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Rapid deforestation has been well-documented in Colombia after the 2016 peace agreement with FARC. While many analysis using remote sensing identify land cover change, structural connectivity variables are less studied for understanding landscape transformation. In this work, we used data from the Landsat archive from 2011 to 2021, the Continuous Change Detection and Classification algorithm (CCDC), and Morphological Spatial Pattern Analysis (MSPA) to analyze deforestation, land cover change, and landscape connectivity in northwestern Colombia’s Amazon. We examined the spatial patterns in three specific subsets in the Colombian arc of deforestation, with a special focus on the surroundings of the National Natural Park Serranía de Chiribiquete. Our results confirm changes in structural connectivity linked to pasture expansion from the conversion forest to pasture during the analyzed period showing changes in the borders of Serranía de Chiribiquete National Park along rivers and roads, where cattle is transported. Before 2016, the average annual deforested area in the three study areas was 27.93 km²; after 2016, this number increased to 73.36 km². The outcomes of our study contribute to an improved understanding of pasture dynamics. They can aid decision-making in areas that play a key role in ecological networks necessary for long-term conservation efforts. Our quantitative assessments have revealed a temporal shrinking of the core area, indicative of a decline in forest cover. Concurrently, we observed an expansion of the edge and background areas, which is consistent with the proliferation of pastures. This study presents methodology and data in support of policy-making for sustainable land use and ecological connectivity to mitigate further environmental degradation in the area.

Summary: To date, genome-wide association studies (GWAS) have revealed over 200 genetic risk loci associated with prostate cancer; yet, true disease-causing variants remain elusive. Identification of causal variants and their targets from association signals is complicated by high linkage disequilibrium and limited availability of functional genomics data for specific tissue/cell types. Here, we integrated statistical fine-mapping and functional annotation from prostate-specific epigenomic profiles, 3D genome features, and quantitative trait loci data to distinguish causal variants from associations and identify target genes. Our fine-mapping analysis yielded 3,395 likely causal variants, and multiscale functional annotation linked them to 487 target genes. We prioritized rs10486567 as a genome-wide top-ranked SNP and predicted HOTTIP as its target. Deletion of the rs10486567-associated enhancer in prostate cancer cells decreased their capacity for invasive migration. HOTTIP overexpression in enhancer-KO cell lines rescued defective invasive migration. Furthermore, we found that rs10486567 regulates HOTTIP through allele-specific long-range chromatin interaction.