Direct and Indirect Effects of Climate Change on Plant Populations and Communities in Sagebrush Steppe

Understanding how annual climate variation affects population growth rates across a species' range may help us anticipate the effects of climate change on species. We predict that populations in warmer or wetter parts of a species' range should respond negatively to periods of above average temperature or precipitation, respectively, whereas populations in colder or drier areas should respond positively to periods of above average temperature or precipitation. To test this, we estimated the sensitivity of a common shrub species, big sagebrush (Artemisia tridentata), to annual climate variation across its range. Our analysis includes 7934 observations of year-to-year change in sagebrush cover or production from 131 monitoring sites in western North America. We coupled these observations with seasonal weather data for each site and analyzed the effects of spring through fall temperatures and fall through spring accumulated precipitation on annual changes in sagebrush abundance. Sensitivity to annual temperature variation supported our hypothesis: sagebrush responded negatively to warmer years in hotter locations but positively to warmer years in colder locations. In contrast, sensitivity to precipitation ran counter to our hypothesis: sagebrush responded negatively to above average precipitation in drier sites and positively in wetter sites. This pattern of responses suggests that patterns of regional abundance of this species may be more limited by temperature than 1Coauthored with Peter B. Adler. 10 precipitation. Our model predicts that a short-term temperature increase of 2°C would increase sagebrush cover by a factor of 0.67 at the coldest sites and decrease cover by a factor of 0.21 at the warmest sites. This prediction is qualitatively consistent with predictions from species distribution models for sagebrush based on spatial occurrence data, but it provides new mechanistic insight and produces estimates for how much and how fast sagebrush cover may change within its range. Introduction Global climate change is causing species to go extinct in locations where they once thrived and become common in areas where they never before occurred (Parmesan and Yohe 2003, Chen et al. 2011). Changing species distributions and abundances will have profound consequences for ecosystem functioning, the spread of diseases and the future of biodiversity on earth (Pachauri et al. 2015). To anticipate the future effects of climate change, we need detailed species-specific understanding of how climate determines where species will be found in the future, but also how much and how fast their abundances will change (Ehrlén and Morris 2015). One approach for determining how species are affected by climate is to assume that spatial patterns of occurrence are determined by climatic constraints. This is the assumption underlying use of species distribution models (SDMs) to project climate change impacts in biodiversity. But using SDMs to predict how any species will respond to climate change may be problematic. Species distributions are shaped by non-climatic factors such as dispersal barriers, physical variables such as soil type, and stochastic population extinction. Moreover, species may occur in areas outside of their climate niche due to immigration or because remnant populations do not immediately go extinct 11 after climate change (Guisan and Thuiller 2005, Holt and Keitt 2005). Even if species occurrence does coincide with the climate niche, using occurrence alone would still be problematic for predicting the ecological impacts of climate change. Species distribution models are likely to identify as important variables which have no direct connection to individual performance or population growth rates. When projected into the future, these spurious correlations could lead to unreliable forecasts (Dormann 2007). An alternative approach is to study changes in species abundance over time to infer how populations respond to short-term climate variation (Dalgleish et al. 2010, Chen et al. 2010, Munson et al. 2013, Lunn et al. 2016). In many ecosystems, differences in rainfall or temperature between two subsequent years may be as large as the difference in long-term average climate between two locations at opposite ends of a species range, or the difference between the climate of a site today and the projected climate of that site in the distant future (Mora et al. 2013). Studying the effects of short-term variation in climate may help us understand how long-term changes in average climate could affect populations at a local scale (Barber et al. 2000, McLaughlin et al. 2002, Maschinski et al. 2006, Bigler et al. 2007, Jenouvrier et al. 2009, Dalgleish et al. 2010, Pol et al. 2010, Chen et al. 2010, Lunn et al. 2016, Searcy and Shaffer 2016). Of course, such local-scale population studies rarely address the landscape to regional scales relevant to management. Applying the temporal, population modeling approach at multiple locations across a species range could link it to the SDM approach and address the scaling challenge (Doak and Morris 2010, Ehrlén and Morris 2015). For example, Ettinger et al. (2011) showed that annual climate variation was more strongly correlated with growth at the 12 upper elevation range limit of several tree species than at their lower elevation range limit. The implication is that climate limits the growth and, by extension, the upper elevation range limit of these tree species, but that climate does not directly influence their lower elevation range limit. This mechanistic insight into the role of temperature and climate would be hidden in a purely spatial SDM approach. Similarly, temporal analyses can strengthen our confidence in the use of SDMs for prediction. For instance, Searcy and Shaffer (2016) analyzed time series of annual recruitment data from salamander populations and found that the same annual climate variables that were most influential for this local population-level process matched the long-term average climate variables identified by a SDM as being most important in controlling that species' geographic distribution. We propose that the population response of a species to annual climate variation across its range can provide valuable insight about how that species' abundance and distribution will change in response to long-term climate change. For example, if a species is sensitive to temperature, we would expect that at the hottest parts of its range, populations will decrease after warmer than average years, whereas in colder parts of its range, populations will increase after warmer than average years (Fig. 2.1 upper plots). Such a pattern can be tested statistically by examining whether the short-term effect of a climate driver changes from positive to negative with increases in the average of that climate driver (Fig. 2.1 a middle plot). This pattern indicates a strong link between climate driver and the species' long-term abundance and distribution. On the other hand, if sensitivity to short-term climate variation is always positive or negative or is unrelated to average climate, we would conclude that while the climate variable may influence 13 local population dynamics, it may not play an important role in determining the species' geographic distribution. Of course it would still be possible that long-term effects may actually be different or even the reverse of short-term sensitivity (Suttle et al. 2007), but we would argue that the simpler hypothesis that short-term sensitivity of species vital rates, behavior, or population change, should be a good indicator of the direction of longterm response (Ludwig et al. 2006). Case study with Big Sagebrush (Artemisia tridentata) Big sagebrush is a dominant shrub found across much of western North America, occurring from forest edges to prairies and from low elevation deserts to high elevation mountains (Kuchler 1970). Sagebrush provides unique and critical habitat for many endemic species of conservation concern such as the greater sage-grouse (Centrocercus urophasianus) (Davies et al. 2011). Distribution models for sagebrush typically indicate that climate change will cause large decreases in the total area suitable for sagebrush in the future (Neilson et al. 2005, Bradley 2010, Schlaepfer et al. 2012a, Still and Richardson 2015). Climate change could cause a decrease in snow cover and an increase in evaporation, both of which would lead to decreased soil moisture during the growing season and reduce sagebrush growth (Schlaepfer et al. 2012a, 2012b). Direct evidence for the effects of short-term climate variation on sagebrush come from a multi-year global warming experiment, analysis of sagebrush growth rings and new remote sensing data. Harte et al. (2015) found that sagebrush cover increased substantially in response to 20 years of artificial warming at high elevation in the southern Rocky Mountains. This increase was linked to a longer snow-free growing season at higher elevations with warming (Perfors et al. 2003). Likewise, Tredennick et 14 al. (2016) found a positive response of sagebrush cover to growing season temperature in a 30-year remote sensing dataset from southwestern Wyoming. In contrast, at lower elevation sites that are warmer and drier, sagebrush growth appears to decrease in response to warmer than average years (Poore et al. 2009, Apodaca 2013). Complicating detection of relationships between climate and sagebrush performance is the fact that sagebrush comprises many different ecologically distinct subspecies or varieties. The three most common subspecies, mountain big sagebrush (A. t. var. vaseyana), basin big sagebrush (A. t. var. tridentata), and Wyoming big sagebrush (A. t. var. wyomingensis), have more or less distinct climate niches (Bonham et al. 1991, Rosentreter 2001). Mountain sagebrush is dominant where snowfall is high, basin big sagebrush is found in warmer areas with moderate amounts of rainfall, and