Dataset Information

Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico.

ABSTRACT: This study identifies the hotspots of land use cover change (LUCC) under two socioeconomic and climate change scenarios [business as usual (BAU) and a pessimistic scenario] at the national level for Mexico for three-time periods. Modelling suggests that by 2050 grassland and tropical evergreen forest will be the most endangered ecosystems, having lost 20-33% (BAU) or 43-46% (pessimistic scenario) of their extent in comparison to 1993. Agricultural expansion would be the major driver of LUCC, increasing from 24.4% of the country in 1993 to 30% (BAU) or 34% (pessimistic) in 2050. The most influential variables were distance from roads and human settlements, slope, aridity, and evapotranspiration. The hotspots of LUCC were influenced by environmental constraints and socioeconomic activities more than by climate change. These findings could be used to build proposals to reduce deforestation, including multiple feedbacks among urbanization, industrialization and food consumption.

SUBMITTER: Mendoza-Ponce A

PROVIDER: S-EPMC6411666 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico.

Mendoza-Ponce Alma A Corona-Núñez Rogelio O RO Galicia Leopoldo L Kraxner Florian F

Ambio 20180820 4

This study identifies the hotspots of land use cover change (LUCC) under two socioeconomic and climate change scenarios [business as usual (BAU) and a pessimistic scenario] at the national level for Mexico for three-time periods. Modelling suggests that by 2050 grassland and tropical evergreen forest will be the most endangered ecosystems, having lost 20-33% (BAU) or 43-46% (pessimistic scenario) of their extent in comparison to 1993. Agricultural expansion would be the major driver of LUCC, inc ...[more]

PMID: 30128861

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Project description:There is a need to revise the framework used to project species risks under climate change (CC) and land-use/cover change (LUCC) scenarios. We built a CC risk index using the latest Intergovernmental Panel on Climate Change framework, where risk is a function of vulnerability (sensitivity and adaptive capacity), exposure, and hazard. We incorporated future LUCC scenarios as part of the exposure component. We combined a trait-based approach based on biological characteristics of species with a correlative approach based on ecological niche modeling, assigning risk scores to species, taxonomic (orders), and functional (trophic, body size, and locomotion) groups of terrestrial mammals occurring in Mexico. We identified 15 species projected to lose their climatic suitability. Of the 11 taxonomic orders, Eulipotyphla, Didelphimorphia, Artiodactyla, and Lagomorpha had the highest risk scores. Of the 19 trophic groups, piscivores, insectivores under canopy, frugivores-granivores, herbivores browser, and myrmecophagous had the highest risk scores. Of the five body-sized groups, large-sized species (>15 kg) had highest risk scores. Of the seven locomotion groups, arboreal and semi-aquatics had highest risk scores. CC and LUCC scenarios reduced suitable areas of species potential distributions by 37.5% (with CC), and 51% (with CC and LUCC) under a limited full-dispersal assumption. Reductions in suitable areas of species potential distributions increased to 50.2% (with CC), and 52.4% (with CC and LUCC) under a non-dispersal assumption. Species-rich areas (>75% species) projected 36% (with CC) and 57% (with CC and LUCC) reductions in suitability for 2070. Shifts in climatic suitability projections of species-rich areas increased in number of species in northeast and southeast Mexico and decreased in northwest and southern Mexico, suggesting important species turnover. High-risk projections under future CC and LUCC scenarios for species, taxonomic, and functional group diversities, and species-rich areas of terrestrial mammals highlight trends in different impacts on biodiversity and ecosystem function.

Project description:The Congo Basin tropical forests are home to many endemic and endangered species, and a global hotspot for forest fragmentation and loss. Yet, little has been done to document the region's rapid deforestation, assess its effects and consequences, or project future forest cover loss to aid in effective planning. Here we applied the Random Forest (RF) supervised classification algorithm in Google Earth Engine (GEE) to map and quantify decadal changes in forest cover and land use (LCLU) in the Congo Basin between 1990 and 2020. We cross-validated our LCLU maps with existing global land cover products, and projected our validated results to 2050 under three climate change scenarios, using the Multiperceptron Artificial Neural Network and Markov chain algorithms of the Idrissi Land Change modeller from TerrSet. We found that, over 5.2% (215,938 km2), 1.2% (50,046 km2), and a 2.1% (86,658 km2) of dense forest cover were lost in the Congo Basin between 1990-2000, 2000-2010, and 2010-2020, totaling approximately 8.5% (352,642 km2) loss estimated between 1990-2020. For the period 2020-2050, we estimated a projected 3.7-4.0% (174,860-204,161 km2) loss in dense forest cover under all three climate change scenarios (i.e., 174,860 km2 loss projected for SSP1-2.6, 199,608 km2 for SSP2-4.5, and 204,161 km2 for SSP5-8.5), suggesting that approximately 12.3-12.6% (527,502 km2-556,803 km2) of dense forest cover could be lost over a 60-year period (1990-2050). Our study represents a novel application of spatial modeling tools and Machine Learning algorithms for assessing long-term deforestation and forest degradation within the Congo Basin, under human population growth and IPCC climate change scenarios. We provide spatial and quantitative results required for supporting long-term deforestation and forest degradation monitoring within Congo Basin countries, especially under the United Nations Framework Convention on Climate Change (UNFCCC) REDD+ (Reduce Emissions from Deforestation and Forest Degradation) program.

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Dataset Information

Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico.

Publications

Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico.

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