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Keywords:

  • Carbon emissions;
  • conservation;
  • deforestation;
  • distribution;
  • mangrove loss;
  • REDD+;
  • remote sensing;
  • threat;
  • uncertainty

Abstract

Aim

To quantify the variability in estimates of change in mangrove area for key countries throughout the tropics, and to highlight potential implications for the assessment of global mangrove ecology, function and conservation.

Location

Pan-tropical, covering 15 countries, and accounting for c. 67% of global mangrove area.

Methods

We review 314 national mangrove area estimates (authoritative statements with defined time periods) from various sources. Linear regression models of mangrove area over time were applied to various data point combinations, defined by data source. These included various FAO resources, as well as academic and government statistics and remote sensing studies.

Results

National trends of change in mangrove area exhibited high variability depending on which data points were modelled. Many countries showed high variability in deforestation rate when the results of all models were combined, e.g. Indonesia had an average deforestation rate of 331.65 (± 222.26) km2 yr−1, Nigeria lost 92.09 (± 188.95) km2 yr−1 and Cuba lost 34.82 (± 142.17) km2 yr−1. The standard deviations were large due to high variability in national estimates, and hence the number of contradictory trends that could be extracted. Conflicting trends of long-term mangrove loss and gain could be derived for eight of the 15 countries analysed, including Indonesia, Brazil and the Philippines.

Main conclusions

The great variability in trends of mangrove extent suggests that estimates of ecosystem functional loss, biodiversity threat assessments and science-based conservation policies will be hampered by low confidence in mangrove area dynamics. Variable and inaccurate baseline data feed into various estimates of biodiversity and ecosystem functional loss, such as IUCN Red List assessments and estimates of global carbon emissions from mangrove deforestation. We also require robust information to inform conservation actions and policies hoping to reduce functional loss. Variability has important implications for all ecosystems where statistics are required over large scales.