Predicting the probable impact of climate change on the distribution of threatened Shorea robusta forest in Purbachal, Bangladesh

Abstract Detecting the determinants of spatiotemporal distribution are important, along with the identification of drivers for the decline of the species, for ecological conservation and restoration. Here, we applied maximum entropy (Maxent)-type species-distribution modeling to investigate current and future potential distributions of an endangered canopy tree, Shorea robusta C. F. Gaertn. (Dipterocarpaceae) in Purbachal, Bangladesh. The model was constructed using 280 location records covering the entire range of S. robusta, with nine environmental variables related to climate, geography, and soil conditions included. Two scenarios [representative concentration pathways (RCPs): 4.5 and 8.5] were used to predict altered S. robusta distribution due to climate change. The precision of predicted distributions was supported sufficiently by the binomial test of omission (~P = 0.00) and area under the curve analysis (0.79–0.89). The current distributions were mostly determined by precipitation and soil nitrogen. Maxent modeling predicted that the suitable area for S. robusta forests will decline by 21% and 24% (Global Climate Models) and 26% and 28% (Regional Climate Models) relative to the present area according to ACCESS1-0 and CCSM4, respectively, under RCP8.5 by 2070 due to temperature rise, precipitation variability, seasonal dryness, and drought stress. These results showed that precipitation and soil nitrogen are important predictors of the current distribution and conservation of S. robusta forests. Furthermore, our results accentuate the potential negative impact of climate change, thereby encouraging further development of conservation and restoration plans for S. robusta by identifying suitable habitats in the region.

• Publication year

  2020

• Venue

  Global Ecology and Conservation

• Publication date

  2020-12-01

• Fields of study

  Environmental Science

• Identifiers
  DOI 10.1016/j.gecco.2020.e01250
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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