Response to Comments on “Disentangling the Drivers of b Diversity Along Latitudinal and Elevational Gradients”

Both Qian et al. (1) and Tuomisto and Ruokolainen (2) critique the Gentry data sets. Qian et al. state that b diversity among Gentry subplots is not comparable across locations because the spatial orientation of the subplots varies among locations. Subplot spatial orientation varies haphazardly across sites, and no systematic trends in spatial orientation exist. Therefore, gradients in Gentry plot b diversity are neither biased nor invalid. Qian et al. also assert that because the Gentry subplots tend to be located to minimize coarse environmental variability among subplots at a location, they cannot be used to study the ecological processes determining species composition. We originally stressed that the Gentry data are not appropriate for testing how coarse-grained environmental heterogeneity structures communities among subplots within a location (3). We disagree, however, that the scale of the Gentry plots makes them inappropriate for studying themyriad processes that structure community composition. Indeed, considerable b diversity exists within each location in the Gentry data estimated by the b partition either before [figure 1C in (3)] or after [figure 3C in (3)] implementing our sampling-based null model. This second point is a misunderstanding shared by both Qian et al. and Tuomisto and Ruokolainen. Specifically, after conditioning the observed b diversity within each location on location-level g diversity, we still find extensive and substantial nonrandom patterns of species turnover at all points along both latitudinal and elevational gradients [i.e., the b deviation was >0 for almost all points in figure 3C and for all points in figure 3D in (1)]. Our key result, therefore, is not that species co-occurrence patterns can be explained by a null model, as Tuomisto and Ruokolainen state. Instead, we find that the b partition shows no trend with latitude or elevation after accounting for g diversity with an appropriate null model. Although broader-scale sampling at each location might capture b diversity driven by coarser-grained environmental factors, there is pervasive, nonrandom b diversity at the spatial scale measured by the Gentry data. Our paper does not state that “latitudinal trends in b diversity...lack ecological relevance,” as Tuomisto and Ruokolainen suggest. Nonrandom patterns in the smaller-scale turnover that we documented are both real and ecologically relevant. Qian et al. critique our use of latitude instead of temperature and suggest a correction to latitude that accounts for plot elevation as a proxy for mean temperature (1). We question the validity of this simple correction, given that many factors besides mean temperature differ among locations. Nevertheless, when we apply this adjustment to the full 197-location data set, the results agree with those of our previous analysis (3). Specifically, we find that the negative correlation between latitude and b diversity can be explained by our null model in the original data [Fig. 1, A and B, after (3)] and after adjusting latitude for elevation in the manner proposed (Fig. 1, C and D). We reach the same conclusion when we remove high-elevation sites from the analysis [Fig. 1, E and F; defining high elevation as >1000 m after figure 2 in (1)]. Qian et al. examined whether b-diversity patterns in one particular subset of the Gentry data differ from patterns seen in the entire data set. We disagree that any nonrandom subset of a larger data set would be expected to show the same pattern as the entire data set. Nevertheless, if we restrict our analysis to include only the Gentry plots from the New World (158 out of 197 locations), our original conclusions are upheld, regardless of whether we adjust latitude for elevation following (1) or remove high-elevation sites (Fig. 2). However, if we reduce the data set further, as Qian et al. have done, to include only the 72 South American locations south of the equator (37% of the full data set, thereby excluding >50% of the NewWorld data and 33% of the South American data), a statistically significant relationship between the b deviation and latitude emerges. What is surprising is that the pattern is the opposite of what is expected. Specifically, although b diversity for South America south of the equator is highest at the equator (R = 0.38) [figure 1B in (1)], after correcting for variation in g diversity, the b deviation is highest in southern South America (R = 0.11) [figure 1C in (1)], effectively reversing the gradient. Our global analysis demonstrated how differences in b diversity across a broad gradient can be explained by differences in g diversity, resulting in no gradient in b diversity across latitude. By focusing on a nonrandom subset of the data, Qian et al. show that the effect of g diversity can be so strong that, once it is accounted for, the pattern along the latitudinal gradient is actually reversed. This gives further strong evidence for the importance of the null-model-based approach that we have developed for analyzing b-diversity trends (3). We fully support attempts to apply our nullmodel approach to other data sets, as Qian et al. attempt to do with a new North American tree data set. Importantly, however, our null-model approach requires data on individual abundances within subplots, because it operates by randomizing individuals among samples. The additional data set does not include abundance information, so unfortunately cannot be analyzed using our null-model approach. Qian et al. propose to instead account for g diversity using least-squares multiple regression. This approach fails to properly TECHNICALCOMMENT