Predicting Habitat Suitability and Adaptation Strategies of an Endangered Endemic Species, Camellia luteoflora Li ex Chang (Ericales: Theaceae) under Future Climate Change

Abstract

Camellia luteoflora Li ex Chang is an endangered plant endemic to the East Asian flora with high ornamental value as well as phylogenetic and floristic research value. Predicting the impact of climate change on its distribution and suitable habitat is crucial until scientific conservation measures are implemented. Based on seven environmental variables and 17 occurrence records, this study optimized the MaxEnt model using the kuenm data package to obtain the optimal parameter combinations (RM = 1.3, FC = LPT) and predicted the potential distribution pattern of C. luteoflora in various future periods. The results revealed that the mean diurnal range, temperature annual range, and precipitation of the wettest month were the influential factors determining the distribution pattern of C. luteoflora, contributing 60.2%, 14.4%, and 12.3% of the variability in the data, respectively. Under the current conditions, the area of suitable habitats for C. luteoflora was only about 21.9 × 104 km2. Overall, the suitable area around the C. luteoflora distribution points will shrink in a circular pattern in response to future global warming, but some potentially suitable distribution areas will expand and migrate to higher latitudes and the Hengduan Mountains region, representing a survival strategy for coping with climate change. It is hypothesized that the future climate refugia will be the highly suitable area and the Hengduan Mountains region. Furthermore, a retrospective validation method was employed to assess the reliability of the predictions and estimate the model’s predictive performance in the future. This study proposes a survival strategy and adaptation measures for C. luteoflora in response to climate change, and the proposed measures can be generalized for application in conservation planning and restoration processes. We also recommend that future studies incorporate factors such as the anthropogenic disturbances and associated socio-economic activities related to C. luteoflora into the model and to further predict the distribution pattern for C. luteoflora in response to historical climatic changes, tracing the evolutionary history of its population.

Prediction of Suitable Habitat of Alien Invasive Plant Ambrosia trifida in Northeast China under Various Climatic Scenarios

“…Therefore, it is necessary to optimize feature combination (FC) and regularization multiplier (RM) in the MaxEnt model to build the optimal MaxEnt model [28]. By testing the combination of different FC and RM parameters, the AIC values of different parameter models were obtained to evaluate the complexity of the model, and the model with the lowest complexity was selected to build the optimal model [29]. In this study, the parameters of the A. trifida prediction model were optimized by using the Kuenm package in R, setting the RM parameter interval [0.1-4], each interval 0.1, and 29 FC feature function combinations.…”