{'fraction_non_alphanumeric': 0.032506415739948676, 'fraction_numerical': 0.020958083832335328, 'mean_word_length': 5.779710144927536, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Drivers of forest cover changes in the Chocó‐Darien Global Ecoregion of South America

Tropical rain forests are suffering the highest deforestation and reforestation ever recorded. Interactions between direct (proximate or direct causes) and indirect (underling or indirect causes) drivers could cluster these forest cover changes forming hotspots (areas that exhibit significant spatial correlation of deforestation or reforestation transitions). Using land use–land cover maps and global (I) and local (Iᵢ) Moran's tests, we identified these hotspots in the Choco‐Darien Global Ecoregion (CGE) of South America, a natural region that was declared one of the top 25 hotspots for conservation priorities in the world. Subsequently, we tested and studied the effects and interactions between deforestation and reforestation hotspots and their direct and indirect drivers using Bayesian Structural Equation Modeling (Bayesian SEM). We found that deforestation and reforestation were spatially auto‐correlated forming hotspots (I = 0.49, P = 0.001 for deforestation transitions and I = 0.48, P = 0.001 for reforestation transitions). Also, hotspots of deforestation and reforestation were auto‐correlated within municipality borders (I = 0.5, P = 0.001 for deforestation transitions; I = 0.49, P = 0.001 for reforestation transitions). Eighteen municipalities located on the border between Colombia and Ecuador showed significant aggregations of deforestation hotspots, while thirty‐four municipalities in three areas of Colombia and the area between the Colombian and Ecuadorian border showed significant clustering of reforestation hotspots. Eleven of these municipalities presented significant clustering of both reforestation and deforestation hotspots. The Bayesian SEM for deforestation showed that population growth and road density were indirect drivers of deforestation hotspots (0.191 and 0.127 standard deviation units). The Bayesian SEM for reforestation found that armed conflicts, Gross Domestic Product, and average annual rain were indirect drivers related to reforestation hotspots (0.228, 0.076, and 0.081 standard deviation units, respectively). Our assessment shows a novel methodology to study interactions among direct and indirect drivers of forest change and their potential dissimilar effects on forest transitions.