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15 results for “diet choice”
Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders 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. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). 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 and its approximate dimensions were recorded, the latter calculated as approximate web area. 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 subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which 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>Extraction and high-throughput sequencing of spider gut DNA</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 <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to 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 <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). 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 (Supplementary Table 1) 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., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran 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. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. 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 Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird
<p>The diet of an individual animal is subject to change over time, both in response to short-term food fluctuations and over longer time scales as an individual ages and meets different challenges over its life cycle. A metabarcoding approach was used to elucidate the diet of different life stages of a migratory songbird, the Eurasian reed warbler (<em>Acrocephalus scirpaceus</em>) over the 2017 summer breeding season in Somerset, UK. The faeces of adult, juvenile and nestling warblers were screened for invertebrate DNA, enabling the identification of prey species. Dietary analysis was coupled with monitoring of Diptera in the field using yellow sticky traps. Seasonal changes in warbler diet were subtle whereas age class had a greater influence on overall diet composition. Age classes showed high dietary overlap, but significant dietary differences were mediated through the selection of prey; i) from different taxonomic groups, ii) with different habitat origins (aquatic versus terrestrial) and iii) of different average approximate sizes. Our results highlight the value of metabarcoding data for enhancing ecological studies of insectivores in dynamic environments. </p>
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
<p>Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behavior and foraging patterns are also influenced by abiotic and biotic factors. Fine-scale mechanisms of density-dependent foraging at the bite scale are likely consistent with density-dependent behavioral patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density-dependent effects observed at broader scales in caribou. We specifically assessed how behavioral choices are affected by space use intensity and environmental processes using behavioral state and forage selection data from caribou (<i>Rangifer tarandus granti</i>) observed from GPS video-camera collars using a multivariate discrete-choice modeling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of <i>Salix</i> spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine-scale foraging behavior by reducing the overall probability of eating. Strong eastward winds mitigated the negative effects of insects and resulted in higher probabilities of eating lichen. Lastly, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine-scale foraging were consistent with density-dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight the potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviors provides exciting future applications where spatial models can identify high-quality habitats for conservation.</p>
Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird
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Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
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Fish resist temptation from junk food: State-dependent diet choice in reproductive Atlantic cod (Gadus morhua) facing seasonal fluxes of lipid-rich prey
<p>In ecological sciences, animal diets are often simplified to "resources" or "caloric quantities". However, in the present study, we investigated the optimal foraging strategy of Atlantic cod (Gadus morhua) when both macro- and micro-nutritional requirements are accounted for. Proteins cannot be synthesized from fatty acids, so the proteins for gonad development must come from other dietary sources. In addition, micronutrients are required in smaller quantities. For example, for cod, arachidonic acid (ARA) acts as a micronutrient precursor for prostaglandins, which is important for reproduction. We formulated a dynamic state-dependent model to make predictions about optimal diet choice and foraging behavior. We applied the model to a case study in the strait between Denmark and Sweden. The model predicted that energy acquired from dietary protein should be twice that acquired from lipids, with a small increase in the lipid requirements when gonads are growing. The model also predicted that the "energy sparing effect of lipids" made it beneficial to engage in risky foraging activity to supplement a lean diet with a little bit of fat. When we re-constructing the model to also optimize ARA uptake, the cod consumed relatively more ARA-rich crabs in the months prior to spawning, despite the otherwise poor energetic value of this prey. In support of the model predictions, field observations indicated that lipid stores reached a peak shortly after the arrival of the lipid-rich migrating herring and the fatty acid signal of these herring were evident in the liver of nearly all cod. Three month later, only half of the cod contained the herring-derived fatty acid signal, supporting the predicted shift in prey type prior to spawning. From these model predictions and field observations, we conclude that, also in the wild, nutritional requirements can be at least as important as pure energy acquisition.</p>
A nutrition-defense tradeoff drives diet choice in a toxic plant generalist
<p>Plant toxicity shapes the dietary choices of herbivores. Especially when herbivores sequester plant toxins, they may experience a tradeoff between gaining protection from natural enemies and avoiding toxicity. The availability of toxins for sequestration may additionally trade off with the nutritional quality of a potential food source for sequestering herbivores. We hypothesized that diet mixing might allow a sequestering herbivore to balance nutrition and defense (via sequestration of plant toxins). Accordingly, here we address diet mixing and sequestration of large milkweed bugs (<em>Oncopeltus fasciatus</em>) when they have differential access to toxins (cardenolides) in their diet. In the absence of toxins from a preferred food (milkweed seeds), large milkweed bugs fed on nutritionally adequate non-toxic seeds, but supplemented their diet by feeding on nutritionally poor, but cardenolide-rich milkweed leaf and stem tissues. This dietary shift corresponded to reduced insect growth but facilitated sequestration of defensive toxins. Plant production of cardenolides was also substantially induced by bug feeding on leaf and stem tissues, perhaps benefitting this cardenolide-resistant herbivore. Thus, sequestration appears to drive diet mixing in this toxic plant generalist, even at the cost of feeding on nutritionally poor plant tissue.</p>
A nutrition-defense tradeoff drives diet choice in a toxic plant generalist
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Fish resist temptation from junk food: State-dependent diet choice in reproductive Atlantic cod (Gadus morhua) facing seasonal fluxes of lipid-rich prey
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Diet Choices Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Cut Down on Carbohydrate Usage in the Diet of Type 2 Diabetes - Mechanisms of Effective Therapy of Diabetes by Selective Choice of Macronutrients - The Phenotype Study
ClinicalTrials.gov study NCT02472951. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: Diet complexity in early life affects survival in released pheasants by altering foraging efficiency, food choice, handling skills and gut morphology
1. Behavioural and physiological deficiencies are major reasons why reintroduction programmes suffer from high mortality when captive animals are used. Mitigation of these deficiencies is essential for successful reintroduction programmes. 2. Our study manipulated early developmental diet to better replicate foraging behaviour in the wild. Over 2 years, we hand-reared 1800 pheasants (Phasianus colchicus), from 1 day old, for 7 weeks under different dietary conditions. In year one, 900 pheasants were divided into three groups and reared with (i) commercial chick crumb, (ii) crumb plus 1% live mealworm or (iii) crumb plus 5% mixed seed and fruit. In year two, a further 900 pheasants were divided into two groups and reared with (i) commercial chick crumb or (ii) crumb plus a combination of 1% mealworm and 5% mixed seed and fruit. In both years, the commercial chick crumb acted as a control treatment, whilst those with live prey and mixed seeds and fruits mimicking a more naturalistic diet. After 7 weeks reared on these diets, pheasants were released into the wild. 3. Postrelease survival was improved with exposure to more naturalistic diets prior to release. We identified four mechanisms to explain this. Pheasants reared with more naturalistic diets (i) foraged for less time and had a higher likelihood of performing vigilance behaviours, (ii) were quicker at handling live prey items, (iii) were less reliant on supplementary feed which could be withdrawn and (iv) developed different gut morphologies. 4. These mechanisms allowed the pheasants to (i) reduce the risk of predation by reducing exposure time whilst foraging and allowing more time to be vigilant; (ii) be better at handling and discriminating natural food items and not be solely reliant on supplementary feed; and (iii) have a better gut system to cope with the natural forage after the cessation of supplementary feeding in the spring. 5. Learning food discrimination, preference and handling skills by the provision of a more naturalistic diet is essential prior to the release of pheasants in a reintroduction programme. Subsequent diet, foraging behaviour, gut morphology and digestive capabilities all work together as one nutritional complex. Simple manipulations during early development can influence these characteristics to better prepare an individual for survival upon release.
Data from: Diet complexity in early life affects survival in released pheasants by altering foraging efficiency, food choice, handling skills and gut morphology
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Protecting Kidneys Through a Low Protein Diet: A Stepwise Multiple-Choice System Approach
ClinicalTrials.gov study NCT03979534. IPD Sharing: NO. Countries: 1. Publications: 0.
DiEt ChoIce to Promote Type 2 Diabetes REmission
ClinicalTrials.gov study NCT05710900. IPD Sharing: NO. Countries: 1. Publications: 0.
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