Aim:The stratification of organisms along elevational gradients is widely reported, with montane communities characterized by species occurring in relatively small and isolated populations; these species are of considerable interest to ecologists and conservationists. This stratification is generally attributed to climatic zonation. Evidence that species are shifting upward in elevation has fuelled speculation that species are tracking their climatic niches in response to climate change. Uncertainty regarding the degree to which climate directly influences species abundance versus the degree to which climate has an indirect influence via vegetation represents a key impediment to understanding the ecology of montane species; here, we evaluate these direct and indirect effects. Location: White Mountains, New Hampshire, USA. Methods: We used N-mixture models to correct for imperfect detection of species, principal component analysis to represent gradients in vegetation structure and composition and structural equation models to assign variation to the direct and indirect effects of climate upon birds.Results: Analysis of 13 species revealed that climate exerts direct influences on bird abundance and indirect influences mediated by vegetation composition and structure. All species exhibited indirect effects of climate via forest habitat, with 77% exhibiting both direct and indirect effects and 53% exhibiting stronger indirect effects. Main conclusions:We provide insight into the mechanistic pathways of how climate influences the distribution of species along elevational gradients, underscoring the complex vulnerability of species to climate change. Our results reveal that the majority of species experience both direct and indirect effects of climate, implying that forests play a key role in mediating climate effects. For species that are primarily influenced by climate directly, typical climate envelope models may continue to be informative, but for the majority of the species included in this study, we show that distribution models should also include measures of habitat. K E Y W O R D S climate change, climate effects, montane bird abundance, spruce-fir ecosystems, structural equation modelling | 1671 DUCLOS et aL.

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS) gridded predictions using data from three winters (2014)(2015)(2016). Snow depth observations were correlated with SNODAS predictions for sites (R 2 = 0.20) and regions (R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer-dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species' responses to climate change.