reef Marine Park Authority, Townsville, Australia, 2017).
    Google Scholar 

    16.
    O’Brien, D. et al. Spatial and temporal variability in pesticide exposure downstream of a heavily irrigated cropping area: application of different monitoring techniques. J. Agric. Food Chem. 64, 3975–3989. https://doi.org/10.1021/acs.jafc.5b04710 (2016).
    CAS  Article  Google Scholar 

    17.
    Radcliffe, J. Pesticide use in Australia. A review undertaken by the Australian Academy of Technological Sciences, Victoria, Australia. https://www.atse.org.au/ (2002).

    18.
    Oettmeier, W. Herbicides of photosystems II. in Structure, Function and Molecular Biology (Barber, J., ed) (Elsevier, Amsterdam, 349–408). https://doi.org/10.1016/B978-0-444-89440-3.50018-7 (1992).

    19.
    Lewis, S. E. et al. Using monitoring data to model herbicides exported to the Great Barrier Reef, Australia. in The 19th International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand. MODSIM2011, 2051–2056 (2011).

    20.
    Kennedy, K. et al. The influence of a season of extreme wet weather events on exposure of the World Heritage Area Great Barrier Reef to pesticides. Mar. Pollut. Bull. 64, 1495–1507. https://doi.org/10.1016/j.marpolbul.2012.05.014 (2012).
    CAS  Article  Google Scholar 

    21.
    Kennedy, K. et al. Long term monitoring of photosystem II herbicides: correlation with remotely sensed freshwater extent to monitor changes in the quality of water entering the Great Barrier Reef, Australia. Mar. Pollut. Bull. 65, 292–305. https://doi.org/10.1016/j.marpolbul.2011.10.029 (2012).
    CAS  Article  Google Scholar 

    22.
    Mercurio, P., Mueller, J. F., Eaglesham, G., Flores, F. & Negri, A. P. Herbicide persistence in seawater simulation experiments. PLoS ONE 10, e0136391. https://doi.org/10.1371/journal.pone.0136391 (2015).
    CAS  Article  Google Scholar 

    23.
    Mercurio, P. et al. Degradation of herbicides in the tropical marine environment: influence of light and sediment. PLoS ONE 11, e0165890. https://doi.org/10.1371/journal.pone.0165890 (2016).
    CAS  Article  Google Scholar 

    24.
    Gallen, C. et al. Marine Monitoring Program: Annual report for inshore pesticide monitoring 2017–18. Report for the Great Barrier Reef Marine Park Authority, Great Barrier Reef Marine Park Authority, Townsville, Australia. https://elibrary.gbrmpa.gov.au/jspui/handle/11017/3489 (2019).

    25.
    Davis, A., Lewis, S., Brodie, J. & Benson, A. The potential benefits of herbicide regulation: a cautionary note for the Great Barrier Reef catchment area. Sci. Total Environ. 490, 81–92. https://doi.org/10.1016/j.scitotenv.2014.04.005 (2014).
    ADS  CAS  Article  Google Scholar 

    26.
    Thomas, M. C., Flores, F., Kaserzon, S., Fisher, R. & Negri, A. P. Toxicity of ten herbicides to the tropical marine microalgae Rhodomonas salina. Sci. Rep. 10, 7612. https://doi.org/10.1038/s41598-020-64116-y (2020).
    ADS  CAS  Article  Google Scholar 

    27.
    ANZG. Revised Australian and New Zealand guidelines for fresh and marine water quality. Australian and New Zealand Environment and Conservation Council and Agriculture and Resource Management Council of Australia and New Zealand, Canberra, Australia. https://www.waterquality.gov.au/anz-guidelines/guideline-values/default/water-quality-toxicants/toxicants (2018).

    28.
    Warne, M. St. J. et al. Revised method for deriving Australian and New Zealand water quality guideline values for toxicants: update of 2015 version. Prepared for the revision of the Australian and New Zealand guidelines for fresh and marine water quality. Australian and New Zealand Governments and Australian state and territory governments, Canberra, Australia. 48 pp, https://www.waterquality.gov.au/anz-guidelines/guideline-values/derive/warne-method-derive. https://doi.org/10.13140/RG.2.2.36577.35686 (2018).

    29.
    Warne, M. St. J., Smith, R. & Turner, R. Analysis of pesticide mixtures discharged to the lagoon of the Great Barrier Reef, Australia. Environ. Pollut. 265, 114088. https://doi.org/10.1016/j.envpol.2020.114088 (2020).
    CAS  Article  Google Scholar 

    30.
    Magnusson, M., Heimann, K., Quayle, P. & Negri, A. P. Additive toxicity of herbicide mixtures and comparative sensitivity of tropical benthic microalgae. Mar. Pollut. Bull. 60, 1978–1987. https://doi.org/10.1016/j.marpolbul.2010.07.031 (2010).
    CAS  Article  Google Scholar 

    31.
    Faust, M. et al. Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants. Aquat. Toxicol. 56, 13–32. https://doi.org/10.1016/S0166-445X(01)00187-4 (2001).
    CAS  Article  Google Scholar 

    32.
    Wilkinson, A. D., Collier, C. J., Flores, F. & Negri, A. P. Acute and additive toxicity of ten photosystem-II herbicides to seagrass. Sci. Rep. 5, 17443. https://doi.org/10.1038/srep17443 (2015).
    ADS  CAS  Article  Google Scholar 

    33.
    Traas, T. P. et al. The potentially affected fraction as a measure of ecological risk. in Species sensitivity distributions in ecotoxicology (L. Posthuma, & G. W. Suter, Eds.) (pp. 315–344). https://doi.org/10.1201/9781420032314-20 (2002).

    34.
    Negri, A. P. et al. Adjusting tropical marine water quality guideline values for elevated ocean temperatures. Environ. Sci. Technol. 54, 1102–1110. https://doi.org/10.1021/acs.est.9b05961 (2019).
    ADS  CAS  Article  Google Scholar 

    35.
    King, O., Smith, R., Mann, R. & Warne, M. St. J. Proposed aquatic ecosystem protection guideline values for pesticides commonly used in the Great Barrier Reef catchment area: Part 1 (amended): 2,4-D, Ametryn, Diuron, Glyphosate, Hexazinone, Imazapic, Imidacloprid, Isoxaflutole, Metolachlor, Metribuzin, Metsulfuron-methyl, Simazine, Tebuthiuron. Department of Environment and Science, Brisbane, Australia. https://www.publications.qld.gov.au/dataset/proposed-guideline-values-27-pesticides-used-in-the-gbr-catchment (2017).

    36.
    King, O., Smith, R., Warne, M. St. J. & Mann, R. Proposed aquatic ecosystem protection guideline values for pesticides commonly used in the Great Barrier Reef catchment area: Part 2: Bromacil, Chlorothalonil, Fipronil, Fluometuron, Fluroxypyr, Haloxyfop, MCPA, Pendimethalin, Prometryn, Propazine, Propiconazole, Terbutryn, Triclopyr and Terbuthylazine. Department of Science, Information Technology and Innovation, Brisbane, Australia. https://www.publications.qld.gov.au/dataset/proposed-guideline-values-27-pesticides-used-in-the-gbr-catchment (2017).

    37.
    Fleeger, J. W., Carman, K. R. & Nisbet, R. M. Indirect effects of contaminants in aquatic ecosystems. Sci. Total Environ. 317, 207–233. https://doi.org/10.1016/S0048-9697(03)00141-4 (2003).
    ADS  CAS  Article  Google Scholar 

    38.
    Ralph, P. J. & Gademann, R. Rapid light curves: a powerful tool to assess photosynthetic activity. Aquat. Bot. 82, 222–237. https://doi.org/10.1016/j.aquabot.2005.02.006 (2005).
    CAS  Article  Google Scholar 

    39.
    Schreiber, U. Pulse-amplitude-modulation (PAM) fluorometry and saturation pulse method: an overview. in Chlorophyll a fluorescence (Springer, Dordrecht, 2004) 279–319.

    40.
    Magnusson, M., Heimann, K. & Negri, A. P. Comparative effects of herbicides on photosynthesis and growth of tropical estuarine microalgae. Mar. Pollut. Bull. 56, 1545–1552. https://doi.org/10.1016/j.marpolbul.2008.05.023 (2008).
    CAS  Article  Google Scholar 

    41.
    Sjollema, S. B. et al. Hazard and risk of herbicides for marine microalgae. Environ. Pollut. 187, 106–111. https://doi.org/10.1016/j.envpol.2013.12.019 (2014).
    CAS  Article  Google Scholar 

    42.
    Muller, R. et al. Rapid exposure assessment of PSII herbicides in surface water using a novel chlorophyll a fluorescence imaging assay. Sci. Total Environ. 401, 51–59. https://doi.org/10.1016/j.scitotenv.2008.02.062 (2008).
    ADS  CAS  Article  Google Scholar 

    43.
    Bengston-Nash, S. M., Quayle, P. A., Schreiber, U. & Muller, J. F. The selection of a model microalgal species as biomaterial for a novel aquatic phytotoxicity assay. Aquat. Toxicol. 72, 315–326. https://doi.org/10.1016/j.aquatox.2005.02.004 (2005).
    CAS  Article  Google Scholar 

    44.
    Duggleby, R. G., McCourt, J. A. & Guddat, L. W. Structure and mechanism of inhibition of plant acetohydroxyacid synthase. Plant Physiol. Biochem. 46, 309–324. https://doi.org/10.1016/j.plaphy.2007.12.004 (2008).
    CAS  Article  Google Scholar 

    45.
    Grossmann, K. Auxin herbicides: current status of mechanism and mode of action. Pest Manage. Sci. 66, 113–120. https://doi.org/10.1002/ps.1860 (2010).
    CAS  Article  Google Scholar 

    46.
    OECD. Organisation for Economic Cooperation and Development (OECD) guidelines for the testing of chemicals: freshwater alga and cyanobacteria, growth inhibition test. Test No. 201, https://search.oecd.org/env/test-no-201-alga-growth-inhibition-test-9789264069923-en.htm (2011).

    47.
    Lewis, K. A., Tzilivakis, J., Warner, D. J. & Green, A. An international database for pesticide risk assessments and management. Hum. Ecol. Risk Assess. Int. J. 22, 1050–1064. https://doi.org/10.1080/10807039.2015.1133242 (2016).
    CAS  Article  Google Scholar 

    48.
    Rutherford, A. W. & Krieger-Liszkay, A. Herbicide-induced oxidative stress in photosystem II. Trends Biochem. Sci. 26, 648–653. https://doi.org/10.1016/S0968-0004(01)01953-3 (2001).
    CAS  Article  Google Scholar 

    49.
    Chen, S., Yin, C., Strasser, R. J., Yang, C. & Qiang, S. Reactive oxygen species from chloroplasts contribute to 3-acetyl-5-isopropyltetramic acid-induced leaf necrosis of Arabidopsis thaliana. Plant Physiol. Biochem. 52, 38–51. https://doi.org/10.1016/j.plaphy.2011.11.004 (2012).
    CAS  Article  Google Scholar 

    50.
    Chesworth, J., Donkin, M. & Brown, M. The interactive effects of the antifouling herbicides Irgarol 1051 and Diuron on the seagrass Zostera marina (L.). Aquat. Toxicol. 66, 293–305. https://doi.org/10.1016/j.aquatox.2003.10.002 (2004).
    CAS  Article  Google Scholar 

    51.
    Jones, R. J. & Kerswell, A. P. Phytotoxicity of photosystem II (PSII) herbicides to coral. Mar. Ecol. Prog. Ser. 261, 149–159. https://doi.org/10.3354/meps261149 (2003).
    ADS  CAS  Article  Google Scholar 

    52.
    van Dam, J. W., Negri, A. P., Mueller, J. F. & Uthicke, S. Symbiont-specific responses in foraminifera to the herbicide diuron. Mar. Pollut. Bull. 65, 373–383. https://doi.org/10.1016/j.marpolbul.2011.08.008 (2012).
    CAS  Article  Google Scholar 

    53.
    Negri, A. P., Flores, F., Röthig, T. & Uthicke, S. Herbicides increase the vulnerability of corals to rising sea surface temperature. Limnol. Oceanogr. 56, 471–485. https://doi.org/10.4319/lo.2011.56.2.0471 (2011).
    ADS  CAS  Article  Google Scholar 

    54.
    USEPA. ECOTOX User Guide: ECOTOXicology Database System. Version 5.0. United States Environmental Protection Agency. https://cfpub.epa.gov/ecotox/ (2019).

    55.
    Bao, V. W., Leung, K. M., Qiu, J.-W. & Lam, M. H. Acute toxicities of five commonly used antifouling booster biocides to selected subtropical and cosmopolitan marine species. Mar. Pollut. Bull. 62, 1147–1151. https://doi.org/10.1016/j.marpolbul.2011.02.041 (2011).
    CAS  Article  Google Scholar 

    56.
    Gatidou, G., Thomaidis, N. S. & Zhou, J. L. Fate of Irgarol 1051, diuron and their main metabolites in two UK marine systems after restrictions in antifouling paints. Environ. Int. 33, 70–77. https://doi.org/10.1016/j.envint.2006.07.002 (2007).
    CAS  Article  Google Scholar 

    57.
    Jung, S. et al. Acute toxicity of organic antifouling biocides to phytoplankton Nitzschia pungens and zooplankton Artemia larvae. Mar. Pollut. Bull. 124, 811–818. https://doi.org/10.1016/j.marpolbul.2016.11.047 (2017).
    CAS  Article  Google Scholar 

    58.
    Koutsaftis, A. & Aoyama, I. The interactive effects of binary mixtures of three antifouling biocides and three heavy metals against the marine algae Chaetoceros gracilis. Environ. Toxicol. Int. J. 21, 432–439. https://doi.org/10.1002/tox.20202 (2006).
    ADS  CAS  Article  Google Scholar 

    59.
    Booij, P. et al. Identification of photosynthesis inhibitors of pelagic marine algae using 96-well plate microfractionation for enhanced throughput in effect-directed analysis. Environ. Sci. Technol. 48, 8003–8011. https://doi.org/10.1021/es405428t (2014).
    ADS  CAS  Article  Google Scholar 

    60.
    DeLorenzo, M. E., Danese, L. E. & Baird, T. D. Influence of increasing temperature and salinity on herbicide toxicity in estuarine phytoplankton. Environ. Toxicol. 28, 359–371. https://doi.org/10.1002/tox.20726 (2013).
    ADS  CAS  Article  Google Scholar 

    61.
    Devilla, R. A. et al. Impact of antifouling booster biocides on single microalgal species and on a natural marine phytoplankton community. Mar. Ecol. Prog. Ser. 286, 1–12. https://doi.org/10.3354/MEPS286001 (2005).
    ADS  CAS  Article  Google Scholar 

    62.
    Mercurio, P. et al. Contribution of transformation products towards the total herbicide toxicity to tropical marine organisms. Sci. Rep. 8, 4808. https://doi.org/10.1038/s41598-018-23153-4 (2018).
    ADS  CAS  Article  Google Scholar 

    63.
    Jones, R. The ecotoxicological effects of Photosystem II herbicides on corals. Mar. Pollut. Bull. 51, 495–506. https://doi.org/10.1016/j.marpolbul.2005.06.027 (2005).
    CAS  Article  Google Scholar 

    64.
    Guasch, H. & Sabater, S. Light history influences the sensitivity to atrazine in periphytic algae. J. Phycol. 34, 233–241. https://doi.org/10.1046/j.1529-8817.1998.340233.x (1998).
    CAS  Article  Google Scholar 

    65.
    Millie, D. F., Hersh, C. M. & Dionigi, C. P. Simazine-induced inhibition in photoacclimated populations of Anabaena circinalis (Cyanophyta). J. Phycol. 28, 19–26. https://doi.org/10.1111/j.0022-3646.1992.00019.x (1992).
    CAS  Article  Google Scholar 

    66.
    Bérard, A. et al. Comparison of the ecotoxicological impact of the triazines Irgarol 1051 and atrazine on microalgal cultures and natural microalgal communities in Lake Geneva. Chemosphere 53, 935–944. https://doi.org/10.1016/S0045-6535(03)00674-X (2003).
    ADS  CAS  Article  Google Scholar 

    67.
    Descolas-Gros, C. & Oriol, L. Variations in carboxylase activity in marine phytoplankton cultures. ß-carboxylation in carbon flux studies. Mar. Ecol. Prog. Ser. 85, 163–169 (1992).
    ADS  CAS  Article  Google Scholar 

    68.
    Tang, J., Hoagland, K. D. & Siegfried, B. D. Uptake and bioconcentration of atrazine by selected freshwater algae. Environ. Toxicol. Chem. 17, 1085–1090. https://doi.org/10.1002/etc.5620170614 (1998).
    CAS  Article  Google Scholar 

    69.
    Magnusson, M., Heimann, K., Ridd, M. & Negri, A. P. Chronic herbicide exposures affect the sensitivity and community structure of tropical benthic microalgae. Mar. Pollut. Bull. 65, 363–372. https://doi.org/10.1016/j.marpolbul.2011.09.029 (2012).
    CAS  Article  Google Scholar 

    70.
    Tuchman, N. C., Schollett, M. A., Rier, S. T. & Geddes, P. Differential heterotrophic utilization of organic compounds by diatoms and bacteria under light and dark conditions. Hydrobiologia 561, 167–177. https://doi.org/10.1007/s10750-005-1612-4 (2006).
    CAS  Article  Google Scholar 

    71.
    APVMA. Australian Pesticides and Veterinary Medicines Authority. https://apvma.gov.au/ (2019).

    72.
    EPA. U.S. Environmental Protection Agency. https://www.epa.gov/pesticides (2020).

    73.
    EC. European Commission. EU Pesticides database. https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/ (2020).

    74.
    Novic, A. J. et al. Monitoring herbicide concentrations and loads during a flood event: a comparison of grab sampling with passive sampling. Environ. Sci. Technol. 51, 3880–3891. https://doi.org/10.1021/acs.est.6b02858 (2017).
    ADS  CAS  Article  Google Scholar 

    75.
    Mercurio, P. Herbicide persistence and toxicity in the tropical marine environment. PhD University of Queensland. 148 p. https://doi.org/10.14264/uql.2016.722 (2016).

    76.
    MacBean, C. The pesticide manual: a world compendium, 6th Edition 598–601 (British Crop Production Council (BCPC), Alton, 2012).

    77.
    Huerlimann, R. & Heimann, K. Comprehensive guide to acetyl-carboxylases in algae. Crit. Rev. Biotechnol. 33, 49–65. https://doi.org/10.3109/07388551.2012.668671 (2013).
    CAS  Article  Google Scholar 

    78.
    Kukorelli, G., Reisinger, P. & Pinke, G. ACCase inhibitor herbicides – selectivity, weed resistance and fitness cost: a review. Int. J. Pest Manage. 59, 165–173. https://doi.org/10.1080/09670874.2013.821212 (2013).
    CAS  Article  Google Scholar 

    79.
    Huerlimann, R., Zenger, K. R., Jerry, D. R. & Heimann, K. Phylogenetic analysis of nucleus-encoded acetyl-CoA carboxylases targeted at the cytosol and plastid of algae. PLoS ONE https://doi.org/10.1371/journal.pone.0131099 (2015).
    Article  Google Scholar 

    80.
    Tang, C. Y., Huang, Z. & Allen, H. C. Interfacial water structure and effects of Mg2+ and Ca2+ binding to the COOH headgroup of a palmitic acid monolayer studied by sum frequency spectroscopy. J. Phys. Chem. B 115, 34–40. https://doi.org/10.1021/jp1062447 (2010).
    CAS  Article  Google Scholar 

    81.
    Brzozowska, A., Duits, M. H. & Mugele, F. Stability of stearic acid monolayers on Artificial Sea Water. Colloids Surf. Physicochem. Eng. Aspects 407, 38–48. https://doi.org/10.1016/j.colsurfa.2012.04.055 (2012).
    CAS  Article  Google Scholar 

    82.
    Bengston-Nash, S. M., Schreiber, U., Ralph, P. J. & Muller, J. F. The combined SPE : ToxY-PAM phytotoxicity assay; application and appraisal of a novel biomonitoring tool for the aquatic environment. Biosens. Bioelectron. 20, 1443–1451. https://doi.org/10.1016/j.bios.2004.09.019 (2005).
    CAS  Article  Google Scholar 

    83.
    Schreiber, U., Quayle, P., Schmidt, S., Escher, B. I. & Mueller, J. F. Methodology and evaluation of a highly sensitive algae toxicity test based on multiwell chlorophyll fluorescence imaging. Biosens. Bioelectron. 22, 2554–2563. https://doi.org/10.1016/j.bios.2006.10.018 (2007).
    CAS  Article  Google Scholar 

    84.
    Haynes, D., Muller, J. & Carter, S. Pesticide and herbicide residues in sediments and seagrasses from the Great Barrier Reef World Heritage Area and Queensland coast. Mar. Pollut. Bull. 41, 279–287. https://doi.org/10.1016/s0025-326x(00)00097-7 (2000).
    CAS  Article  Google Scholar 

    85.
    Ralph, P., Smith, R., Macinnis-Ng, C. & Seery, C. Use of fluorescence-based ecotoxicological bioassays in monitoring toxicants and pollution in aquatic systems. Toxicol. Environ. Chem. 89, 589–607. https://doi.org/10.1080/02772240701561593 (2007).
    CAS  Article  Google Scholar 

    86.
    Lemmermann, E. D. grosse Waterneverstorfer Binnensee: Eine biologische Studie. Forsch. Biol. Station Plön 6, 166–205 (1896).
    Google Scholar 

    87.
    Li, Y. et al. Diversity in the globally distributed diatom genus Chaetoceros (Bacillariophyceae): three new species from warm-temperate waters. PLoS ONE https://doi.org/10.1371/journal.pone.0168887 (2017).
    Article  Google Scholar 

    88.
    Helm, M. M., and Neil Bourne. Hatchery culture of bivalves: a practical manual. Ed. Alessandro Lovatelli. Fisheries Technical Paper 471. Food and Agriculture Organization of the United (FAO), 177 pp (2004).

    89.
    Guillard, R. R. & Ryther, J. H. Studies of marine planktonic diatoms: I Cyclotellanana Hustedt, and Detonulaconfervacea (Cleve) Gran. Can. J. Microbiol. 8, 229–239. https://doi.org/10.1139/m62-029 (1962).
    CAS  Article  Google Scholar 

    90.
    Schreiber, U., Müller, J. F., Haugg, A. & Gademann, R. New type of dual-channel PAM chlorophyll fluorometer for highly sensitive water toxicity biotests. Photosynth. Res. 74, 317–330. https://doi.org/10.1023/A:1021276003145 (2002).
    CAS  Article  Google Scholar 

    91.
    Fisher, R., Ricardo, G., and Fox, D. jags NEC: A Bayesian No Effect Concentration (NEC) package. https://github.com/AIMS/NEC-estimation (2019).

    92.
    Fox, D. R. A Bayesian approach for determining the no effect concentration and hazardous concentration in ecotoxicology. Ecotoxicol. Environ. Saf. 73, 123–131. https://doi.org/10.1016/j.ecoenv.2009.09.012 (2010).
    CAS  Article  Google Scholar  More

  • in

    Plant resistome profiling in evolutionary old bog vegetation provides new clues to understand emergence of multi-resistance

    1.
    World Health Organization. Antimicrobial resistance: global report on surveillance. 2014.
    2.
    Kåhrström CT. Entering a post-antibiotic era? Nat Rev Microbiol. 2013;11:146.
    PubMed  PubMed Central  Google Scholar 

    3.
    Martínez JL. Antibiotics and antibiotic resistance genes in natural environments. Science. 2008;321:365–7.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    4.
    Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ. The structure and diversity of human, animal and environmental resistomes. Microbiome. 2016;4:1–15.
    CAS  Article  Google Scholar 

    5.
    Chen Q-L, Cui H-L, Su J-Q, Penuelas J, Zhu Y-G. Antibiotic resistomes in plant microbiomes. Trends Plant Sci. 2019;24:530–41.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    6.
    Cernava T, Erlacher A, Soh J, Sensen CW, Grube M, Berg G. Enterobacteriaceae dominate the core microbiome and contribute to the resistome of arugula (Eruca sativa Mill.). Microbiome. 2019;7:13.
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Blau K, Jacquiod S, Sørensen SJ, Su J-Q, Zhu Y-G, Smalla K, et al. Manure and doxycycline affect the bacterial community and its resistome in lettuce rhizosphere and bulk soil. Front Microbiol. 2019;10:725.
    PubMed  PubMed Central  Article  Google Scholar 

    8.
    Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, et al. Tackling antibiotic resistance: the environmental framework. Nat Rev Microbiol. 2015;13:310–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Wright GD. The antibiotic resistome: the nexus of chemical and genetic diversity. Nat Rev Microbiol. 2007;5:175–86.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Page SE, Baird AJ. Peatlands and global change: response and resilience. Annu Rev Environ Resour. 2016;41:35–57.
    Article  Google Scholar 

    11.
    Kostka JE, Weston DJ, Glass JB, Lilleskov EA, Shaw AJ, Turetsky MR. The Sphagnum microbiome: new insights from an ancient plant lineage. N Phytologist. 2016;211:57–64.
    CAS  Article  Google Scholar 

    12.
    Bragina A, Oberauner-Wappis L, Zachow C, Halwachs B, Thallinger GG, Müller H, et al. The Sphagnum microbiome supports bog ecosystem functioning under extreme conditions. Mol Ecol. 2014;23:4498–510.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Opelt K, Chobot V, Hadacek F, Schönmann S, Eberl L, Berg G. Investigations of the structure and function of bacterial communities associated with Sphagnum mosses. Environ Microbiol. 2007;9:2795–809.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Bragina A, Berg C, Cardinale M, Shcherbakov A, Chebotar V, Berg G. Sphagnum mosses harbour highly specific bacterial diversity during their whole lifecycle. ISME J. 2012;6:802–13.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Opelt K, Berg C, Schönmann S, Eberl L, Berg G. High specificity but contrasting biodiversity of Sphagnum-associated bacterial and plant communities in bog ecosystems independent of the geographical region. ISME J. 2007;1:502–16.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Bragina A, Berg C, Berg G. The core microbiome bonds the Alpine bog vegetation to a transkingdom metacommunity. Mol Ecol. 2015;24:4795–807.
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Opelt K, Berg C, Berg G. The bryophyte genus Sphagnum is a reservoir for powerful and extraordinary antagonists and potentially facultative human pathogens. FEMS Microbiol Ecol. 2007;61:38–53.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Allen HK, Donato J, Wang HH, Cloud-Hansen KA, Davies J, Handelsman J. Call of the wild: antibiotic resistance genes in natural environments. Nat Rev Microbiol. 2010;8:251–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Mondav R, Woodcroft BJ, Kim EH, Mccalley CK, Hodgkins SB, Crill PM, et al. Discovery of a novel methanogen prevalent in thawing permafrost. Nat Commun. 2014;5:1–7.
    Article  CAS  Google Scholar 

    21.
    Müller CA, Oberauner-Wappis L, Peyman A, Amos GCA, EMH Wellington, Berg G. Mining for NRPS and PKS genes revealed a high diversity in the Sphagnum bog metagenome. Appl Environ Microbiol. 2015;81:5064–72.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    22.
    Bragina A, Maier S, Berg C, Müller H, Chobot V, Hadacek F, et al. Similar diversity of Alphaproteobacteria and nitrogenase gene amplicons on two related Sphagnum mosses. Front Microbiol. 2011;2:275.
    PubMed  PubMed Central  Google Scholar 

    23.
    Wardwell LH, Jude BA, Moody JP, Olcerst AI, Gyure RA, Nelson RE, et al. Co-selection of mercury and antibiotic resistance in sphagnum core samples dating back 2000 years. Geomicrobiol J. 2009;26:351–60.
    CAS  Article  Google Scholar 

    24.
    McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426. 2020.

    25.
    Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37:38–47.
    CAS  Article  Google Scholar 

    26.
    Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    27.
    Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26:1340–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Streit M, Lex A, Gratzl S, Partl C, Schmalstieg D, Pfister H, et al. Guided visual exploration of genomic stratifications in cancer. Nat Methods. 2014;11:884–5.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, Baylay AJ, et al. The comprehensive antibiotic resistance database. Antimicrobial agents Chemother. 2013;57:3348–57.
    CAS  Article  Google Scholar 

    30.
    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Elbehery AHA, Aziz RK, Siam R. Antibiotic resistome: improving detection and quantification accuracy for comparative metagenomics. OMICS: A J Integr Biol. 2016;20:229–38.
    CAS  Article  Google Scholar 

    32.
    Boyd JA, Woodcroft BJ, Tyson GW. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 2018;46:e59.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    33.
    Allaire J. RStudio: Integrated development environment for R. Boston, MA: RStudio Inc.; 2012. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6194118/#!po=0.819672.

    34.
    Core Team R. R: a language and environment for statistical computing. R Foundation for statistical computing, Vienna. 2013.

    35.
    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS ONE. 2013;8:e61217.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Commun Ecol Package. 2007;10:631–7.
    Google Scholar 

    37.
    Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27:209–20.
    Google Scholar 

    38.
    Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.
    PubMed  PubMed Central  Article  Google Scholar 

    41.
    Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Sieber CM, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy T, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018;6:1–15.
    Article  Google Scholar 

    47.
    Huerta-Cepas J, Serra F, Bork P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol Biol Evol. 2016;33:1635–8.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.

    49.
    Mauri M, Elli T, Caviglia G, Uboldi G, Azzi M. RAWGraphs: a visualisation platform to create open outputs. In: Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter. New York, New York, USA: ACM Press; 2017. p. 28:1–28:5.

    50.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Vercammen K, Garcia-Armisen T, Goeders N, Van Melderen L, Bodilis J, Cornelis P. Identification of a metagenomic gene cluster containing a new class A beta-lactamase and toxin-antitoxin systems. MicrobiologyOpen. 2013;2:674–83.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Allen HK, Moe LA, Rodbumrer J, Gaarder A, Handelsman J.Functional metagenomics reveals diverse β-lactamases in a remote Alaskan soil. ISME J. 2009;3:243–51.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID). Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin Microbiol Infect. 2003;9:1–7.
    Google Scholar 

    54.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    55.
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678–9.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    57.
    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids. Res 2004;32:1792–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–25.
    CAS  PubMed  PubMed Central  Google Scholar 

    61.
    Nei M, Kumar S. Molecular Evolution and Phylogenetics. New York: Oxford University Press; 2000.

    62.
    Collignon PC, Conly JM, Andremont A, McEwen SA, Aidara-Kane A, Agerso Y, et al. World Health Organization ranking of antimicrobials according to their importance in human medicine: a critical step for developing risk management strategies to control antimicrobial resistance from food animal production. Clin Infect Dis. 2016;63:1087–93.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Philippon A, Slama P, Dény P, Labia R. A structure-based classification of class A β-lactamases, a broadly diverse family of enzymes. Clin Microbiol Rev. 2016;29:29–57.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Ambler RP. The structure of β-lactamases. Philos Trans R Soc B: Biol Sci. 1980;289:321–31.
    CAS  Google Scholar 

    65.
    D’Costa VM, McGrann KM, Hughes DW, Wright GD. Sampling the antibiotic resistome. Science. 2006;311:374–7.
    PubMed  Article  PubMed Central  Google Scholar 

    66.
    Forsberg KJ, Patel S, Wencewicz TA, Dantas G. Bacterial phylogeny structures soil resistome across habitats. Nature. 2014;509:612–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Van Goethem MW, Pierneef R, Bezuidt OKI, Van De Peer Y, Cowan DA, Makhalanyane TP. A reservoir of ´historical´antibiotic resistance genes in remote pristine Antarctic soils. Microbiome. 2018;6:40.
    PubMed  PubMed Central  Article  Google Scholar 

    68.
    Mahnert A, Moissl-Eichinger C, Zojer M, Bogumil D, Mizrahi I, Rattei T, et al. Man-made microbial resistances in built environments. Nat Commun. 2019;10:968.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    69.
    Carr VR, Witherden EA, Lee S, Shoaie S, Mullany P, Proctor GB, et al. Abundance and diversity of resistomes differ between healthy human oral cavities and gut. Nat Commun. 2020;11:693.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Bragina A, Cardinale M, Berg C, Berg G, Schmid M, Zentrum H. Vertical transmission explains the specific Burkholderia pattern in Sphagnum mosses at multi-geographic scale. Front Microbiol. 2013;4:394.
    PubMed  PubMed Central  Article  Google Scholar 

    71.
    Crofts TS, Gasparrini AJ, Dantas G. Next-generation approaches to understand and combat the antibiotic resistome. Nat Rev Microbiol. 2017;15:422.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    72.
    Tyson GH, McDermott PF, Li C, Chen Y, Tadesse DA, Mukherjee S, et al. WGS accurately predicts antimicrobial resistance in Escherichia coli. J Antimicrobial Chemother. 2015;70:2763–9.
    CAS  Article  Google Scholar 

    73.
    Schmidt K, Mwaigwisya S, Crossman L, Doumith M, Munroe D, Pires C, et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicrobial Chemother. 2016;72:104–14.
    Article  CAS  Google Scholar 

    74.
    Andersen H, Connolly N, Bangar H, Staat M, Mortensen J, Deburger B, et al. Use of shotgun metagenome sequencing to detect fecal colonization with multidrug-resistant bacteria in children. J Clin Microbiol. 2016;54:1804–13.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    75.
    Martinez JL, Sánchez MB, Martínez-Solano L, Hernandez A, Garmendia L, Fajardo A, et al. Functional role of bacterial multidrug efflux pumps in microbial natural ecosystems. FEMS Microbiol Rev. 2009;33:430–49.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    76.
    Pehrsson EC, Forsberg KJ, Gibson MK, Ahmadi S, Dantas G. Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs. Front Microbiol. 2013;4:1–11.
    Article  Google Scholar 

    77.
    Leclercq R. Mechanisms of resistance to macrolides and lincosamides: nature of the resistance elements and their clinical implications. Clin Infect Dis. 2002;34:482–92.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    78.
    Belova SE, Pankratov TA, Dedysh SN. Bacteria of the genus Burkholderia as a typical component of the microbial community of Sphagnum peat bogs. Microbiology. 2006;75:90–96.
    CAS  Article  Google Scholar 

    79.
    Le Fléche-MatéosA, Kügler JH, Hansen SH, Syldatk C, Hausmann R, Lomprez F, et al. Rouxiella badensis sp. nov. and Rouxiella silvae sp. nov. isolated from peat bog soil and emendation description of the genus Rouxiella. Int J Syst Evolut Microbiol. 2017;67:1255–9.
    Article  CAS  Google Scholar 

    80.
    Coenye T, Falsen E, Hoste B, Ohle M, Goris J, Govan JRW, et al. Description of Pandoraea gen. nov. with Pandoraea apista sp. nov., Pandoraea pulmonicola sp. nov., Pandoraea pnomenusa sp. nov., Pandoraea sputorum sp. nov. and Pandoraea norimbergensis comb. nov 2000;50:887–99.
    CAS  Google Scholar 

    81.
    Green H, Jones AM. Emerging Gram-negative bacteria: pathogenic or innocent bystanders. Curr Opin Pulm Med. 2018;24:592–8.
    PubMed  Article  PubMed Central  Google Scholar 

    82.
    Schneider I, Queenan AM, Bauernfeind A. Novel carbapenem-hydrolyzing oxacillinase OXA-62 from Pandoraea pnomenusa. Antimicrobial Agents Chemother. 2006;50:1330–5.
    CAS  Article  Google Scholar 

    83.
    Mahlen SD. Serratia infections: from military experiments to current practice. Clin Microbiol Rev. 2011;24:755–91.
    PubMed  PubMed Central  Article  Google Scholar 

    84.
    Silby MW, Winstanley C, Godfrey SAC, Levy SB, Jackson RW. Pseudomonas genomes: Diverse and adaptable. FEMS Microbiol Rev. 2011;35:652–80.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    85.
    Mitter B, Pfaffenbichler N, Flavell R, Compant S, Antonielli L, Petric A, et al. A new approach to modify plant microbiomes and traits by introducing beneficial bacteria at flowering into progeny seeds. Front Microbiol. 2017;8:11.
    PubMed  PubMed Central  Google Scholar 

    86.
    Vandamme P, Opelt K, Knochel N, Berg C, Schonmann S, De Brandt E, et al. Burkholderia bryophila sp. nov. and Burkholderia megapolitana sp. nov., moss-associated species with antifungal and plant-growth-promoting properties. Int J Syst Evolut Microbiol. 2007;57:2228–35.
    CAS  Article  Google Scholar 

    87.
    Berg G, Eberl L, Hartmann A. The rhizosphere as a reservoir for opportunistic human pathogenic bacteria. Environ Microbiol. 2005;7:1673–85.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    88.
    Lycus P, Lovise Bøthun K, Bergaust L, Peele Shapleigh J, Reier Bakken L, Frostegård Å. Phenotypic and genotypic richness of denitrifiers revealed by a novel isolation strategy. ISME J. 2017;11:2219–32.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    89.
    Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E, Rott M, et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature. 2015;528:364–9.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    90.
    Papanicolaou GA, Medeiros AA, Jacoby GA. Novel plasmid-mediated β-lactamase (MIR-1) conferring resistance to oxyimino- and α-methoxy β-lactams in clinical isolates of Klebsiella pneumoniae. Antimicrobial Agents Chemother. 1990;34:2200–9.
    CAS  Article  Google Scholar 

    91.
    Bauernfeind A, Stemplinger I, Jungwirth R, Ernst S, Casellas JM. Sequences of β-lactamase genes encoding CTX-M-1 (MEN-1) and CTX-M-2 and relationship of their amino acid sequences with those of other β-lactamases. Antimicrobial Agents Chemother. 1996;40:509–13.
    CAS  Article  Google Scholar 

    92.
    Dahmen S, Mansour W, Charfi K, Boujaafar N, Arlet G, Bouallègue O. Imipenem resistance in Klebsiella pneumoniae is associated to the combination of plasmid-mediated CMY-4 AmpC β-lactamase and loss of an outer membrane protein. Microb Drug Resistance. 2012;18:479–83.
    CAS  Article  Google Scholar 

    93.
    Forsberg KJ, Patel S, Gibson MK, Lauber CL, Knight R, Fierer N, et al. Bacterial phylogeny structures soil resistomes across habitats. Nature. 2014;509:612–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    94.
    Kurm V, van der Putten WH, de Boer W, Naus-Wiezer S, Hol WHG. Low abundant soil bacteria can be metabolically versatile and fast growing. Ecology. 2017;98:555–64.
    PubMed  Article  PubMed Central  Google Scholar 

    95.
    Vadstein O, Attramadal KJK, Bakke I, Olsen Y. K-Selection as microbial community management strategy: a method for improved viability of larvae in aquaculture. Front Microbiol. 2018;9:2730.
    PubMed  PubMed Central  Article  Google Scholar  More