Climate change increases the extinction risk of species, and studying the impact of climate change on endangered species is of great significance to biodiversity conservation. In this study, the endangered plant Meconopsis punicea Maxim (M. punicea) was selected as the research object. Four species distribution models (SDMs): the generalized linear model, the generalized boosted regression tree model, random forest and flexible discriminant analysis were applied to predict the potential distribution of M. punicea under current and future climates scenarios. Among them, two emission scenarios of sharing socio-economic pathways (SSPs; i.e., SSP2-4.5 and SSP5-8.5) and two global circulation models (GCMs) were considered for future climate conditions. Our results showed that temperature seasonality, mean temperature of coldest quarter, precipitation seasonality and precipitation of warmest quarter were the most important factors shaping the potential distribution of M. punicea. The prediction of the four SDMs consistently indicated that the current potential distribution area of M. punicea is concentrated between 29.02° N–39.06° N and 91.40° E–105.89° E. Under future climate change, the potential distribution of M. punicea will expand from the southeast to the northwest, and the expansion area under SSP5-8.5 would be wider than that under SSP2-4.5. In addition, there were significant differences in the potential distribution of M. punicea predicted by different SDMs, with slight differences caused by GCMs and emission scenarios. Our study suggests using agreement results from different SDMs as the basis for developing conservation strategies to improve reliability.
Prediction of the Potential Distribution of the Endangered Species Meconopsis punicea Maxim under Future Climate Change Based on Four Species Distribution Models
Published 2023 in Plants
ABSTRACT
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- Publication year
2023
- Venue
Plants
- Publication date
2023-03-01
- Fields of study
Biology, Medicine, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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