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Land-cover Change in Cuba and Implications for the Area of Distribution of a Specialist's Host-plant

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Journal PeerJ
Date 2024 Jul 1
PMID 38948225
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Abstract

Changes in land cover directly affect biodiversity. Here, we assessed land-cover change in Cuba in the past 35 years and analyzed how this change may affect the distribution of plants and moths. We analyzed the vegetation cover of the Cuban archipelago for 1985 and 2020. We used Google Earth Engine to classify two satellite image compositions into seven cover types: forest and shrubs, mangrove, soil without vegetation cover, wetlands, pine forest, agriculture, and water bodies. We considered four different areas for quantifications of land-cover change: (1) Cuban archipelago, (2) protected areas, (3) areas of potential distribution of , and (4) areas of potential distribution of the plant within the protected areas. We found that "forest and shrubs", which is cover type in which populations have been reported, has increased significantly in Cuba in the past 35 years, and that most of the gained forest and shrub areas were agricultural land in the past. This same pattern was observed in the areas of potential distribution of ; whereas almost all cover types were mostly stable inside the protected areas. The transformation of agricultural areas into forest and shrubs could represent an interesting opportunity for biodiversity conservation in Cuba. Other detailed studies about biodiversity composition in areas of forest and shrubs gain would greatly benefit our understanding of the value of such areas for conservation.

References
1.
Biswas J, Jobaer M, Haque S, Islam Shozib M, Limon Z . Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh. Heliyon. 2023; 9(11):e21245. PMC: 10633608. DOI: 10.1016/j.heliyon.2023.e21245. View

2.
Osman M, Abdel-Rahman E, Onono J, Olaka L, Elhag M, Adan M . Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model. PLoS One. 2023; 18(7):e0288694. PMC: 10365312. DOI: 10.1371/journal.pone.0288694. View

3.
Vermote E, Justice C, Claverie M, Franch B . Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ. 2020; Volume 185(Iss 2):46-56. PMC: 6999666. DOI: 10.1016/j.rse.2016.04.008. View

4.
Baude M, Meyer B, Schindewolf M . Land use change in an agricultural landscape causing degradation of soil based ecosystem services. Sci Total Environ. 2019; 659:1526-1536. DOI: 10.1016/j.scitotenv.2018.12.455. View

5.
Basset Y, Lamarre G . Toward a world that values insects. Science. 2019; 364(6447):1230-1231. DOI: 10.1126/science.aaw7071. View