Prediction of the Potential Distribution of the Endangered Species Meconopsis punicea Maxim under Future Climate Change Based on Four Species Distribution Models

Abstract

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.

Current and Future Spatial Distribution of the Genus Cinchona in Peru: Opportunities for Conservation in the Face of Climate Change

“…We collected 567 records of historical occurrences for the genus Cinchona from two data sources: (i) the Global Biodiversity Information Facility (GBIF) platform, available at https://www.gbif.org/, accessed on 16 November 2022; (ii) Web of Science and Scopus database of research publications and citations. Subsequently, duplicate occurrences of data points were filtered out and removed with the same longitude and latitude in a specific spatial resolution area [36,37], yielding 165 occurrence points which were used to build the model.…”

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm

“…Our comparative analysis reveals that the predictive performance of GNNA was better than that of GLM and GBM, and delivering predictive results on compare with MaxEnt and RF when species with small sample sizes. Despite the notable superiority of GNNA over the four commonly used SDMs in certain cases (e.g., S. dareiformis and C. flavum ), relying solely on a single SDM could result in skewed interpretations within ecological research 3 , 59 . It is well-established that no single SDM can consistently deliver high predictive performance across diverse species and regions 29 , 35 , 60 .…”

The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models

“…Algorithms of SDMs have been found to be one of the major drivers of uncertainty in predicting species potential distributions (Buisson et al., 2010; Garcia et al., 2012; Thuiller et al., 2019; Zhang & Wang, 2023). The SDMs algorithms selected in this study are all popular for predicting species distributions except ANN (Hao et al., 2019; Li & Wang, 2013).…”