MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia

Abstract

Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using 37 different environmental variables. The maximum entropy (MaxEnt) algorithm in MaxEnt software was used to forecast the potential habitat suitability under current and future (i.e., 2050s and 2070s) climate change scenarios (i.e., Shared Socio-economic Pathways (SSPs): 245 and 585). A total of 238 species occurrence records were collected from Afghanistan, Pakistan and India, and employed to build the predictive distribution model. The results showed that normalized difference vegetation index, mean temperature of coldest quarter, isothermality, precipitation of driest month and volumetric fraction of the coarse soil fragments (>2 mm) were the leading predictors of species presence prediction. High accuracy values (>0.9) of predicted distribution models were recorded, and remarkable shrinkage of potentially suitable habitat of Chilgoza pine was followed by Afghanistan, India and China. The estimated extent of occurrence (EOO) of the species was about 84,938 km2, and the area of occupancy (AOO) was about 888 km2, with 54 major sub-populations. This study concluded that, as the total predicted suitable habitat under current climate scenario (138,782 km2) is reasonably higher than the existing EOO, this might represent a case of continuous range contraction. Hence, the outcomes of this research can be used to build the future conservation and management plans accordingly for this economically valuable species in the region.

The Future Potential Distribution and Sustainable Management of Ancient Pu’er Tea Trees (Camellia sinensis var. assamica (J. W. Mast.) Kitam.)

“…We also provided an example in which climate change facilitates a range expansion of ancient tea trees. This is inconsistent with previous studies indicating the negative effects of climate change on trees and forests [22,51]. Hence, climate change's effects on different species and the effects of different temperature bands are worth exploring.…”

“…We included all 28 parameters in the initial model, and some studies have indicated that potential correlations among variables might induce uncertainty related to multicollinearity in the results [51,53,57]. However, we did not omit any variable in the first stage because these variables are theoretically independent and possess unique significance.…”

“…Our methodology minimized the theoretical uncertainty but increased the statistical uncertainty. To deal with this problem, we used the average of 10 replicates instead of only the 10th replicate, as has been performed in some previous studies [51]. We also set the maximum number of background points as 10,000 and the maximum number of iterations as 500 to account for potentially redundant calculations.…”

“…The accuracy of the maximum entropy method of species distribution modeling (SDM) depends on the quality of presence-only data [50]; thus, we included as many occurrence records as possible. To assess the degree of uncertainty in the predicted occurrence probability, we built response curves of the means of ten replicate Maxent runs [51]. These curves showed clear patterns with acceptable ranges of standard deviation; therefore, we decided to not calibrate variables further nor present redundant figures.…”

“…To facilitate interpretation of the logistic values, we categorized them into three classes, high, medium, and low, using the Jenks natural breaks to visualize environmental suitability. Interpretation of three classes is more straightforward for our data compared with the current five probability classes [51,52]. We also calculated areas at the province level to evaluate distributional changes corresponding to climate change in China.…”