Global patterns and drivers of species and genera richness of Fabaceae

Abstract

The Fabaceae, a highly diverse and ecologically vital plant family, thrives across diverse biomes with remarkable nitrogen-fixation potential and functional adaptability. Despite its key role in global carbon and nitrogen cycles, the biogeographic patterns and environmental drivers of this important family remain understudied compared to other major angiosperm families. Here, we consolidate and curate a global dataset comprising 27,421 taxa of Fabaceae obtained from the World Checklist of Vascular Plants (WCVP) and the Global Inventory of Floras and Traits (GIFT) to investigate the geographical patterns of species and genera richness and their environmental determinants. Using generalized linear models with a negative binomial approach and hierarchical partitioning analysis, we assessed the influence of climatic, geographic, and topographic predictors derived from WorldClim and PaleoClim on the species and genera richness of Fabaceae. The results reveal heterogeneous patterns of species and genera richness of Fabaceae, with maximum richness centers in tropical regions, particularly in seasonally dry tropical biomes, followed by temperate and subtropical biomes. Across the globe, Southern America turns out to be the dominant source of this botanical family, followed by Africa and Asia-Temperate. The results also reveal unequal representation of species belonging to different biomes in different continents. I found different sets of climatic and geographic drivers that shape the taxonomic levels of Fabaceae across countries, with a maximum contribution of elevation range, temperature diurnal range, precipitation seasonality, annual mean temperature, temperature seasonality, and geographical area. The findings provide data-based evidence of climatic variability and topographic heterogeneity in influencing the patterns of species and genera richness by enhancing niche differentiation and microhabitat diversity. The results concur with the latitudinal diversity gradient and the tropical conservatism hypothesis, which posits that stable tropical environments promote high species diversification and persistence. The present study will serve as a model to be replicated in other families to bridge the existing knowledge gaps. Furthermore, the findings of this study will aid in understanding the ecological adaptations of Fabaceae, which have immediate implications for ecological restoration and sustainable management strategies.

Keywords: Fabaceae; biogeography; drivers; global patterns; species richness.

Figure 1

Geographic patterns of genera richness of Fabaceae among the countries across the globe. Islands are represented by circles, with size reflecting the number of genera. The map was generated using the Mollweide projection.

Figure 2

Relationship of (a) species and (b) genera richness of Fabaceae with the latitude.

Figure 3

Geographic patterns of species and genera richness of Fabaceae among the continents of the globe.

Figure 4

Mosaic plot showing the comparison of observed and expected numbers of Fabaceae species (a) and genera (b) according to their biome affiliation in different countries of the world. The corresponding Pearson’s residuals, which were obtained from the contingency table, are shown by color. Positive residual values indicate greater observed values than predicted, whilst negative residual values indicate lower observed values than expected. [DDS, desert or dry shrubland; MTR, montane tropical; SDT, seasonally dry tropical; SUB, subalpine or subarctic; SUT, subtropical; TRO, tropical; TEM, temperate; WET, wet tropical; AFR, Africa; ATM, Asis-Temperate; ATR, Asia-Tropical; AUS, Australasia; EUR, Europe; NAM, Northern America; PAC, Pacific; and SAM, Southern America].

Figure 5

Standardized coefficient of the negative binomial model for (a) species richness and (b) genera richness of Fabaceae. Significant variables are shown as ***p < 0.001, **p < 0.01, and *p < 0.05. The red lines represent a negative effect, while as blue lines indicate a positive effect of variables. [Tmean, mean annual temperature; Tmin, minimum temperature of the coldest month; Tseas, temperature seasonality; Tdur, temperature diurnal range; Pmean, annual precipitation; Pseas, precipitation seasonality, area; ELR, elevation range; TPI, topographic position index; TRI, terrain ruggedness index; Panom, precipitation anomaly; Tstab, temperature stability].