Analysis of mangrove distribution and suitable habitat in Beihai, China, using optimized MaxEnt modeling: improving mangrove restoration efficiency - Lifeng, Li; Wenai, Liu; Mo, Wang; Shuangjiao, Cai; Fuqin, Liu; Xiaoling, Xu; Yancheng, Tao; Yunhong, Xue; Weiguo, Jiang (Frontiers in Forests and Global Change, 2024)
Introduction: Mangroves are an important component of coastal ecosystems, and determining the spatial dispersion of prevalent mangrove species and the most suitable land-use source for mangrove growth is of great importance for judicious restoration and effective conservation approaches. Maximum entropy (MaxEnt) models are well suited for this task; however, the default parameterization such models for distribution prediction has limitations and may produce results with low accuracy, requiring elucidation of useful parameter settings. Further, a focus on predicting only the mangrove distribution is insufficient for mangrove restoration, and clarification of suitable habitats is required. Here, we examined the geographical distribution of six mangrove species in Beihai, China (Aricennia marina, Aegiceras corniculatum, Kandelia obovata, Rhizophora stylosa, Bruguiera gymnorrhiza, and Acanthus ilicifolius). Methods: We used the ENMTools tool to select 16 variables from environmental factors, including bioclimate, terrain, sediment type, land-use classification, and sea-surface salinity and temperature. Using the selected variables and mangrove distribution data, a MaxEnt model optimized using the “kuenm” package in R was used to establish a mangrove prediction distribution model for Beihai City. Transition analyses of land-use types within suitable zones further clarified their current and potential functional roles. Results and Discussion: The spatial occurrences of A. marina, A. corniculatum, and K. obovata were strongly driven by topographical features, those of R. stylosa and B. gymnorrhiza mostly depended on bioclimatic variables, and that of A. ilicifolius was driven mostly by edaphic conditions, notably the substrate type. The predicted optimal suitable area for mangrove growth in Beihai City was 50.76 km2, of which 55.04% are currently officially protected. Unprotected areas suitable for mangrove growth were mainly located in Lianzhou Bay, Tieshangang Bay, Dafengjiang, and Xicun Port. The majority of these regions were derived from land-use transitions from wetlands and aquaculture ponds to forested ecosystems. We suggest that careful development of selected wetland ecosystems and transmutation of aquaculture ponds into forested landscapes are crucial for effective mangrove restoration. Our results will assist in selecting suitable species for mangrove restoration sites and improving mangrove restoration efficiency.