Decoding the drivers and effects of deforestation in Peru: a national and regional analysis

Abstract

High deforestation rates in tropical forests of South America lead to biodiversity loss, climate change and alterations in nature’s contributions to people. Deforestation drivers vary across scales due to the heterogeneity of environmental and socioeconomic conditions and forest types. Here, we test the effects of deforestation drivers on deforestation rate from 2000 to 2020 at national and regional scales using Peru as a study case. To do that, we selected nine deforestation drivers commonly used in tropical deforestation analyses. We used the forest cover loss dataset of Global Forest Change to calculate deforestation rates. We conducted five path analyses, one for the national scale and the others for the four regions, using the district as a spatial unit. The national path model explained 34% of the total observed variance and showed that temperature, agriculture, transport network, precipitation, rural population and fire had a positive effect on deforestation, while the slope had a negative effect. The regional path models (63% of the total observed variance in the Coast region, 32% in the Andean, 60% in the High Rainforest and 75% in the Low Rainforest) showed that many national drivers remained at the regional scale. However, we found that the strength, relation (positive/negative) and type (direct/indirect) may vary. Therefore, identifying regional differences in deforestation dynamics is crucial for forest conservation planning and for addressing effective policies in tropical countries. However, improving the quality and availability of national data is essential for further advancing our understanding of this complex process.