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Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9-51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.Predicción de la Recuperación de la Biodiversidad a Escala de Paisaje según la Regeneración Natural del Bosque Tropical Resumen La regeneración natural del bosque es una solución rentable para la recuperación de la biodiversidad basada en la naturaleza, sin embargo, los diferentes factores socioambientales pueden derivar en resultados variables. Cómo predecir la ubicación en donde la regeneración natural del bosque recuperará los niveles de biodiversidad, los cuales son un indicador del valor de la conservación y un suministro potencial de diferentes servicios ambientales, es un vacío de conocimiento importante en la planeación de la restauración forestal. Buscamos predecir y mapear la recuperación a escala de paisaje de la riqueza de especies y la abundancia total de vertebrados, invertebrados y plantas en bosques tropicales y subtropicales de segundo crecimiento para guiar la planeación de la restauración. Primero, realizamos un metaanálisis mundial para cuantificar la medida a la que se desvió la recuperación de la riqueza y la abundancia total de especies en los bosques de segundo crecimiento de los valores de biodiversidad en los bosques antiguos referenciales en el mismo paisaje. Después, utilizamos un algoritmo de aprendizaje automático y un conjunto integral de factores socioambientales para predecir espacialmente la desviación a escala de paisaje para después mapearla. Los modelos explicaron en promedio el 34% de la varianza observada en la recuperación (rango de 9-51%). La recuperación de la biodiversidad a escala de paisaje en los bosques de segundo crecimiento pudo predecirse espacialmente con base en los factores socioambientales del paisaje (demografía humana, uso y cobertura del suelo, alteraciones naturales y antropogénicas, productividad del ecosistema, tipo de topografía y de suelo); fue significativamente más alta para la riqueza de especies que para la abundancia total de vertebrados (desviación media pronosticada ajustada al rango de 0.09 versus 0.34) e invertebrados (0.2 versus 0.35) pero no para las plantas (las cuales mostraron una recuperación similar para ambas medidas [0.24 versus 0.25]); y tuvo una correlación positiva para la abundancia de especies de plantas y vertebrados (Pearson r =0.45, p=0.001). Nuestra estrategia puede ayudar a identificar los paisajes de bosques tropicales y subtropicales con un potencial alto para la recuperación de la biodiversidad por medio de la regeneración natural del bosque.

Natural regeneration is key for large‐scale forest restoration, yet it may lead to different biodiversity outcomes depending on socio‐environmental context. We combined the results of a global meta‐analysis to quantify how biodiversity recovery in naturally regenerating forests deviates from biodiversity values in reference old‐growth forests, with structural equation modeling, to identify direct and indirect associations between socioeconomic, biophysical and ecological factors and deviation in biodiversity recovery at a landscape scale. Low deviation within a landscape means higher chances of multiple sites in naturally regenerating forests successfully recovering biodiversity compared to reference forests. Deviation in biodiversity recovery was directly negatively associated with the percentage of cropland, forest cover, and positively associated with the percentage of urban areas in the surrounding landscape. These three factors mediated the indirect associations with rural population size, recent gross deforestation, time since natural regeneration started, mean annual temperature, mean annual water deficit, road density, land opportunity cost, percentage cover of strictly protected forest areas, and human population variation in the surrounding landscape. We suggest that natural forest restoration should be prioritized in landscapes with both low socioeconomic pressures on land use conversion to pasturelands and urban areas, and high percentage of forest cover.

High costs of tree planting are a barrier to meeting global forest restoration targets. Natural forest regeneration is more cost‐effective than tree planting, but its potential to foster restoration at scale is poorly understood. We predict, map, and quantify natural regeneration potential within 75.5 M ha of deforested lands in the Brazilian Atlantic Forest. Of 34.1 M ha (26.4%) of current forest cover, 2.7 M ha (8.0%) regenerated naturally from 1996 to 2015. We estimate that another 2.8 M ha could naturally regenerate by 2035, and a further 18.8 M ha could be restored using assisted regeneration methods, thereby reducing implementation costs by US$ 90.6 billion (77%) compared to tree planting. These restored forests could sequester 2.3 GtCO2 of carbon, reduce the mean number of expected species at risk of extinction by 63.4, and reduce fragmentation by 44% compared to current levels. Natural regeneration planning is key for achieving cost‐effective large‐scale restoration.

Assessment of socioenvironmental benefits and feasibility identifies priority areas for restoring global tropical rainforests., Over 140 Mha of restoration commitments have been pledged across the global tropics, yet guidance is needed to identify those landscapes where implementation is likely to provide the greatest potential benefits and cost-effective outcomes. By overlaying seven recent, peer-reviewed spatial datasets as proxies for socioenvironmental benefits and feasibility of restoration, we identified restoration opportunities (areas with higher potential return of benefits and feasibility) in lowland tropical rainforest landscapes. We found restoration opportunities throughout the tropics. Areas scoring in the top 10% (i.e., restoration hotspots) are located largely within conservation hotspots (88%) and in countries committed to the Bonn Challenge (73%), a global effort to restore 350 Mha by 2030. However, restoration hotspots represented only a small portion (19.1%) of the Key Biodiversity Area network. Concentrating restoration investments in landscapes with high benefits and feasibility would maximize the potential to mitigate anthropogenic impacts and improve human well-being.

Forest loss is mainly due to the conversion of forest to agriculture, mostly in private lands. Forest restoration is a global priority, yet restoration targets are ambitious and budget-limited. Therefore, assessing the outcome of alternative decisions on land-use within private lands is paramount to perform cost-effective restoration. We present a novel framework that incorporates spatial planning for forest restoration within private lands with conservation co-benefits at the landscape scale. As a case study, we used three real landscapes of 10.000 ha with differing amounts of forest cover in the Atlantic Forest region of Brazil, and three hypothetical animal species with different dispersal abilities. We estimated the total amount of forest that landholders must restore to comply with the Native Vegetation Protection Law, which requires landholders to reforest 20% of their land within a 20-year time frame. We compared the cost-effectiveness of five restoration strategies based on the improvement in habitat availability and restoration costs. The most cost-effective strategy depends on a landscape’s initial amount of forest cover and the species of concern. We revealed that spatial planning for restoration in private lands increased habitat availability up to 12 times more than random restoration, which was always the least cost-effective strategy. Cost-effective large-scale restoration in Brazil depends on public policies that assist landholders to comply with the law and on prioritizing areas for restoration within private lands. We show that by adding habitat availability as target in spatial prioritization, benefits for biodiversity can be hastened at low additional cost, even in real world scenarios with severe spatial constraints. Despite constraints, spatially planned restoration for law compliance in Brazil increased landscape permeability by creating corridors and stepping stones. Our framework should be used to plan restoration in Brazilian private lands and can be customized for other regions worldwide.

International commitments for ecosystem restoration add up to one-quarter of the world’s arable land. Fulfilling them would ease global challenges such as climate change and biodiversity decline but could displace food production and impose financial costs on farmers. Here, we present a restoration prioritization approach capable of revealing these synergies and trade-offs, incorporating ecological and economic efficiencies of scale and modelling specific policy options. Using an actual large-scale restoration target of the Atlantic Forest hotspot, we show that our approach can deliver an eightfold increase in cost-effectiveness for biodiversity conservation compared with a baseline of non-systematic restoration. A compromise solution avoids 26% of the biome’s current extinction debt of 2,864 plant and animal species (an increase of 257% compared with the baseline). Moreover, this solution sequesters 1 billion tonnes of CO2-equivalent (a 105% increase) while reducing costs by US$28 billion (a 57% decrease). Seizing similar opportunities elsewhere would offer substantial contributions to some of the greatest challenges for humankind. A restoration prioritization approach applied to the Brazilian Atlantic Forest biodiversity hotspot considers 362 scenarios for synergies and trade-offs between ecological and economic costs, benefits and scales.

We used association rules for extracting patterns of co-occurrences.We obtained pairs and groups of species with positive and negative correlation.One multiscale, spatially explicit and multi-species method was proposed.Data mining can be useful to produce lists of species with co-occurrences. The continuous growth of biodiversity databases has led to a search for techniques that can assist researchers. This paper presents a method for the analysis of occurrences of pairs and groups of species that aims to identify patterns in co-occurrences through the application of association rules of data mining. We propose, implement and evaluate a tool to help ecologists formulate and validate hypotheses regarding co-occurrence between two or more species. To validate our approach, we analyzed the occurrence of species with a dataset from the 50-ha Forest Dynamics Project on Barro Colorado Island (BCI). Three case studies were developed based on this tropical forest to evaluate patterns of positive and negative correlation. Our tool can be used to point co-occurrence in a multi-scale form and for multi-species, simultaneously, accelerating the identification process for the Spatial Point Pattern Analysis. This paper demonstrates that data mining, which has been used successfully in applications such as business and consumer profile analysis, can be a useful resource in ecology.

Natural regeneration provides multiple benefits to nature and human societies, and can play a major role in global and national restoration targets. However, these benefits are context specific and impacted by both biophysical and socioeconomic heterogeneity across landscapes. Here we investigate the benefits of natural regeneration for climate change mitigation, sediment retention and biodiversity conservation in a spatially explicit way at very high resolution for a region within the global biodiversity hotspot of the Atlantic Forest. We classified current land-use cover in the region and simulated a natural regeneration scenario in abandoned pasturelands, areas where potential conflicts with agricultural production would be minimized and where some early stage regeneration is already occurring. We then modelled changes in biophysical functions for climate change mitigation and sediment retention, and performed an economic valuation of both ecosystem services. We also modelled how land-use changes affect habitat availability for species. We found that natural regeneration can provide significant ecological and social benefits. Economic values of climate change mitigation and sediment retention alone could completely compensate for the opportunity costs of agricultural production over 20 years. Habitat availability is improved for three species with different dispersal abilities, although by different magnitudes. Improving the understanding of how costs and benefits of natural regeneration are distributed can be useful to design incentive structures that bring farmers’ decision making more in line with societal benefits. This alignment is crucial for natural regeneration to fulfil its potential as a large-scale solution for pressing local and global environmental challenges.

Metzgeriaceae are a cosmopolitan family that is particularly diverse in the Neotropics. In Brazil, the species of Metzgeria Raddi preferentially inhabit the coastal ecosystem Atlantic Rain Forest (from northeastern to southern Brazil). For bryophytes, the IUCN distribution criteria are of critical importance to evaluate threat status. In this study, we propose the use of ecological niche modeling methods to estimate the extent of potential occurrence of five Metzgeria species. Herbarium collection data were used to estimate the potential distribution of the species based on the method MAXENT. Understanding the spatial distribution of species is essential for the conservation of biodiversity, and the use of potential distribution models in biogeographic analysis is an important tool for the conservation of rare or endangered species. Modeling the distribution of five Metzgeria species endemic and/or threatened reveals that the species are currently not known from several areas that are, however, p...