The "niche variation hypothesis" (NVH) predicts that populations with wider niches should display higher among-individual variability. This prediction originally stated at the intra-specific level may be extended to the inter-specific level: individuals of generalist species may differ to a greater extent than individuals of a specialist species. We tested the NVH at intra- and inter-specific levels based on a large diet database of three large herbivore feces collected in the field and analyzed using DNA metabarcoding. The three herbivores (roe deer Capreolus capreolus, chamois Rupicapra rupicapra and mouflon Ovis musimon) are highly contrasted in terms of sociality (solitary to highly gregarious) and diet. The NVH at the intraspecific level was tested by relating, for the same population, diet breadth and inter-individual variation across the four seasons. Compared to null models, our data supported the NVH both at the intra- and inter-specific levels. Inter-individual variation of the diet of solitary species was not larger than in social species, although social individuals feed together and could therefore have more similar diets. Hence, the NVH better explained diet breadth than other factors such as sociality. The expansion of the population niche of the three species was driven by resource availability, and achieved by an increase in inter-individual variation, and the level of inter-individual variability was larger in the generalist species (mouflon) than in the specialist one (roe deer). This mechanism at the base of the NVH appears at play at different levels of biological organization, from populations to communities.

Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R 2 validation = 0.94 and RMSEP = 0.20% for N, R 2 validation = 0.76 and RMSEP = 0.05% for P and R 2 validation = 0.82 and RMSEP = 1.16% for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.

Aim When modelling the distribution of animals under current and future conditions, both their response to environmental constraints and their resources’ response to these environmental constraints need to be taken into account. Here, we develop a framework to predict the distribution of large herbivores under global change, while accounting for changes in their main resources. We applied it to Rupicapra rupicapra, the chamois of the European Alps. Location The Bauges Regional Park (French Alps). Methods We built sixteen plant functional groups (PFGs) that account for the chamois’ diet (estimated from sequenced environmental DNA found in the faeces), climatic requirements, dispersal limitations, successional stage and interaction for light. These PFGs were then simulated using a dynamic vegetation model, under current and future climatic conditions up to 2100. Finally, we modelled the spatial distribution of the chamois under both current and future conditions using a point‐process model applied to either climate‐only variables or climate and simulated vegetation structure variables. Results Both the climate‐only and the climate and vegetation models successfully predicted the current distribution of the chamois species. However, when applied into the future, the predictions differed widely. While the climate‐only models predicted an 80% decrease in total species occupancy, including vegetation structure and plant resources for chamois in the model provided more optimistic predictions because they account for the transient dynamics of the vegetation (−20% in species occupancy). Main conclusions Applying our framework to the chamois shows that the inclusion of ecological mechanisms (i.e., plant resources) produces more realistic predictions under current conditions and should prove useful for anticipating future impacts. We have shown that discounting the pure effects of vegetation on chamois might lead to overpessimistic predictions under climate change. Our approach paves the way for improved synergies between different fields to produce biodiversity scenarios.