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33 results for “trophic group”
Fig. 2 in Structure and Composition of Dung Beetle Assemblages (Coleoptera: Scarabaeidae) in a Livestock Ranch in Central Uruguay: Responses of Functional Groups and Species to Local Habitats and Trophic Resources
Fig. 2. Diversity profiles for dung beetle assemblages in three habitats in Puntas de Sauce Maciel, Florida, Uruguay as a function of Hill numbers (q) with 95% confidence intervals (shaded areas). PHS = pasture on humid soil; PDS = pasture on dry soil; EP = Eucalyptus plantation.
Fig. 4 in Structure and Composition of Dung Beetle Assemblages (Coleoptera: Scarabaeidae) in a Livestock Ranch in Central Uruguay: Responses of Functional Groups and Species to Local Habitats and Trophic Resources
Fig. 4. Detrended correspondence analysis ordination of dung beetle species sampled with baited pitfall traps in three different habitats in Puntas de Sauce Maciel, Florida, Uruguay. PHS = pasture on humid soil; PDS = pasture on dry soil; EP = Eucalyptus plantation.
Multi-level warming alters energy flux by nonlinear effects on soil nematode trophic groups in a mature forest
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A global meta-analysis of temperature effects on marine fishes’ digestion across trophic groups
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Data from: Soil fauna responses to invasive alien plants are determined by trophic groups and habitat structure: a global meta-analysis
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Warming and top predator loss drive direct and indirect effects on multiple trophic groups within and across ecosystems
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Data from: Importance of biotic niches versus drift in a plant-inhabiting arthropod community depends on rarity and trophic group
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Data from: Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes
<p>The file "Zarco-Perello et al Temperate Reef Fish Trophic Guilds Complete Diet Dataset.xlsx" contains several spreadsheet tabs related to the analyses carried out in the paper: <i><strong>Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes: </strong></i><a href="https://doi.org/10.32942/X2CC97">https://doi.org/10.32942/X2CC97</a></p><p>All analyses, with the exception of the network calculations, of the study were carried out in the computer software R. The code is contained in the file "Zarco-Perello et al Temperate Reef Fish Trophic Ecology.R". For trophic network analyses we used the computer program Gephi v0.1 <a href="https://sciwheel.com/work/citation?ids=15257446&amp;pre=&amp;suf=&amp;sa=0">(Bastian et al. 2009).</a></p><p><strong>DATASET DESCRIPTION</strong></p><p><strong>Region of Study</strong></p><p>The region of study encompasses all the temperate reefs of south-western Australia (SWA). Extending along ~1600 km of coast, from Jurien Bay Marine Park (30° 18.6 S, 115° 0.1 E) to the Recherche Archipelago Nature Research (33° 53.7 S, 123° 52.3 E; supplementary Fig. S1), the temperate reefs of SWA are distributed across the Leeuwin and Houtman biogeographical ecoregions <a href="https://sciwheel.com/work/citation?ids=1796477&pre=&suf=&sa=0">(Spalding et al. 2007)</a>, conforming approximately ⅓ of the total distribution of temperate Australia, known as the Great Southern Reef <a href="https://sciwheel.com/work/citation?ids=4498783&pre=&suf=&sa=0">(Bennett et al. 2016).</a></p><p><strong>Species Composition</strong></p><p>The species composition of the metacommunity of temperate reef fishes of the region was obtained from a total of 4589 underwater visual surveys conducted across 206 reefs in 12 locations by the Reef Life Survey (RLS) citizen science program, and the Australian Temperate Reef Collaboration (ATRC, with support from the Department of Biodiversity Conservation and Attractions; https://www.atrc.au) from 1997 to 2021.</p><p><strong>Trophic Information</strong></p><p>All fish species listed in the RLS-ATRC database were classified in trophic guilds based on collected diet information from studies of gut content analyses in SWA, or other Australian and international regions in the absence of local information. A total of 298 fish species composed the metacommunity. For every species, we obtained diet information from the scientific literature reported on Fishbase <a href="https://sciwheel.com/work/citation?ids=10423542&pre=&suf=&sa=0">(Froese and Pauly 2019)</a> and through the search engine Scopus using the search terms: TS = (<i>name of species</i>* OR *<i>common name of species</i>*) AND TS = (diet OR *stomach content* OR *gut content* OR consump* OR herbi* OR predat* OR feeding). Diet information consisted of the average proportions of food items represented as the number of items (%N), percent volume (%V), or biomass (%W) in a population of each species. Preference was given to diet studies conducted in the region of study and those presenting biomass proportions. Species that lacked diet information globally were assigned diet proportions based on phylogenetically related species with similar size and habitat preferences based on the Fish Tree of Life <a href="https://sciwheel.com/work/citation?ids=10720381&pre=&suf=&sa=0&dbf=0">(Chang et al. 2019)</a>.</p><p><i><< The tab "Guilds Complete Diet Dataset" contains all the diet information (stomach content proportions) and its sources for all fish species considered in the study >></i></p><p><strong>Trophic guilds classification</strong></p><p>To quantify the diversity of trophic guilds and identify important fish consumers of specific groups of prey, we classified the fish species into trophic guilds performing a multi-step cluster analysis. Firstly, species were grouped into main trophic guilds using the mutually exclusive major categories of prey items. The diet proportions in these categories were used to create a dissimilarity matrix among species based on the Bray-Curtis linkage method using the function <i>vegdist</i> of the R package Vegan <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>, which was used to run a sequential divisive hierarchical cluster analysis using the function <i>diana</i> (divisive analysis) of the R package Cluster <a href="https://sciwheel.com/work/citation?ids=15165291&pre=&suf=&sa=0">(Maechler et al. 2022)</a>. Subsequently, because there are mismatches in the resolution of diet identification between species belonging to different trophic levels (<i>e.g.</i> the diets of herbivorous fish tend to have higher resolution on macrophytes, while carnivorous species tend to have higher resolution on animal prey), species within each identified main trophic guild were subject to a cluster analysis with higher definition of prey items to identify groups of species with diet specializations using sequential agglomerative hierarchical cluster analysis based on Ward's Method and Bray-Curtis or Euclidean dissimilarity matrix <a href="https://sciwheel.com/work/citation?ids=205080&pre=&suf=&sa=0">(Pineda‑Munoz and Alroy 2014)</a>.</p><p>The stomach content of most scarid species (parrotfish; Labridae: Scarinae) is very difficult to identify due to their pharyngeal mill, which grinds all food items to indiscernible particles. However, they are well identified as a special group that ingest detritus and algae by scraping the reef substrate with their specialized fused teeth. Thus, for the sake of differentiating their trophic guild, the proportions of diet for species of parrotfish was arbitrarily defined based on field observations as sediment and detritus (90%) and short filamentous algae (10%) <a href="https://sciwheel.com/work/citation?ids=11332249&pre=&suf=&sa=0&dbf=0">(Bonaldo et al. 2014)</a>. Additionally, cleaner fish and false cleaners are a special group of fishes that are difficult to group by diet given that they feed on prey that could be identified as zooplankton or zoobenthos, while in fact true cleaners forage, at least in part, on parasitic invertebrates attached to bigger fish, in addition to fish skin and scales <a href="https://sciwheel.com/work/citation?ids=13921938&pre=&suf=&sa=0">(Grutter 1997)</a>; thus, given their particular trophic ecology these labrid and blenny species were arbitrarily grouped in the major trophic group "fish cleaners" for the subsequent specialized trophic group classifications.</p><p>Visual analysis of the differences in multidimensional space between trophic guilds was done with Non-metric Multidimensional Scaling based on the dissimilarity matrix calculated for clustering using the function <i>metaMDS</i> of the R package vegan (reported in supplementary materials; <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>. Statistical significance in dietary differences among major and specialized trophic guilds (diet proportions ~ trophic guilds) was tested with permutational analysis of variance (PERMANOVA) using the function <i>adonis2 </i>of the R package vegan <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>, followed by pairwise comparisons using the function <i>pairwise.adonis2</i> of the R package pairwiseAdonis <a href="https://sciwheel.com/work/citation?ids=15190336&pre=&suf=&sa=0">(Martinez 2017)</a>.</p><p><i><< The tabs in the dataset called "Major Guilds Diet Data", "Herbivores Diet Data", "Cleaners Diet Data", "Zoobenthivores Diet Data, "Zooplanktivores Diet Data", and "Piscivores Diet Data" are the datasets with selected diet categories for each guild without "unidentified diet items" and standardized to 100 proportion which were used for the classification of each major trophic guild into specialized trophic guilds. >></i></p><p><strong>Trophic Network Links Between Specialized Guilds</strong></p><p>The trophic links between fishes and their invertebrate and macrophyte prey groups were identified by our trophic guild classification (Other Guilds Links tab in dataset); however, the trophic role of piscivores is faced with what here we called a "matrioshka paradox", because to know their links with other guilds, we must first know the trophic links of their prey. Moreover, this is not straightforward because the highest taxonomic identification of piscivorous prey is usually limited to family level, which could belong to multiple trophic guilds. This paradox is usually not explicitly stated in the literature, and it is unclear how trophic links have been drawn in previous studies without performing detailed quantitative trophic classifications. Here we estimated the trophic links between piscivorous guilds and the rest of fish guilds by (i) assigning each fish family identified in the diets of piscivorous fishes into their respective specialized guilds based in our trophic classification, (ii) pooling their diet proportions into each specialized trophic guilds they could belong to, (iii) standardizing values by number of species in each piscivorous guild, and (iv) dividing by the total sum of diet proportions to estimate their potential predation (0-100%) on other trophic guilds in the trophic network. Trophic links that had pooled diet proportions with values <5% were discarded for clarity of the network (Piscivores Trophic Links tab in dataset). This information was joined with the trophic information from non-piscivorous trophic guilds and formatted as a list of nodes (guilds and prey groups), and links between nodes (source-target) to create the trophic network of the entire temperate reef fish metacommunity (Nodes Network List and Edges Network Lisk tabs in dataset). All network analyses were done using the computer program for network visualization and analyzes Gephi v0.1 <a href="https://sciwheel.com/work/citation?ids=15257446&pre=&suf=&sa=0">(Bastian et al. 2009)</a>.</p><p><i><< The tabs "Piscivores Trophic Links" and "Other Guilds Links" are datasets containing the calculations of the links between specialized trophic links for Piscivores and other guilds respectively used to create the data of the tabs "Nodes Network List" and "Edges Network List" to create the trophic network of the system of study. >></i></p><p><i><< The tab "Herbivory, Omnivory and Carnivory" contains diet proportion data of all fish species of the study formated to build the barplot (Fig. 4) in the manuscript showing the distribution of consumption of macrophytes, invertebrates and fishes >></i></p><p> </p>
Supplementary Material 1 related to: Distribution of pesticides in agroecosystem food webs differ among trophic groups and between annual and perennial crops.
<p>Supplementary Material 1 related to the publication:</p><p>Radek Michalko, Luboš Purchart, Jakub Hofman & Ondřej Košulič. Distribution of pesticides in agroecosystem food webs differ among trophic groups and between annual and perennial crops. <i>Agronomy for Sustainable Development</i>, xx: xx.</p><p>The repository contains the comprehensive list of applied and detected pesticide compounds, including their metabolites detected during the study. The data obtained were used to compare the application by farmers with the pesticide residues detected.</p><p><strong>Table caption:</strong></p><p><i><strong>Location number/type</strong></i><strong> - shows the localities with annual or perennial crops; </strong><i><strong>Crop type</strong></i><strong> - shows the type of crops (vineyards, orchards, oilseed rape, wheat, barley, pea, mustard and sugar beet); </strong><i><strong>Applied by farmers</strong></i><strong> - shows the list of pesticides used by farmers; </strong><i><strong>Pesticide type</strong></i><strong> - shows type of pesticides (e.g. herbicide, fungicide, insecticide, etc.); </strong><i><strong>Year of Application</strong></i><strong> - shows what year the respective pesticide was applied; </strong><i><strong>Detection </strong></i><strong>- shows what pesticide residues were detected in respective locality (green colour indicates molecules that were not applied by farmers).</strong></p>
Global synthesis of effects of plant species diversity on trophic groups and interactions
<p>Numerous studies have demonstrated that plant species diversity enhances ecosystem functioning in terrestrial ecosystems, including diversity effects on insects (herbivores, predators and parasitoids) and plants. However, the effects of increased plant diversity across trophic levels in different ecosystems and biomes have not yet been explored on a global scale. Through a global meta-analysis of 2,914 observations from 351 studies, we found that increased plant species richness reduced herbivore abundance and damage but increased predator and parasitoid abundance, predation, parasitism and overall plant performance. Moreover, increased predator/parasitoid performance was correlated with reduced herbivore abundance and enhanced plant performance. We conclude that increasing plant species diversity promotes beneficial trophic interactions between insects and plants, ultimately contributing to increased ecosystem services.</p>
Global synthesis of effects of plant species diversity on trophic groups and interactions
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Arthropod Trophic Group Composition Data
*We conducted a field study to test the hypothesis that creosotebush (Larrea tridentata) shrubs growing in naturally nutrient-rich sites had better quality foliage and supported greater populations of foliage arthropods than shrubs growing in nutrient-poor sites. This is data for foliage arthropods sampled from LVAR creosotebush shrubs. Sampling was done in April of 1985 and 1986. Shrubs were sampled from 5 separate sites designated A-E. 10 shrubs of 3 different types, R=random, H=high quality, L=low quality were sampled at each site. Total numbers of taxa and individuals of each major trophic group, herbivores, predators, omnivores, from each of the 30 shrubs/site are listed.
R codes for plant genetic diversity effects on trophic groups and interactions
<p>R codes for plant genetic diversity effects on trophic groups and interactions</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.