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Projecting shifts in the distributions of Chinese endemic vertebrate species under climate and land-use change

  • Yiming Deng
  • , Eben Goodale
  • , Anran Dong
  • , Demeng Jiang
  • , Aiwu Jiang*
  • , Zhixin Zhang*
  • , Christos Mammides
  • *Corresponding author for this work
  • Guangxi University
  • Peking University
  • CAS - South China Sea Institute of Oceanology
  • Frederick University

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

Human-induced climate and land-use change impact species’ habitats and survival ability. A growing body of research uses species distribution models (SDMs) to predict potential changes in species ranges under global change. We constructed SDMs for 411 Chinese endemic vertebrates using Maximum Entropy (MaxEnt) modeling and four shared socioeconomic pathways (SSPs) spanning to 2100. We compared four different approaches: (1) using only climatic and geographic factors, (2) adding anthropogenic factors (land-use types and human population densities), but only using current data to project into the future, (3) incorporating future estimates of the anthropogenic variables, and (4) processing species occurrence data extracted from IUCN range maps to remove unsuitable areas and reflect each species’ area of habitat (AOH). The results showed that the performance of the models (as measured by the Boyce index) improved with the inclusion of anthropogenic data. Additionally, the predicted future suitable area was most restricted and diminished compared to the current area, when using the fourth approach. Overall, the results are consistent with other studies showing that species distributions will shift to higher elevations and latitudes under global change, especially under higher emission scenarios. Species threatened currently, as listed by the IUCN, will have their range decrease more than others. Additionally, higher emission scenarios forecast more threatened species in the future. Our findings show that approaches to optimizing SDM modeling can improve accuracy, predicting more direct global change consequences, which need to be anticipated. We also show that global change poses a significant threat to endemic species even in regions with extensive protected land at higher latitudes and elevations, such as China.

Original languageEnglish
Article number1174495
JournalFrontiers in Ecology and Evolution
Volume11
DOIs
Publication statusPublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action
  2. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • biodiversity hotspots
  • endemic species
  • global change
  • IUCN red list
  • MaxEnt
  • optimization model
  • shared socioeconomic pathways
  • species distribution modeling

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