Global patterns of vascular plant alpha diversity

Abstract

Global patterns of regional (gamma) plant diversity are relatively well known, but whether these patterns hold for local communities, and the dependence on spatial grain, remain controversial. Using data on 170,272 georeferenced local plant assemblages, we created global maps of alpha diversity (local species richness) for vascular plants at three different spatial grains, for forests and non-forests. We show that alpha diversity is consistently high across grains in some regions (for example, Andean-Amazonian foothills), but regional 'scaling anomalies' (deviations from the positive correlation) exist elsewhere, particularly in Eurasian temperate forests with disproportionally higher fine-grained richness and many African tropical forests with disproportionally higher coarse-grained richness. The influence of different climatic, topographic and biogeographical variables on alpha diversity also varies across grains. Our multi-grain maps return a nuanced understanding of vascular plant biodiversity patterns that complements classic maps of biodiversity hotspots and will improve predictions of global change effects on biodiversity.

Fig. 1. Global distribution of estimated vascular plant alpha diversity in forests.

Spatial grains: A 400 m²; B 1000 m²; C 1 ha. The maps on the left show the median estimated species richness at the corresponding spatial grain for each 2.5 arcminute grid cell of the World, averaged over 99 boosted regression tree models based on different resampled datasets. Colors are on a log₂ scale. The maps on the right show the distribution of hotspots (red) and coldspots (blue), i.e., areas where species richness is above the 95th or below the 5th global percentile, respectively. We only show alpha diversity estimates for locations where forests would grow under current climate conditions and without human influence. Hatching represents data-poor regions, i.e., regions farther than 500 km from any vegetation plots, for which we did not generate predictions. Global maps with predictions for these data-poor regions can be found in Supplementary Fig. 3. Values are averaged over 2600 km² hexagons. Source data are provided as a Source Data file.

Fig. 2. Global distribution of estimated vascular plant alpha diversity in non-forest ecosystems.

Spatial grains: A 10 m²; B 100 m²; C 1000 m². The maps on the left show the median estimated species richness at the corresponding spatial grain for each 2.5 arcminute grid cell of the World, averaged across 99 boosted regression tree models based on different resampled datasets. Colors are on a log₂ scale. The maps on the right show the distribution of hotspots (red) and coldspots (blue), i.e., areas where species richness is above the 95th or below the 5th global percentile, respectively. We only show alpha diversity estimates for locations where the land cover ‘herbaceous vegetation’ occurs based on a consensus map that integrates multiple global remote sensing-derived land-cover products. Hatching represents data-poor regions, i.e., regions farther than 500 km from any vegetation plots, for which we did not generate predictions. Global maps with predictions for these data-poor regions can be found in Supplementary Fig. 5. Values are averaged over 2600 km² hexagons. Source data are provided as a Source Data file.

Fig. 3. Relative influence of environmental and biogeographic variables on alpha diversity of vascular plants.

Points represent the median relative importance of a predictor across 99 runs of a boosted regression tree model that jointly models vascular plant species richness in forest and non-forest formations. The bars connect the 2.5th and the 97.5th percentiles of the relative importance distribution across runs. The vertical dashed line separates variables with relative influence higher or lower than expected, i.e., those variables whose relative influence is higher or lower than 100% divided by the number of variables (n = 20).

Fig. 4. Partial dependence plots for the main determinants of plant alpha diversity at different grain sizes.

These plots show the fitted function of the most influential variables explaining vascular plant alpha diversity at three different spatial grains, while holding all other predictor variables constant at their mean value. The fitted function is the difference between the response value at a given value of each predictor and the mean response value. Each line represents the fitted function for one of the 99 boosted regression tree model runs. Fine, intermediate and coarse grains correspond to 400 m², 1000 m² and 1 ha in forests (A) and 10, 100, and 1000 m² in non-forest ecosystems (B), respectively. Variables are sorted by decreasing relative influence. The rug plots on the x-axis display the distribution of the calibration data. Note the different range of plot sizes between forest and non-forest ecosystems.

Fig. 5. Regional scaling anomalies in species richness across grain sizes.

Correspondence between estimates of plant alpha diversity at fine and coarse grains for A forest and B non-forest ecosystems. Fine-grained alpha diversity was calculated at 400 m² and 10 m² for forest and non-forest ecosystems, respectively. Coarse-grained alpha diversity was calculated at 1 ha and 1000 m² for forest and non-forest ecosystems, respectively. We only show alpha diversity estimates where (A) forests would grow under current climate conditions and without human influence, or B the land cover ‘herbaceous vegetation’ occurs, based on a consensus map integrating multiple global remote sensing-derived land-cover products. Color codes are based on quartile distributions of species richness at the two grains. Parallel hatching represents data-poor regions, i.e., regions farther than 500 km from any vegetation plots. Values are averaged over 7700 km² hexagons. SR: species richness. Source data are provided as a Source Data file.