GEOGRAPHY AND RESOURCE LIMITATION COMPLICATE METABOLISM-BASED PREDICTIONS OF SPECIES RICHNESS

Assessments of general theoretical frameworks likeneutral ecological theory and the metabolic theory ofecology (MTE) often use only one or two data sets, andthus do not provide much evidence about the particularprediction tested, much less the validity or usefulness ofthe general theory. By compiling a diverse group of datasets from a range of terrestrial taxa, regions, and scales,Hawkins et al. (2007) attempt a more comprehensive testof predictions made by proponents of the metabolictheory of ecology about the relationship of temperatureand species richness. The predictions are that tempera-ture is the dominant factor controlling species richnesspatterns, and therefore, that observed species richnesswill scale log-linearly with (rescaled) temperature with aslope of about 0.65 (Allen et al. 2003, Brown et al.2004). A wide-ranging survey is necessary to assess suchgeneral predictions, but also raises a familiar challenge:what is the best way to pool information across diversedata sets and to reach an overall conclusion with theappropriate degree of conﬁdence? Here, I argue that ahierarchical statistical framework provides a ﬂexible,robust way of dealing with precisely these issues. Byreanalyzing a subset of the data in such a framework, Iconﬁrm the main ﬁndings of the paper: that terrestrialrichness patterns do not generally conform to the MTE’spredictions. The reanalysis also reveals a surprisingassociation between the slope inferred for a data set andthe data set’s latitudinal extent, showing that tempera-ture cannot be the sole important driver of terrestrialspecies richness patterns. MTE’s predictions fail in thiscase probably for two reasons. (1) At high temperatures,other resources inversely correlated with temperature(mostly water) control species richness, often producinga decline in species richness at the highest temperaturesand, hence, a shallower slope or nonlinearity. (2)Dispersal of organisms from their place of speciationtends dilute the richness–temperature relationshipthroughout its range, producing shallower slopes.To use a collection of data such as the impressive onepresented here, it is necessary to synthesize the multipledata sets and arrive at an overall conclusion. Theauthors score individual data sets as ‘‘consistent,’’‘‘possibly consistent,’’ or ‘‘inconsistent’’ with MTE,and then perform a meta-analysis. This approachprovides a summary, but the scoring system, althoughlogical, does not take into account differences amongthe data sets in sample size and informativeness toprovide an integrated measure of uncertainty. As acomplement to the authors’ approach, I reanalyzed asubset of the data, using hierarchical Bayesian regres-sion models, with OpenBUGS 2.0 (Thomas et al. 2006).These models are included as a Supplement. The modelsﬁt slopes and intercepts to individual data sets, whileallowing these individual slopes to inform an overallcommon slope and intercept. The models thus provideinference simultaneously about a ‘‘consensus’’ slope towhich slopes for the individual data sets are related, andthe degree to which individual slopes depart from thatconsensus slope (Gelman et al. 1995); see Model 1 of theSupplement. The results also quantify the uncertaintyaround the ﬁtted slopes, so that we can assess thestrength of agreement or disagreement of the data withthe slope of about 0.65 predicted by MTE.I limited the reanalysis to the 23 data sets that theauthors identify as linear. It would be difﬁcult tointerpret slopes ﬁtted through the clearly nonlinear datasets, and without a geographical basis for splitting theminto subgroups and lacking the authors’ intimatefamiliarity with the data, interpretation of ﬁts to partialdata sets is also not straightforward. A disadvantage ofusing the subset is that it removes some of the data setswith broadest geographical coverage, and thus weakensthe conclusions as to richness patterns at the globalscale. On the other hand, these large data sets areobviously nonlinear and are therefore not consistentwith MTE, so arguably they ought not to be used to testthe more precise prediction of the value of the linearslope. The reanalysis does not use RMA (reduced majoraxis) regression, so it might be criticized for sensitivity toerror in the explanatory variable (temperature). As analternative to the all-or-nothing choice between OLS(ordinary least squares) and RMA regression, it isstraightforward in the Bayesian framework to add a