ABSTRACT
Global mangrove forests have exhibited distinct changes in the past decades owing to anthropogenic activities, with land-use pressure being among the main causes of mangrove loss. Thus, understanding the inherent conflicts between conservation/restoration and land-use demands is fundamental for mangrove management. To predict how land-use changes will drive the spatiotemporal patterns of mangrove habitats, a novel integrated framework coupling MaxEnt and Dyna-CLUE modeling was proposed. The coupled model can identify suitable mangrove afforestation habitats and predict the impact of land-use change on potential mangrove habitats. In this study, the model was used to predict the mangrove habitat change in 2030 in the province with the most mangrove forests in China. The potential suitable habitat of 14 mangrove species under three coastal land-use scenarios were mapped using the coupled model. Under the current trend scenario, only 41.2% of the existing wetland would be retained, whereas the potential distribution area of all the mangrove species will decrease by an average of 30%. Under the sustainable development and ecological protection scenarios, the mangrove habitat could be increased by 11% to 61%, depending on the species. Different mangrove species showed varied sensitivity to the improved land-use policies, with several species being harder to restore than others, even under aggressive protection and restoration policies. The combined use of both MaxEnt and Dyna-CLUE models proved complementary and offered insights into the impacts of different land-use policies on the spatiotemporal change of mangrove habitats.
ABSTRACT
Mangrove forests support numerous ecosystem services and contribute to coastal ecological risk reduction. However, they are one of the most severely threatened ecosystems in the world. China has carried out national mangrove restoration projects, but there is still insufficient scientific information for the strategic planning of this restoration. In this study, we carried out mangrove suitability assessments using the genetic algorithm for rule-set prediction (GARP) and maximum entropy (MaxEnt) models, and we mapped the restoration potential of mangrove forests in China for the first time. The restoration potential index (RPI), which combines suitability and land use data, is proposed as a rapid estimator method for locating theoretically available areas for restoration. The results showed that the MaxEnt model performed better than GARP in predicting potential mangrove distributions. Temperature was the most important environmental factor for determining large scale distribution of mangroves. The predicted northern limit of mangrove distribution was around 28°27' N-28°35' N. Using the RPI approach, 16,800 ha with the potential to restore mangrove forests was identified. According to both models, the largest area with restoration potential occurs along the Guangdong and Guangxi coast. Nationwide, about 75% of the potential area suitable for mangrove forests has been lost as a consequence of land use and is no longer available for restoration. Around 6400 ha of ponds is currently used for aquaculture, accounting for 38% of theoretically restorable areas. These areas can be a priority for mangrove forest restoration. In conclusion, our findings provide a better scientific understanding of mangrove distribution in China and can underpin strategic design and planning of mangrove restoration.
