In November 2016, after 52 years of armed conflict, the Colombian government and the primary rebel group, the FARC (Fuerzas Armadas Revolucionarias de Colombia) reached a peace agreement. The agreement incorporated three changes to institutions governing forest land occupation and use: (a) the demobilization of FARC from forested places, (2) the future distribution of legal land titles and new road construction into forests, and (3) the eradication of illicit crops. However, we document unprecedented rates of forest disturbance in the months following the peace agreement in biodiversity hotspots across the country. Are the declaration of peace and the increased rates of forest disturbance related? Here, we present the first systematic assessment of the impact of the Colombian peace agreement on forest disturbance. Focusing on the Andes-Amazon Transition Belt (AATB), we used automated satellite image disturbance detection methods and ethnographic data to quantify and interpret forest cover change from 2010 to 2018 that span wartime, peace negotiation, and post-peace agreement stages. Our findings indicate that during the post-peace agreement period (2017-2018), the area of forest disturbance increased by 50% (about 238 000 ha) across the AATB in comparison with the four-year peace negotiation stage (2013-2016); these changes reflect the end of FARC-led gunpoint conservation in the region. Forest disturbance also spread deeper into the Amazon watershed and increased in area by 187% within the AATB's protected areas. We find that following the peace agreement and the withdrawal of FARC, key actors (viz. drug cartels, large landowners, campesinos and dissidents) with expectations of favorable land tenure policies swept into the region; this led to increases in large-scale cattle ranching, coca cultivation dispersal, and speculative illegal land markets each of which contributed to the widespread forest disturbance that we mapped. The rapid increase in forest disturbance occurred despite the interest of the international community in promoting forest conservation initiatives in the AATB and Colombia's existing conservation and land titling frameworks for public lands. Our findings underscore the need for conservation strategies sensitive to rapid institutional and demographic changes in the course of the peace agreement to prevent forests from becoming an unexpected casualty of premature and unstable peace.

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Illicit cattle ranching and coca farming have serious negative consequences on the Colombian Amazon’s land systems. The underlying causes of these land activities include historical processes of colonization, armed conflict, and narco-trafficking. We aim to examine how illicit cattle ranching and coca farming are driving forest cover change over the last 34 years (1985–2019). To achieve this aim, we combine two pixel-based approaches to differentiate between coca farming and cattle ranching using hypothetical observed patterns of illicit activities and a deep learning algorithm. We found evidence that cattle ranching, not coca, is the main driver of forest loss outside the legal agricultural frontier. There is evidence of a recent, explosive conversion of forests to cattle ranching outside the agricultural frontier and within protected areas since the negotiation phase of the peace agreement. In contrast, coca is remarkably persistent, suggesting that crop substitution programs have been ineffective at stopping the expansion of coca farming deeper into protected areas. Countering common narratives, we found very little evidence that coca farming precedes cattle ranching. The spatiotemporal dynamics of the expansion of illicit land uses reflect the cumulative outcome of agrarian policies, Colombia’s War on Drugs, and the 2016 peace accord. Our study enables the differentiation of illicit land activities, which can be transferred to other regions where these activities have been documented but poorly distinguished spatiotemporally. We provide an applied framework that could be used elsewhere to disentangle other illicit land uses, track their causes, and develop management options for forested land systems and people who depend on them.