Data from: Increased intake of tree forage by moose is associated with intake of crops rich in non-structural carbohydrates
Data files
May 30, 2024 version files 125.06 KB
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DNA_results_fecal_samples_for_Dryad.xlsx
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README.md
Abstract
Animals representing a wide range of taxonomic groups are known to select specific food combinations to achieve a nutritionally balanced diet. The nutrient balancing hypothesis suggests that, when given the opportunity, animals select foods to achieve a particular target nutrient balance, and that balancing occurs between meals and between days. For wild ruminants who inhabit landscapes dominated by human land use, nutritionally imbalanced diets can result from ingesting agricultural crops rich in starch and sugar (non-structural carbohydrates, NC), which can be provided to them by people as supplementary feeds. Here, we test the nutrient balancing hypothesis by assessing potential effects that the ingestion of such crops by Alces alces (moose) may have on forage intake. We predicted that moose compensate for an imbalanced intake of excess NC by selecting tree forage with macro-nutritional content better suited for their rumen microbiome during wintertime. We applied DNA metabarcoding to identify plants in faecal and rumen content from the same moose during winter in Sweden. We found that the concentration of NC-rich crops in faeces predicted the presence of Picea abies (Norway spruce) in rumen samples. The finding is consistent with the prediction that moose use tree forage as a nutritionally complementary resource to balance their intake of NC-rich foods, and that they ingested P. abies in particular (normally a forage rarely eaten by moose) because it was the most readily available tree. Our finding sheds new light on the foraging behaviour of a model species in herbivore ecology, and on how habitat alterations by humans may change the behaviour of wildlife.
README: Increased intake of tree forage by moose is associated with intake of crops rich in non-structural carbohydrates
https://doi.org/10.5061/dryad.f4qrfj73v
Description of the data and file structure
The excel file includes results from DNA metabarcoding of faecal samples from moose. Faecal samples (F) were collected from shot moose during the winter 2014/15 in six moose management areas in southern Sweden. DNA was extracted from faecal samples, and then we carried out PCR and Illumina sequencing to identify plant species present. The data set includes proportional data (relative read abundances) of the 128 identified taxa (molecular operational taxonomic units, MOTU). MOTUs are given as latin names. We also provide metadata for the samples, such as place of origin (moose management area, MMA), moose management unit (MMU), as well as sex and age of the moose individual. Age was determined by analysis of tooth cemental layers. Data included in this data file represent fecal samples in Category 1 and 2 as described in the article. Category 1 are individuals without any traces of any crops rich in non-structural carbohydrates (NCC) in either rumen or faecal samples; Category 2 are individuals with identified NCC in faecal samples. Empty cells in the excel file means that information is unavailable for that particular parameter and sample.
The word file contains the JAGS code used to perform the Bayesian model for estimating the probability of spruce being present in moose rumens (recent intakes) based on prior consumption of energy-dense and natural food groups (faecal data).
Sharing/Access information
Code/Software
Bayesian analysis - general model structure:
The differences in the effect of different predictors can be directly quantified by subtracting the posterior distributions (A-B) within the JAGS model structure. Here we describe the Bayesian model used to estimate the probability of spruce being present in moose rumens (recent intakes) based on prior consumption of energy-dense and natural food groups (faecal data). Data are indexed to ‘i’ at the observation level and to ‘j’ for the predictions. The priors were chosen to be vague (minimally informative) to allow the data to determine their shape and range. Beta coefficients were centered on zero to avoid influencing whether their effects were negative or positive. For additional details see the BUGS code in the attached word file. Analyses were carried out using JAGS (Plummer 2003) via the *rjags *package in R (Plummer et al. 2016).
Methods
Fecal samples were collected from shot moose during the winter 2014/15 in six moose management areas in southern Sweden. DNA was extracted from fecal samples, and then we carried out PCR and Illumina sequencing to identify plant species present. The data set includes proportional data (relative read abundances, RRA) of the 128 identified taxa (molecular operational taxonomic units, MOTU). We also provide metadata for the samples, such as place of origin (moose management area, MMA), moose management unit (MMU), as well as sex and age of the moose individual. Age was determined by analysis of tooth cemental layers. Data included in this data file represent fecal samples in Category 1 and 2 as described in the article. Category 1 are individuals without any traces of any crops rich in non-structural carbohydrates (NCC) in either rumen or faecal samples; Category 2 are individuals with identified NCC in faecal samples.