Response to Comments on “Changes in Climatic Water Balance Drive Downhill Shifts inPlantSpecies’OptimumElevations”

We recently reported that changes in climatic water balance across much of the state of California have led to downhill shifts in plant species’ distributions (1). Wolf and Anderegg (2), Hijmans (3), and Stephenson and Das (4) contend that our analysis was flawed.We address their comments here. Wolf and Anderegg (2) state that our results are narrowly applicable because of the limited number of species examined. We agree that overstating the scope of inference in scientific studies should be avoided and did not claim that our results were representative of all species in our study area.We simply stated that downhill shifts of plant species have occurred as reported elsewhere (5) and, under certain circumstances, may continue to occur. Wolf and Anderegg (2) note that the Wieslander surveys were not strictly presenceabsence surveys. Although true for rare and herbaceous species, for the prominent woody species examined in (1), the Wieslander Vegetation Type Map (VTM) data set effectively contains the same level of detail as any presence-absence data set. For example, many plots in the VTM data set contain occurrence records for >12 woody species, suggesting that species need not be dominant over the entire plot to be recorded. This level of detail has previously led to the use of the VTM data for presence-absence modeling of woody species (6, 7). Wolf and Anderegg conclude by noting the lack of strong correlation between the magnitude of shifts in optimum deficit and optimum elevation as evidence against our hypothesis of niche tracking of water balance. However, under perfect niche tracking of water balance, we would expect no change in species optimums of water balance through time and hence no correlation between these variables. The Technical Comment authors state that latitudinal bias in our data sets may invalidate our conclusions. Wolf and Anderegg (2) claim that the central Sierra Nevada and Central Coast regions were unsampled in the contemporary data set. This is false, as these two areas contained >1100 and >2300 vegetation plots, respectively. Hijmans (3) uses a linear regression between latitudinal bias and shifts in optimum elevation to suggest that latitudinal bias can account for our findings. We note that his critique omits critical details necessary for interpretation, such as a measure of fit and a simple plot. Consequently, we emulated his analysis and found that his regression model had low predictive power (r = 0.11) and was sensitive to three outliers (species) that exert excessive leverage on the regression, as is apparent from a simple examination of Fig. 1A and corroborated using Cook’s distance and hat (H ) valuemetrics (Fig. 1A; see caption for further detail). Further, Juniperus occidentalis, the species Hijmans presents in figure 1 of (3) as a graphical example of the influence of latitudinal bias, is identified as an outlier here. When these outliers are removed from our original data set, the purported relationship between latitudinal bias and shift in optimum elevation disappears (r = 0.02, P = 0.26) (Fig. 1B) and our finding of significant downhill shifts is unchanged (–77 m, P = 0.02, n = 61). Although latitudinal bias did exist between our two data sets, Fig. 1B demonstrates that it had a nominal effect on the reported changes in species optimum elevations. The basis for the criticism leveled by the Technical Comment authors is the supposition that the greater sample density at higher latitudes in the modern data translates to a downward shift in the measure of central tendency of the distribution of the sample gradient (elevation). Indeed, this would be the case if we used means or medians. Instead, we use Gaussian optimums that use presence and absence data and are insensitive to sample effort along a gradient (8, 9), given that increased sample effort should leave the ratio of presence to absence (prevalence) unaffected. To confirm this, we compared species prevalence between the two data sets (historical and modern) within the northwestern and Cascade ecoregions (10), the area identified as the principle source of geographic bias by the technical comment authors. We found no discernible difference in species prevalence between the historical and modern data set for these ecoregions (paired t test, P = 0.14, n = 61). Hijmans (3) presents results from a new bias correction approach. Unfortunately, he does not provide a minimum level of information necesTECHNICALCOMMENT