Upscaling the niche variation hypothesis from the intra- to the inter-specific level.

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.

Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species.

Temperatures in mountain areas are increasing at a higher rate than the Northern Hemisphere land average, but how fauna may respond, in particular in terms of phenology, remains poorly understood. The aim of this study was to assess how elevation could modify the relationships between climate variability (air temperature and snow melt-out date), the timing of plant phenology and egg-laying date of the coal tit (Periparus ater). We collected 9 years (2011-2019) of data on egg-laying date, spring air temperature, snow melt-out date, and larch budburst date at two elevations (~1,300 m and ~1,900 m asl) on a slope located in the Mont-Blanc Massif in the French Alps. We found that at low elevation, larch budburst date had a direct influence on egg-laying date, while at high-altitude snow melt-out date was the limiting factor. At both elevations, air temperature had a similar effect on egg-laying date, but was a poorer predictor than larch budburst or snowmelt date. Our results shed light on proximate drivers of breeding phenology responses to interannual climate variability in mountain areas and suggest that factors directly influencing species phenology vary at different elevations. Predicting the future responses of species in a climate change context will require testing the transferability of models and accounting for nonstationary relationships between environmental predictors and the timing of phenological events.

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content.

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 (R2validation = 0.94 and RMSEP = 0.20% for N, R2validation = 0.76 and RMSEP = 0.05% for P and R2validation = 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.