Global models for predicting woody plant richness from climate: comment

Combining climatic and soil properties better predicts covers of Brazilian biomes

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km² for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Revealing patterns of local species richness along environmental gradients with a novel network tool

How species richness relates to environmental gradients at large extents is commonly investigated aggregating local site data to coarser grains. However, such relationships often change with the grain of analysis, potentially hiding the local signal. Here we show that a novel network technique, the “method of reflections”, could unveil the relationships between species richness and climate without such drawbacks. We introduced a new index related to potential species richness, which revealed large scale patterns by including at the local community level information about species distribution throughout the dataset (i.e., the network). The method effectively removed noise, identifying how far site richness was from potential. When applying it to study woody species richness patterns in Spain, we observed that annual precipitation and mean annual temperature explained large parts of the variance of the newly defined species richness, highlighting that, at the local scale, communities in drier and warmer areas were potentially the species richest. Our method went far beyond what geographical upscaling of the data could unfold, and the insights obtained strongly suggested that it is a powerful instrument to detect key factors underlying species richness patterns, and that it could have numerous applications in ecology and other fields.

Species richness-environment relationships of European arthropods at two spatial grains: habitats and countries

We study how species richness of arthropods relates to theories concerning net primary productivity, ambient energy, water-energy dynamics and spatial environmental heterogeneity. We use two datasets of arthropod richness with similar spatial extents (Scandinavia to Mediterranean), but contrasting spatial grain (local habitat and country). Samples of ground-dwelling spiders, beetles, bugs and ants were collected from 32 paired habitats at 16 locations across Europe. Species richness of these taxonomic groups was also determined for 25 European countries based on the Fauna Europaea database. We tested effects of net primary productivity (NPP), annual mean temperature (T), annual rainfall (R) and potential evapotranspiration of the coldest month (PET_min) on species richness and turnover. Spatial environmental heterogeneity within countries was considered by including the ranges of NPP, T, R and PET_min. At the local habitat grain, relationships between species richness and environmental variables differed strongly between taxa and trophic groups. However, species turnover across locations was strongly correlated with differences in T. At the country grain, species richness was significantly correlated with environmental variables from all four theories. In particular, species richness within countries increased strongly with spatial heterogeneity in T. The importance of spatial heterogeneity in T for both species turnover across locations and for species richness within countries suggests that the temperature niche is an important determinant of arthropod diversity. We suggest that, unless climatic heterogeneity is constant across sampling units, coarse-grained studies should always account for environmental heterogeneity as a predictor of arthropod species richness, just as studies with variable area of sampling units routinely consider area.

Environmental effects on vertebrate species richness: testing the energy, environmental stability and habitat heterogeneity hypotheses

Background

Explaining species richness patterns is a central issue in biogeography and macroecology. Several hypotheses have been proposed to explain the mechanisms driving biodiversity patterns, but the causes of species richness gradients remain unclear. In this study, we aimed to explain the impacts of energy, environmental stability, and habitat heterogeneity factors on variation of vertebrate species richness (VSR), based on the VSR pattern in China, so as to test the energy hypothesis, the environmental stability hypothesis, and the habitat heterogeneity hypothesis.

Methodology/Principal Findings

A dataset was compiled containing the distributions of 2,665 vertebrate species and eleven ecogeographic predictive variables in China. We grouped these variables into categories of energy, environmental stability, and habitat heterogeneity and transformed the data into 100×100 km quadrat systems. To test the three hypotheses, AIC-based model selection was carried out between VSR and the variables in each group and correlation analyses were conducted. There was a decreasing VSR gradient from the southeast to the northwest of China. Our results showed that energy explained 67.6% of the VSR variation, with the annual mean temperature as the main factor, which was followed by annual precipitation and NDVI. Environmental stability factors explained 69.1% of the VSR variation and both temperature annual range and precipitation seasonality had important contributions. By contrast, habitat heterogeneity variables explained only 26.3% of the VSR variation. Significantly positive correlations were detected among VSR, annual mean temperature, annual precipitation, and NDVI, whereas the relationship of VSR and temperature annual range was strongly negative. In addition, other variables showed moderate or ambiguous relations to VSR.

Conclusions/Significance

The energy hypothesis and the environmental stability hypothesis were supported, whereas little support was found for the habitat heterogeneity hypothesis.

Patterns, determinants and models of woody plant diversity in China

What determines large-scale patterns of species richness remains one of the most controversial issues in ecology. Using the distribution maps of 11 405 woody species in China, we compared the effects of habitat heterogeneity, human activities and different aspects of climate, particularly environmental energy, water-energy dynamics and winter frost, and explored how biogeographic affinities (tropical versus temperate) influence richness-climate relationships. We found that the species richness of trees, shrubs, lianas and all woody plants strongly correlated with each other, and more strongly correlated with the species richness of tropical affinity than with that of temperate affinity. The mean temperature of the coldest quarter was the strongest predictor of species richness, and its explanatory power for species richness was significantly higher for tropical affinity than for temperate affinity. These results suggest that the patterns of woody species richness mainly result from the increasing intensity of frost filtering for tropical species from the equator/lowlands towards the poles/highlands, and hence support the freezing-tolerance hypothesis. A model based on these results was developed, which explained 76-85% of species richness variation in China, and reasonably predicted the species richness of woody plants in North America and the Northern Hemisphere.

A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients

We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) In-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near -0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression, one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.