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Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator
<p>The arrival of novel predators can trigger trophic cascades driven by shifts in prey numbers. Predators also elicit behavioural change in prey populations, via phenotypic plasticity and/or rapid evolution, and such changes may also contribute to trophic cascades. Here we document rapid demographic and behavioural changes in populations of a prey species (grassland melomys <em>Melomys burtoni</em>, a granivorous rodent) following the introduction of a novel marsupial predator (northern quoll <em>Dasyurus hallucatus</em>). Within months of quolls appearing, populations of melomys exhibited reduced survival and population declines relative to control populations. Quoll-invaded populations (<em>n </em>= 4) were also significantly shyer than nearby, quoll-free populations (<em>n </em>= 3) of conspecifics. This rapid but generalised response to a novel threat was replaced over the following two years with more threat-specific antipredator behaviours (i.e. predator-scent aversion). Predator-exposed populations, however, remained more neophobic than predator-free populations throughout the study. These behavioural responses manifested rapidly in changed rates of seed predation by melomys across treatments. Quoll-invaded melomys populations exhibited lower per-capita seed take rates, and rapidly developed an avoidance of seeds associated with quoll scent, with discrimination playing out over a spatial scale of tens of metres. Presumably the significant and novel predation pressure induced by quolls drove melomys populations to fine-tune behavioural responses to be more predator-specific through time. These behavioural shifts could reflect individual plasticity (phenotypic flexibility) in behaviour or may be adaptive shifts from natural selection imposed by quoll predation. Our study provides a rare insight into the rapid ecological and behavioural shifts enacted by prey to mitigate the impacts of a novel predator and shows that trophic cascades can be strongly influenced by behavioural as well as numerical responses.</p>
BOP_RODENT - Rodent specialized birds of prey (Circus, Asio, Buteo) in Flanders (Belgium)
<p><em>BOP_RODENT - Rodent specialized birds of prey (Circus, Asio, Buteo) in Flanders (Belgium)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>BOP_RODENT</strong>, using trackers developed by Ornitela (<a href="https://www.ornitela.com">https://www.ornitela.com</a>). The study has been operational since 2020. In total 35 individuals of 5 bird of prey species have been tagged at several locations in Flanders (Belgium), mainly to study their habitat use and migration behaviour. Data are automatically synced with Movebank and from there periodically archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>).</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1278021460">1278021460</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/12567894/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json:</strong> technical description of the data files.</li> <li><strong>BOP_RODENT-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>BOP_RODENT-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. Additional funding was provided by Agentschap voor Natuur en Bos (ANB).</p>
Feeding trials of Leptagrion andromache and prey species
<p>We quantified the consumption rate for one damselfly larvae predator and many of its prey. The top predator Leptagrion andromache (Zygoptera: Odonata, dry mass = 3.31 mg, se = 2.45, n = 29) was fed several densities of each prey. The prey were chosen because they were the most abundant prey in bromeliads. All prey are aquatic insect larvae. We chose Culex sp 1 (Culicidae: Diptera, density range = 1- 50, mean dry mass = 0.17 mg, se = 0.04, n = 25), Culex sp 2 (Culicidae: Diptera, 1-20, 0.09 mg, 0.02, 14), Forcipomyia (Ceratopogonidae: Diptera, 1-60, 0.07 mg, 0.01, 5), Dero superterrenus (Naididae: Haplotaxida, 1-60, 0.12 mg, 0.01, 2), Psychodidae (Psychodidae: Diptera, 1-30, 0.22 mg, 0.11, 18), Scirtes sp 1 (Scirtidae: Coleoptera, 1-30, 0.33 mg, 0.12, 13), Scirtes sp 2 (Scirtidae: Coleoptera, 1-6, 0.43 mg, 0.27, 65), and Trentepohlia (Tipulidae: Diptera, 1-7, 0.29 mg, 0.19, 43).</p>
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><em><span>Fieldwork</span></em></h2> <p><a name="_Hlk173879015"></a><a name="_Hlk56335325"></a><span><span>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 m<sup>2</sup> 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. </span></span><span><span>Following collection of spiders, 4 m<sup>2</sup> 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.</span></span></p> <p><span>All invertebrates were identified to family level using morphological keys: Araneae </span><span><span>(Roberts, 1993)</span></span><span>, Diptera </span><span><span>(Ball, 2008)</span></span><span>, Coleoptera </span><span><span>(Duff, 2012)</span></span><span>, Hymenoptera </span><span><span>(Goulet & Huber, 1993)</span></span><span>, Hemiptera </span><span><span>(Unwin, 2001)</span></span><span>, Collembola </span><span><span>(Dallimore & Shaw, 2013)</span></span><span> and Chilopoda </span><span><span>(Barber, 2008)</span></span><span>. 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></span></p> <p><span><span>Extraction, amplification and sequencing of DNA from the individually collected spiders, and its bioinformatic analysis are described by </span></span><span><span><span>Cuff, Tercel, et al. (2022)</span></span></span><span><span> and </span></span><span><span><span>Drake et al. (2022)</span></span></span><span><span> 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 </span></span><span><span><span>(Cuff, Kitson, et al., 2023)</span></span></span><span><span>. 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 </span></span><span><span><span>(Drake et al., 2022)</span></span></span><span><span>.</span></span></p> <p><span> </span></p> <h2><a name="_Toc58843582"></a><em><span>Macronutrient determination</span></em></h2> <p><span>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 </span><span><span><span>(Cuff, Wilder, et al., 2021; Cuff & Wilder, 2021)</span></span></span><span><span> with minor alterations to account for the small size of most of the invertebrates processed </span></span><span><span><span>(Cuff, 2021)</span></span></span><span><span> and with the omission of exoskeletal measurement. </span></span><span>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<sup>-1</sup>). 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<sup>-1</sup>) to ensure signals overcame the higher limit of detection relative to typical invertebrate carbohydrate content. <span> </span><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.</span></p> <p><span> </span></p> <h2><a name="_Toc58843584"></a><em><span>Statistical analysis</span></em></h2> <p><span>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 </span><span><span>(R Core Team, 2020)</span></span><span>. </span></p> <p><span>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 </span><span><span>(Wang et al., 2012)</span></span><span>.<span> </span><span>Differences were visualised using ternary plots via ‘ggtern’ </span></span><span><span>(Hamilton & Ferry, 2018)</span></span><span> and ‘ggplot2’ </span><span><span>(Wickham, 2016)</span></span><span>. 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; </span><span><span>(Brown et al., 2014)</span></span><span>. </span><span>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 </span><span><span>(Wang et al., 2012)</span></span><span> with a binomial error family<span>. </span>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 </span><span><span>(Brown et al., 2014)</span></span><span>. </span></p> <p><span>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’ </span><span><span>(Vaughan et al., 2018)</span></span><span> with the ‘generate_null_net’ command. The ‘generate_null_net_indiv’ function </span><span><span>(Cuff, Windsor, et al., 2023)</span></span><span> 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 </span><span>using a MLM in mvabund, and significant differences visually represented through a ternary plot using ggtern<span>. 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.</span></span></p> <p><span>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. </span></p>
Ferry et al. 2024 - Prey that is attractive but not repelled by predators suggests an asymmetric investment in the encounter-avoid-escape sequence. - R Code and Datasets
<p>R code for formating data and running PAMMs for all different combinations of predator-prey.</p> <p>Data of camera trap observation.</p> <p>Data of environmental variable associated to camera trap sites.</p>
Dataset for: Owner-ascribed personality profiles distinguish domestic cats that capture and bring home wild animal prey
<p>Dataset allowing repetition of the analyses in the above paper, comprising personality scores and predation data, with details of cat characteristics. See readme.txt file.</p>
Supplementary data for "Influence of prey availability on habitat selection during the non-breeding period in a resident bird of prey"
<p><strong>Abstract</strong></p> <p>Background: For resident birds of prey in the temperate zone, the cold non-breeding period can have strong impacts on survival and reproduction with implications for population dynamics. Therefore, the non-breeding period should receive the same attention as other parts of the annual life cycle. Birds of prey in intensively managed agricultural areas are repeatedly confronted with unpredictable, rapid changes in their habitat due to agricultural practices such as mowing, harvesting, and ploughing. Such a dynamic landscape likely affects prey distribution and availability and may even result in changes in habitat selection of the predator throughout the annual cycle.</p> <p>Methods: In the present study, we 1) quantified barn owl prey availability in different habitats across the annual cycle, 2) quantified the size and location of barn owl breeding and non-breeding home ranges using GPS-data, 3) assessed habitat selection in relation to prey availability during the non-breeding period, and 4) discussed differences in habitat selection during the non-breeding period to habitat selection during the breeding period.</p> <p>Results: The patchier prey distribution during the non-breeding period compared to the breeding period led to habitat selection towards grassland during the non-breeding period. The size of barn owl home ranges during breeding and non-breeding were similar, but there was a small shift in home range location which was more pronounced in females than males. The changes in prey availability led to a mainly grassland-oriented habitat selection during the non-breeding period. Further, our results showed the importance of biodiversity promotion areas and undisturbed field margins within the intensively managed agricultural landscape. </p> <p>Conclusions: We showed that different prey availability in habitat categories can lead to changes in habitat preference between the breeding and the non-breeding period. Given these results we show how important it is to maintain and enhance structural diversity in intensive agricultural landscapes, to effectively protect birds of prey specialised on small mammals. Hereafter we provide the datasets and R script to reproduce the resource selection functions.</p>
Temporal variation in spider trophic interactions is explained by the influence of weather on prey communities, web building and prey choice
<p>Materials and Methods</p> <p><em>Fieldwork</em><em> and sample processing</em></p> <p>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 (mm<sup>2</sup>). 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 m<sup>2</sup> 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.</p> <p> </p> <p><em>Weather data</em></p> <p>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 1<sup>st</sup> January 2018 to 17<sup>th</sup> September 2018 (the last field collection). Weather data were also separately extracted for the week preceding each of the two 2017 collection dates (3<sup>rd</sup> to 9<sup>th</sup> August and 29<sup>th</sup> August to 4<sup>th</sup> September 2017). Specifically, daily average temperatures (°C), daily average dew point (°C), maximum daily wind speed (km h<sup>-1</sup>), 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).</p> <p> </p> <p><em>Statistical Analysis</em></p> <p>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.</p> <p> </p> <p><em>Sampling completeness and diversity assessment</em></p> <p>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).</p> <p> </p> <p><em>Relationships between weather, spider trophic interactions and prey community composition</em></p> <p>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.</p> <p>All MGLMs were fitted using the ‘manyglm’ function in the ‘mvabund’ package (Wang et al., 2012). ‘Temporal model’ independent variables were calendar day (<em>day</em>), mean day length in minutes for the preceding week (<em>day length</em>), 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.</p> <p>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).</p> <p> </p> <p><em>Relationships between web characteristics and weather variables</em></p> <p>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.</p> <p> </p> <p><em>Variation in spider prey choice across weather conditions</em></p> <p>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).</p> <p>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.</p> <p> </p> <p><em>Validating and predicting relationships between years</em></p> <p>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 (<em>Tenuiphantes tenuis</em> and <em>Erigone</em> spp.) collected using the same methods by the same researchers and in the same locality (Cuff, Drake, et al., 2021).</p> <p>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:</p> <p> </p> <p>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).</p> <p>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:</p> <p> </p> <p>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.</p> <p>Null models were generated as above (<em>Variation in spider prey choice across weather conditions</em>) 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 <em>t</em>-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).</p> <p> </p> <p><strong>Supplementary Information 1: Field collection and sample processing</strong></p> <p>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 m<sup>2</sup> 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 (mm<sup>2</sup>). 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.</p> <p>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 m<sup>2</sup> 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.</p> <p> </p> <p><strong>Supplementary Information 2: Molecular analysis and bioinformatics</strong></p> <p><em>Extraction and high-throughput sequencing of spider gut DNA</em></p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. 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).</p> <p>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.</p> <p>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 <em>et al. </em>(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).</p> <p><em>Bioinformatic analysis</em></p> <p>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.</p> <p>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.</p> <p>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 <em>Blattella germanica</em>), 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.</p> <p> </p> <p><strong>Supplementary Information 3: Cluster analysis</strong></p> <p>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 <em>k</em> values; this was calculated using the ‘dunn’ function in the “clValid” package (Brock et al., 2008) for each cluster <em>k</em> value above five until the Dunn index decreased. The <em>k</em> 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.</p> <p>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).</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Alberdi, A., Aizpurua, O., Gilbert, M. T. P., & Bohmann, K. (2017). Scrutinizing key steps for reliable metabarcoding of environmental samples. <em>Methods in Ecology and Evolution</em>, <em>9</em>(1), 1–14. https://doi.org/10.1111/2041-210X.12849</p> <p>Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid: an R package for cluster validation. <em>Journal of Statistical Software</em>, <em>25</em>(4), 1–22.</p> <p>Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: architecture and applications. <em>BMC Bioinformatics</em>, <em>10</em>, 1–9. https://doi.org/10.1186/1471-2105-10-421</p> <p>Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). Fastp: An ultra-fast all-in-one FASTQ preprocessor. <em>Bioinformatics</em>, <em>34</em>(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560</p> <p>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. <em>Ecological Entomology</em>, <em>46</em>(2), 249–261.</p> <p>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. <em>Environmental DNA</em>, <em>4</em>(3), 549–564.</p> <p>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. <em>Methods in Ecology and Evolution</em>, <em>13</em>(3), 694–710.</p> <p>Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. <em>Bioinformatics</em>, <em>26</em>(19), 2460–2461. https://doi.org/10.1093/bioinformatics/btq461</p> <p>Garnier, S. (2018). <em>viridis: default color maps from ‘matplotlib’</em> (0.5.1). https://cran.r-project.org/package=viridis</p> <p>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. <em>PLoS Computational Biology</em>, <em>12</em>(6), 1–12. https://doi.org/10.1371/journal.pcbi.1004957</p> <p>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. <em>Methods in Ecology and Evolution</em>, <em>8</em>, 126–134. https://doi.org/10.1111/2041-210X.12647</p> <p>Neuwirth, E. (2014). <em>RColorBrewer: ColorBrewer palettes</em> (1.1-2). https://cran.r-project.org/package=RColorBrewer</p> <p>Roberts, M. J. (1993). <em>The Spiders of Great Britain and Ireland (Compact Edition)</em> (3rd ed.). Harley Books.</p> <p>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. <em>Applied and Environmental Microbiology</em>, <em>75</em>(23), 7537–7541. https://doi.org/10.1128/AEM.01541-09</p> <p>Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). <em>Environmental DNA</em>. Oxford University Press.</p> <p>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). <em>gplots: Various R programming tools for plotting data</em> (R package version 3.1.0). https://cran.r-project.org/package=gplots</p>
Are you scared yet? Variations to cue components elicits differential prey behavioral responses even when gape limited predators are relatively small.
Anti-predator behavior is often evoked based on measurements of risk calculated from sensory cues emanating from predators independent of physical attack. Yet, the exact sensory indices of cues used in risk assessment remain largely unknown. To examine how different predatory cue indices of information are used in risk assessment, we presented prey with various cues from sublethal gape-limited predators. Rusty crayfish (Faxonius rusticus (Girard, 1852)) were exposed to predatory odors from sublethal-sized largemouth bass (Micropterus salmoides (Lacepède, 1802)) to test effects of changing predator abundance, relative size relationships, and total predator length in flow through mesocosms. Foraging, shelter use, and movement behavior were used to measure cue effects. Foraging time depended jointly upon predator abundance and total predator size (p = 0.030). Specifically, high predator abundance resulted in decreased foraging efforts as gape ratio increased. Similarly, sheltering time depended on the interaction between predator abundance and gape ratio when predator abundance was highest (p = 0.020). Crayfish significantly increased exploration time when gape ratio increased (p = 0.010). Thus, this study shows crayfish can use different indices of predatory cues, namely total predator abundance and relative size ratios, in risk assessment but do so in context-specific ways.
Sublethal effects of pesticides on predator-prey interactions in amphibians, 2008.
Increasing evidence suggests that contaminants in the environment can have important consequences on organismal interactions. While we have a good understanding of the lethal effects of contaminants on organisms, we have a weak understanding of how contaminants can affect organisms by altering the interactions that they have with other species in the community. Using tadpoles of two anuran species (Bullfrogs, Lithobates [Rana] catesbeianus; Green Frogs, L. clamitans), we investigated the effects of low nominal concentrations (1 and 10 ppb) of two pesticides (malathion and endosulfan) on tadpole activity and survival when exposed to four predator treatments (no predators; water bugs, Belostoma flumineum; newts, Notophthalmus viridescens; and dragonfly larvae, Anax junius). In both anuran species, adding predators reduced tadpole activity and survival, with increasing rates of mortality occurring with water bugs, newts, and dragonflies, respectively. Additionally, the highest concentration of endosulfan caused tadpole mortality after 48 hrs. Most significant, tadpole species also experienced interactive effects of predators and pesticides on survival after 48 hrs. In Bullfrog treatments, all predators reduced the amount of tadpole mortality when exposed to endosulfan. In Green Frogs, additive negative effects occurred, except that newts increased the tadpole mortality when exposed to endosulfan. Our findings illustrate that pesticide effects on predator–prey interactions are often complex and have the potential to alter aquatic community composition.
Bottom-up meets top-down: Leaf litter inputs influence predator-prey interactions in wetlands, 2011.
While the common conceptual role of resource subsidies is one of bottom-up nutrient and energy supply, inputs can also alter the structural complexity of environments. This can further impact resource flow by providing refuge for prey and decreasing predation rates. However, the direct influence of different organic subsidies on predator–prey dynamics is rarely examined. In forested wetlands, leaf litter inputs are a dominant energy and nutrient resource and they can also increase benthic surface cover and decrease water clarity, which may provide refugia for prey and subsequently reduce predation rates. In outdoor mesocosms, we investigated how inputs of leaf litter that alter benthic surface cover and water clarity influence the mortality and growth of gray treefrog tadpoles (Hyla versicolor) in the presence of free-swimming adult newts (Notophthalmus viridiscens), which are visual predators. To manipulate surface cover, we added either oak (Quercus spp.) or red pine (Pinus resinosa) litter and crossed these treatments with three levels of red maple (Acer rubrum) litter leachate to manipulate water clarity. In contrast to our predictions, benthic surface cover had no effect on tadpole survival while darkening the water caused lower survival. In addition, individual tadpole mass was lowest in the high maple leachate treatments, suggesting an interaction between bottom-up effects of leaf litter and topdown effects of predation risk that altered mortality and growth of tadpoles. Our results indicate that realistic changes in forest tree composition, which cause concomitant changes in litter inputs to wetlands, can substantially alter community interactions.
What doesn’t kill you makes you sluggish: How sublethal pesticides alter predator-prey interactions, 2004.
Pesticides commonly occur in ecological communities at relatively low concentrations, leading to growing interest in determining the sublethal effects of pesticides. Such effects should affect individuals and, in turn, alter interspecific interactions. We sought to determine how sublethal concentrations (0.1 and 1.0 mg/L) of two common pesticides (carbaryl and malathion) affected predator and prey behavior as well as subsequent predation rates. We conducted a series of experiments using three species of larval amphibians (Gray Treefrogs, Hyla versicolor; Green Frogs, Rana clamitans; and American Bullfrogs, R. catesbeiana) and three species of their predators (larval dragonflies, Anax junius; adult water bugs, Belostoma flumineum; and adult Red-spotted Newts, Notophthalmus viridescens). We found that the pesticides frequently reduced the activity of all three tadpole species. For the two invertebrate predators (Anax and Belostoma), the pesticides were lethal, precluding us from examining sublethal effects on predator–prey interactions. However, newt survival was high and the addition of the pesticides reduced the predation rates of newts in one of the three tadpole species. There were no effects of the pesticides on the striking frequency of the newts or on their prey capture efficiency. Thus, the mechanism underlying the pesticide-induced reduction in predation rates remains unclear. What is clear is that sublethal concentrations of pesticides have the potential to alter prey behavior and species interactions and thereby alter the composition of ecological communities.
Interpreting the smells of predation: How alarm cues and kairomones induce different prey defenses.
1. For phenotypically plastic organisms to produce phenotypes that are well matched to their environment, they must acquire information about their environment. For inducible defences, cues from damaged prey and cues from predators both have the potential to provide important information, yet we know little about the relative importance of these separate sources of information for behavioural and morphological defences. We also do not know the point during a predation event at which kairomones are produced, i.e. whether they are produced constitutively, during prey attack or during prey digestion. 2. We exposed leopard frog tadpoles (Rana pipiens) to nine predator cue treatments involving several combinations of cues from damaged conspecifics or heterospecifics, starved predators, predators only chewing prey, predators only digesting prey or predators chewing and digesting prey. 3. We quantified two behavioural defences. Tadpole hiding behaviour was induced only by cues from crushed tadpoles. Reduced tadpole activity was induced only by cues from predators digesting tadpoles or predators chewing + digesting tadpoles. 4. We also quantified tadpole mass and two size-adjusted morphological traits that are known to be phenotypically plastic. Mass was unaffected by the cue treatments. Relative body length was affected (i.e. there were differences among some treatments), but none of the treatments significantly differed from the no-predator control. Relative tail depth was affected by the treatments and deeper tails were induced only when tadpoles were exposed to cues from predators digesting tadpoles or cues from predators chewing + digesting tadpoles. 5. These results demonstrate that some prey species can discriminate among a diverse set of potential cues from heterospecific prey, conspecific prey and predators. Moreover, the results illustrate that the cues responsible for the full suite of behavioural and morphological defences are not induced by tadpole crushing nor
Damage, digestion, and defense: The roles of alarm cues and kairomones for inducing prey defenses.
Inducible defences are widely used for studying phenotypic plasticity, yet frequently we know little about the cues that induce these defences. For aquatic prey, defences are induced by chemical cues from predators (kairomones) and injured prey (alarm cues). Rarely has anyone determined the separate and combined effects of these cues, particularly across phylogenetically diverse prey types. We examined how tadpoles (Hyla versicolor) altered their defences when 10 different prey were either crushed by hand or consumed by predators. Across all prey types, crushing induced only a subset of the defences induced by consumption. Consuming vs. crushing produced additive responses for behaviour but synergistic responses for morphology and growth. Moreover, we discovered the first extensive evidence that prey responses to different alarm cues depends on prey phylogeny. These results suggest that the amount of information available to the prey affects both the quantitative and qualitative nature of the defended phenotype.
When should prey respond to heterospecific alarm cues? Testing the hypotheses of perceived risk.
In aquatic systems, a long-standing question is why chemical cues from some diets consumed by a predator induce strong anti-predator responses in prey while other diets induce weak or no responses. We performed an experiment to determine if strong prey responses to particular predator diets are due to prey being closely related to the predator’s diet (i.e., phylogenetic relatedness) or due to prey coexisting with the predator’s diet and thereby sharing a risk of predation. We compared the behavior of Gray Treefrog tadpoles (Hyla versicolor) to cues from a dragonfly nymph (Anax junius) that consumed either conspecific Gray Treefrogs, one of six diets that commonly coexist with Gray Treefrogs (spanning a wide range of phylogenetic relatedness), or one diet that is closely related to Gray Treefrogs but has an allopatric range that has not overlapped for at least 20,000 yrs. We found that tadpoles could discriminate among the diets and that the magnitude of behavioral response supported the hypothesis of diet phylogenetic relatedness and refuted the hypothesis of diet coexistence.
Gut Fluorescence measurements of mesozooplankton grazing on autotrophic prey. Samples collected in the CCE-LTER region on Process Cruises from 2006 to the present. Summaries for each Lagrangian Cycle.
Mesozooplankton are collected with plankton nets (typically a 71-cm diameter, 202-um mesh Bongo net) and samples flash frozen at sea in liquid N2 for subsequent shore-based measurements of ingested phytoplankton chlorophyll-a. Measurements of mesozooplankton gut fluorescence are done by fluorometric analysis on a Turner Designs fluorometer of gut pigments extracted in 90% acetone. Analyses are done on mesozooplankton size-fractionated into 5 different categories on Nitex mesh (> 0.2 mm, 0.5 mm, 1.0 mm, 2.0 mm, 5.0 mm). The pigment content (as Chl-a and phaeopigments) is then expressed as mass of pigment ingested per m3 of water filtered, or divided by the dry weight biomass of the mesozooplankton in the same sample in order to obtain mass-specific ingestion per m3 of water. Application of published values of the temperature-dependent gut passage time are used to estimate the mesozooplankton grazing rate, as pigments ingested per m3 per unit time, or the corresponding mass-specific rate of ingestion. Samples for gut fluorescence assays have been collected on CCE-LTER Process Cruises since 2006 and these collections are ongoing.
Figure 10. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait
Figure 10. Spheniopsis brasiliensis. A transverse section through the heart. AM, Amoebocyte; AU, auricle; PE, pericardium; PEG, pericardial gland; R, rectum; SM, suspensory membrane; V, ventricle.
Figure 3. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait
Figure 3. Spheniopsis brasiliensis. A ventral view of the septum, foot and mouth. BG, Byssal groove; F, foot; F(T), 'toe' of foot; M, mouth; SE, septum; SEM, margin of septal membrane; SEP(1),(2),(3),(4), septal pores.
Figure 1 in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait
Figure 1. Spheniopsis brasiliensis. SEM views of the siphonal apparatus. (A) Posterior view of the exhalant and inhalant siphons, with three and four siphonal papillae, respectively. (B) Higher magnification view of a single siphonal papilla with a terminal array of sensory cilia. CI, Cilia; ES, exhalant siphon; IS, Inhalant siphon; SP, sensory papilla; SPB, base of sensory papillae.
Figure 9. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait
Figure 9. Spheniopsis brasiliensis. A transverse section through the pedal ganglia and the statocysts. PEGA, Pedal ganglia; STAT, statocyst; STL, statolith.
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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.
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.