Functional trait space and the latitudinal diversity gradient

The processes causing the latitudinal gradient in species richness remain elusive. Ecological theories for the origin of biodiversity gradients, such as competitive exclusion, neutral dynamics, and environmental filtering, make predictions for how functional diversity should vary at the alpha (within local assemblages), beta (among assemblages), and gamma (regional pool) scales. We test these predictions by quantifying hypervolumes constructed from functional traits representing major axes of plant strategy variation (specific leaf area, plant height, and seed mass) in tree assemblages spanning the temperate and tropical New World. Alpha-scale trait volume decreases with absolute latitude and is often lower than sampling expectation, consistent with environmental filtering theory. Beta-scale overlap decays with geographic distance fastest in the temperate zone, again consistent with environmental filtering theory. In contrast, gamma-scale trait space shows a hump-shaped relationship with absolute latitude, consistent with no theory. Furthermore, the overall temperate trait hypervolume was larger than the overall tropical hypervolume, indicating that the temperate zone permits a wider range of trait combinations or that niche packing is stronger in the tropical zone. Although there are limitations in the data, our analyses suggest that multiple processes have shaped trait diversity in trees, reflecting no consistent support for any one theory.Species richness increases toward the equator (1, 2) in major clades of both extant and extinct species of plants and animals (3, 4). The generality of the pattern hints at a correspondingly general explanation, yet the latitudinal gradient in species richness remains one of ecology’s greatest unsolved puzzles. Long-running debates over the causes of the latitudinal gradient of species richness have focused on ecological, evolutionary, and geographic explanations (5–10). Although there has been some progress (11), it is also increasingly clear that there are numerous obstacles to understanding the primary drivers of the latitudinal gradient, including an ever-increasing number of hypotheses (12, 13), challenges in clearly separating their interdependencies (14, 15), and difficulties in rigorously falsifying their assumptions and predictions (16).More powerful tests of biodiversity theories need to move beyond species richness and instead explicitly focus on the mechanisms generating the gradient, by recasting the theories in terms of other measures of diversity, such as functional diversity (17–19). For example, explanations that assume species richness is limited by resource availability have often focused on the strength of species interactions, life history differences, and environmental constraints on how species pack into niche space (20). Evolutionary hypotheses have focused on differences in diversification rates, as well as the influence of species interactions on diversification rates (9). These interaction-based explanations implicitly refer to the degree of ecological differentiation among species, and therefore to trait dispersion within clades and assemblages, suggesting that patterns of functional diversity may provide a more powerful test of theory than taxonomic richness (21).A particularly important concept that unifies many ecological and evolutionary theories is the concept of the Hutchinsonian multidimensional niche (22). Hutchinsonian niches can be quantified by assessing the functional trait hypervolumes that characterize phenotypic space occupied by a set of species. Quantifying the volume, overlap, and packing of functional trait space at different spatial scales enables inferences about how differing ecological and evolutionary processes structure functional diversity and ecological strategies (23, 24).Here, we recast several contrasting hypotheses for the latitudinal gradient in terms of functional trait space. We focus on the proximate ecological mechanisms that ultimately can influence evolutionary processes. We quantify tree functional trait space across latitude at three spatial scales: (i) within assemblages (alpha), (ii) among assemblages (beta), and (iii) among biomes (gamma). For alpha and beta analyses, we use tree species assemblage data from 620 standardized 0.1-ha forest plots (Fig. 1A); for gamma analyses, we calculated the latitudinal range distributions for 520 New World tree species where we had sufficient data on geographic distribution and functional traits. In total, across all analyses, we used paired geographic occurrence data with trait data for 6,839 tree species.Open in a separate windowFig. 1.(A) Spatial distribution of the 620 0.1-ha forest plots used in this study. Plots are colored by richness. Plots cover most of the New World forested climate space (Fig. S1). (B) Relationship between absolute latitude and alpha hypervolume for tropical (red triangles) and temperate (blue pluses) plots. (C) Alpha hypervolume as a function of effective species richness (number of species with full trait coverage). We compare this hypervolume with a null expectation based on sampling the same number of species from the regional pool (median, dark gray line; 90% quantile range, light gray envelope).We primarily measured hypervolumes for three central traits hypothesized to characterize major axes of ecological strategy variation (25): specific leaf area (SLA), maximum height, and seed mass. SLA represents the tradeoff between leaf longevity and maximum photosynthetic rate (26); height is important for light competition and dispersal (27); and seed mass represents tradeoffs between fecundity, dispersal, and seedling survival (27). Although whole-plant resource strategies can be more fully assessed in higher dimensions (28, 29), we focus on these traits because of data availability (Materials and Methods). We use a hypervolume algorithm for calculating the volume and overlap of trait space (30) (Materials and Methods). All hypervolumes are reported in units of SDs of centered and scaled log-transformed trait values, raised to the power of the number of trait dimensions used.At all scales, our overall results and conclusions are similar (i) with and without gap-filling missing data, (ii) if we use convex hulls instead of hypervolumes to calculate trait spaces, and (iii) if we include additional trait axes. Additional details are given in Figs. S2–S7.