Abstract. Given the multitude of ecosystem services provided by mangroves, it is important to understand their potential responses to global climate change. Extensive reviews of the literature and manipulative experiments suggest that mangroves will be impacted by climate change, but few studies have tested these predictions over large scales using statistical models. We provide the first example of applying species and community distribution models (SDMs and CDMs, respectively) to coastal mangroves worldwide. Species distributions were modeled as ensemble forecasts using BIOMOD. Distributions of mangrove communities with high species richness were modeled in three ways: as the sum of the separate SDM outputs, as binary hotspots (with .3 species) using a generalized linear model, and continuously using a general boosted model. Individual SDMs were projected for 12 species with sufficient data and CDMs were projected for 30 species into 2080 using global climate model outputs and a range of sea-level rise projections. Species projected to shift their ranges polewards by at least 2 degrees of latitude consistently experience a decrease in the amount of suitable coastal area available to them. Central America and the Caribbean are forecast to lose more mangrove species than other parts of the world. We found that the extent and grain size, at which continuous CDM outputs are examined, independent of the grain size at which the models operate, can dramatically influence the number of pseudo-absences needed for optimal parameterization. The SDMs and CDMs presented here provide a first approximation of how mangroves will respond to climate change given simple correlative relationships between occurrence records and environmental data. Additional, precise georeferenced data on mangrove localities and concerted efforts to collect data on ecological processes across large-scale climatic gradients will enable future research to improve upon these correlative models.

Offsetting carbon (C) emissions and reducing nitrogen (N) pollution have been goals of mangrove restoration programs around the world. There is a common, yet dubious expectation that mangrove restoration will result in immediate and perpetual delivery of ecosystem services. There are expected time lags between mangrove clearing and C and N losses, and between restoration and C and N gains. Obtaining accurate rates of losses and gains requires frequent and long-term sampling, which is expensive and time consuming. To address this knowledge gap, we used a chronosequence of mangrove forests in mangroves in Matang Mangrove Forest Reserve (MMFR) in Malaysia, a region with one of the most C dense forests in the world. In this site, we assessed the ecosystem C and N stocks, including soil, downed wood, downed litter, and trees. The objective was to measure C and N changes through time. After mangrove clearing, C and N losses in soil and downed wood were rapid, with stocks halved after just one year. In the first 10 years after replantation, the forest recovered quickly, with rates of C accumulation of 9.5 Mg C ha-1 yr-1. After ten years, the rate of accumulation decreased to 2.8 Mg C ha-1 yr-1. However, 40 years after replantation, mangroves were still about 26% lower in C and 15% lower in N compared to our reference forest. The trajectory of recovery of C and N stocks in these forests was different among mangrove components: forest litter recovered rapidly, but downed wood and soil recovered much slower. Programs aimed at reducing C emissions and N pollution should consider that there are temporal lags and ecosystem trade-offs when assessing the effectiveness of mangrove protection and restoration as climate change mitigation strategies.

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