Precipitation of the warmest quarter and temperature of the warmest month are key to understanding the effect of climate change on plant species diversity in Southern African savannah

Abstract

In this study, we test for the key bioclimatic variables that significantly explain the current distribution of plant species richness in a southern African ecosystem as a preamble to predicting plant species richness under a changed climate. We used 54,000 records of georeferenced plant species data to calculate species richness and spatially interpolated climate data to derive nineteen bioclimatic variables. Next, we determined the key bioclimatic variables explaining variation in species richness across Zimbabwe using regression analysis. Our results show that two bioclimatic variables, that is, precipitation of the warmest quarter (R2 = 0.92, P < 0.001) and temperature of the warmest month (R2 = 0.67, P < 0.001) significantly explain variation in plant species richness. In addition, results of bioclimatic modelling using future climate change projections show a reduction in the current bio‐climatically suitable area that supports high plant species richness. However, in high‐altitude areas, plant richness is less sensitive to climate change while low‐altitude areas show high sensitivity. Our results have important implications to biodiversity conservation in areas sensitive to climate change; for example, high‐altitude areas are likely to continue being biodiversity hotspots, as such future conservation efforts should be concentrated in these areas.

Quantifying Changes in Plant Species Diversity in a Savanna Ecosystem Through Observed and Remotely Sensed Data

“…These results imply that NDVI can be used as a surrogate for vegetative species diversity. An increase in vegetative species diversity corresponds with an increase in NDVI, but the increase does not continue beyond 2.3 of H. The decrease in diversity as temperatures increase implies that some vegetative species' tolerance decreases with an increase in maximum temperatures of the warmest month, as also suggested by Gwitira et al [47]. Furthermore, the results indicate that the regression analysis between NDVI and plant species richness is consistent with the Spectral Variation Hypothesis (SVH), which envisages a direct correlation between differences in reflectance of remote sensing images with environmental The decrease in diversity as temperatures increase implies that some vegetative species' tolerance decreases with an increase in maximum temperatures of the warmest month, as also suggested by Gwitira et al [47].…”

“…An increase in vegetative species diversity corresponds with an increase in NDVI, but the increase does not continue beyond 2.3 of H. The decrease in diversity as temperatures increase implies that some vegetative species' tolerance decreases with an increase in maximum temperatures of the warmest month, as also suggested by Gwitira et al [47]. Furthermore, the results indicate that the regression analysis between NDVI and plant species richness is consistent with the Spectral Variation Hypothesis (SVH), which envisages a direct correlation between differences in reflectance of remote sensing images with environmental The decrease in diversity as temperatures increase implies that some vegetative species' tolerance decreases with an increase in maximum temperatures of the warmest month, as also suggested by Gwitira et al [47]. Furthermore, the results indicate that the regression analysis between NDVI and plant species richness is consistent with the Spectral Variation Hypothesis (SVH), which envisages a direct correlation between differences in reflectance of remote sensing images with environmental heterogeneity and beta diversity [50].…”

“…The warmest quarter is the three-month period, which receives the highest amount of radiation, and consequently, temperatures throughout the year. In the subtropical region in general and Zimbabwe in particular, this period falls between October and December [47]. Thus, precipitation data for the warmest quarter reveal a statistically significant (P = 0.048, α = 0.05) trend over a forty-year period.…”

“…It is also observed that the trend for MTWM has increased over time and it is statistically significant (p = 0.011, α = 0.05). October is the warmest month of the year in this climatic region [47]. The selection of the month was based on a preliminary analysis of temperature characteristics of all months over a thirty-year period, i.e., 1974-2004.…”

Modeling Ecological Niche of Tree Species in Brazilian Tropical Area

“…This method is useful to understand the species biogeography and its potential occurrence through the development of maps as results. These techniques are widely applied for different goals, such as: conservation of rare or endangered species (QUEIROZ et al, 2012;HAMILTON et al, 2015); identification of climate change impacts (CHUN;LEE, 2013;GWITIRA et al, 2014); reintroduction of species (HIRZEL et al, 2002;MEINERI et al, 2015); identifying of potential areas for invasive species (VACLAVIK;MEENTEMEYER, 2009;GALLIEN et al, 2012). Several techniques for ecological niche modeling are available and can be classified in two models groups: 1) classical statistical and 2) non-classical statistical.…”