Abstract
Climate change has significantly influenced the growth and distribution of plant species, particularly those with a narrow ecological niche. Understanding climate change impacts on the distribution and spatial pattern of endangered species can improve conservation strategies. The MaxEnt model is widely applied to predict species distribution and environmental tolerance based on occurrence data. This study investigated the suitable habitats of the endangered Ormosia microphylla in China and evaluated the importance of bioclimatic factors in sha** its distribution. Occurrence data and environmental variables were gleaned to construct the MaxEnt model, and the resulting suitable habitat maps were evaluated for accuracy. The results showed that the MaxEnt model had an excellent simulation quality (AUC = 0.962). The major environmental factors predicting the current distribution of O. microphylla were the mean diurnal range (bio2) and precipitation of the driest month (bio14). The current core potential distribution areas were concentrated in Guangxi, Fujian, Guizhou, Guangdong, and Hunan provinces in south China, demonstrating significant differences in their distribution areas. Our findings contribute to develo** effective conservation and management measures for O. microphylla, addressing the critical need for reliable prediction of unfavorable impacts on the potential suitable habitats of the endangered species.
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Introduction
Climate change has influenced the growth and survival of plant species, especially those with a narrow ecological niche1,2. Extreme high temperatures and droughts, incurring abiotic and biotic stresses, can harm plant growth and forest health3. Moreover, changes in environmental conditions could alter physiological responses, metabolic processes, phenology, and seed dispersal. Plants may respond by adapting to the new conditions or shifting or shrinking their geographical range4,5.
Consequently, climate change has brought far-reaching impacts on the distribution patterns of plant species in recent decades, constituting a primary cause of their decline and loss6. Temperature and precipitation changes brought by climate change modify plant physiological processes, affecting growth, development, reproduction, stability, and geographical distribution7,8. Notable and fast climate change can induce serious degradation or loss of species habitats. Plants with weak adaptability and poor dispersal could be driven to local extinction9. Therefore, assessing climate change impacts on the distribution area and spatial pattern of threatened and endangered plants is crucial for monitoring and restoring native populations in their natural habitats. The results can enhance the formulation of sustainable conservation and management strategies to maintain habitat integrity10.
In recent years, species distribution modeling (SDM) (e.g., MaxEnt, Random Forests, Bioclim, and Climex) has emerged as an analytical tool for conservation planning and biodiversity management. It is especially useful in poorly surveyed regions beset by increasing habitat degradation and loss pressure11,12. SDMs can integrate species occurrence data with environmental variables to simulate and predict habitats suitable for species growth and map the distribution of potential suitable habitats across space and time13,14.
Among various SDM algorithms, the Maximum Entropy (MaxEnt) model combines machine learning and maximum entropy principles to predict the potential distribution areas of species15,16. The method’s many advantages have triggered extensive application to species distribution modeling. It can utilize both continuous and categorical data and incorporate interactions between variables. It performs better than similar models in forecasting species distribution with small sample size or presence-only data5. Additionally, the probability distribution obtained from Maxent has a concise mathematical expression, allowing direct generation of a habitat suitability map17. Each environmental variable's relative importance (%) can be evaluated using the software’s built-in jackknife test18. These MaxEnt capabilities provide an effectual way to predict the potential distribution of endangered species, which often have a limited number of observed occurrences and grow in remote areas with terrain and access constraints, making field data collection difficult19.
Ormosia microphylla (Merr. & H. Y. Chen) is a dioecious tree species of the Fabaceae family. It is a first-class national protected plant in China. This evergreen tree can reach 15–20 m in height. The legume fruits ripen from October to November. Each fruit holds 3–4 extremely hard seeds that are brightly colored in shades of red. The seeds (150–180 g/1000 grain) rely on wind dispersal. Importantly, the species has high economic value. The attractive seeds are used in jewelry and handicrafts. The priced wood, strong and heavy with handsome grain, is used for furniture and flooring, constituting the main reason for its extensive logging20.
The natural distribution range of O. microphylla is very narrow. It is restricted to forests at 600–800 m altitude on slopes and foot slopes in central and south China, covering Guizhou, Hunan, Guangxi, Guangdong, and Fujian provinces21. Moreover, the species is beset by low genetic diversity, poor natural regeneration ability, and low seed germination rate, which have jointly depressed its population growth, density, and distribution. Unfortunately, its high economic value, mainly as a valuable timber source has incurred excessive felling by humans. Consequently, the species has suffered from massive tree losses, a severely fragmented range, and increasing scarcity in the wild22.
Previous O. microphylla studies have focused on its population structure22, with little attention on geographical distribution and factors influencing its habitat. However, its habitat has been seriously damaged, and the natural distribution area has been drastically reduced due to the acute impacts of climate change and human disturbance. Therefore, it is important to study the potential impacts of climate change on the species to yield information about its distribution range, habitat preference, and population dynamics.
This study aimed to investigate the suitable habitat distribution of O. microphylla in China with two research objectives: (1) to predict the current potential spatial distribution; and (2) to identify the key environmental factors highly correlated with O. microphylla distribution range. The findings will be useful in develo** conservation and management measures for the species.
Materials and Methods
Establishing species occurrence records
Occurrence data of O. microphylla in China were obtained from the Chinese Virtual Herbarium (http://www.cvh.ac.cn), Plant Photo Bank of China (http://www.Plantphotophoto.cn), National Specimen Information Infrastructure (http://www.nsii.org.cn/), field surveys, and the published literature. Each occurrence record was scrutinized for data quality and suitability with reference to the model's requirements. Records with unclear latitude and longitude information and duplicated distribution points were discarded. A total of 45 verified distribution points of O. microphylla were kept for final analysis to generate the potential distribution based on MaxEnt modeling. The occurrence-point data were stored in a CSV file, sorted by species name, longitude and latitude, and plotted on a map (Fig. 1).