Identifying global and local drivers of change in mangrove cover and the implications for management

• Published in: Global ecology and biogeography Vol. 30; no. 10; pp. 2057 - 2069
• Main Authors: Maina, J. M., Bosire, J. O., Kairo, J. G., Bandeira, S. O., Mangora, M. M., Macamo, C., Ralison, H., Majambo, G., Poulter, Benjamin
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01-10-2021
• Subjects: Adaptive management; Biodiversity; Climate change; drivers of change; Ecological conditions; Environmental impact; Exposure; Geomorphology; Indicators; Learning algorithms; Machine learning; mangrove conservation; mangrove exposure; Mangroves; Normalized difference vegetative index; Satellites; Sea level; sea‐level rise; spatially adaptive management; Vegetation; Vegetation index; Western Indian Ocean
• ISSN: 1466-822X; 1466-8238
• DOI: 10.1111/geb.13368

Background
Climate change and human activities continue to drive a widespread decline in global mangrove coverage, undermining their capacity to provide ecosystem benefits. While global and local scale drivers of change on mangroves are widely acknowledged, the relative importance and the exposure of mangroves to climatic, geomorphological, and direct human threats vary spatially. Understanding the role and relative importance of the multiscale and multiple threats to mangroves and how these vary spatially is fundamental for formulating a spatially adaptive approach to their management and conservation.
Aim
Our study investigated the role of multiple threats on mangroves and the relative exposure.
Location
Western Indian Ocean (WIO) region.
Time period
Recent past (2002–2019 and future (2050–2060).
Major taxa studied
Mangrove.
Methods
Using satellite‐derived indicators of mangrove condition aggregated over 19 years (2002 to 2019) and 14 proxies of climate, human activity, and geomorphology, we applied machine learning methods to determine the role and relative importance of the change drivers. Using outputs from this deductive statistical process, we applied inductive methods to map mangrove exposure spatially.
Results
Model results highlight the importance of catchment erosion, human pressure, sea level, and macroclimate as the main drivers of the present‐day ecological condition of mangroves in the WIO. Normalized difference vegetation index (NDVI) was more sensitive to the identified drivers than the vegetation condition index (VCI), with the relative importance of variables varying across the two vegetation indicators.
Main conclusions
In anticipation of a stronger manifestation of climate change impacts, the resilience of mangroves in the WIO could be improved through adaptive management over time and space. Testing the efficacy of the essential biodiversity variables (EBV) is critical for understanding the mechanisms of ecosystem change and managing biodiversity change.