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17 results for “Prey association”
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>
Selection against early flowering in geothermally heated soils is associated with pollen but not prey availability in a carnivorous plant
<p>This data set includes data on flowering phenology, rosette diameters and fitness of the perennial herb Pinguicula vulgaris, as well as data on soil temperature and experimental treatment applied. The data was collected during the summer of 2020 in 287 plant individuals located in a sub-arctic geothermal area in Ölfus municipality in SW-Iceland, Hengill (64°03’N; 21°18’W, ~360 m.a.s.l.).</p>
Gyrfalcon prey abundance and their habitat associations in a changing Arctic
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Cross-continental comparison of parasite communities in a wide-ranging carnivore suggests associations with prey diversity and host density
<p><span>Parasites are integral to ecosystem functioning yet often overlooked. Improved understanding of host-parasite associations is important, particularly for wide-ranging species for which host range shifts and climate change could alter host-parasite interactions and their effects on ecosystem function. </span></p> <p><span>Among the most widely distributed mammals with diverse diets, grey wolves (<i>Canis lupus</i>) host parasites that are transmitted among canids and via prey species. Grey wolf-parasite associations may therefore influence the population dynamics and ecological functions of both wolves and their prey. Our goal was to identify large-scale processes that shape host-parasite interactions across populations, with the grey wolf as a model organism. </span></p> <p><span>By compiling data from various studies, we examined the faecal prevalence of gastrointestinal parasites in six wolf populations from two continents in relation to wolf density, diet diversity, and other ecological conditions.</span></p> <p><span>As expected, we found that the faecal prevalence of parasites transmitted directly to wolves via contact with other canids or their excreta was positively associated with wolf density. Contrary to our expectations, the faecal prevalence of parasites transmitted via prey was negatively associated with prey diversity. We also found that parasite communities reflected landscape characteristics and specific prey items available to wolves. </span></p> <p><span>Several parasite taxa identified in this study, including hookworms and coccidian protozoans, can cause morbidity and mortality in canids, especially in pups, or in combination with other stressors. The density-prevalence relationship for parasites with simple lifecycles may reflect a regulatory role of gastrointestinal parasites on wolf populations. Our result that faecal prevalence of parasites was lower in wolves with more diverse diets could provide insight into the mechanisms by which biodiversity may regulate disease. A diverse suite of predator-prey interactions could regulate the effects of parasitism on prey populations and mitigate the transmission of infectious agents, including zoonoses, spread via trophic interactions. </span></p>
Food talk: 40-Hz fin whale calls are associated with prey biomass
<p>Animals use varied acoustic signals that play critical roles in their lives. Understanding the function of these signals may inform about key life-history processes relevant for conservation. In the case of fin whales (<em>Balaenoptera physalus</em>), that produce different call types associated with different behaviours, several hypotheses have emerged regarding call function, but the topic still remains in its infancy. Here, we investigate the potential function of two fin whale vocalizations, the song-forming 20-Hz call and the 40-Hz call, by examining their production in relation to season, year and prey biomass. Our results showed that the production of 20-Hz calls was strongly influenced by season, with a clear peak during the breeding months, and secondarily by year, likely due to changes in whale abundance. These results support the reproductive function of the 20-Hz song used as an acoustic display. Conversely, season and year had no effect on variation in 40-Hz calling rates, but prey biomass did. This is the first study linking 40-Hz call activity to prey biomass, supporting the previously suggested food-associated function of this call. Understanding the functions of animal signals can help identifying functional habitats and predict the negative effects of human activities with important implications for conservation.</p>
Food talk: 40-Hz fin whale calls are associated with prey biomass
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Cross-continental comparison of parasite communities in a wide-ranging carnivore suggests associations with prey diversity and host density
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Association of prey quality with environmental odors in the foraging behavior of Pardosa milvina
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Data from: Morphological convergence in a Mexican garter snake associated with the ingestion of a novel prey
Morphological convergence is expected when organisms which differ in phenotype experience similar functional demands, which lead to similar associations between resource utilization and performance. To consume prey with hard exoskeletons, snakes require either specialized head morphology, or to deal with them when they are vulnerable, e.g. during molting. Such attributes may in turn reduce the efficiency with which they prey on soft-bodied, slippery animals such as fish. Snakes which consume a range of prey may present intermediate morphology, such as that of Thamnophiine (Natricidae) which may be classified morphometrically across the soft-hard prey dietary boundary. In this study, we compared the dentition and head structure of populations of Thamnophis melanogaster that have entered the arthropod-crustacean (crayfish)-eating niche and those that have not, and tested for convergence between the former and two distantly related crayfish specialists of the genus Regina (R. septemvittata and R. grahamii). As a control, we included the congener T. eques. Multivariate analysis of jaw length, head length, head width, and number of maxillary teeth yielded three significant canonical variables that together explained 98.8 % of the variance in the size-corrected morphological data. The first canonical variable significantly discriminated between the three species. The results show that head dimensions and number of teeth of the two Regina species are more similar to those of crayfish-eating T. melanogaster than to non-crayfish-eating snakes or of T. eques. It is unclear how particular head proportions or teeth number facilitates capture of crayfish, but our results and the rarity of soft crayfish ingestion by T. melanogaster may reflect the novelty of this niche expansion, and are consistent with the hypothesis that some populations of T. melanogaster have converged in their head morphology with the two soft crayfish-eating Regina species, although we cannot rule out the possibility of a morphological preadaptation to ingest crayfish.
Fig. 6 in Prey-associated genetic differentiation in two species of silver fly (Diptera: Chamaemyiidae), Leucotaraxis argenticollis and L. piniperda
Fig. 6. Haplotype network of Leucotaraxis piniperda DNA barcode sequences.The area of each pie chart is proportional to the number of samples sharing that haplotype. Small black dots represent unsampled haplotypes. Pie charts indicate the proportions of flies sampled from different host plant genera of adelgid prey.
Fig. 3. STRUCTURE plot for Leucotaraxis argenticollis genotyped with 15 in Prey-associated genetic differentiation in two species of silver fly (Diptera: Chamaemyiidae), Leucotaraxis argenticollis and L. piniperda
Fig. 3. STRUCTURE plot for Leucotaraxis argenticollis genotyped with 15 microsatellite loci.The height of each bar represents the proportion of an individual′s genotype assigned to each of K = 4 clusters.The names of the clusters correspond to those in Fig. 1C. Vertical black lines separate groups of individuals collected in different states or provinces and on different adelgid host plant genera.
Fig. 4. STRUCTURE plot for Leucotaraxis piniperda genotyped with 16 in Prey-associated genetic differentiation in two species of silver fly (Diptera: Chamaemyiidae), Leucotaraxis argenticollis and L. piniperda
Fig. 4. STRUCTURE plot for Leucotaraxis piniperda genotyped with 16 microsatellite loci. The height of each bar represents the proportion of an individual′s genotype assigned to: A) each of K = 2 clusters for analysis of all individuals, and B) each of K = 2 clusters for analysis of only western individuals.The names of the clusters correspond to those in Fig. 1D. Vertical black lines separate groups of individuals collected in different states or provinces and on different adelgid host plant genera.
Fig. 2 in Prey-associated genetic differentiation in two species of silver fly (Diptera: Chamaemyiidae), Leucotaraxis argenticollis and L. piniperda
Fig. 2. Individual-based isolation by distance for eastern and western groups of both Leucotaraxis species. Pairwise genetic distance was calculated as linear genotypic distance and Euclidean geographic distance was calculated from collection site coordinates. Points are differentially shaded for distances between two individuals collected from the same adelgid prey host plant genus (e.g., Tsuga—Tsuga), or for two individuals collected from different adelgid prey host plant genera (e.g., Tsuga—Pinus).
Fig. 1 in Prey-associated genetic differentiation in two species of silver fly (Diptera: Chamaemyiidae), Leucotaraxis argenticollis and L. piniperda
Fig. 1. Maps showing sample sites indicating the host plant genus of the adelgid prey from which flies were collected for: A) L. argenticollis, and B) L. piniperda; and pie charts showing population genetic structure for: C) L. argenticollis, and D) L. piniperda. Structure results correspond to the plots shown in Figs. 3 and 4.
Fig. 5 in Prey-associated genetic differentiation in two species of silver fly (Diptera: Chamaemyiidae), Leucotaraxis argenticollis and L. piniperda
Fig. 5. Haplotype network of Leucotaraxis argenticollis DNA barcode sequences.The area of each pie chart is proportional to the number of samples sharing that haplotype. Small black dots represent unsampled haplotypes. Pie charts indicate the proportions of flies sampled from different host plant genera of adelgid prey.
Data from: Associational resistance or susceptibility: the indirect interaction between chemically-defended and non-defended herbivore prey via a shared predator
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Data from: Morphological convergence in a Mexican garter snake associated with the ingestion of a novel prey
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