From 1990 and up until 2010, South America was the region in the world with the highest rate of forest loss, with an estimated 5.2 million hectares of net forest lost per year in the first decade of this century. Since then, the destruction of South American forests has slowed down to an average of 2.6 million hectares per year, the second largest forest loss rate in the world after Africa. The figures suggest that, despite reforestation efforts, forest areas in South America continue to be endangered by massive deforestation and wildfires.

Brazil is the Latin American country that registered the largest tree cover loss in the past decade. In 2022 alone, over 3.3 million hectares of tree cover were destroyed in the South American country. That same year, Bolivian forests saw their area reduced by approximately 600,000 hectares, the second highest tree cover loss in the region. Overall, more than 70 million hectares of tree cover were lost in Latin America and the Caribbean between 2010 and 2022. These estimates do not take into account tree cover gain throughout the same period.

Throughout 2024, Brazil reported nearly 279,000 wildfire outbreaks, by far the highest figure in South America. Bolivia registered the second-largest number of wildfires in the region that year, at over 90,000. On the other hand, 276 wildfires were detected in Uruguay during the reported period, the lowest number amongst South American countries and territories. The lungs of the world Spanning nine different countries, the Amazon rainforest makes up approximately 40 percent of South America. Predominantly encompassed by Brazil, the government has made some efforts towards protecting what is left of the world's most abundant tropical rainforest. Over five million square kilometers of land area lie under a special regime designated as the Legal Amazon in Brazil. Nevertheless, wildfires in the region remain a cause for concern. In 2024, over 192,000 outbreaks were registered in the Legal Amazon, accounting for more than half of the country's occurrences that year. Brazil's blind ambition At the heart of the issue of wildfires is deforestation as a technique to expand land for farming and pastures. Along with the incremental rise in wildfires, the Amazon's deforestation rate has seen a continual increase for most of the decade. As Brazil climbs global markets to become the leading producer of soybean and beef, the country's agricultural ambitions have led to the lightening of environmental restrictions and the re-allocation of land for farming purposes. In turn, the area planted with crops in the Legal Amazon has nearly tripled since 2000.

This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.

DeepRainForest_daily_rainfall_with_observed_treecover.nc: contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover.

DeepRainForest_daily_rainfall_with_2000_treecover.nc: contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards.

DeepRainForest_daily_rainfall_with_1982_treecover.nc:contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.

This data set provides active fire locations and estimates of annual fire frequencies for South America from 2000-2007. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra (2000-€“2007) and Aqua (2003-2007) satellite platforms were analyzed to determine spatial and temporal patterns in satellite fire detections.

The analysis considered a high-confidence subset of all MODIS fire detections to reduce the influence of false fire detections over small forest clearings in Amazonia (Schroeder et al., 2008). The number of unique days on which the active fire detections were recorded within a 1 km radius was estimated from the subset of active fire detections and the ArcGIS neighborhood variety algorithm.

There are 14 data files with this data set: 7 GeoTIFF (.tif) files of fire frequency at MODIS 250 m resolution, where each grid cell value represents the number of days in that year on which active fires were detected, and 7 shape files of active fire locations for the years 2001-2007.

OverviewThis data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions.CautionsThis analysis is limited by various data and attribution issues and methodological assumptions, including the following:Commodity data sets have limited coverage and quality. Only oil palm has recent, detailed maps of extent at a global level. The analysis also uses detailed data on South American soy. Outside of these regions and commodities, the analysis relies on global 10-kilometer resolution data on crop and pasture extent. These data are from 2010 (2000 for pasture), so the amount of forest replaced by a specific commodity is assumed to be proportional to its area during that year and may be misrepresented if significant expansion or contraction of that commodity has occurred since then. While Goldman et al. (2020) presents results using detailed pasture data for Brazil, this data set includes pasture results for the coarse method only.The data cannot capture complex land-use change transitions. The analysis does not consider other possible land uses between the deforestation event and the establishment of the commodity. The analysis also does not consider any forms of indirect land-use change (e.g., the target commodity displacing other activities that may, in turn, expand into forested areas).The data measure tree cover loss rather than deforestation directly. All tree cover loss in an area later used for one of the target commodities is assumed to be deforestation because forest replaced with a crop or pasture represents a permanent land-use change. Historical data from Indonesia and Malaysia were used to filter out older oil palm plantations from the analysis to avoid counting old, unproductive oil palm trees being felled as tree cover loss.The data may miss some forms of tree cover loss. The Hansen et al. (2013) tree cover loss data may not detect all changes related to commodity production. Much of the production of cocoa and coffee occurs on very small farms (less than one hectare) that may not be captured by the tree cover loss data. The analysis may also underestimate the conversion of dry forest and woody savanna areas, which are not well represented in the tree cover loss data. For the detailed soy analysis, we define tree cover as any woody vegetation with a minimum of 10 percent canopy cover (analyses for other commodities use 30 percent) to minimize underestimations in South American biomes such as the Cerrado and the Chaco.Further discussion about the methods, assumptions, and limitations of this analysis is available in Goldman et al. (2020).CitationGoldman, E., M.J. Weisse, N. Harris, and M. Schneider. 2020. “Estimating the Role of Seven Commodities in Agriculture-Linked Deforestation: Oil Palm, Soy, Cattle, Wood Fiber, Cocoa, Coffee, and Rubber.” Technical Note. Washington, DC: World Resources Institute. Available online at: wri.org/publication/estimating-the-role-of-sevencommodities-in- agriculture-linked-deforestationLicenseCreative Commons Attribution 4.0 International License (CC-BY 4.0)

In this paper we address two topical questions: How do the quality of governance and agricultural intensification impact on spatial expansion of agriculture? Which aspects of governance are more likely to ensure that agricultural intensification allows sparing land for nature? Using data from the Food and Agriculture Organization, the World Bank, the World Database on Protected Areas, and the Yale Center for Environmental Law and Policy, we estimate a panel data model for six South American countries and quantify the effects of major determinants of agricultural land expansion, including various dimensions of governance, over the period 1970–2006. The results indicate that the effect of agricultural intensification on agricultural expansion is conditional on the quality and type of governance. When considering conventional aspects of governance, agricultural intensification leads to an expansion of agricultural area when governance scores are high. When looking specifically at environme...

Côte d'Ivoire lost half of its forest area between 1990 and 2022, making the African country one of the most affected by deforestation. Densely wooded countries in Central-South America, South East Asia, and Africa were among those seeing the greatest level of deforestation in the past three decades.

RAdar for Detecting Deforestation (RADD) is a deforestation alert product that uses data from the European Space Agency’s Sentinel-1 satellites to detect forest disturbances in near-real-time . The RADD alerts use a detection methodology produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing. These alerts are particularly advantageous in monitoring tropical forests, as Sentinel-1’s cloud-penetrating radar and frequent revisit times (6-12 days) allow for more consistent monitoring than alert products based on optical satellite images. Alerts are available for the primary humid tropical forest areas of South America, sub-Saharan Africa and insular Southeast Asia at a 10m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South America and Southeast Asia. Pre-processed Sentinel-1 images are collected from Google Earth Engine, then quality controlled and normalized using historical time-series metrics. Forest disturbance alerts are then detected using a probabilistic algorithm. Each disturbance alert is detected from a single observation in the latest image if the forest disturbance probability is above 85%. If the forest disturbance probability reaches 97.5% in subsequent imagery within a maximum 90-day period, alerts are then marked as "high confidence". The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. Alerts are detected within areas of primary humid tropical forest, defined by Turubanova et al. (2018) and with 2001-2018 forest loss (Hansen et al. 2013) and mangroves (Bunting et al. 2018) removed. For more information on methodology and validation, please refer to Reiche et. al. (2021). The version presented here (v1) has been updated from that described in the paper (v0), with changes to the forest mask and a reduction of the minimum mapping unit. The RADD alerts were made possible thanks to the support of a coalition of ten major palm oil producers and buyers. Under the project, Wageningen University and Research (WUR) developed the detection method and Satelligence first scaled the system in Indonesia and Malaysia and provided additional prioritization of alerts for on-the-ground follow up. Additional support was provided by the US Forest Service and Norway’s International Climate and Forest Initiative. The alerts are currently generated by WUR using Google Earth Engine.*This data product utilizes a special encoding*Each pixel (alert) encodes the date of disturbance and confidence level in one integer value. The leading integer of the decimal representation is 2 for a low-confidence alert and 3 for a high-confidence alert, followed by the number of days since December 31, 2014. 0 is the no-data value. For example:20001 is a low confidence alert on January 1st, 201530055 is a high confidence alert on February 24, 201521847 is a low confidence alert on January 21, 20200 represents no alert

Processed dataset used for the models in "Links between deforestation, conservation areas, and conservation funding in major deforestation regions of South America" Authors: Siyu Qin, Ana Buchadas, Patrick Meyfroidt, Yifan He, Arash Ghoddousi, Florian Pötzschner, Matthias Baumann, Tobias Kuemmerle

The genus Restrepia occurs throughout Central and South America in areas of montane forest heavily affected by deforestation. The current study was designed to test the feasibility of using available online resources to establish the threats facing these orchids and their conservation status for later inclusion in the IUCN online database. Online resources were searched for primary data on the distribution of species of Restrepia. The Geospatial Conservation Assessment Tool (GeoCAT) was used to produce semi-automated IUCN Red List assessments. Locations of populations were examined in Google Earth to establish habitat loss. A comparison of the data produced a Red List assessment for each species. The observed losses of Restrepia habitat were: Venezuela 45% of recorded locations for 15 species, Colombia 28% for 30 species, Ecuador 36% for 18 species, Peru 41% for eight species, Costa Rica 81% and Panama 32% for three species. This habitat loss coincided with the route of the Pan-American Highway in these countries. It was possible to establish the Red List Status of Restrepia species even with minimal data. The degree of threat facing these and other epiphytic orchid genera in these habitats was shown to be considerable.

WWF developed a global analysis of the world's most important deforestation areas or deforestation fronts in 2015. This assessment was revised in 2020 as part of the WWF Deforestation Fronts Report.An Emerging Hotspot Analysis was used to derive deforestation fronts using the ArcGIS Emerging Hot Spot Analysis tool. This analysis was undertaken in 10km² hexagons, within country boundaries, based on the remote sensing data series from Terra-i data for Latin America, Africa, Asia and Oceania for the period from 2004 to 2017 for which validated data was available. Locations with the highest incidence of deforestation were selected in the tropical and subtropical biomes in each country. Following the hotspot analysis, a visual interpretation of the spatial clustering of deforestation hotspots in the selected biomes by country was conducted in order to delineate the boundaries of deforestation fronts, which comprise all countries in which deforestation hotspots were detected. Deforestation fronts are places that contain an important area of remaining forests where there is a relatively larger spatial concentration or clustering of deforestation hotspots (measured in 10km2 hexagons). As a result of this exercise, based on the Terra-i data, 30 countries were retained in the analysis.

How to download this data:Click on "View Map" button at the top (the Download button allows you to download the footprint of tiles but not the actual alerts)Click on the tile where your area of interest is locatedCopy the whole URL from the pop-up and paste it into your internet browser. Download will begin automaticallyAdditional DetailsThis data set, assembled by Global Forest Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated. The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30m GLAD Landsat-based alerts are up-sampled to match the 10m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels. Alerts are classified as high confidence when detected twice by a single alert system. Alerts detected by multiple alert systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that it was not a false positive and may not need to wait for additional satellite imagery to increase confidence in detected loss. *This data product utilizes a special encoding*Each pixel (alert) encodes the date of disturbance and confidence level in one integer value. The leading integer of the decimal representation is 2 for a low-confidence alert, 3 for a high-confidence alert, and 4 for an alert detected by multiple alert systems, followed by the number of days since December 31, 2014. 0 is the no-data value. For example:20001 is a low confidence alert on January 1st, 201530055 is a high confidence alert on February 24, 201521847 is a low confidence alert on January 21, 202041847 is a highest confidence alert (detected by multiple alert systems) on January 21, 2020. Alert date represents the earliest detection0 represents no alertResolution: 10 x 10mGeographic Coverage: 30°N to 30°SFrequency of Updates: DailyDate of Content: January 1st, 2015 – presentCautionsConfidence level may change retroactively as source data is updated GLAD-L: Available for entire tropics (30°N to 30°S) from January 1, 2018 to the present, and from 2015 to the present for select countries in the Amazon, Congo Basin, and insular Southeast Asia GLAD-S2: Available for the primary humid tropical forest areas of South America from January 2019 to the present RADD: Available for the primary humid tropical forest areas of South America, sub-Saharan Africa and insular Southeast Asia at a 10m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South America and Southeast Asia In order to integrate the three alerting systems on a common grid, GLAD-L is resampled from 30m resolution to 10m resolution to match GLAD-S2 and RADD. As a result, pixels in the integrated layer may not exactly align with pixels in the individual GLAD-L layer. Each pixel in the integrated layer preserves the earliest date of detection from any alerting system, even if multiple systems have reported an alert in that pixel. In some situations, this may lead to inconsistent visualizations when switching from the integrated layer to individual alerting system layers. It is advisable to use in the integrated layer when you are interested in the earliest date of detection by any alerting system. However, it is better to use the individual alerting system layers if you are interested in a specific alert type. Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests (more likely to happen in the GLAD-L system), alerts may indicate timber harvesting operations, without a conversion to a non-forest land use. The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this. LicenseCC by 4.0SourcesGLAD Alerts:Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3). GLAD-S2 Alerts:Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. RADD Alerts:Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. https://doi.org/10.1088/1748-9326/abd0a8

To advance our understanding of forest cover changes, given the discrepancies, this work providesan original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) toestimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, forwhich data was available. This assumes that no single approach or data source can capture majortrends everywhere; therefore, an all-available data approach is needed to overcome shortcomings ofindividual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends tounderestimate the conversion in dry forests ecosystems and lacks explicit assessment ofuncertainties across the different datasets.“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing fromforest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m giventhe original resolutions of the datasets incorporated and on the understanding that forest areasshould be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.species distribution, ecology, ecosystem services), rather than depicting individual trees or groups oftrees.According to our analysis, about 20% of total forest cover loss takes place in core forest, which welabel “primary forest loss”, while the remaining 80% results from the conversion of edge andpatched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss inthe period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperateforests. A portion of the loss in temperate and boreal forests will not be permanent and might referto other types of natural forest disturbances produced by insects, fire, and severe weather, as wellas by felling of plantations or semi-natural forests as part of forest management.Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss ismainly in South America and Africa. When looking at countries by income levels, as defined by theWorld Bank, much of deforestation takes place in upper middle and lower middle-income countries.To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurswhen countries increase economic development. In the tropics, upper-middle income countriesdominate forest cover loss in South America, due to the influence of Brazil, and lower middle-income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occursmainly in Brazil and Argentina in South America, many lower-middle income countries in SouthAmerica, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest coverloss, likely not all permanent, occurs in high-income countries (Russia), and North America (UnitedStates and Canada) Unfortunately, this data does not identify changes over time or land use interactions amongcountries. Reduced forest cover loss in some mainly high-income countries, except North America, isassociated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. Interactions are informed by the “forest transition” effect. Forest transition dynamics occur whennet forest restoration replaces net forest cover loss in some specific place. The countries thatunderwent a forest transition that reduced forest loss and encouraged regrowth may have placedadditional pressure on forests outside their borders, thus displacing deforestation. The debate onforest transitions and leakage is quite controversial given its policy implications.Recent analysis, based on a land-balance model that quantifies deforestation due to global trade atcountry level in the tropics and sub-tropics, linked to a country-to-country trade model, found thatfrom 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.About 26% of total deforestation was attributed to international demand, 87% of which wasexported to countries with decreasing deforestation or increasing forest cover in Europe and Asia(i.e. China, India). Some of this displacement pressure may be reduced by land intensification. Global patterns of forest fragmentationIn this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-factorial phenomenon that includes amongst others loss of native species, appearance of invasivespecies, pollution damage, structural changes, selective timber removal and many more. Here weuse fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect offorest degradation but does not capture all aspects. The change in spatial pattern and structure byfragmentation of forest into smaller patches or “islands” damages forest ecosystem services such ascarbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. These impacts are created by changes at forest edges, which include increased exposure to differentclimate, fire, wind, mortality, and human access. The increasing isolation of forest patchescontributes to long-term changes in biodiversity, including species richness and productivity,creating fundamental changes in forest ecosystems.We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, todetermine forest cover transitions between different type of fragmentation classes (i.e. stable core,inner edges, outer edges, and patches). Changes between fragmentation classes over time aredefined as primary and secondary degradation based on their initial state, in contrast to forestswhich remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. Inthis definition, primary degradation is a result of the fragmentation of core forests into forest withmore edges, reducing the area of continuous forest extent, and resulting in greater losses of carbonand associated ecosystem services such as biodiversity present in intact forests. Secondarydegradation is the conversion of edge forests into more fragmented classes, occurring in secondaryforests which may already be degraded and are more accessible and easier to deforest

Africa has seen the greatest loss of forest area out of any region in the world. Between 1990 and 2022, over 15 percent of forest area was lost on the African continent. South America has also recorded a prominent rise in deforestation, with the region's combined forest area shrinking by 13.8 percent. Côte d'Ivoire was found to have the greatest negative forest area percentage change of any country.

Approximately 20% of the Brazilian Amazon has now been deforested, and the Amazon is currently experiencing the highest rates of deforestation in a decade, leading to large-scale land-use changes. Roads have consistently been implicated as drivers of ongoing Amazon deforestation and may act as corridors to facilitate species invasions. Long-term data, however, are necessary to determine how ecological succession alters avian communities following deforestation and whether established roads lead to a constant influx of new species.

We used data across nearly 40 years from a large-scale deforestation experiment in the central Amazon to examine the avian colonization process in a spatial and temporal framework, considering the role that roads may play in facilitating colonization.

Since 1979, 139 species that are not part of the original forest avifauna have been recorded, including more secondary forest species than expected based on the regional species pool. Among the 35 species considered to have colonized and become established, a disproportionate number were secondary forest birds (63%), almost all of which first appeared during the 1980s. These new residents comprise about 13% of the current community of permanent residents.

Widespread generalists associated with secondary forest colonized quickly following deforestation, with few new species added after the first decade, despite a stable road connection. Few species associated with riverine forest or specialized habitats colonized, despite road connection to their preferred source habitat. Colonizing species remained restricted to anthropogenic habitats and did not infiltrate old-growth forests nor displace forest birds.

Deforestation and expansion of road networks into terra firme rainforest will continue to create degraded anthropogenic habitat. Even so, the initial pulse of colonization by non-primary forest bird species was not the beginning of a protracted series of invasions in this study, and the process appears to be reversible by forest succession.

Methods We generated the avian regional species pool (n=725 species) for the Biological Dynamics of Forest Fragments Project (BDFFP; 2°20′ S, 60°W), ~80 km north of Manaus, Amazonas, Brazil. To do so, we used a few simple criteria: 1) the species must have been previously recorded in the Amazon (total ~1300 species), 2) for terra firme species, we only included birds that are known from the Guiana area of endemism, and 3) we imposed distance cutoffs of ~500 km from the BDFFP for resident species and ~1000 km for migratory species. We then curated the resulting list by hand to ensure that the final list matched current knowledge. These criteria necessarily mean that all species that have already been detected from the BDFFP are included.

To this list, we added three additional columns. The "BDFFP list" column denotes all species that have been recorded at the BDFFP between 1979 and 2017 (n=407 species; see Rutt et al., 2017). Another column includes a list of the birds that are part of the "core forest avifauna" at the project (n=268 species), or those species that are regularly found in primary terra firme forest. Terra firme species are only designated as part of the core avifauna if they reached a relative abundance of rare, uncommon, or common during one of the last two avifaunal inventories (Cohn-Haft et al., 1997; Rutt et al., 2017). Finally, the last column categorizes each species by habitat according to the Parker et al. (1996) databases. When appropriate, we used the first (primary) habitat type that was listed therein; however, we made adjustments if the primary code suggested the species occurred in habitat not found in the central Amazon (e.g., montane forest, temperate grassland). In those cases, we accepted secondary or tertiary habitat codes. We then collapsed these 22 categories (21 distinct habitats plus ‘Edge’) for the regional species pool into a more manageable seven that adequately captured habitat diversity in the immediate vicinity of the BDFFP: aquatic, primary forest, riverine, secondary forest, white sand, palm, and grassland/pasture (see Appendix 1 in the paper).

Taxonomy follows the South American Classification Committee (J. V. Remsen, Jr. et al., 2018). See the Methods section of the paper for further details.

References

Cohn-Haft, M., Whittaker, A., & Stouffer, P. C. (1997). A new look at the "species-poor" central Amazon: the avifauna north of Manaus, Brazil. Ornithological Monographs, 48, 205-235.

Parker, T. A., Stotz, D. F., & Fitzpatrick, J. W. (1996). Ecological and distributional databases. In D. F. Stotz, J. W. Fitzpatrick, T. A. Parker, & D. Moskovits (Eds.), Neotropical Birds: Ecology and Conservation (pp. 113-407). Chicago, IL: University of Chicago Press.

Remsen, J. V., Jr., Cadena, C. D., Jaramillo, A., Nores, M., Pacheco, J. F., Pérez-Emán, J., . . . Zimmer, K. J. (2018). A classification of the bird species of South America. American Ornithologists' Union. http://www.museum.lsu.edu/~Remsen/SACCBaseline.htm

Rutt, C. L., Jirinec, V., Johnson, E. I., Cohn-Haft, M., Vargas, C. F., & Stouffer, P. C. (2017). Twenty years later: an update to the birds of the Biological Dynamics of Forest Fragments Project, Amazonas, Brazil. Revista Brasileira de Ornitologia, 25(4), 259-278.

In 2023, the deforested area in Brazil amounted to approximately 1.8 million hectares, a decrease of 11.6 percent compared to 2022. Also in 2023, Brazil ranked third among the countries with the largest tree cover loss worldwide, after Canada and Russia.

On average, deforestation was considered one of the most important environmental issues faced by Latin American countries, according to a survey conducted in 2020. When asked about the top three environmental issues faced by their country, more than half of Brazilians and ** percent of Colombians picked deforestation. Meanwhile, ** and ** percent of Colombians and Mexicans, respectively, included air pollution on their top lists, whereas Peruvians and Argentinians were the most concerned with water pollution.

This study employs mixed methods combining quantitative quasi-experimental analysis to estimate the effect of Afro-descendant Peoples' (ADP) lands on deforestation with a qualitative social-environmental-historical assessment of ADP conservation practices approach to contextualize the findings of the study.

How to Attribute:

Please use the following attribution when using or referencing this data:

Sangat et al. (2025). Afro-descendant lands in South America contribute to biodiversity conservation and climate change mitigation. Communications Earth & Environment.

Terms of Use:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License: https://creativecommons.org/licenses/by-nc-nd/4.0/

Palm oil is the most widely traded vegetable oil globally, with demand projected to increase substantially in the future. Almost all oil palm grows in areas that were once tropical moist forests, some of them quite recently. The conversion to date, and future expansion, threatens biodiversity and increases greenhouse gas emissions. Today, consumer pressure is pushing companies toward deforestation-free sources of palm oil. To guide interventions aimed at reducing tropical deforestation due to oil palm, we analysed recent expansions and modelled likely future ones. We assessed sample areas to find where oil palm plantations have recently replaced forests in 20 countries, using a combination of high-resolution imagery from Google Earth and Landsat. We then compared these trends to countrywide trends in FAO data for oil palm planted area. Finally, we assessed which forests have high agricultural suitability for future oil palm development, which we refer to as vulnerable forests, and identified critical areas for biodiversity that oil palm expansion threatens. Our analysis reveals regional trends in deforestation associated with oil palm agriculture. In Southeast Asia, 45% of sampled oil palm plantations came from areas that were forests in 1989. For South America, the percentage was 31%. By contrast, in Mesoamerica and Africa, we observed only 2% and 7% of oil palm plantations coming from areas that were forest in 1989. The largest areas of vulnerable forest are in Africa and South America. Vulnerable forests in all four regions of production contain globally high concentrations of mammal and bird species at risk of extinction. However, priority areas for biodiversity conservation differ based on taxa and criteria used. Government regulation and voluntary market interventions can help incentivize the expansion of oil palm plantations in ways that protect biodiversity-rich ecosystems.