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In the last decades, population growth worldwide boosts agricultural demand for food production. This huge driver rendered global food production more and more specialized, so agricultural landscapes became uniform and monotonic. The loss of diversity is a strong evidence of how modern agricultural landscapes have been disconnecting from nature. Evidence of environmental side-effects from this pathway are abundant in literature. Now, society is pressing towards changing practices aiming for healthy diets and sustainable food production systems. This raises the question: how to reconnect nature and agriculture in the context of future food production? In this review we propose a reconnection process based on the principles of ecological intensification or sustainable intensification. The integrated crop-livestock systems (ICLS) are the most consolidated technological pathway for reconciling crop production with natural processes. These systems are diverse and can partially mimic natural ecosystems exploring the synergies of natural biological processes, while achieving high levels of food production. ICLS promote soil improvements and mitigate greenhouse gas (GHG) emissions, reducing the agricultural share of global warming and climate change. Besides, these systems are more efficient in the use of nutrients and can optimize the use of other inputs such as pesticides. We present evidence of soil health and biogeochemical cycle restoration in addition to system stability improvement, and assume those symptoms as evidence of mixing crops and livestock fostering reconnection with natural processes.

Although genetic diversity ultimately determines the ability of organisms to adapt to environmental changes, conservation assessments like the widely used International Union for Conservation of Nature (IUCN) Red List Criteria do not explicitly consider genetic information. Including a genetic dimension into the IUCN Red List Criteria would greatly enhance conservation efforts, because the demographic parameters traditionally considered are poor predictors of the evolutionary resilience of natural populations to global change. Here we perform the first genomic assessment of genetic diversity, gene flow, and patterns of local adaptation in tropical plant species belonging to different IUCN Red List Categories. Employing RAD-sequencing we identified tens of thousands of single-nucleotide polymorphisms in an endangered narrow-endemic and a least concern widespread morning glory (Convolvulaceae) from Amazonian savannas, a highly threatened and under-protected tropical ecosystem. Our results reveal greater genetic diversity and less spatial genetic structure in the endangered species. Whereas terrain roughness affected gene flow in both species, forested and mining areas were found to hinder gene flow in the endangered plant. Finally we implemented environmental association tests and genome scans for selection, and identified a higher proportion of candidate adaptive loci in the widespread species. These mainly contained genes related to pathogen resistance and physiological adaptations to life in nutrient-limited environments. Our study emphasizes that IUCN Red List Criteria do not always prioritize species with low genetic diversity or whose genetic variation is being affected by habitat loss and fragmentation, and calls for the inclusion of genetic information into conservation assessments. More generally, our study exemplifies how landscape genomic tools can be employed to assess the status, threats and adaptive responses of imperiled biodiversity.

As Amazon deforestation rates reach the highest levels observed in the past decade, it is extremely important to direct conservation efforts to regions containing preserved forests with a high risk of deforestation. This requires forecasting deforestation, a complex endeavor due to the interplay of multiple socioeconomic, political, and environmental factors across different spatial and temporal scales. Here we couple high-resolution land-cover maps with Bayesian hierarchical spatial models to identify the main drivers of recent deforestation in the Brazilian Amazon and predict which areas are likely to lose a larger proportion of forest in the next 3 years. Recent deforestation was positively associated with forest edge density, the length of roads and waterways, elevation and terrain slope; and negatively associated with distance to urban areas, roads, and indigenous lands, area designated as protected or indigenous territory, and municipality GDP per capita. From these variables, forest edge density and distance to roads showed the largest effect sizes and highest predictive power. Predictive accuracy was highest for shorter time windows and larger grid sizes. Predicted deforestation was largely concentrated in the North-Eastern portions of the Brazilian Amazon, and amounted to roughly 3, 5, and 6 million hectares for 2020, 2021, and 2022, respectively. About 50% of this predicted deforestation is expected to occur inside protected areas or indigenous lands. Our short-term forecasts can help plan preventive measures to limit deforestation while meeting the specific needs of local areas.