Which are the best methods of mangrove cover change detection and anthropogenic drivers of cover loss?

Despite their crucial role in marine biodiversity conservation, ecological services provisioning and protection of downstream coastal habitats and species, mangrove cover continues to decline worldwide. Yet selection of... [ view full abstract ]

Despite their crucial role in marine biodiversity conservation, ecological services provisioning and protection of downstream coastal habitats and species, mangrove cover continues to decline worldwide. Yet selection of assessment method for mangrove cover change, and natural or anthropogenic factors driving it, remains a challenge for researchers and managers. To determine important anthropogenic mangrove cover change drivers within Kenya’s four coastal Counties, I used 3 cover change detection methods and 6 socio-economic predictors, for 2005-2016 period. The detection methods were: remote-sensed satellite imagery; modelling-based on known annual change-rate; mapping instantaneous cover for 2016. Socio-economic predictors were population density; poverty rates; total protected-areas cover; tourist-facility density; agricultural opportunities; fishing opportunities. I used generalized linear modelling to test influence of the predictors on cover estimates from each detection method, and identify cross-cutting change predictors. From instantaneous estimates, mangrove cover was negatively influenced by poverty rate (t=-22.27, p=0.029), tourist-facility density (t=-13.81, p=0.046), agricultural intensity (t=-41.80, p=0.015) and total protected area size (t=-36.26, p=0.018) but positively by fishing opportunities (t=45.83, p=0.014). When modelled as annual change-rate, negative influence was by tourist-facility density (t=-12.43, p=0.048) but positive by fishing opportunities (t=21.42, p=0.023). Remote-sensed cover estimation showed negative effect of population density (t=-14.22, p=0.046). Instantaneous cover assessment was best for capturing a comprehensive range of anthropogenic predictors of cover change while fishing opportunities and tourist-facility density the most resilient and suitable predictors for change monitoring. Management action for reducing mangrove loss should involve expanding community benefits from tourism, and facilitating fisheries opportunities through improved infrastructure and equipment.