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97 results for “Trophic ecology”
Data from: Assessing the trophic ecology of top predators across a recolonisation frontier using DNA metabarcoding of diets
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Data from: The trophic ecology of a desert river fish assemblage: influence of season and hydrologic variability
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Data from: Novel trophic niches drive variable progress toward ecological speciation within an adaptive radiation of pupfishes
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Data from: Climate-warming alters the structure of farmland tri-trophic ecological networks and reduces crop yield
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Data from: SIDER: an R package for predicting trophic discrimination factors of consumers based on their ecology and phylogenetic relatedness
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Data from: Molecular detection of invertebrate prey in vertebrate diets: trophic ecology of Caribbean island lizards
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Data from: The cryptic origins of evolutionary novelty: 1,000-fold-faster trophic diversification rates without increased ecological opportunity or hybrid swarm
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Data from: Incorporating disturbance into trophic ecology: fire history shapes mesopredator suppression by an apex predator
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Data from: Molecular characterisation of trophic ecology within an island radiation of insect herbivores (Curculionidae: Entiminae: Cratopus).
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Data from: Trophic response to ecological conditions of habitats: evidence from trophic variability of freshwater fish
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Supplementary material 1 from: Zarzoso-Lacoste D, Bonnaud E, Corse E, Dubut V, Lorvelec O, De Meringo H, Santelli C, Meunier J-Y, Ghestemme T, Gouni A, Vidal E (2019) Stuck amongst introduced species: Trophic ecology reveals complex relationships between the critically endangered Niau kingfisher and introduced predators, competitors and prey. NeoBiota 53: 61-82. https://doi.org/10.3897/neobiota.53.35086
: Data type: species data
Supplementary material 4 from: Zarzoso-Lacoste D, Bonnaud E, Corse E, Dubut V, Lorvelec O, De Meringo H, Santelli C, Meunier J-Y, Ghestemme T, Gouni A, Vidal E (2019) Stuck amongst introduced species: Trophic ecology reveals complex relationships between the critically endangered Niau kingfisher and introduced predators, competitors and prey. NeoBiota 53: 61-82. https://doi.org/10.3897/neobiota.53.35086
: Link: https://doi.org/10.3897/neobiota.53.35086.suppl4
Supplementary material 3 from: Zarzoso-Lacoste D, Bonnaud E, Corse E, Dubut V, Lorvelec O, De Meringo H, Santelli C, Meunier J-Y, Ghestemme T, Gouni A, Vidal E (2019) Stuck amongst introduced species: Trophic ecology reveals complex relationships between the critically endangered Niau kingfisher and introduced predators, competitors and prey. NeoBiota 53: 61-82. https://doi.org/10.3897/neobiota.53.35086
: Data type: measurement
Supplementary material 2 from: Zarzoso-Lacoste D, Bonnaud E, Corse E, Dubut V, Lorvelec O, De Meringo H, Santelli C, Meunier J-Y, Ghestemme T, Gouni A, Vidal E (2019) Stuck amongst introduced species: Trophic ecology reveals complex relationships between the critically endangered Niau kingfisher and introduced predators, competitors and prey. NeoBiota 53: 61-82. https://doi.org/10.3897/neobiota.53.35086
: Data type: measurement
Supposed "snake specialist" consumes monitor lizards: diet and trophic implications of king cobra feeding ecology
<p>King cobra predation events on <em>Varanus nebulosus. </em>Included: photographic evidence of each event, king cobra biometric data from most recent measurements and location information.</p> <p>.csv file column headings:</p> <p>folderid: The Zenodo folder ID containing the photographic evidence of event.</p> <p>obvdate: Date of observation (yyyy-mm-dd)</p> <p>obvtime: Time of observation (24hr)</p> <p>snakeid: Unique ID given to individual king cobras captured</p> <p>svlmm: King cobra snout-to-vent length (mm)</p> <p>tlmm: King cobra tail length (mm)</p> <p>totalmm: King cobra total length (mm)</p> <p>massg: King cobra mass (g)</p> <p>easting: UTM easting (UTM Zone 47N; Datum WGS84)</p> <p>northing: UTM northing (UTM Zone 47N; Datum WGS84)</p> <p>gpsaccm: Accuracy of GPS location (m)</p> <p>notes: Comments on predation event</p>
Molecular ecological network analyses: An effective conservation tool for the assessment of biodiversity, trophic interactions, and community structure
<p>Global biodiversity is threatened by the anthropogenic restructuring of animal communities, which rewires species interaction networks in real-time as individuals are extirpated or introduced. Conservation science and adaptive ecosystem management demands more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity and quantifying species interactions. Here we develop molecular ecological network analyses (MENA) as an ecosystem assessment tool to address these needs. To construct the ecological network, we used environmental DNA from feces to identify the plant and mammal diet of two carnivores: puma (<i>Puma concolor</i>) and bobcat (<i>Lynx rufus</i>); two omnivores: coyote (<i>Canis latrans</i>) and gray fox (<i>Urocyon cinereoargenteus</i>); and two herbivores: black-tailed deer (<i>Odocoileus hemionus</i>) and black-tailed jackrabbit (<i>Lepus californicus)</i> in a well-studied Californian reserve<i>. </i>To evaluate MENA as a comprehensive biodiversity tool, we applied our framework to identify the structure of the network, patterns of trophic interactions, key species, and to assess its utility in capturing the biodiversity of the area. The high dietary taxonomic resolution enabled the assessment of species diversity, niche breadth and overlap. The network analysis revealed a dense ecological network with a high diversity of weakly connected species and a community that is highly modular and non-nested. The significant prevalence of tri-trophic chain and exploitative competition patterns indicates (i) the removal or reintroduction of a top predator would trigger a trophic cascade within this community, directly affecting their prey and indirectly the plant communities, and (ii) the potential impact of indirect effects between two predators that consume the same prey. These results suggest that the recent resurgence of puma in the study area may impact the herbaceous and woody vegetation and the population size of other predators. This effect of fluctuating predator populations and plant communities could be predicted through MENA's fine-scale assessment of the diet selection and the identified keystone species. Although just using a subset of species, MENA more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies. MENA reconstructed and unveiled the hidden complexity in trophic structure and interaction networks within the community, providing a promising toolkit for biodiversity and ecosystem management.</p>
Data from: Mercury exposure in an endangered seabird: long-term changes and relationships with trophic ecology and breeding success
<p class="MsoNoSpacing">Mercury (Hg) is an environmental contaminant which, at high concentrations, can negatively influence avian physiology and demography. Albatrosses (Diomedeidae) have higher Hg burdens than all other avian families. Here, we measure total Hg (THg) concentrations of body feathers from adult grey-headed albatrosses (<i>Thalassarche chrysostoma</i>) at South Georgia. Specifically, we: (i) analyse temporal trends at South Georgia (1989–2013) and make comparisons with other breeding populations; (ii) identify factors driving variation in THg concentrations; and, (iii) examine relationships with breeding success. Mean ± SD feather THg concentrations were 13.0 ± 8.0 µg g<sup>-1</sup> dw, which represents a threefold increase over the past 25 years at South Georgia and is the highest recorded in the <i>Thalassarche</i> genus. Foraging habitat, inferred from stable isotope ratios of carbon (<i>δ</i><sup>13</sup>C), significantly influenced THg concentrations – feathers moulted in Antarctic waters had far lower THg concentrations than those moulted in subantarctic or subtropical waters. THg concentrations also increased with trophic level (<i>δ</i><sup>15</sup>N), reflecting the biomagnification process. There was limited support for the influence of sex, age and previous breeding outcome on feather THg concentrations. However, in males, Hg exposure was correlated with breeding outcome – failed birds had significantly higher feather THg concentrations than successful birds. These results provide key insights into the drivers and consequences of Hg exposure in this globally important albatross population.</p>
Data from: Incorporating anthropogenic effects into trophic ecology: predator-prey interactions in a human-dominated landscape
Apex predators perform important functions that regulate ecosystems worldwide. However, little is known about how ecosystem regulation by predators is influenced by human activities. In particular, how important are top-down effects of predators relative to direct and indirect human-mediated bottom-up and top-down processes? Combining data on species' occurrence from camera traps and hunting records, we aimed to quantify the relative effects of top-down and bottom-up processes in shaping predator and prey distributions in a human-dominated landscape in Transylvania, Romania. By global standards this system is diverse, including apex predators (brown bear and wolf), mesopredators (red fox) and large herbivores (roe and red deer). Humans and free-ranging dogs represent additional predators in the system. Using structural equation modelling, we found that apex predators suppress lower trophic levels, especially herbivores. However, direct and indirect top-down effects of humans affected the ecosystem more strongly, influencing species at all trophic levels. Our study highlights the need to explicitly embed humans and their influences within trophic cascade theory. This will greatly expand our understanding of species interactions in human-modified landscapes, which compose the majority of the Earth's terrestrial surface.
Data from: Species-specific differences in adaptive phenotypic plasticity in an ecologically relevant trophic trait: hypertrophic lips in Midas cichlid fishes
The spectacular species richness of cichlids and their diversity in morphology, coloration, and behaviour have made them an ideal model for the study of speciation and adaptive evolution. Hypertrophic lips evolved repeatedly and independently in African and Neotropical cichlid radiations. Cichlids with hypertrophic lips forage predominantly in rocky crevices and it has been hypothesized that mechanical stress caused by friction could result in larger lips through phenotypic plasticity. To test the influence of the environment on the size and development of lips, we conducted a series of breeding and feeding experiments on Midas cichlids. Full-sibs of Amphilophus labiatus (thick-lipped) and A. citrinellus (thin-lipped) each were split into a control group which was fed food from the water column and a treatment group whose food was fixed to substrates. We found strong evidence for phenotypic plasticity on lip area in the thick-lipped species, but not in the thin-lipped species. Intermediate phenotypic values were observed in hybrids from thick- and thin-lipped species reared under "control" conditions. Thus, both a genetic, but also a phenotypic plastic component is involved in the development of hypertrophic lips in Neotropical cichlids. Moreover, species-specific adaptive phenotypic plasticity was found, suggesting that plasticity is selected for in recent thick-lipped species.
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>
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