Mangrove distribution maps are used for a variety of applications, ranging from estimates of mangrove extent, deforestation rates, quantify carbon stocks, to modelling response to climate change. There are multiple mangrove distribution datasets, which were derived from different remote sensing data and classification methods, and so there are some discrepancies among these datasets, especially with respect to the locations of their range limits. We investigate the latitudinal discrepancies in poleward mangrove range limits represented by these datasets and how these differences translate climatologically considering factors known to control mangrove distributions. We compare four widely used global mangrove distribution maps - the World Atlas of Mangroves, the World Atlas of Mangroves 2, the Global Distribution of Mangroves, the Global Mangrove Watch. We examine differences in climate among 21 range limit positions by analysing a set of bioclimatic variables that have been commonly related to the distribution of mangroves. Global mangrove maps show important discrepancies in the position of poleward range limits. Latitudinal differences between mangrove range limits in the datasets exceed 5°, 7° and 10° in western North America, western Australia and northern West Africa, respectively. In some range limit areas, such as Japan, discrepancies in the position of mangrove range limits in different datasets correspond to differences exceeding 600 mm in annual precipitation and > 10 °C in the minimum temperature of the coldest month. We conclude that dissimilarities in mapping mangrove range limits in different parts of the world can jeopardise inferences of climatic thresholds. We expect that global mapping efforts should prioritise the position of range limits with greater accuracy, ideally combining data from field-based surveys and very high-resolution remote sensing data. An accurate representation of range limits will contribute to better predicting mangrove range dynamics and shifts in response to climate change.
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DOI:
https://doi.org/10.1016/j.scitotenv.2022.160380Altmetric score:
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Publication year
2022
Authors
Ximenes, A.C.; Cavanaugh, K.C.; Arvor, D.; Murdiyarso, D.; Thomas, N.; Arcoverde, G.; da C. Bispo, P.; Van der Stocken, T.
Language
English
Keywords
mangroves, remote sensing, satellite imagery, deforestation, spatial distribution, climate change, species distribution, wetlands, coastal areas, mapping
