Drivers of elevational richness peaks, evaluated for trees in the east Himalaya

Abstract

Along elevational gradients, species richness often peaks at intermediate elevations and not the base. Here we refine and test eight hypotheses to evaluate causes of a richness peak in trees of the eastern Himalaya. In the field, we enumerated trees in 50 plots of size 0.1 ha each at eight zones along an elevational gradient and compared richness patterns with interpolation of elevational ranges of species from a thorough review of literature, including floras from the plains of India. The maximum number of species peaks at similar elevations in the two data sets (at 500 m in the field sampling and between 500 m and 1,000 m in range interpolation); concordance between the methods implies that statistical artefacts are unlikely to explain the peak in the data. We reject most hypotheses (e.g., area, speciation rate, mixing of distinct floras). We find support for a model in which climate (actual evapotranspiration [AET] or its correlates) sets both the number of species and each species optimum, coupled with a geometric constraint. We consider that AET declines with elevation, but an abrupt change in the association of AET with geographical distance into the plains means that the location of highest AET, at the base of the mountain, receives range overlaps from fewer species than the location just above the base. We formalize this explanation with a mathematical model to show how this can generate the observed low‐elevation richness peak.

Plant species richness across the Himalaya driven by evolutionary history and current climate

“…Seemingly contradicting this finding, fieldwork in the eastern Himalaya has shown that in small plots, herbs peak at the point where shrubs decline (Bhatta et al 2018); we also found that the decline in herbs shallows at this point (Fig. For trees, the highest over-dispersion (1700 m) is at about the line of regular freezing (White et al 2019), associated with a steep decline in the number of species that belong to primarily tropical genera (Rana et al 2019). In our dataset, the positive correlations between trees and shrubs, and between shrubs and herbs, may reflect underlying associations with benign conditions not captured by the climatic variables we studied, but alternative hypotheses based in dispersal and diversification are also possible (see below).…”

“…However, temperature range, a measure of seasonality, is weakly correlated with mean temperature (r = 0.27). S5 in Rana et al 2019). S1).…”

“…Minimum temperature is potentially a more important influence on a species persistence than the annual mean but is captured by the mean (mean temperature is correlated with both minimum temperature of coldest month and quarter; r = 0.99 for both variables). 1b for CHELSA, Price et al 2011, Rana et al 2019 for WorldClim), but CHELSA precipitation is more variable at a given elevation (see Fig. We did not include ❖ www.esajournals.org it in our analyses, because similar ranges are found in dry low-elevation locations that never freeze, as well as in wet high-elevation locations that regularly freeze, and in the northwest midelevations, they are the least variable (Appendix S2: Fig.…”

Spatial and climatic variables independently drive elevational gradients in ant species richness in the Eastern Himalaya

“…Range locations of such species are then adjusted to the nearest geometrically feasible value. Here, the assumption is that the observed extents of species are truncated subsets of actual distributions that extend beyond the domain boundary [39]. This model is similar to the range spread model where chances for species occurring at domain boundaries are much higher compared to hard boundary models [40,52].…”

“…Most studies test geometric constraints on elevational gradients using random unconstrained models with hard boundaries and the few that combine climatic predictor variables with geometric constraints also assume that all species are confined within the observed spatial extent [12,38] (but see Rana et. al [39] for a conceptual model). However, elevational extents present in most empirical data are likely to be subsets of actual geographical extents of species.…”