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In mountain landscapes, surface temperatures vary over short distances due to interacting influences of topography and overstory vegetation on local energy and water balances. At two study landscapes in the Sierra Nevada of California, characterized by foothill oak savanna at 276–481 m elevation and montane conifer forest at 1977–2135 m, we deployed 288 near-surface (5 cm above the surface) temperature sensors to sample site-scale (30 m) temperature variation related to hillslope orientation and vegetation structure and microsite-scale (2–10 m) variation related to microtopography and tree overstory. Daily near-surface maximum and minimum temperatures for the 2013 calendar year were related to topographic factors and vegetation overstory characterized using small footprint LiDAR imagery acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). At both landscapes we recorded large site and microsite spatial variation in daily maximum temperatures, and less absolute variation in daily minimum temperatures. Generalized boosted regression trees were estimated to measure the influence of tree canopy density, understory solar radiation, cold-air drainage and pooling, ground cover and microtopography on daily maximum and minimum temperatures at site and microsite scales. Site-scale models based on indices of understory solar radiation and landscape position explained an average of 61–65% of daily variation in maximum temperature; site-scale models based on tree canopy density and landscape position explained 65–83% of variation in minimum temperatures. Models explained

Climate change has emerged as a major threat to biodiversity, but there have been few assessments of variability in sensitivity to climate across species’ ranges. We examined the sensitivity of ponderosa pine (Pinus ponderosa), a common, commercially important tree species found throughout the western United States, to climate variability using tree rings mostly from the International Tree Ring Data Bank (161 chronologies) and multiple climate products for 1930–1979. We compared interannual tree ring variation to water-year precipitation (wy ppt), May-July vapor pressure deficit (MJJ VPD), April-July precipitation (AMJJ ppt) and average winter (Dec-Feb) minimum temperature (winter tmn). We mapped growth-climate correlations for each variable and applied hierarchical agglomerative cluster analysis to identify five main groups of climate sensitivities, one of which contained mostly climate-insensitive populations. Ponderosa pine growth was positively correlated with wy ppt and AMJJ ppt and negatively correlated with MJJ VPD throughout its range, but sensitivities were greater in the Southern Rockies and Northern Great Plains than in Pacific and Southwestern populations. Sensitivities to winter tmn were weaker than responses to other climate variables, but Pacific and interior populations mostly demonstrated positive and negative responses, respectively. In summary, our analyses revealed the general pattern that interior populations of ponderosa pine were more climate-sensitive than Pacific populations, but there is considerable heterogeneity in sensitivity throughout the species’ range. Sensitivities to climate variables were related to position along corresponding climate gradients for all variables except MJJ VPD, but the unevenness of these relationships suggests a role of local adaptation. Our results illustrate the variation in climate response for a uniquely wide-ranging species and suggest that “leading” and “trailing” range edge concepts can oversimplify patterns of species exposure to climate change.

Context: Predicting climate-driven species’ range shifts depends substantially on species’ exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species’ exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change. Objectives: We demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040–2069) and end-of-century (2070–2099). Methods: We produced a 149-year dataset (1951–2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951–1980 mean and historical range of variability in CWD in individual years and 3-year moving windows. Results: Climate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change. Conclusions: In moisture-limited, Mediterranean-climate landscapes, high elevations may experience the greatest exposure to climate change in the 21st century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energy-limited locations become increasingly moisture-limited in the future.