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1,551 results for “prey”
Predator discrimination of prey promotes the predator-mediated coexistence of prey species
<p><span>The predator discrimination of prey can affect predation intensity and the prey density-dependence of predators, which has the potential to alter the coexistence of prey species. We used a predator–prey population dynamics model accounting for the predator's adaptive diet choice and predator discrimination of prey to investigate how the latter influences prey coexistence. The model revealed that: (1) prey species that are perceived as belonging to the same species by a predator are attacked in the same manner, and it is more difficult for them to coexist than those that are recognised as different prey species; and (2) prey species that are not discriminated by a predator—and therefore cannot coexist—may coexist in the presence of an alternative predator that does discriminate between them. These results suggest that prey diversity, which favours the predator discrimination of prey, and the different capabilities of predators to identify prey species both enhance prey coexistence.</span></p>
Data from: Tall, heterogenous forests improve prey capture, delivery to nestlings, and reproductive success for Spotted Owls in southern California
<p>Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (<em>Strix</em> <em>occidentalis</em> <em>occidentalis</em>) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (<em>Neotoma</em> spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions.</p>
Extremely low seasonal prey capture efficiency in a deep-diving whale, the narwhal
<p><span>Successful foraging is essential for individuals to maintain the positive energy balance required for survival and reproduction. Yet, prey capture efficiency is poorly documented in marine apex predators, especially deep-diving mammals. We deployed acoustic tags and stomach temperature pills in summer to collect concurrent information on presumed foraging activity (through buzz detection) and successful prey captures (through drops in stomach temperature), providing estimates of feeding efficiency in narwhals. Compared to the daily number of buzzes (706.9 </span><span>± </span><span>368), the daily rate of feeding events was particularly low in summer (19.8 </span><span>± </span><span>8.9), and only 8–14% of the foraging dives were successful (i.e., with a detectable prey capture). This extremely low success rate resulted in a very low daily food consumption rate (< 0.5% of body mass), suggesting that narwhals rely on body reserves accumulated in winter to sustain year-round activities. </span><span>The expected changes or disappearance of their wintering habitats in response to climate change may therefore have severe fitness consequences for narwhal populations.</span></p>
Arthropod food webs predicted from body length ratios are improved by incorporating prey defensive properties
<p>This dataset was used to run for Van de Walle et al. (2023). Arthropod food webs predicted from body length ratios are improved by incorporating prey defensive properties. Journal of Animal Ecology, 92(4), 913-924. <a href="https://doi.org/10.1111/1365-2656.13905">https://doi.org/10.1111/1365-2656.13905</a></p> <p>"Arthropod_feeding_trials.csv" contains the results of the experimental feeding trials. Each row contains information on species taxonomy, body size and hunting strategy within a single trial.</p> <p><strong>Abstract</strong></p> <p>Trophic interactions are often deduced from body size differences between predators and potential prey, assuming predators prefer prey smaller than themselves because larger prey are more difficult to subdue. This hypothesis has mainly been confirmed in aquatic ecosystems, but rarely in terrestrial ecosystems, especially in arthropods. Our goal was to validate whether body size ratios can accurately predict trophic interactions in a terrestrial, plant-associated arthropod community. Additionally, we tested whether predator hunting strategy and prey taxonomy could explain possible deviations from this general rule.</p> <p>We collected arthropods from marram grass in coastal dunes and conducted pairwise feeding trials to explicitly test whether two individuals, of the same or different species, would predate each other. From the trial results, we constructed one of the most complete, empirically derived food webs for terrestrial arthropods associated with a single plant species. We contrasted this empirical food web with a theoretical web based on body size ratios, literature and expert knowledge.</p> <p>In our feeding trials, predator-prey interactions were indeed largely size-based. Moreover, the theoretical food web based on body size, activity period, microhabitat and expert knowledge converged quite well with the food web based on experimental feeding trials for both predator and prey species. However, predator hunting strategy, but mainly prey taxonomy improved predictions of predation events. Well-defended taxa, such as hard-bodied beetles, were less frequently consumed than expected based on their body size.</p> <p>Body size ratios predict trophic interactions among plant-associated arthropods fairly well. However, traits such as hunting strategy and anti-predator defences can explain why certain trophic interactions do not adhere to size-based rules. Feeding trials can generate insights into multiple traits underlying real-life trophic interactions among arthropods. Such insights are much needed as the dramatic global decline in arthropod species richness and abundance is knocking out many trophic interactions on which services such as pest control and nutrient cycling depend.</p>
Data and code from: Three decades of wildlife-vehicle collisions in a protected area: main roads and long-distance commuting trips to migratory prey increase spotted hyena roadkills in the Serengeti
<p>This is the first release. Potential updates will be available on GitHub: <a href="https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area">https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area</a></p>
Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
<p>Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space therefore provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled faeces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analysis of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes.</p>
Moonlight synchronous flights across three western palearctic swifts mirror size dependent prey preferences
<p><strong>Abstract</strong></p> <p>Recent studies have suggested the presence of moonlight mediated behaviour in avian aerial insectivores, such as swifts. At the same time swift species also show differences in prey (size) preferences. Here, we use the combined analysis of state-of-the-art activity logger data across three swift species, the Common, Pallid and Alpine swifts, to quantify flight height and activity responses to crepuscular and nocturnal light conditions. Our results show a significant response in flight heights to moonlight illuminance for Common and Pallid swifts, while a moonlight driven response is absent in Alpine swifts. Swift flight responses followed the size dependent altitude gradient of their insect prey. We show a weak relationship between night-time illuminance driven responses and twilight ascending behaviour, suggesting a decoupling of both crepuscular and night-time behaviour. We suggest that swifts optimise their flight behaviour to adapt to favourable night-time light conditions, driven by light responsive and size-dependent vertical insect stratification and weather conditions.</p> <blockquote> <p>You are required to cite both the Zenodo data repository as well as the BioRXiv pre-print when using this data, as:</p> <p>Hufkens et al. 2023. Moonlight synchronous flights across three western palearctic swifts mirror size dependent prey preferences. doi://10.5281/zenodo.7814214</p> <p>Hufkens et al. 2023. Moonlight synchronous flights across three western palearctic swifts mirror size dependent prey preferences. bioRxiv 2023.04.25.538243; doi: https://doi.org/10.1101/2023.04.25.538243</p> </blockquote> <p><strong>Use</strong></p> <p>This is a deposited version of the releases on Github.</p> <p>Either download this Zenodo repository or clone or download the project Github <a href="https://github.com/bluegreen-labs/swift_lunar_synchrony/archive/refs/heads/main.zip">zip file</a>.</p> <pre><code class="language-bash">git clone https://github.com/bluegreen-labs/swift_lunar_synchrony.git</code></pre> <p>Unzip the downloaded data if required. The repository is an `R` project and can be opened in <a href="https://posit.co/download/rstudio-desktop/">RStudio</a>, which will set the correct relative path.</p> <p><strong>Data structure & analysis</strong></p> <p>Analysis data is saved as compressed R serial files (.rds) in the <a href="https://github.com/bluegreen-labs/swift_lunar_synchrony/tree/main/data">`data` folder</a>. Scripts to reproduce the main statistical results are provided in the <a href="https://github.com/bluegreen-labs/swift_lunar_synchrony/tree/main/analysis">`analysis` folder</a>. A matching render of the analysis using the shared data is provided as <a href="http://bluegreen-labs.github.io/swift_lunar_synchrony/">dynamic webpage</a>.</p> <p><strong>Licensing</strong></p> <p>Be mindful of the CC-BY 4.0 license of the data and figures. Reuse is permitted on the condition of proper attribution and documentation of any changes.</p>
Virtual prey with Lévy motion are preferentially attacked by predatory fish
<p>Of widespread interest in animal behaviour and ecology is how animals search their environment for resources, and whether these search strategies are optimal. However, movement also affects predation risk through effects on encounter rates, the conspicuousness of prey, and the success of attacks. Here we use predatory fish attacking a simulation of virtual prey to test whether predation risk is associated with movement behaviour. Despite often being demonstrated to be a more efficient strategy for finding resources such as food, we find that prey displaying Lévy motion are twice as likely to be targeted by predators than prey utilising Brownian motion. This can be explained by the predators, at the moment of the attack, preferentially targeting prey that were moving with straighter trajectories rather than prey that were turning more. Our results emphasise that costs of predation risk need to be considered alongside the foraging benefits when comparing different movement strategies.</p>
Fast prediction in marmoset reach-to-grasp movements for dynamic prey - Reach Data and Supplemental Video
<p>Supplemental video and data corresponding to Shaw, L., Wang, K.H., Mitchell, J. (2023) Fast prediction in marmoset reach-to-grasp movements for dynamic prey.</p> <p>1. Video Files</p> <p>MarmoReach 1 is an illustrative example.</p> <p>MarmoReach 2 illustrates the reaching trial shown in Figure 3D.</p> <p>MarmoReach 3-5 are example reach to grasps from grasp clusters found in Figure 2. </p> <p>2. Data</p> <p>marmo_reach_model.mat is a Matlab struct.</p> <p>2D position data of hand and cricket used for analyses related to Figure 3 and Figure 4. </p> <p>x.hand,y.hand = position data of the central hand marker for each trial.</p> <p>x.cricket,y.cricket = position data of the cricket marker for each trial.</p> <p>x.cricketexfull,y.cricketexfull = position data of the cricket marker preceding reach onset for delay analyses. </p> <p>To reconstruct cricket position from beginning to end with the inclusion of the exfull data (prior to reach to reach end) for the second reach, for example, [model.x.cricketexfull{2}' model.x.cricket{2}'].</p> <p> </p>
Dazzled by shine: gloss as an antipredator strategy in fast moving prey
<p>Previous studies on stationary prey have found mixed results for the role of gloss in predator avoidance – some have found that gloss can act as warning colouration or improve camouflage, whereas others detected no survival benefit. An alternative untested hypothesis is that gloss could provide protection in the form of dynamic dazzle. Fast-moving animals that are glossy produce flashes of light that increase in frequency at higher speeds, which could make it harder for predators to track and accurately locate prey. We tested this hypothesis by presenting praying mantids with glossy or matte targets moving at slow and fast speeds. Mantids were less likely to strike glossy targets, independently of speed. Additionally, we found that compared to matte targets, mantids were less likely to track glossy targets and more likely to hit the target with one rather than both raptorial arms, but only when targets were moving fast. These results support the hypothesis that gloss may have a function as an antipredator strategy by reducing the ability of predators to track and accurately target fast-moving prey. </p>
Data and code for: Changes in prey body size differentially reduces predation risk across predator and prey abundances
<p>Trophic interactions underpin the structure of ecological communities by describing the rate at which consumers exploit their resources. The rates at which predators consume their prey are influenced by prey traits, with many species inducing defensive modifications to prey traits following the threat of predation. Here we use different clonal lines of the protist <em>Paramecium</em> being consumed by <em>Stenostomum</em> predators to highlight how differences in prey traits impact rates of predation. Clonal lines differed in their body width traits and in their ability to induce changes in body width. By using a factorial cross of predator and prey abundances for different clonal lines we demonstrate how evolutionary or induced alterations in prey traits can impact the relative threat of predation. Our experiments show how interference among predators impacts predation rate and how increased body width increased predator handling times. Given that reductions in the strength of interspecific interactions are associated with increased levels of overall community stability, our results indicate how individual-level changes may scale up to impact whole communities. </p>
Data from: Dynamic balancing of risks and rewards in a large herbivore: Further extending predator-prey concepts to road ecology
<p>Animal behavior is shaped by the ability to identify risks and profitably balance the levels of risks encountered with the payoffs experienced. Anthropogenic disturbances like roads generate novel risks and opportunities that wildlife must accurately perceive and respond to. Basic concepts in predator-prey ecology are often used to understand responses of animals to roads (e.g., increased vigilance, selection for cover in their vicinity). However, prey often display complex behaviors such as modulating space use given varying risks and rewards, and it is unclear if such dynamic balancing is used by animals in the context of road crossings.</p> <p>We tested whether animals dynamically balance risks and rewards relative to roads using extensive field -based and GPS collar data from elk in Yoho National Park (British Columbia, Canada) where a major highway completely bisects their range during most of the year.</p> <p>We analyzed elk behavior by combining hidden Markov movement models with a step-selection function framework. Rewards were indexed by a dynamic map of available forage biomass and risks were indexed by road crossings and traffic volumes.</p> <p>We found that elk generally selected intermediate and high forage biomass and avoided crossing the road. Most of the time, elk modulated their behavior given varying risks and rewards. When crossing the highway compared with not crossing, elk selected for greater forage biomass and this selection was stronger as the number of highway crossings increased. However, with traffic volume, elk only balanced foraging rewards when they crossed a single time during a travel sequence.</p> <p>Using a road ecology system, we empirically tested an important component of predator-prey ecology – the ability to dynamically modulate behavior in response to varying levels of risks and rewards. Such a test articulates how decision-making processes that consider the spatiotemporal variation in risks and rewards allow animals to successfully and profitably navigate busy roads. Applying well-developed concepts in predator-prey theory helps understand how animals respond to anthropogenic disturbances and anticipate the adaptive capacity for individuals and populations to adjust to rapidly changing environments.</p>
The influence of prey density and pollen on the predation and oviposition rate of Amblyseius swirskii on Echinothrips americanus
<p>Data set of the research: "The influence of prey density and pollen on the predation and oviposition rate of Amblyseius swirskii on Echinothrips americanus". The dataset contains observations of predation and oviposition of Amblyseius swirskii on Echinothrips americanus. The different conditions were leaf area and prey density.</p>
Sources of prey availability data alter interpretation of outputs from prey choice null networks
<p><em>Spider surveys</em></p> <p>Data collection was described previously by Cuff, Tercel, et al., (2022). This study pertains to a subset of those data, collected between 1<sup>st</sup> May and 9<sup>th</sup> July 2018 at 19 separate locations, for which paired sticky trap and vacuum sample data were collected (described below). Briefly, 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 webs and the ground. Transects were randomly distributed across the entire field. Along these transects, 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 spiders were also collected. Spiders were taken to Cardiff University, transferred to fresh ethanol and stored at -80 °C in 100 % ethanol until DNA extraction. 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 below.</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). Bioinformatic analysis followed Drake et al. (2022).</p> <p><em>Bioinformatic analysis</em></p> <p>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. </p> <p>The resultant sequencing read counts were converted into relative proportions (all values made to sum to one within each sample) and a mean value across the two primer pairs retained for each taxon within each sample. Relative read abundances were converted to presence-absence data of each detected prey taxon in each individual spider, but relative read abundance data were also retained for separate analyses to compare experimental outcomes between treatments.</p> <p><em>Invertebrate surveys</em></p> <p>To estimate prey availability using sticky traps, we placed one white dry 100 mm x 125 mm trap (Oecos) in the 4 m<sup>2</sup> quadrat centred at the position where the spider was captured. The trap was suspended with wire approximately 25 mm above the ground to catch falling, crawling and flying invertebrates, and left in place for 72 hours. Invertebrates were identified on the traps under a stereomicroscope. To estimate prey availability using suction sampling, ground and crop stems were sampled using a ‘G-vac’ for approximately 30 seconds at each location. 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 following suction sampling.</p> <p><em>Statistical Analysis</em></p> <p>All analyses were conducted in R v4.0.3 (R Core Team, 2021) and carried out on invertebrate data at the family or superfamily level. Alongside the dietary data derived from metabarcoding, and prey availability as determined directly by suction sampling (abundance) and sticky trapping (activity density), three additional datasets were generated where two were designed to combine data from the two trapping methods. The first approach simply set all invertebrate taxa detected in the field to have equal abundance, to provide a baseline against which to assess the effects of different prey abundance estimates. When generating the two combined data sets, it was apparent that simply adding them together would underrepresent one of the datasets as abundance and activity density are measured in different units. Therefore, a ‘proportional combined’ dataset was generated by converting counts to relative proportions of each sample (to equally weight the two methods), which were then combined by summing proportions between the two methods for each sample, multiplied by the total count of individuals across both methods for each sample (to create realistic abundance values), and then rounded to the nearest integer (to return count data). In addition, a ‘frequency of occurrence (FOO) combined’ dataset was generated by converting counts to binary presence-absence values of each sample, which were then summed between the two methods for each sample. To assess the diversity represented by the two sampling methods and their combinations, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell et al., 2021) using the ‘iNEXT’ package with families represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016).</p> <p>The remaining analyses were performed using both presence-absence and relative read abundance dietary data separately to show how differences in the treatment of the observed data are reflected in the outcomes of the analyses. Figures and outputs given in the main text relate to the presence-absence data, while relative read abundance figures and outputs are presented in the Supplementary Information. Prey preferences of spiders were analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ function. Econullnetr generates null models based on prey availability to predict how consumers would forage if based on the availability of resources alone. These null models are then compared against the observed interactions of consumers (e.g., interactions of spiders with their prey based on dietary metabarcoding) to ascertain the extent to which resource consumption deviated from random. In five separate null models, prey availability was represented separately by the datasets described above: abundance (suction sampling), activity density (sticky trapping), proportional combined, FOO combined and equal prey abundance.</p> <p>To compare effect sizes between null models for each resource taxon, mean prey preference standardised effect size (SES) values were calculated from the individual spiders per model. The SES values were plotted and joined between taxa to visualise paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via an econullnetr null model with 999 simulations with outputs extended to allow the comparison of the null interactions for individual consumers (generate_null_net_indiv; Cuff, Kitson, et al., 2023). A visualisation of the per-individual differences in null model and observed data was generated via non-metric multi-dimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 9999 simulations, with Euclidean distance. Centroid coordinates for each null model and the observed data were extracted and pairwise distances calculated between model centroids:</p> <p>The ‘observed’ network (i.e., the network determined solely by dietary data, not necessarily the objectively ‘true’ network) and each null network were visualised with the associated prey choice effect sizes as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016) via an ‘igraph’ object (Csardi & Nepusz, 2006). The degree of each prey node, weighted nestedness and linkage density were generated using the ‘bipartite’ package (Dormann et al., 2008) for each network and compared visually via ggplot2.</p>
Metadata for prey choice in insectivorous steppe passerines Paper
<p>This dataset comprises metadata which corresponds to a paper prepared for submission entitled "Prey choice in insectivorous steppe passerines: new insights from DNA metabarcoding".</p> <p>Sample identifiers are given for faecal samples collected from shrub-steppe passerines at the province of Soria, Spain. Information such as location, date of collection, sex and other data are supplied.</p>
Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey
<p><strong>Data and file overview:</strong></p> <ol> <li>Kamaru_Path_Analysis_Data.csv</li> <li>Kamaru_Path_Analysis.R</li> <li>Kamaru_Zebra_RSF_Data.csv</li> <li>Kamaru_Zebra_RSF.R</li> </ol> <p><strong>Layers used to build Zebra RSF:</strong></p> <ol> <li>Kamaru_DWater: distance to water</li> <li>Kamaru_DGlade: distance to glade</li> <li>Kamaru_DSettlement: distance to human settlement</li> <li>Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others)</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong></p> <ol> <li>Number of variables: 11</li> <li>Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020)</p> <p>(B) Kill_ID: kill identifier.</p> <p>(C) Lion_ID: individual lion pride identifier.</p> <p>(D) Date (Day, Month, Year) when a specific kill occurred.</p> <p>(E) Zebra_kill (1 = kill site, 0 = paired random location).</p> <p>(F). Species: Zebra.</p> <p>(G) Visibility: openness measurement using a rangefinder in (m).</p> <p>(H) Lion_activity: Utilization distributions (UD) of lions.</p> <p>(I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants).</p> <p>(J) zeb.rsf: resource selection function value.</p> <p>(K) zeb.density: zebra density estimated from resource selection functions.</p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong></p> <ol> <li>Number of variables: 10</li> <li>Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) Species: Zebra.</p> <p>(B) Date (Day, Month, Year) for that sighting.</p> <p>(C) Survey: count identifier (Survey 2 to 21).</p> <p>(D) GPS location (X and Y), longitude and latitude of that sighting location.</p> <p>(E) Transect: Transect number.</p> <p>(F) Used: (1= zebra sighting, 0 = paired point).</p> <p>(G) zebra.ct: total number of zebras in each sighting.</p> <p> </p> <p><strong>R CODE</strong></p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models.</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.</li> </ol>
Landscape heterogeneity provides co-benefits to predator and prey
<p>Predator populations are imperiled globally, due in part to changing habitat and trophic interactions. Theoretical and laboratory studies suggest that heterogeneous landscapes containing prey refuges acting as source habitats can benefit both predator and prey populations, although the importance of heterogeneity in natural systems is uncertain. Here, we tested the hypothesis that landscape heterogeneity mediates predator-prey interactions between the California spotted owl (<em>Strix occidentalis occidentalis</em>) – a mature forest species – and one of its principal prey, the dusky-footed woodrat (<em>Neotoma</em> <em>fuscipes</em>) – a younger forest species – to the benefit of both. We did so by combining estimates of woodrat density and survival from live-trapping and VHF tracking with direct observations of prey deliveries to dependent young by owls in both heterogeneous and homogeneous home ranges. Woodrat abundance was approximately 2.5x higher in owl home ranges (1,412 hectares) featuring greater heterogeneity in vegetation types (1,805.0 ± 50.2 SE) compared to those dominated by mature forest (727.3 ± 51.9 SE), in large part because of high densities in young forests appearing to act as sources promoting woodrat densities in nearby mature forests. Woodrat mortality rates were low across vegetation types and did not differ between heterogeneous and homogeneous home ranges, yet all observed predation by owls occurred within mature forests, suggesting young forests may act as woodrat refuges. Owls exhibited a type 1 functional response, consuming approximately 2.5x more woodrats in heterogeneous (31.1/month ± 5.2 SE) versus homogeneous (12.7/month ± 3.7 SE) home ranges. While consumption of smaller-bodied alternative prey partially compensated for lower woodrat consumption in homogeneous home ranges, owls nevertheless consumed 30% more biomass in heterogeneous home ranges – approximately equivalent to the energetic needs of producing one additional offspring. Thus, a mosaic of vegetation types including young forest patches increased woodrat abundance and availability that, in turn, provided energetic and potentially reproductive benefits to mature forest-associated spotted owls. More broadly, our findings provide strong empirical evidence that heterogeneous landscapes containing prey refuges can benefit both predator and prey populations. As anthropogenic activities continue to homogenize landscapes globally, promoting heterogeneous systems with prey refuges may benefit imperiled predators.</p>
FIGURE 4 in Oviparity, viviparity or plasticity in reproductive mode of the olm Proteus anguinus: an epic misunderstanding caused by prey regurgitation?
FIGURE 4 Proteus anguinus larva (captive-bred from Tular Cave Laboratory, photograph by Gregor Aljančič) and Salamandra salamandra larva in comparison. Both larvae are about 3 cm in size. Note the difference in size and shape of the head and trunk length, but similarities in presence of eyes and pigmentation. PHOTOGRAPH OF proteus anguinus LARVA BY GREGOR ALJANČIČ AND salamandra salamandra LARVA BY JAMES BURGON
FIGURE 3 in Oviparity, viviparity or plasticity in reproductive mode of the olm Proteus anguinus: an epic misunderstanding caused by prey regurgitation?
FIGURE 3 Scanning electron micrographs of the olm's teeth marks on regurgitated salamander (Salamandra salamandra) larvae. Heads of all larvae are facing towards the left. A) Teeth marks (arrowheads) on the dorsal side of the head corresponding to the position of the olm's jaw and B) parallel teeth marks on the left side of the head above the gills (g) of the larvae shown in fig. 2A. C) Teeth marks on the anterior edge of laceration above the left gills (g) and D) above the right eye (e) of the head of the larva shown in fig. 2B and C. (Scale bars represents 500 µm).
FIGURE 1 in Oviparity, viviparity or plasticity in reproductive mode of the olm Proteus anguinus: an epic misunderstanding caused by prey regurgitation?
FIGURE 1 Image of an olm (Proteus anguinus) regurgitating a fire salamander (Salamandra salamandra) larva. A) Image of an olm (22.5 cm of total length), moments before B) it started to regurgitate a salamander larva (3.1 cm of total length). C) shows a close-up of the salamander head and the larva fully regurgitated, with D) showing details of the still alive salamander larva.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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