Moose selection for resource stoichiometry
Cite this dataset
Balluffi-Fry, Juliana (2021). Moose selection for resource stoichiometry [Dataset]. Dryad. https://doi.org/10.5061/dryad.crjdfn32x
- Herbivores consider the variation of forage qualities (nutritional content and digestibility) as well as quantities (biomass) when foraging. Such selection patterns may change based on the scale of foraging, particularly in the case of ungulates that forage at many scales.
- To test selection for quality and quantity in free-ranging herbivores across scales, however, we must first develop landscape-wide quantitative estimates of both forage quantity and quality. Stoichiometric distribution models (StDMs) bring opportunity to address this because they predict the elemental measures and stoichiometry of resources at landscape extents.
- Here, we use StDMs to predict elemental measures of understory white birch quality (% nitrogen) and quantity (g carbon/m2) across two boreal landscapes. We analyzed GPS collared moose (n = 14) selection for forage quantity and quality at the landscape, home range, and patch extents using both individual and pooled resource selection analyses. We predicted that as the scale of resource selection decreased from the landscape to the patch, selection for white birch quantity would decrease and selection for quality would increase.
- Counter to our prediction, pooled-models showed selection for our estimates of quantity and quality to be neutral with low explanatory power and no scalar trends. At the individual-level, however, we found evidence for quality and quantity trade-offs, most notably at the home range scale where resource selection models explain the largest amount of variation in selection. Furthermore, individuals did not follow the same trade-off tactic, with some preferring forage quantity over quality and vice-versa.
- Such individual trade-offs show that moose may be flexible in attaining a limiting nutrient. Our findings suggest that herbivores may respond to forage elemental compositions and quantities, giving tools like StDMs merit towards animal ecology applications. The integration of StDMs and animal movement data represents a promising avenue for progress in the field of zoogeochemistry.
The moose GPS data has already been cleaned and prepared for resource selection analyses. Resource stoichiometry landscapes are provided. We provide code on GitHub for extracting used and available points for three scales of resource selection: the landscape (resource selection function; RSF), home range (RSF), and patch level (integrated step selection analysis; iSSA). We also provide code on GitHub for running models at all scales and outputing selection coefficients into a table (leads to output folder).