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edi56/100

Impacts of microplastics on wetland ecosystem dynamics: a mesocosm study of trophic interactions and community responses, 2021-2025

This dataset documents a mesocosm experiment conducted to evaluate the ecological impacts of microplastics (MP) on wetland communities representative of eastern USA wetlands. The study focused on key organisms across trophic levels, including tadpoles (Lithobates pipiens), snails (Helisoma trivolvis), zooplankton (Daphnia pulex), phytoplankton, and periphyton communities, to assess the effects of three microplastic types (low-density polyethylene–LDPE, medium-density polystyrene–PS, and high-density polyester–PES) at two concentrations (1 mg/L and 5 mg/L), alongside a no-microplastic control. Experimental units consisted of 70 mesocosms (19-L buckets) with 10 replicates per treatment, established between July 1–15, 2021, at Binghamton University’s Ecological Research Facility. Response variables included survival and developmental traits (mass, snout-vent length, shell width) of tadpoles and snails, microplastic ingestion, zooplankton abundance, phytoplankton biomass (chlorophyll and phycocyanin concentrations), and periphyton mass. The dataset provides comprehensive measurements of community responses and water quality parameters, offering insights into the ecological consequences of microplastic pollution in wetland ecosystems. This dataset is suitable for researchers studying ecotoxicology, wetland ecology, and the impacts of anthropogenic pollutants on aquatic food webs.

openCC (other)Jul 2025View details →

edi56/100

Rhizaria abundance, biomass, and trophic interactions at the Northern Gulf of Alaska LTER site during summer 2023 cruise KM2308

This dataset consists of Rhizaria abundance, biomass, number of individuals with captured prey, and number of individuals that interacted with algal cells, by taxonomic group. Taxonomic group ID characteristics and biomass calculation details are also presented. Sampling occurred on NGA-LTER research cruise KM2308 during summer 2023 at four stations and four different depth-intervals. Seawater samples were collected from CTD-secured Niskin bottles, concentrated by reverse filtration with 50 μm mesh, and analyzed back in the lab with inverted epi-fluorescence microscopy. Excel spreadsheets were converted to CSV files for preservation. These data are part of the Northern Gulf of Alaska Long Term Ecological Research (NGA LTER) program. The LTER program is a National Science Foundation–funded network of 28 sites nationwide that focus on the influence of long-term and large-scale phenomenon on ecosystems. Additional funding for sampling is provided by the North Pacific Research Board (NPRB), the Alaska Ocean Observing System (AOOS), and the Exxon Valdez Oil Spill Trustee Council (EVOS) via the Gulf Watch Alaska program.

openCC0Apr 2025View details →

zenodo44/100

Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness"

Raw data for the article: Bestion, E, Soriano-Redondo, A,  Cucherousset, J, Jacob, S,  White, J,  Zinger, L,  Fourtune, L,  Di Gesu, L, Teyssier, A, Cote, J. Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness. Proceedings of the Royal Society: B. 2019. 286:20192227. https://doi.org/10.1098/rspb.2019.2227 This data should be cited as: Bestion, E, Soriano-Redondo, A,  Cucherousset, J, Jacob, S,  White, J,  Zinger, L,  Fourtune, L,  Di Gesu, L, Teyssier, A, Cote, J (2019). Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness", Bestion et al 2019 Proceedings B. (Version 1). Zenodo. https://doi.org/10.5281/zenodo.3475402 This data is composed of one dataset with 21 columns and a README file Composition of the Bestion_2019_isotopy_dataset_for_zenodo.csv dataset - Individual: numerical index corresponding to each of the 327 individuals in the dataset - Age: age class, J = juvenile (<1 year old), A = adult (1 and 2+ year old) - Sex: F (female) or M (male) - Climate: Present-day climate or Warm climate - Enclosure: enclosure number (10 enclosures, 5 per climatic treatment) - delta13C_september: stable isotope values for delta13C in september - delta15N_september: stable isotope values for delta15N in september - delta13C_september_corrected: stable isotope values for delta13C in september corrected for the stable isotope value of the three invertebrate prey categories - delta15N_september_corrected: stable isotope values for delta15N in september corrected for the stable isotope value of the three invertebrate prey categories - Prop_predator_eaten: proportion of predatory invertebrates eaten by each individual derived from the corrected stable isotope values - Prop_phytophagous_eaten: proportion of phytophagous invertebrates eaten by each individual derived from the corrected stable isotope values - Prop_detritivorous_eaten: proportion of detritivorous invertebrates eaten by each individual derived from the corrected stable isotope values - Levins_diet_index: levins' dietary index corresponding to lizard diet specialization (with 3 = completely generalist and 1 = completely specialist lizard) - Body_Size_september: lizard body size (snout-vent length in mm) - Body_Mass_september: lizard body mass (in g) - Body_Condition_september: lizard body condition (residuals of body mass by body size) - Microbiota_shannon_index: shannon index representing gut microbial bacteria community diversity - Survival_winter: survival during the winter (1 = survived, 0 = died) - Abundance_predator_enclosure: abundance of predatory invertebrates within the enclosure - Abundance_phytophagous_enclosure: abundance of phytophagous invertebrates within the enclosure - Abundance_detitivorous_enclosure: abundance of detritivorous invertebrates within the enclosure

opencc-by-4.0Oct 2019View details →

zenodo44/100

Prey nutrient content is associated with the trophic interactions of spiders and their prey selection under field conditions

<h2>Materials and Methods</h2> <h2><a name="_Toc58843581"></a>Fieldwork</h2> <a name="_Hlk173879015"></a><a name="_Hlk56335325"></a>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae), the two most abundant spider groups in this study, were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground in daylight hours between April and September 2018. Each belt transect was adjacent to a randomly selected crop tramline and were distributed across the entire field and ran its length. The areas searched were 4 m2 quadrats at least 10 m apart and all observed linyphiids and lycosids were collected. The 300 spiders taken forward for molecular dietary analysis in this study were taken from 64 randomly selected locations along the aforementioned transects. Following collection of spiders, 4 m2 of ground and crop stems was suction sampled <a name="_Hlk173879230"></a>in each of these 64 sampling locations for approximately 30 seconds, with the collected material emptied into a bag and any organisms immediately killed with ethyl-acetate. Suction sampling used a ‘G-vac’ modified garden leaf-blower. All material was later frozen at -20 ºC for storage before sorting in the lab. Sticky trap data were also collected, but were not used in this study as suction sampling was found to represent the interactions of spiders more closely (Cuff, Tercel et al., 2024). These invertebrates were collected for background population densities and macronutrient analysis, not for molecular dietary analysis. All invertebrates were identified to family level using morphological keys: Araneae (Roberts, 1993), Diptera (Ball, 2008), Coleoptera (Duff, 2012), Hymenoptera (Goulet & Huber, 1993), Hemiptera (Unwin, 2001), Collembola (Dallimore & Shaw, 2013) and Chilopoda (Barber, 2008). Further identifications were not carried out due to the inability to identify some of the invertebrate groups further via the associated metabarcoding-derived dietary data (e.g., Sciaridae), and the difficulty associated with finer taxonomic resolution of many damaged or immature specimens. The only taxa not identified to family level were springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to differentiate them) which were left at super-family, mites (many of which were immature or in poor condition, or lacked appropriate taxonomic keys) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage); in these cases, these taxonomic assignments were pooled to family-level for later analyses. <a name="_Hlk96098198"></a> Extraction, amplification and sequencing of DNA from the individually collected spiders, and its bioinformatic analysis are described by Cuff, Tercel, et al. (2022) and Drake et al. (2022) and are also detailed in Supplementary Information 1. In short, dietary metabarcoding was carried out using two primer pairs, one excluding predator DNA and the other amplifying it, to overcome the problem of overamplification of predator DNA (Cuff, Kitson, et al., 2023). Amplified DNA was sequenced on an Illumina MiSeq V3 2x300 cartridge, and resultant data screened for false positives following bioinformatic processing via minimum sequence copy thresholds applied according to read counts in controls and control DNA counts present in samples (Drake et al., 2022).   <h2><a name="_Toc58843582"></a>Macronutrient determination</h2> Specimens were taken for macronutrient analysis from the same suction samples collected for invertebrate community identification. Representatives were taken from each family found in the community samples for which specimens were intact, in visually good condition and relatively clean of soil and other contaminants. If specimens were from a relatively uncommon family but unclean, soil and other surface contaminants were physically removed, and the specimen then momentarily dipped in water to remove remaining surface contaminants without greatly dislodging surface lipids. <a name="_Hlk173879439"></a>Macronutrient contents were determined following the MEDI protocol (Cuff, Wilder, et al., 2021; Cuff & Wilder, 2021) with minor alterations to account for the small size of most of the invertebrates processed (Cuff, 2021) and with the omission of exoskeletal measurement. During extraction, half volumes (i.e., 500 µl) of solvents were used. For the lipid assays, 15 µl of sulfuric acid was added for a 15 min incubation, followed by only 200 µl of vanillin reagent to increase the concentration and development of analyte for more accurate readings from smaller invertebrates. Lipid and protein standard series were diluted to 50% of the concentration specified in the original protocol (i.e., 0-1 mg ml-1). Carbohydrate assays used 140 µl of reagent with 30 min incubation at 92 °C followed by a further 30 min at room temperature. Carbohydrate standard series were diluted to 1 % of the concentrations specified in the original protocol (i.e., 0-0.02 mg ml-1) to ensure signals overcame the higher limit of detection relative to typical invertebrate carbohydrate content.  <a name="_Hlk173926384"></a>Mean macronutrient contents were calculated for each taxon and converted into proportions of the total macronutrient mass detected for each taxon (i.e., macronutrient values are given as % total macronutrient mass). Macronutrient data were allocated to each prey taxon. Where macronutrient data were not available for a family (due to no or very few individuals being present in vacuum samples), average data for that order were used.   <h2><a name="_Toc58843584"></a>Statistical analysis</h2> We have assessed nutritional dynamics through a combination of multivariate models and network-based null modelling. All analyses were conducted in R v.4.0.3 (R Core Team, 2020). To compare the nutritional balance of prey consumed by different spider groups, the mean nutrient contents of all prey consumed by each spider were calculated and compared using a multivariate linear model (MLM) via the ‘manylm’ command in mvabund (Wang et al., 2012). Differences were visualised using ternary plots via ‘ggtern’ (Hamilton & Ferry, 2018) and ‘ggplot2’ (Wickham, 2016). How spider diets differ between spider groups (genera, sexes and life stages) and how this is related to the nutrient contents of those prey was assessed using a fourth corner analysis (FCA). Fourth corner analyses assess how the relationship between the presence of species (or consumed resources in a dietary context) and environmental (or consumer) traits relates to species traits (or prey traits; (Brown et al., 2014). First, overall relationships between dietary composition and spider traits were assessed using a multivariate generalized linear model (MGLM) via the ‘manyglm’ command in the ‘mvabund’ package (Wang et al., 2012) with a binomial error family. These relationships were identified via likelihood ratio test using the ‘anova.manyglm’ command. A fourth corner analysis was performed using the ‘trait.glm’ command in mvabund with the ‘R’, ‘Q’ and ‘L’ matrices representing dietary detections of prey families in each spider, spider trait data (genus (a proxy for many unmeasured traits such as morphology), sex and life stage) and prey proportional macronutrient contents, respectively, with a binomial error family. Log-likelihood ratio tests were carried out using the ‘anova.traitglm’ command with 999 bootstrap iterations and Monte-Carlo resampling. The model was repeated with the least absolute shrinkage and selection operator (LASSO) applied, which is a method of penalised likelihood that reduces model terms to zero if they lack predictive power (i.e., do not reduce the Bayesian information criterion), thereby selecting models with greater predictive accuracy (Brown et al., 2014). To assess whether the proportions of mean prey nutrient contents deviated from those expected based on random foraging, null diets were simulated using network-based null models in ‘econullnetr’ (Vaughan et al., 2018) with the ‘generate_null_net’ command. The ‘generate_null_net_indiv’ function (Cuff, Windsor, et al., 2023) was used to generate null diets for each individual spider based on local prey communities determined via suction sampling. The mean prey macronutrient contents of spider diets were compared between expected and observed diets using a MLM in mvabund, and significant differences visually represented through a ternary plot using ggtern. To ascertain how differences between spider groups factor into any deviations from random nutrient intake, the difference in macronutrient proportions between expected and observed spider diets was also compared between spider genera, life stages and sexes in a MLM. To relate prey preferences of different spider groups to different prey and their macronutrient contents, observed interactions were compared against null models based on prey abundances using the ‘generate_null_net’ command in econullnetr (as above) for each of the spider groups and, separately, for individual spiders. Ternary plots representing preference effect sizes for prey of varying macronutrient contents were generated using the group-specific data via ‘ggtern’. The observed interactions of individual spiders were divided by the interactions expected in the null model; infinite values (i.e., zero interactions expected and more than zero observed) and NAs (e.g., no interactions expected nor observed) were converted to zero. These observed/expected values were compared between spider groups via permutational multivariate analysis of variance (PerMANOVA). These results were visualised by plotting mean standardised effect sizes for each spider genus, sex and life stage from the prey choice null models via ggplot2.

opencc-by-4.0Aug 2024View details →

zenodo44/100

Temporal variation in spider trophic interactions is explained by the influence of weather on prey communities, web building and prey choice

Materials and Methods Fieldwork and sample processing Field collection and sample processing has been described previously by Cuff, Tercel, et al., (2022), but is briefly described in Supplementary Information 1. In short, money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were collected from occupied webs and the ground in barley fields between April and September 2018. Linyphiids occupying webs (n = 78) were prioritised for collection, but ground-active linyphiid and lycosid spiders were also collected. For each linyphiid taken from a web, the height of the web from the ground (mm) and its approximate dimensions were recorded, the latter calculated as approximate web area (mm2). To obtain data on local prey density, ground and crop stems were suction sampled using a ‘G-vac’ for approximately 30 seconds at each 4 m2 quadrat from which spiders were collected. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al. (2022) and Drake et al. (2022), and is also detailed in Supplementary Information 2. Amplification was carried out using two complementary PCR primer pairs: one targeting invertebrates generally, and one intended to exclude amplification of spider DNA to reduce the prevalence of ‘host’ reads in the data output (Cuff et al. 2023). Amplicons were sequenced via Illumina MiSeq V3 with 2x300 bp paired-end reads. The resultant sequencing read counts were converted to presence-absence data of each detected prey taxon in each individual spider. Given the prevalence of sequencing reads associated with each spider analysed and the impossibility of disentangling these from detections of intraspecific predation (i.e., cannibalism), all such reads were removed (Cuff et al. 2023), although intrageneric and intrafamilial predation were still detected.   Weather data Weather data were taken from publicly available reports from the Cardiff Airport weather station (6.6 km from the study site) via “Wunderground” (Wunderground, 2020), to represent local weather conditions. This does not necessarily reflect smaller-scale effects (e.g., microclimate-scale; Bell, 2014; Holtzer et al., 1988), but the timescale of detection for dietary metabarcoding reduces the value of that resolution given that spiders may forage across multiple microclimates. We collated data from 1st January 2018 to 17th September 2018 (the last field collection). Weather data were also separately extracted for the week preceding each of the two 2017 collection dates (3rd to 9th August and 29th August to 4th September 2017). Specifically, daily average temperatures (°C), daily average dew point (°C), maximum daily wind speed (km h-1), daily sea level pressure (hPa) and day length (min; sunrise to sunset) were recorded. Precipitation data were downloaded via the UK Met Office Hadley Centre Observation Data (UK Met Office, 2020) as regional precipitation (mm) for South West England & Wales. Weather data were converted to mean values for seven days preceding the collection of spider samples to correspond with the longevity of DNA in the guts of spiders (Greenstone et al., 2014).   Statistical Analysis All analyses were conducted in R v4.0.3 (R Core Team, 2020). To assess how weather affects spider trophic interactions over time, we analysed dietary changes across weather gradients using multivariate models. To identify whether this was likely to be driven by changes in prey abundance, we assessed the corresponding changes in the prey communities and then used null models to ascertain whether spiders were responding to prey abundance changes through prey choice. Given the dependence of linyphiid spiders on webs for foraging, we also compared web height and area over weather gradients to assess whether this may be a component of adaptive foraging. To assess the inter-annual consistency of prey choices in response to weather conditions, we also assessed whether prey preference data could be used to improve the predictive power of prey choice models. For this, we generated null models for 2017 data with prey abundance weighted by prey preferences estimated with the 2018 data. This allowed us to assess the consistency of prey choice under similar conditions, but also provides insight as to whether this framework can be used to predict predator responses to diverse prey communities under dynamic conditions. We detail the specific stages of this analytical framework in the below sections.   Sampling completeness and diversity assessment To assess the diversity represented by the dietary analysis and the invertebrate community sampling, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell, Dushoff, & Winfree, 2021). This was performed using the ‘iNEXT’ package with species represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016; Figures S4 & S6).   Relationships between weather, spider trophic interactions and prey community composition Prey species that occurred in only one spider individual were removed before further analyses to prevent outliers skewing the results. Spider trophic interactions were related to temporal and weather variables in multivariate generalized linear models (MGLMs) with a binomial error family (Wang, Naumann, Wright, & Warton, 2012). Trophic interactions were related to temporal variables and their pairwise interactions (including spider genus to account for any confounding effect), weather variables and their pairwise interactions, and weather variables and their interactions with spider genus and time (to account for any confounding effects) in three separate MGLMs. These variables were separated into different models (Temporal model, Weather interaction model and Confounding effects model) to improve model fit and reduce singularity. Invertebrate communities from suction sampling were related to temporal and weather variables in identically structured MGLMs (excluding the spider genus variable) with a Poisson error family. All MGLMs were fitted using the ‘manyglm’ function in the ‘mvabund’ package (Wang et al., 2012). ‘Temporal model’ independent variables were calendar day (day), mean day length in minutes for the preceding week (day length), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths) and all two-way interactions between these variables. ‘Weather interaction model’ independent variables were mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and pairwise interactions between weather variables. ‘Confounding effects model’ independent variables were day (to investigate the interaction between time and weather), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths), mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and two-way interactions of each weather variable with day and genus. Trophic interaction and community differences were visualised by non-metric multidimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 999 simulations, with Jaccard distance for spider diets and Bray-Curtis distance for invertebrate communities. For the dietary NMDS, outliers (n = 21; samples containing rare taxa) obscured variation on one axis and were thus removed to facilitate separation of samples and achieve minimum stress. For visualization of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the ‘ordisurf’ function in the ‘ggplot’ package (Wickham, 2016).   Relationships between web characteristics and weather variables Web area and height were compared against weather and temporal variables using a multivariate linear model (MLM) with the ‘manylm’ command in ‘mvabund’ (Wang et al., 2012). Log-transformed web area and height comprised the multivariate dependent variable, and day, spider genus, temperature, precipitation, dewpoint, wind, pressure and two-way interactions between each of these and day and genus comprised the independent variables.   Variation in spider prey choice across weather conditions To separately represent spiders from different weather conditions in prey choice analyses, sample dates for every spider were clustered based on the mean weather conditions (temperature, precipitation, dewpoint, wind and pressure) of the week before collection (7 days, to align approximately with spider gut DNA half-life; Greenstone et al., 2014).  Alongside data from 2018 (n = 24 collection dates), two sampling periods from 2017 were included in the clustering to ascertain similarity of weather conditions for additional inter-annual prey choice analyses described below. The clustering process is described in Supplementary Information 3. Five clusters were generated: High Pressure (HPR), Hot (HOT), Wet Low Dewpoint (WLD), Dry Windy (DWI), Wet Moderate Dewpoint (WMD), and 2017 (2017 sampling periods). Prey preferences of spiders in each of the weather clusters was analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ command. Consumer nodes in this case represented spiders belonging to each of the weather clusters. Econullnetr generates null models based on prey abundance, represented here by suction sample data, to predict how consumers will forage if based on the abundance of resources alone. These null models are then compared against the observed interactions of consumers (i.e., interactions of spiders within each weather cluster with their prey) to ascertain the extent to which resource choice deviated from random (i.e., density dependence). The trophic network was visualised with the associated prey choice effect sizes using ‘igraph’ (Csardi & Nepusz, 2006) with a circular layout, and as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016). The normalised degree of each weather cluster node was generated using the ‘bipartite’ package (Dormann, Gruber, & Fruend, 2008) and compared against the normalised degree of the same node in the null network to determine whether spiders were more or less generalist than expected by random. Prior to the prey choice analysis, an hemipteran prey identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty.   Validating and predicting relationships between years To test how generalisable the results are and the extent to which weather drives prey preferences, we used a measure of prey preference (observed/expected values; observed interaction frequencies divided by interaction frequencies expected by null models) from the above prey choice analysis to assess whether we could more accurately predict observed trophic interactions under similar weather conditions for data from a linked study at the same location in 2017. These additional data represent a subset of the spider taxa analysed above (Tenuiphantes tenuis and Erigone spp.) collected using the same methods by the same researchers and in the same locality (Cuff, Drake, et al., 2021). The similarity in weather conditions between the 2017 study period and each of the five 2018 weather clusters was determined via NMDS of the weather data in two dimensions with Euclidean distance. Centroid coordinates for each 2018 weather cluster and the 2017 data were extracted and pairwise distances calculated between weather clusters:   In order, the most proximate weather clusters to the 2017 weather data were HPR (mean Euclidean distance = 8.845), HOT (9.290), WMD (13.626), DWI (13.817) and WLD (18.682; Figure S3). To facilitate comparison between the two years, observed/expected values from the 2018 prey choice models were extracted separately for each of the weather clusters and scaled between 0.1 and 1. For this, 0.1 was used as a minimum since 0 would result in interactions being excluded altogether in the null models, and one as a maximum given the limits of econullnetr but also because this is a multiplier applied to the prey abundances, so greater values would skew prey abundances beyond realistic proportions. Scaling was achieved by the following equation:   Missing values (e.g., prey that were absent in certain weather conditions) were represented as 1 to prevent transformation of their abundances in the null models; this treats prey for which data were absent naively, but could increase perceived preferences for them. The scaled values were used to weight the abundance of prey available to the spiders in the 2017 data using the weighting option in econullnetr, whereby values less than 1 proportionally reduce the probability of that taxon being predated in the null models. This effectively redistributes the 2017 relative prey abundance data according to the preference effect sizes generated for each of the 2018 weather clusters. If prey preferences are similar between the 2017 spiders and those from the weather cluster being used to weight the model, the composition of simulated diets should more closely resemble observed diets and fewer significant deviations from the null model should be found. Null models were generated as above (Variation in spider prey choice across weather conditions) but based on the prey availability and trophic interactions from 2017 samples. Three types of model were run: i) a conventional model based on observed prey abundances; ii) a model with prey abundances set to be equal across all prey taxa; and iii) observed prey abundances weighted by prey preferences determined for each of the weather clusters in the 2018 prey choice analysis. A separate model was run for each 2018 weather cluster with abundances weighted by the corresponding scaled observed/expected values. The unweighted conventional model was compared against weighted models to ascertain whether the prey preference weightings from 2018 improved the predictive power of the null models. To compare effect sizes between the unweighted and each other null model for each resource taxon, mean standardised effect size (SES) values were calculated from the paired ‘pre-harvest’ and ‘post-harvest’ data from each model, and paired t-tests were carried out with these between the unweighted and each weighted model. The SES values were plotted for each model and joined between taxa to visualise these paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via a modified ‘econullnetr’ function (generate_null_net_indiv) which produces outputs at the individual level to generate simulated diets for individual spiders to compare dietary composition between null model predictions and observed data. These models were run with 2300 simulations to represent 50 simulations per individual spider in the 2017 dataset (n = 46). Null diets were associated with sample IDs by aggregating the 50 simulations per sample and retaining a mean incidence of prey (i.e., mean occurrence across all 50 simulations). A visualisation of the per-sample differences in null model and observed data was generated via NMDS. Mean centroid coordinates for the observed 2017 data and the predicted diets of each model were extracted and the Euclidean distance between the observed data centroid and that of each model was calculated (as above for weather conditions).   Supplementary Information 1: Field collection and sample processing Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018 (five visits per week of which spiders from 24 collection dates were used). Transects were randomly distributed across the entire field. Along these transects, 64 separate 4 m2 quadrats, at least 10 m apart, were searched and all observed linyphiids and lycosids were collected. Spiders were placed in 100 % ethanol using an aspirator, regularly changing meshing to limit potential cross-contamination. Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground (mm) and its approximate dimensions were recorded, the latter calculated as approximate web area (mm2). Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until DNA extraction. To obtain data on local prey density, ground and crop stems were suction sampled using a ‘G-vac’ for approximately 30 seconds at each 4 m2 quadrat (n = 64) from which spiders were collected. The collected material was emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level to match the resolution of the least resolved of the metabarcoding-derived trophic interaction data, and due to difficulties associated with identification to finer taxonomic resolution for many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level, and wasps of the superfamily Ichneumonoidea which were identified no further due to obscurity of wing venation due to damage.   Supplementary Information 2: Molecular analysis and bioinformatics Extraction and high-throughput sequencing of spider gut DNA Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera Erigone, Tenuiphantes, Bathyphantes and Microlinyphia (Araneae: Linyphiidae), and the Lycosidae genus Pardosa. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key (Roberts, 1993). Abdomens were removed from spiders and again transferred to and washed in fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens (Krehenwinkel et al., 2017). For amplification of DNA, two primer pairs were used. BerenF-LuthienR (Cuff et al., 2021) amplified a broad range of invertebrates including spiders, and TelperionF-LaureR (Cuff et al., 2022), amplified a range of invertebrates but fewer spiders. Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively. Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet et al. (2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis Bioinformatic analysis followed Drake et al., (2022). The Illumina run generated 11,165,405 and 10,959,010 reads for BerenF-LuthienR and TelperionF-LaureR, respectively, which were quality-checked and paired via FastP (Chen et al., 2018)  to retain only sequences of at least 200 bp with a quality threshold of 33, resulting in 10,561,874 and 9,355,112 paired reads. The paired reads were demultiplexed and assigned to their respective spider sample according to their MID-tags via the “trim.seqs” command in Mothur v1.39.5 (Schloss et al., 2009), leaving 7,854,610 and 7,437,929 reads with exact matches to the primer and MID-tags. Replicates were removed, and denoising and clustering to zero-radius operational taxonomic units (ZOTUs; clustered without % identity to avoid multiple species represented within a single operational taxonomic unit (OTU)) completed via Unoise3 in Usearch11 (Edgar, 2010). The resultant sequences were assigned a taxonomic identity from GenBank via BLASTn v2.7.1 (Camacho et al., 2009) using a 97 % identity threshold (Alberdi et al., 2017). The BLAST output was analysed in MEGAN v6.15.2 (Huson et al., 2016). Where the top BLAST hit, determined by lowest e-value, was resolved at a higher taxonomic level than species-level, the results were checked; where possibly erroneous entries were preventing species-level assignment (e.g., poorly resolved identifications on GenBank), finer resolution was assigned based on the next-closest match. Where ZOTUs were assigned the same taxon, these were aggregated. Data clean-up used the optimal minimum sequence copy thresholds identified by Drake et al. (2022). The maximum value for a ZOTU present in blank or negative controls was identified and subtracted from all read counts for that ZOTU to remove background contaminants. Simultaneously, known lab contaminants (e.g., German cockroach Blattella germanica), artefacts and errors of the sequencing process, unexpected reads in positive controls and positive control taxon reads in dietary samples were identified. These were calculated as a percentage of their respective sample’s read count and any read counts lower than the highest of these percentages for their respective sample were removed to eliminate additional instances of contamination. These thresholds were defined as 0.38 % and 0.39 % for BerenF-LuthienR and TelperionF-LaureR, respectively. The data from the two libraries (i.e., from each primer pair) were then aggregated together by sample and aggregated again by taxon. Non-target taxa (e.g., fungi) and instances in which predator DNA was amplified (i.e., ZOTUs with high read counts matching the individual’s morphological identity) were removed. All remaining read counts were converted to presence-absence.   Supplementary Information 3: Cluster analysis Prior to clustering, weather variables were scaled by subtracting the mean and dividing by the standard deviation. A Euclidean distance matrix was calculated using the ‘dist’ function, and this scaled distance matrix was hierarchically clustered using the ‘hclust’ function. Optimal clustering solutions were determined by comparison of Dunn’s index between methods and k values; this was calculated using the ‘dunn’ function in the “clValid” package (Brock et al., 2008) for each cluster k value above five until the Dunn index decreased. The k value after which Dunn’s index decreased was deemed the optimal solution for each clustering method. Clustering methods based on ‘average’, ‘complete’, ‘single’, ‘median’, ‘centroid’ and ‘mcquitty’ linkages were compared, and the ‘complete’ method selected for subsequent analysis as it resulted in the smallest number of clusters (6; thus, the most efficient simplification of the data; Figure S1). Different clustering methods altered the composition of some clusters, but most sampling dates showed consistent clustering between methods. A heatmap dendrogram was produced using the ‘heatmap.2’ function in the ‘gplots’ package (Warnes et al., 2020), with cluster colours assigned with the ‘Accent’ palette of ‘RColorBrewer’ (Neuwirth, 2014) and relative weather value colour scaling generated using the ‘viridis’ package (Garnier 2018; Figure S2). Weather clusters were named according to unique characteristics relative to the other clusters. These names comprise: High Pressure (HPR; days 142, 253, 256, 250, 173), Hot (HOT; days 162, 204, 205, 208, 201, 187, 198, 197, 183, 184, 194, 190 and 191), Wet Low Dewpoint (WLD; day 121), Dry Windy (DWI; days 131, 169 and 170), Wet Moderate Dewpoint (WMD; days 149 and 152), and 2017 (pre- and post-harvest 2017 sampling periods).     References Alberdi, A., Aizpurua, O., Gilbert, M. T. P., & Bohmann, K. (2017). Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods in Ecology and Evolution, 9(1), 1–14. https://doi.org/10.1111/2041-210X.12849 Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid: an R package for cluster validation. Journal of Statistical Software, 25(4), 1–22. Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: architecture and applications. BMC Bioinformatics, 10, 1–9. https://doi.org/10.1186/1471-2105-10-421 Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560 Cuff, J. P., Drake, L. E., Tercel, M. P. T. G., Stockdale, J. E., Orozco-terWengel, P., Bell, J. R., Vaughan, I. P., Müller, C. T., & Symondson, W. O. C. (2021). Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. Ecological Entomology, 46(2), 249–261. Cuff, J. P., Tercel, M. P. T. G., Drake, L. E., Vaughan, I. P., Bell, J. R., Orozco-terWengel, P., Müller, C. T., & Symondson, W. O. C. (2022). Density-independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops. Environmental DNA, 4(3), 549–564. Drake, L. E., Cuff, J. P., Young, R. E., Marchbank, A., Chadwick, E. A., & Symondson, W. O. C. (2022). An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. Methods in Ecology and Evolution, 13(3), 694–710. Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26(19), 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 Garnier, S. (2018). viridis: default color maps from ‘matplotlib’ (0.5.1). https://cran.r-project.org/package=viridis Huson, D. H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H. J., & Tappu, R. (2016). MEGAN Community Edition - interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Computational Biology, 12(6), 1–12. https://doi.org/10.1371/journal.pcbi.1004957 Krehenwinkel, H., Kennedy, S., Pekár, S., & Gillespie, R. G. (2017). A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. Methods in Ecology and Evolution, 8, 126–134. https://doi.org/10.1111/2041-210X.12647 Neuwirth, E. (2014). RColorBrewer: ColorBrewer palettes (1.1-2). https://cran.r-project.org/package=RColorBrewer Roberts, M. J. (1993). The Spiders of Great Britain and Ireland (Compact Edition) (3rd ed.). Harley Books. Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J., & Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. https://doi.org/10.1128/AEM.01541-09 Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). Environmental DNA. Oxford University Press. Warnes, G. R., Bolker, B., Bonebakker, L., Gentleman, R., Huber, W., Liaw, A., Lumley, T., Maechler, M., Magnusson, A., Moeller, S., Schwartz, M., & Venables, B. (2020). gplots: Various R programming tools for plotting data (R package version 3.1.0). https://cran.r-project.org/package=gplots

opencc-by-4.0Nov 2022View details →

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Trophic Interactions, Habitat Use, and Pollution Loads of Bottlenose Dolphins (Tursiops Truncatus) in the Florida Coastal Everglades, Florida, USA, 2013-2019

Cetaceans can feed at upper trophic levels and occur from freshwater to open-ocean ecosystems. Due to their abundance, mobility, and high metabolic rates, they have the potential to affect the structure and function of ecosystems through both top-down and bottom-up pathways. To better understand what ecological roles they may play in a system, it is important to understand patterns and drivers of their abundance, habitat use, and trophic interactions. I investigated the trophic interactions and pollutant exposure of common bottlenose dolphins (Tursiops truncatus) of the Florida Coastal Everglades. Based on bulk stable isotope analysis of tissue samples collected using biopsy sampling, it appears that despite their high mobility, bottlenose dolphins restrict their foraging within the habitats where they were sampled. Trophic position and foraging locations affected exposure to pollutants, with high levels of mercury found in dolphins estimated to forage at higher trophic levels and feeding within an inland bay. Mercury levels also varied with age and sex. Dolphins and their prey both contained substantial mercury levels and dolphins’ health could be impacted by this exposure, but the selenium levels we measured might counteract these negative effects.

openCustomNov 2023View details →

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Database of trophic interactions

This database builds upon the collection assembled by Brose et al. (2005), and represents the largest standardised collection of trophic links for freshwater organisms, of which we are aware. https://sites.google.com/site/foodwebsdatabase/

opencc-zeroOct 2015View details →

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Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model

Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 years of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by Laminaria hyperborea). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age class 0 Atlantic cod (Gadus morhua) and kelp and European lobster (Homarus gammarus), specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50-year period, incorporating harvesting cycles of 2, 5, and 9 years, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5- and 9-year harvesting cycles, whereas a decline was observed with a 2-year cycle. Furthermore, the non-trophic interaction facilitated a higher pre-harvest biomass for both the European lobster and the Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.

opencc-zeroNov 2023View details →

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Data from: Restoration of native saltmarshes can reverse arthropod assemblages and trophic interactions changed by a plant invasion

Plant invasions profoundly impact both natural and managed ecosystems, and removal of the invasive plants addresses only part of the problem of restoring impacted areas. The rehabilitation of diverse communities and their ecosystem functions following removal of invasive plants is an important goal of ecological restoration. Arthropod assemblages and trophic interactions are important indicators of the success of restoration, but have largely been overlooked in saltmarshes. We determined how arthropod assemblages and trophic interactions changed with the invasion of the exotic plant Spartina alterniflora and with the restoration of the native plant Phragmites australis following Spartina removal in a Chinese saltmarsh. We investigated multiple biotic and abiotic variables to gain insight into the factors underlying the changes in arthropod assemblages and trophic structure. We found that although Spartina invasion had changed arthropod diversity, community structure, feeding-guild composition, and the diets of arthropod natural enemies in the saltmarsh, these changes could be reversed by the restoration of native Phragmites vegetation following removal of the invader. The variation in arthropod assemblages and trophic structure were critically associated with four biotic and abiotic variables (aboveground biomass, plant density, leaf N, and soil salinity). Our findings demonstrate the positive effects of controlling invasive plants on biodiversity and nutrient cycling, and provide a foundation for assessing the efficacy of ecological restoration projects in saltmarshes.

opencc-zeroApr 2022View details →

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Plant-associate interactions and diversification across trophic levels

Interactions between species are widely understood to have promoted the diversification of life on Earth, but how interactions spur the formation of new species remains unclear. Interacting species often become locally adapted to each other, but they may also be subject to shared dispersal limitations and environmental conditions. Moreover, theory predicts that different kinds of interactions have different effects on diversification. To better understand how species interactions promote diversification, we compiled population genetic studies of host plants and intimately associated herbivores, parasites, and mutualists. We used Bayesian multiple regressions and the BEDASSLE modeling framework to test whether host and associate population structures were correlated over and above the potentially confounding effects of geography and shared environmental variation. We found that associates' population structure often paralleled their hosts' population structure, and that this effect is robust to accounting for geographic distance and climate. Associate genetic structure was significantly explained by plant genetic structure somewhat more often in antagonistic interactions than in mutualistic ones. This aligns with a key prediction of coevolutionary theory, that antagonistic interactions promote diversity through local adaptation of antagonists to hosts, while mutualistic interactions more often promote diversity via the effect of hosts' geographic distribution on mutualists' dispersal.

opencc-zeroSep 2022View details →

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Fig. 2. SNSB-BSPG 2020 XCIII 18 containing a in A new glimpse on trophic interactions of 100-million-year old lacewing larvae

Fig. 2. SNSB-BSPG 2020 XCIII 18 containing a neuropteran larva with attached mite from Hukawng Valley, Kachin State, Myanmar; Turonian– Cenomanian, Cretaceous, 90–100 mya; in dorsal (A1) and ventral (A2) views.

opencc-by-4.0Dec 2020View details →

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Fig. 1 in A new glimpse on trophic interactions of 100-million-year old lacewing larvae

Fig. 1. Neuropteran larvae from Hukawng Valley, Kachin State, Myanmar; Turonian–Cenomanian, Cretaceous, 90–100 mya. A. SNSB-BSPG 2020 XCIII 19 with assemblage of neuropteran stylets; A1, overview; A2, close-up image of enclosed stylets; A3, stylets, with five clearly visible (1–5) and possibly three additional stylets (6?–8?); A4, close-up image of representative of Hymenoptera. B. SNSB-BSPG 2020 XCIII 26 (previously BUB 033 in Haug et al. 2019c); B1, head of neuropteran larva with stylets pair in situ; B2, drawing of a single stylet of the specimen in B1; note the high number of teeth, similar to that of stylets 1 and 2 in A3.

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FIGURE 6 in Fishers' knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers

FIGURE 6 | The mean number of prey items of Plagioscion squamosissimus is cited by the interviewed fishers in the Tapajós and Tocantins rivers. Mean (dark vertical line), data distribution (bean gray area), data (points) correspond to individual fishers, confidence interval (rectangle). The median was 2 for

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FIGURE 3 in Fishers' knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers

FIGURE 3 | Paired comparison between the relative abundance (% of biomass in kg) in samples and relative importance for fisheries (% of citations in interviews) of Plagioscion squamosissimus in the Tapajós River. Dots indicate the sampling sites for biomass and fishing communities for interviews.

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FIGURE 4 in Fishers' knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers

FIGURE 4 | Mean size (standard length in cm) of Plagioscion squamosissimus sampled in the Tapajós and Tocantins rivers. Mean (dark horizontal line), data distribution (bean gray area), data (points), confidence interval (rectangle). The median sizes of P. squamosissimus were 23.6 cm in the Tapajós and 22.7 cm in the Tocantins.

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FIGURE 5 in Fishers' knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers

FIGURE 5 | Diagram depicting the main trophic interactions (simplified food web) of Plagioscion squamosissimus based on fishers' LEK (interviews) indicating the 10 most cited prey (yellow) and the 14 most cited predators (red), in the Tapajós (blue) and Tocantins (green). Numbers inside squares are the percent of citations of prey and predators of P. squamosissimus by the interviewed fishers. We obtained 150 citations of prey and 129 citations of predators by the interviewed fishers in the Tapajós River and 56 citations of prey and 50 citations of predators in the Tocantins River. Line thicknesses indicate the number of citations by the fishers interviewed.

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FIGURE 1 in Fishers' knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers

FIGURE 1 | Map of the studied region in the middle course of the Tapajós, Tocantins-Araguaia rivers, in the Brazilian Amazon, showing the fishing communities and distribution of biomass of Plagioscion squamosissimus (black circle) sampled where the research (fish sampling and interviews) was conducted.

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FIGURE 2 in Fishers' knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers

FIGURE 2 | Comparison of mean relative relevance to fisheries (% of citations in interviews) of Plagioscion squamosissimus according to the interviewed fishers in the Tapajós (n = 7) and Tocantins (n = 4) rivers. Mean (dark horizontal line), data distribution (bean gray area), data (points), confidence interval (rectangle). Points indicate the fishing communities, considered as replicates in this analysis.

opencc-by-4.0Mar 2023View details →

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Fig. 1 in Trophic interactions among sympatric zooplanktivorous fish species in volume change conditions in a large, shallow, tropical lake

Fig. 1. Lake Chapala, Mexico. Numbers in bold represent the sampling sites, in italics depths contours (m).

opencc-by-4.0Feb 2011View details →

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Figure 1 in Parasites as secret files of the trophic interactions of hosts: the case of the rufousbellied thrush Los parásitos como archivos secretos en las interacciones tróficas con sus hospederos: el caso del Zorzal Colorado

Figure 1. Relationship between body weight in grams (independent variable) of adult rufous-bellied thrushes (Turdus rufiventris) and species richness of parasites (dependent variable).

opencc-by-4.0Nov 2010View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record