Net Loss Statistics Hide Massive Carbon Emissions From Land Use and Land Cover Change in Mangroves

Abstract:

“No net loss” policies for conservation are proliferating globally. These policies seek to sequentially avoid, minimize, or offset ecosystem destruction, commonly through restoring or creating equivalent extents of the ecosystem to be impacted. While these policies are well-meaning, shifts in ecosystem quality, location and/or type may result in adverse impacts to ecosystem goods and benefits. Here, we examine how policies based on net loss statistics may hide deterioration of ecosystem services through the case of mangroves and climate change mitigation in Thailand. Specifically, we asked how do the carbon emissions associated with gross loss and gain of mangrove extent compare against carbon emissions estimated from net mangrove loss statistics? To do so, we modeled land use and land cover (LULC) change from 2000-2014 in Thailand’s mangrove holding regions using high-resolution land cover data that we obtained from the Royal Thai Department of Marine and Coastal Resources. Using published datasets of aboveground biomass carbon and soil organic carbon, we then modeled pool-specific gains and losses of carbon associated with each of these transitions. Finally, we compared these values to estimates of carbon emissions derived using statistics of net mangrove loss from 2000 - 2014. Our results show that estimating carbon emissions with net loss statistics may underestimate emissions from LULC change in mangroves by a factor of 4. Specifically, we estimated emissions from 2000-2014 to be 10.4 ± 10.0 million Mg C using a disaggregated approach, whereas we estimated emissions to be 2.3 ± 1.3 million Mg C under a net loss approach. We attribute this finding to the presence of irrecoverable carbon pools in mangroves—or the fact that loss of carbon from mangrove soils are unlikely to be recovered over meaningful timeframes for mitigating climate change. The implications of our study provide strong evidence for the use of gross loss and gain statistics over net loss statistics when monitoring conservation and restoration programs.