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13 results for “mammalian diet”
Body size modulates the extent of seasonal diet switching by large mammalian herbivores in Yellowstone National Park
<div> <p><span>Large mammalian herbivores vary their diets markedly with changes in resource availability yet the ways that seasonal changes in individual foraging behaviors scale up to reconfigure complex trophic networks are poorly understood. Two years of dietary DNA data enabled us to quantify fine-grained dietary variation within and among populations of five large herbivore species at Yellowstone National Park, revealing remarkably strong and significant correlations between body size and five key indicators of diet seasonality (R<sup>2</sup> = 0.71–0.80). Data from GPS collars implicated seasonal changes in each species' movement- and habitat-use patterns as potential determinants of foraging constraints and specializations that give rise to the strong allometry in diet composition. Bison and elk showed relatively muted seasonal changes compared to smaller species that exhibited stronger switches. Whereas the taxonomic breadth of individual diets contracted for all species in winter, larger species generally consumed a greater functional diversity of plants and thus maintained more unique dietary niches under resource limitations.</span></p> </div>
Linking diet switching to reproductive performance across populations of two Critically Endangered mammalian herbivores
<p>Data associated with Harvey Sky, N., Britnell, J., Antwis, R. <em>et al.</em> Linking diet switching to reproductive performance across populations of two critically endangered mammalian herbivores. <em>Commun Biol</em> <strong>7</strong>, 333 (2024). https://doi.org/10.1038/s42003-024-05983-3</p> <p>The data deposited here includes raw metabarcoding output fasta files and some processed metabarcoding and sample data in xslx files. We include a more detailed description of each file below.</p> <p>Data regarding Kenyan black rhino and Grevy’s zebra are treated as sensitive and confidential. There are therefore restrictions on the data that we can make available. Due to these confidentiality considerations, the sample data stored here does not include locations of sample collection within each reserve for either species, or the identity or breeding data for black rhino. It also only includes the final processed values for NDVI and rainfall. The remote sensing data is available from the repositories cited in the methods, but we cannot provide the shapefiles or other data used to calculate the final values for each sample. </p> <p><em><strong>Raw fasta files_plants.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of dietary plants in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Raw fasta files_bacteria.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of microbiome bateria in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Sample data and processed metabarcoding data_Black rhino.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each black rhino sample.</p> <p>SampleID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>IndividualID - We are not able to share rhino names or other identifiers, but we have given each individual a unique number so that it can be seen which samples came from the same individuals. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Season - Post is the post-rain sampling season June-July 2018. Pre is the pre-rain sampling season January-March 2019. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Poaceae, Fabaceae, Ebenaceae - The relative abundance of each of these three dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p> <p><em><strong>Sample data and processed metabarcoding data_Grevy's zebra.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each Grevy's zebra sample.</p> <p>Sample ID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Season - Post is the post-rain sampling season July-August 2018. Pre is the pre-rain sampling season January-February 2019. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. </p> <p>Poaceae, Fabaceae - The relative abundance of each of these two dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Indigofera - The relative abundance of each of this Fabaceae genus was included in our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p>
Figure 2. a in Geographical variation in morphometry, craniometry, and diet of a mammalian species (Stone marten, Martes foina) using data mining
Figure 2. a) Silhouette measure for body size data. b) Silhouette measure for craniometrical data. c) Silhouette measure for dietary data.
Figure 3. a in Geographical variation in morphometry, craniometry, and diet of a mammalian species (Stone marten, Martes foina) using data mining
Figure 3. a) Cluster sizes for body size data. b) Cluster sizes for craniometrical data. c) Cluster sizes for dietary data.
Body size modulates the extent of seasonal diet switching by large mammalian herbivores in Yellowstone National Park
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What is a mammalian omnivore? Insights into terrestrial mammalian diet diversity, body mass, and evolution
<p>Mammalian omnivores are a broad and diverse group of mammals that are often lumped together in ecological studies. As a result, there are open questions about what macroevolutionary and macroecological information can be gained when omnivorous dietary differences are investigated in ecological and phylogenetic comparative studies. In this study, we investigate the frequency at which vertebrate prey, invertebrate prey, fibrous plant material, and non-fibrous plant material co-occur in the diets of omnivorous species. We quantify the body size distributions and phylogenetic signal of terrestrial mammals that consume different omnivorous diets and using multistate reversible jump MCMC, we assess the transition rates between mammalian diet strategies on the mammalian phylogenetic tree. We find that omnivores that consume all four food types are rare and most omnivorous mammals consume only invertebrate prey and non-fibrous plants. We also find that omnivores that only consume invertebrate prey are on average smaller than omnivores that incorporate vertebrate prey as many are from within Rodentia. Our transition rate models show that there are high transition rates from invertivorous omnivory to herbivory, and from vertebrate predation to prey mixing and ultimately invertivory. Our results suggest that prey type is an important aspect of omnivore macroevolution and macroecology, as it is correlated with body mass, evolutionary history, and diet-related evolutionary transition rates.</p>
Effects of intraspecific competition and body mass on diet specialisation in a mammalian scavenger
<p>1. Animals that rely extensively on scavenging rather than hunting must exploit resources that are inherently patchy, dangerous, or subject to competition. Though it may be expected that scavenging species should therefore form opportunistic feeding habits in order to survive, a broad population diet may mask specialisation occurring at an individual level.</p> <p>2. To test this, we used stable isotope analysis to analyse the degree of specialisation in the diet of the Tasmanian devil, one of few mammalian species to develop adaptations for scavenging.</p> <p>3. We found that the majority of individuals were dietary specialists, indicating that they fed within a narrow trophic niche despite their varied diet as a population.</p> <p>4. Even in competitive populations, only small individuals could be classified as true trophic generalists; larger animals in those populations were trophic specialists. In populations with reduced levels of competition, all individuals were capable of being trophic specialists.</p> <p>5. Heavier individuals showed a greater degree of trophic specialisation, suggesting either that mass is an important driver of diet choice or that trophic specialisation is an efficient foraging strategy allowing greater mass gain.</p> <p>6. Devils may be unique among scavenging mammals in the extent to which they can specialise their diets, having been released from the competitive pressure of larger carnivores.</p>
What is a mammalian omnivore? Insights into terrestrial mammalian diet diversity, body mass, and evolution
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Effects of intraspecific competition and body mass on diet specialisation in a mammalian scavenger
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Raw data from: Differentiating siliceous particulate matter in the diets of mammalian herbivores
<p class="MsoNormal"><span>1. Silica is crucial to terrestrial plant life and geochemical cycling on Earth. It is also implicated in the evolution of mammalian teeth, but there is debate over which type of siliceous particle has exerted the strongest selective pressure on tooth morphology.</span></p> <p class="MsoNormal"><span>2. </span><span>Debate revolves around the amorphous silica bodies (phytoliths) in plants and forms of siliceous grit––i.e., crystalline quartz (sand, soil, dust)––on plant surfaces. The problem is that conventional measures of silica often quantify both particle types simultaneously.</span></p> <p class="MsoNormal"><span>3. </span><span>Here we describe a protocol that relies on heavy-liquid flotation to separate and quantify siliceous particulate matter in the diets of herbivores. The method is reproducible and well-suited to detecting species- or population-level differences in silica ingestion. In addition, we detected meaningful variation within the digestive tracts of cows, an outcome that supports the premise of ruminal fluid 'washing' of siliceous grit.</span></p> <p class="MsoNormal"><span>4. </span><span>We used bootstrap resampling to estimate the sample sizes needed to compare species, populations, or individuals in space and time. We found that a minimum sample of 12 individuals is necessary if the species is a browser or as many as 55 if the species is a grazer, which are more variable. But a sample size of 20 is adequate for detecting statistical differences. We conclude by suggesting that our protocol for differentiating and quantifying silica holds promise for testing competing hypotheses on the evolution of dental traits.</span></p>
Raw data from: Differentiating siliceous particulate matter in the diets of mammalian herbivores
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Figure 1 in Geographical variation in morphometry, craniometry, and diet of a mammalian species (Stone marten, Martes foina) using data mining
Figure 1. Landmarks of craniometrical variables (1: length of jaw, 2: distance between the mastoid apophyses, 3: nose width, 4: width of cheekbones, 5: palate length, 6: distance between angular and coronary apophyses, 7: intraophthalmic width, 8: face length, 9: condylobasal length).
Deletion of the Mammalian INDY Homologue in Mice Mimics Aspects of Dietary Restriction and Protects Against Diet and Age-Induced Adiposity and Insulin Resistance
GEO Series GSE29984. Mus musculus. 10 samples. Type: Expression profiling by array.
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