deforestation in the amazon case study

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September 7, 2021 | Combined Reports - UConn Communications

Study Shows the Impacts of Deforestation and Forest Burning on Biodiversity in the Amazon

Since 2001, between 40,000 and 73,400 square miles of Amazon rainforest have been impacted by fires

Ring of fire: Smoke rises through the understory of a forest in the Amazon region. Plants and animals in the Amazonian rainforest evolved largely without fire, so they lack the adaptations necessary to cope with it. (Credit: Paulo Brando)

A new study, co-authored by a team of researchers including UConn Ecology and Evolutionary Biology researcher Cory Merow provides the first quantitative assessment of how environmental policies on deforestation, along with forest fires and drought, have impacted the diversity of plants and animals in the Amazon. The findings were published in the Sept. 1 issue of Nature .

Researchers used records of more than 14,500 plant and vertebrate species to create biodiversity maps of the Amazon region. Overlaying the maps with historical and current observations of forest fires and deforestation over the last two decades allowed the team to quantify the cumulative impacts on the region’s species.

They found that since 2001, between 40,000 and 73,400 square miles of Amazon rainforest have been impacted by fires, affecting 95% of all Amazonian species and as many as 85% of species that are listed as threatened in this region. While forest management policies enacted in Brazil during the mid-2000s slowed the rate of habitat destruction, relaxed enforcement of these policies coinciding with a change in government in 2019 has seemingly begun to reverse this trend, the authors write. With fires impacting 1,640 to 4,000 square miles of forest, 2019 stands out as one of the most extreme years for biodiversity impacts since 2009, when regulations limiting deforestation were enforced.

“Perhaps most compelling is the role that public pressure played in curbing forest loss in 2019,” Merow says. “When the Brazilian government stopped enforced forest regulations in 2019, each month between January and August 2019 was the worse month on record (e.g. comparing January 2019 to previous January’s) for forest loss in the 20-year history of available data. However, based on international pressure, forest regulation resumed in September 2019, and forest loss declined significantly for the rest of the year, resulting in 2019 looking like an average year compared to the 20-year history.  This was big: active media coverage and public support for policy changes were effective at curbing biodiversity loss on a very rapid time scale.”

The findings are especially critical in light of the fact that at no point in time did the Amazon get a break from those increasing impacts, which would have allowed for some recovery, says senior study author Brian Enquist, a professor in UArizona’s Department of Ecology and Evolutionary Biology .

“Even with policies in place, which you can think of as a brake slowing the rate of deforestation, it’s like a car that keeps moving forward, just at a slower speed,” Enquist says. “But in 2019, it’s like the foot was let off the brake, causing it to accelerate again.”

Known mostly for its dense rainforests, the Amazon basin supports around 40% of the world’s remaining tropical forests. It is of global importance as a provider of ecosystem services such as scrubbing and storing carbon from the atmosphere, and it plays a vital role in regulating Earth’s climate. The area also is an enormous reservoir of the planet’s biodiversity, providing habitats for one out of every 10 of the planet’s known species. It has been estimated that in the Amazon, 1,000 tree species can populate an area smaller than a half square mile.

“Fire is not a part of the natural cycle in the rainforest,” says study co-author Crystal N. H. McMichael at the University of Amsterdam. “Native species lack the adaptations that would allow them to cope with it, unlike the forest communities in temperate areas. Repeated burning can cause massive changes in species composition and likely devastating consequences for the entire ecosystem.”

Since the 1960s, the Amazon has lost about 20% of its forest cover to deforestation and fires. While fires and deforestation often go hand in hand, that has not always been the case, Enquist says. As climate change brings more frequent and more severe drought conditions to the region, and fire is often used to clear large areas of rainforest for the agricultural industry, deforestation has spillover effects by increasing the chances of wildfires. Forest loss is predicted reach 21 to 40% by 2050, and such habitat loss will have large impacts on the region’s biodiversity, according to the authors.

“Since the majority of fires in the Amazon are intentionally set by people, preventing them is largely within our control,” says study co-author Patrick Roehrdanz, senior manager of climate change and biodiversity at Conservation International. “One way is to recommit to strong antideforestation policies in Brazil, combined with incentives for a forest economy, and replicate them in other Amazonian countries.”

Policies to protect Amazonian biodiversity should include the formal recognition of Indigenous lands, which encompass more than one-third of the Amazon region, the authors write, pointing to previous research showing that lands owned, used or occupied by Indigenous peoples have less species decline, less pollution and better-managed natural resources.

The authors say their study underscores the dangers of continuing lax policy enforcement. As fires encroach on the heart of the Amazon basin, where biodiversity is greatest, their impacts will have more dire effects, even if the rate of forest burning remains unchanged.

The research was made possible by strategic investment funds allocated by the Arizona Institutes for Resilience at UArizona and the university’s Bridging Biodiversity and Conservation Science group. Additional support came from the National Science Foundation’s Harnessing the Data Revolution program . Data and computation were provided through the Botanical Information and Ecology Network , which is supported by CyVerse , the NSF’s data management platform led by UArizona.

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Case Study: The Amazon Rainforest

The Amazon in context

Tropical rainforests are often considered to be the “cradles of biodiversity.” Though they cover only about 6% of the Earth’s land surface, they are home to over 50% of global biodiversity. Rainforests also take in massive amounts of carbon dioxide and release oxygen through photosynthesis, which has also given them the nickname “lungs of the planet.” They also store very large amounts of carbon, and so cutting and burning their biomass contributes to global climate change. Many modern medicines are derived from rainforest plants, and several very important food crops originated in the rainforest, including bananas, mangos, chocolate, coffee, and sugar cane.

In order to qualify as a tropical rainforest, an area must receive over 250 centimeters of rainfall each year and have an average temperature above 24 degrees centigrade, as well as never experience frosts. The Amazon rainforest in South America is the largest in the world. The second largest is the Congo in central Africa, and other important rainforests can be found in Central America, the Caribbean, and Southeast Asia. Brazil contains about 40% of the world’s remaining tropical rainforest. Its rainforest covers an area of land about 2/3 the size of the continental United States.

There are countless reasons, both anthropocentric and ecocentric, to value rainforests. But they are one of the most threatened types of ecosystems in the world today. It’s somewhat difficult to estimate how quickly rainforests are being cut down, but estimates range from between 50,000 and 170,000 square kilometers per year. Even the most conservative estimates project that if we keep cutting down rainforests as we are today, within about 100 years there will be none left.

How does a rainforest work?

Rainforests are incredibly complex ecosystems, but understanding a few basics about their ecology will help us understand why clear-cutting and fragmentation are such destructive activities for rainforest biodiversity.

High biodiversity in tropical rainforests means that the interrelationships between organisms are very complex. A single tree may house more than 40 different ant species, each of which has a different ecological function and may alter the habitat in distinct and important ways. Ecologists debate about whether systems that have high biodiversity are stable and resilient, like a spider web composed of many strong individual strands, or fragile, like a house of cards. Both metaphors are likely appropriate in some cases. One thing we can be certain of is that it is very difficult in a rainforest system, as in most other ecosystems, to affect just one type of organism. Also, clear cutting one small area may damage hundreds or thousands of established species interactions that reach beyond the cleared area.

Pollination is a challenge for rainforest trees because there are so many different species, unlike forests in the temperate regions that are often dominated by less than a dozen tree species. One solution is for individual trees to grow close together, making pollination simpler, but this can make that species vulnerable to extinction if the one area where it lives is clear cut. Another strategy is to develop a mutualistic relationship with a long-distance pollinator, like a specific bee or hummingbird species. These pollinators develop mental maps of where each tree of a particular species is located and then travel between them on a sort of “trap-line” that allows trees to pollinate each other. One problem is that if a forest is fragmented then these trap-line connections can be disrupted, and so trees can fail to be pollinated and reproduce even if they haven’t been cut.

The quality of rainforest soils is perhaps the most surprising aspect of their ecology. We might expect a lush rainforest to grow from incredibly rich, fertile soils, but actually, the opposite is true. While some rainforest soils that are derived from volcanic ash or from river deposits can be quite fertile, generally rainforest soils are very poor in nutrients and organic matter. Rainforests hold most of their nutrients in their live vegetation, not in the soil. Their soils do not maintain nutrients very well either, which means that existing nutrients quickly “leech” out, being carried away by water as it percolates through the soil. Also, soils in rainforests tend to be acidic, which means that it’s difficult for plants to access even the few existing nutrients. The section on slash and burn agriculture in the previous module describes some of the challenges that farmers face when they attempt to grow crops on tropical rainforest soils, but perhaps the most important lesson is that once a rainforest is cut down and cleared away, very little fertility is left to help a forest regrow.

What is driving deforestation in the Amazon?

Many factors contribute to tropical deforestation, but consider this typical set of circumstances and processes that result in rapid and unsustainable rates of deforestation. This story fits well with the historical experience of Brazil and other countries with territory in the Amazon Basin.

Population growth and poverty encourage poor farmers to clear new areas of rainforest, and their efforts are further exacerbated by government policies that permit landless peasants to establish legal title to land that they have cleared.

At the same time, international lending institutions like the World Bank provide money to the national government for large-scale projects like mining, construction of dams, new roads, and other infrastructure that directly reduces the forest or makes it easier for farmers to access new areas to clear.

The activities most often encouraging new road development are timber harvesting and mining. Loggers cut out the best timber for domestic use or export, and in the process knock over many other less valuable trees. Those trees are eventually cleared and used for wood pulp, or burned, and the area is converted into cattle pastures. After a few years, the vegetation is sufficiently degraded to make it not profitable to raise cattle, and the land is sold to poor farmers seeking out a subsistence living.

Regardless of how poor farmers get their land, they often are only able to gain a few years of decent crop yields before the poor quality of the soil overwhelms their efforts, and then they are forced to move on to another plot of land. Small-scale farmers also hunt for meat in the remaining fragmented forest areas, which reduces the biodiversity in those areas as well.

Another important factor not mentioned in the scenario above is the clearing of rainforest for industrial agriculture plantations of bananas, pineapples, and sugar cane. These crops are primarily grown for export, and so an additional driver to consider is consumer demand for these crops in countries like the United States.

These cycles of land use, which are driven by poverty and population growth as well as government policies, have led to the rapid loss of tropical rainforests. What is lost in many cases is not simply biodiversity, but also valuable renewable resources that could sustain many generations of humans to come. Efforts to protect rainforests and other areas of high biodiversity is the topic of the next section.

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World Wildlife Magazine

The Amazon in crisis: Forest loss threatens the region and the planet

We must act now to protect the amazon from catastrophic and irreversible damage.

• Date: November 08, 2022

The numbers are devastating: 17% of Amazon forests have been wholly lost, and an additional 17% are degraded. And data from the first half of 2022 show the loss continuing to grow.

The Amazon is in crisis as forests are threatened by deforestation, fires, and degradation; surface water has been lost; and rivers are increasingly disconnected and polluted. This immense pressure—if not slowed or stopped—will irreversibly damage the Amazon and the overall planet in the very near future.

With the launch of its Living Amazon Report 2022 , WWF synthesizes the latest information on the region, its critical global role, the threats it faces, and solutions that require an unprecedented global commitment to stop the Amazon’s destruction.

What’s at stake

As the world’s largest tropical forest and river system, the Amazon is an unparalleled and complex biome:

People: 47 million people live in the Amazon region and depend upon it for their livelihoods. This includes 2.2 million Indigenous peoples from more than 500 different groups.

Biodiversity and wildlife: The Amazon is home to a stunning array of the world’s species: 9% of mammals; 14% of birds; 8% of amphibians; 13% of freshwater fish species; and 22% of vascular plant species. Many of these species are found nowhere else in the world, and scientists estimate there are places in the Amazon where up to 90% of the species are yet to be discovered.

Lucinézio Cerdeira de Melo, a teacher; his daughter Louise Sophia; and wife Luciane Melo; are part of the Suruacá village community.

While there are many free-flowing rivers in the Amazon basin, hundreds of dams within the river network threaten connectivity and water quality.

Forests: Without its forests, the Amazon region would lose its biodiversity, release massive amounts of carbon, suffer soil erosion, and face hydrological and climatic devastation. Without its ecosystem services, local communities and people around the world would face a loss of livelihoods, well-being, and ecological stability.

Climate: The Amazon is a major carbon sink that regulates and helps stabilize the planet’s climate. Any loss or degradation of its forests means an increase in carbon emissions. Today, land conversion and fires in the region are already releasing some of that carbon into the atmosphere at record highs.

Food: The Amazon’s “flying rivers” transport moisture outside of the basin to the southern part of the continent, providing the necessary conditions for agriculture in Argentina, Paraguay, Uruguay, and southern Brazil. The health and vitality of the Amazon River basin are also fundamental locally for the millions of people who rely on its waterways for fish.

Freshwater: The Amazon is the largest free-flowing river in the world. Home to 20% of the freshwater discharged into the Earth's oceans, the Amazon must stay free flowing and healthy. Its connectivity and water quality impact not only the river basin but also human health, food security, livelihoods, and the mangroves and surrounding wetlands the river passes on its way to the Atlantic Ocean.

Ancestral knowledge: People have lived in the Amazon for more than 12,000 years, making the region a rich repository of human history and ancestral culture.

The Amazon rivers and forests are home to nearly 10 percent of the world's biodiversity, including squirrel monkeys.

Acai berries are one of the Amazon's many resources produced for human consumption.

The health of the Amazon has both local and far-reaching impacts. Losing the Amazon would drastically change the climate of South America, worsening food security, intensifying the climate crisis, and ultimately affecting the entire planet. The global climate emergency would accelerate, as keeping planetary warming below 1.5 degrees Celsius would be impossible.

Reversing the trend

As a first step, we must recognize the interdependence between ourselves and the Amazon. Our future hinges on its survival, and the Amazon depends on us and the choices we make today.

WWF’s Living Amazon report proposes strategies to reverse current losses and ways for governments, the private sector, and everyday citizens to take urgent action for the Amazon and its conservation. This includes the 80x25 initiative, which aims to conserve 80% of the Amazon by 2025. The plan was presented by the Coordinator of the Indigenous Organizations of the Amazon Basin, adopted as an IUCN motion in 2021, and supported by WWF, but will require further commitment at all levels to succeed.

Amazonian countries must agree to and prioritize the protection and sustainable management of the Amazon and its cross-border and interconnected systems. Indigenous peoples must be included in decision-making for the region.

Corporations that profit from the Amazon’s natural resources should examine their supply chains and ensure sustainable practices are enforced. Consumers can change their consumption patterns and refuse to purchase products that drive deforestation and land conversion in the Amazon.

Together, we can turn the tide on Amazon loss and shift toward social equity, inclusive economic development, and global responsibility.

Read the full Living Amazon Report 2022

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An aerial view showing how agricultural land is encroaching on the forest in Brazil, with illegal burning in the background. Image: Courtesy of Fernando Martinho

Investigating ‘Cattle Laundering’ and Deforestation in the Amazon: Interview with the 2024 Goldman Prize Winner

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For years Brazil’s rainforests have been under attack, with satellite imagery showing the borders of the Amazon jungle slowly, but steadily, receding.

In 2022, deforestation in the country was the highest of anywhere in the world , according to data on tropical primary forest loss from Global Forest Watch.

There are many motivations for transforming forests into fields, ranging from mining to the construction of hydroelectric plants, but the main driver is converting forest to pasture for raising cattle .

Brazil also tops another list, as the world’s largest exporter of beef, exporting to more than 150 countries according to government figures. Last year alone, around 2.5 million tons of fresh and processed beef were sent abroad.

But although the link between deforestation and the sale of beef is widely understood, especially among Brazilians, finding documentary evidence in the paperwork — and tracking the supply chain from point of origin to consumer — is not as simple as it seems.

Often, animals pass through several farms, in different states of the country, before being slaughtered. It’s a process called “cattle laundering” and it makes tracking the livestock practically impossible.

In a multi-year investigation , journalists from Repórter Brasil — a GIJN member since 2018 — worked with researchers from different countries to document the supply chain back from the supermarket to the fields where the cattle are raised, and used data on environmental fines and satellite images on farm locations to track the movement of cattle and beef products to deforestation in Brazil.

That eventually helped the outlet reveal links between supermarket chains in Europe and the United States with Brazilian slaughterhouses supplied by livestock farmers charged with illegal deforestation in different biomes, such as the Amazon, Cerrado, and Pantanal.

After the story was published in 2021, six large retail groups from Belgium, France, the Netherlands, and the United Kingdom suspended the sale of products from the Brazilian meat companies in question.

Marcel Gomes, executive secretary of Repórter Brasil. Photo: Courtesy of Goldman Environmental Prize

“About 65% of the deforestation in the Amazon region is caused by cattle ranching,” explained Marcel Gomes, the executive secretary of Repórter Brasil, who led the project. “It’s our duty in journalism to expose these problems, to hold the authorities and companies accountable.”

The exposé earned Gomes the 2024 Goldman Environmental Prize . Known as the “Nobel of Environmentalism,” Gomes is the first Brazilian journalist to win the award, which is usually given to activists and environmental defenders.

Repórter Brasil’s dogged investigations into topics including deforestation, the environment, and human rights have also earned the outlet a number of journalism awards and recognitions, including a 2023 Gabriel García Márquez Prize and a finalist nomination for the 2023 Javier Valdez Prize .

We spoke with Gomes to understand how the project was carried out, the tips and tools they used to follow the cattle trail, and the risks of reporting on this topic where huge profits — and reputations — are at stake.

Marcel Gomes and colleagues Natália Suzuki (left) and Tatiana Chang Waldman at the Reporter Brasil office. Photo: Courtesy of Goldman Environmental Prize

GIJN: Your investigation dug into meat supply chains and deforestation. In general, how important is the issue of livestock farming in environmental coverage?

Marcel Gomes: It is very important because it is a sector that has a lot of impact. The sector is responsible for deforestation in the Amazon, cases of work similar to slavery, and is also relevant in the country’s economy, both for meat, as we are the largest exporter of meat, and also for the export of biodiesel, which is made with animal fat, and leather, from clothing to automobiles.

Our innovation was to address the issue by considering the entire production chain. It is a very long chain: a cow is born on a farm, from this farm it goes to another farm to be raised, then to another farm where it is fattened, and it often passes through others until it reaches the slaughterhouse. This means that something very important is lost when we talk about the production chain, which is traceability. You don’t know the path this animal has taken throughout its life. At the same time, you have a series of social and environmental impacts linked to this chain. Without traceability you hardly have any influence to improve the situation.

This investigation won awards because we went a step further: we involved the supermarkets. We hired researchers in several countries, including here in Brazil, who went to shops to photograph the barcodes on the packaging. We fed that information into a large spreadsheet that took us to the slaughterhouses where these Brazilian meats spread around the world had originated.

GIJN: Your team created software to track the ‘meat route.’ How did you go about that?

MG: We have had a Public Information Law here in Brazil since 2011. This law requires public bodies to digitize their information. This means that you have a huge volume of information, but this data is super disorganized. Each [piece of data] is on a different server, in a different language, organized in different formats and even in different formats: one is in PDF, another in Word, another in Excel.

Repórter Brasil has been creating databases from the beginning. In 2013, we launched a database on the textile chain in Brazil, Moda Livre, which showed the companies’ links with slave labor and other labor relations. This is available and updated periodically. The secret is to have the ability to organize the data in a way that allows you to perform faster searches and identify headlines and stories. Our partners have access to this database so they can access and search for information. Our database on cattle is updated once a year for those who support Repórter Brasil.

Aerial view of cattle confinement used by farms in the Amazon. Photo: Courtesy of Fernando Martinho

GIJN: The cattle deforestation report had an economic impact as companies stopped buying meat from Brazil. Did you face any retaliation?

MG: The methodology that Repórter Brasil used was to listen to everyone involved. We had a very frank dialogue, both with the meatpacking sector and with supermarkets… [where we presented] data, proof, and evidence.

We did not suffer any type of pressure or retaliation. The biggest risk in this type of investigation is with livestock farmers… and we have had some lawsuits filed by livestock farmers from other reports.

This is a tip I always give: it is essential to have a good team of legal professionals. It is very difficult to do this type of work independently, without the support of legal professionals, because companies in the private sector, or even politicians, sue you. Some of our publications are evaluated by lawyers beforehand, as was the case with this material. That’s why we have a lot of security when we publish material like this. We believe in justice.

GIJN: Do you think that environmental journalism brings a double risk: that of legal harassment and danger in the field?

MG: If you do a risk analysis, there are many things involved with a field trip, with safety in relation to criminals or accidents, right? And in the period after you have the risk of legal harassment, lawsuits, threats. We have a security policy, a risk checklist. We have a security policy that is very time-consuming, but it is essential to avoid tragedies. Recently Repórter Brasil reporters were detained by police thugs who had ties to the militia. We had to send a lawyer to the scene once to free one of our reporters. After the death of Indigenous researcher Bruno Pereira and journalist Dom Philips , we didn’t travel for a long time. Then we strengthened our security policy. We introduced even more criteria that ended up making travel even more expensive. But it’s necessary.

Watch Marcel Gomes discuss his investigation into how cattle ranching fuels illicit deforestation in the Amazon, on YouTube below. 

GIJN: How would you describe Repórter Brasil?

MG: Repórter Brasil is an organization that was created when we were still students at the journalism faculty at the University of São Paulo. We started to discuss the possibility of creating a vehicle that could work on social and environmental topics, topics we thought that the mainstream press did not adequately cover. Repórter Brasil was founded in 2001 and is a unique organization because it does journalism and at the same time is an NGO. We have a research program, an education program, and a political advocacy program. One of the strategies we have is to try to enhance journalistic content so that it really causes impact and social transformation. So, when we carry out an investigation, this material is often used not only by other areas of Repórter Brasil but by our partners. We are producing journalistic work, but at the same time we are fighting for that content to change something in a real way. RB is seen as an outlet, we don’t have copyright, and supply material to UOL, Folha de S. Paulo, and the Guardian. I think it’s a very interesting organizational model.

GIJN: How does this model differ from that of traditional media organizations, and how does that help you?

MG: For us, the ethical issue is fundamental. Although Repórter Brasil is part of a coalition of non-governmental organizations and participates in conversations, our journalism program is independent. Our content is ours, produced by us, and edited by us. We insist on maintaining this way, which is to protect journalistic work so that it is independent. This is why Repórter Brasil has an excellent reputation and is able to supply material to traditional press organizations. The agendas are defined based on the coverage we provide, the perspective we have on the problems: thinking about human rights, labor, and environmental rights.

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Mongabay Series: Amazon Conservation

Deforestation, climate crisis could crash Amazon tree diversity: study

• New research finds that when climate change and deforestation impacts are taken together, up to 58 percent of Amazon tree species richness could be lost by 2050, of which 49 percent would have some degree of risk for extinction.
• Under the deforestation/climate change scenario, half the Amazon (the north, central and west) could be reduced to 53 percent of the original forest. The other half (the east, south and southeast, where agribusiness occurs), could become extremely fragmented, with only 30 percent of forest remaining.
• Studies rarely take both climate change and deforestation into account. But the new study’s results bolster the findings of other scientists who have modeled results showing that when the Amazon is 20-25 percent deforested, it could cross a rainforest to savanna conversion tipping point, a disaster for biodiversity.
• Scientists warn that Jair Bolsonaro’s anti-environmental policies could result in a worst-case scenario, with severe damage to the Amazon rainforest and to its ecological services, including the loss of the sequestration of vast amounts of stored carbon, leading to a regional and global intensification of climate change.

The combined impact of ongoing deforestation and escalating climate change on the Amazon rainforest could radically transform its configuration by 2050, with the biome divided into two distinct blocks — one occupied by still significant but very seriously diminished rainforest, the other dominated by agribusiness and scattered forest inside conserved areas.

That shift, were it to occur, could result in a decline of up to 58 percent of Amazon tree species richness, of which 49 percent would have some degree of risk for extinction (with tree species becoming vulnerable, endangered or critically endangered), according to a new study published in Nature Climate Change . 

The authors of the paper — four researchers from Brazil and the Netherlands — determined that both deforestation and climate change had to be examined together, and not separately as is typically done, in order to determine a realistic future scenario.

To the researchers’ surprise, when the effects were combined in their models, the tree species loss numbers were very high.

Under the deforestation/climate change scenario, one half of the Amazon (the northern, central and western portions) would be reduced to 53 percent of the original forest, although still with continuous areas. The other half (the eastern, southern and southeastern areas, where agribusiness activities currently take place), would become extremely fragmented, with only 30 percent of forest remaining; remnant vegetation would be found primarily in protected areas and indigenous reserves.

Two scenarios for 2050 were highlighted by the study, a more optimistic one — in which Paris Climate Agreement carbon targets are achieved and global temperature warms by less than 2 degrees Celsius (3.6 degrees Fahrenheit) — and a more pessimistic one based on recent rising trends in deforestation and CO 2 emissions.

In the more pessimistic scenario, Amazonian tree species would lose up to 65 percent of their spatial distribution area (where they live and reproduce), and up to 58 percent of their diversity; 49 percent would be threatened with extinction, of which 22 percent would be critically endangered, according to IUCN’s threatened extinction criteria.

Even the more optimistic scenario “does not indicate a promising future,” Vitor Gomes, an environmental scientist at the Federal University of Pará and the lead study author told Mongabay. That scenario predicts a loss of tree species richness of up to 43 percent, and a decline in tree species distribution area of up to 53 percent; under that scenario, 48 percent of tree species would be threatened, of which 11 percent would be critically endangered.

“The study should be seen as a big warning,” said Ima Vieira, a researcher at the Museu Paraense Emílio Goeldi and a study co-author. “It shows that if deforestation is currently the biggest cause of habitat loss in the Amazon, over the next thirty years it will probably be surpassed by climate change, which operates throughout the whole biome and can alone reduce species diversity by up to 37 percent.”

“It was not so clear to us how much the climate could affect the forest in the future,” Gomes added. “Deforestation is no longer the only major threat to the Pan-Amazon,” a biome designation that includes portions of nine South American countries.

Excluding climate change impacts, deforestation alone could cause Amazon species diversity losses of 19 percent (in the best scenario) or 36 percent (in the worst), while climate change could cause reductions of 31 percent to 37 percent, respectively.

No time for migration

The researchers analyzed the current distribution area of 6,394 tree species with available data among the 10,071 known Amazon tree species. They then compared that present data with both historical data (1950-2000) and projected deforestation data (up to 2050), along with current and future climate scenarios as determined by the United Nations IPCC (UN Intergovernmental Panel on Climate Change).

From this total species data, they eliminated rare species with insufficient records available to produce distribution models, and species without statistically significant models, leaving a total of 4,935 species.

Mapping the locations where the species can be found today is important to obtain temperature and precipitation limits in which they are apparently comfortable.

“The trees will find less favorable conditions [as climate change escalates] to keep existing and propagating,” explained Gomes. “Because they are static, they are slow to migrate to new areas through dispersers and pollinators [such as wind, water and animals].” The study gives as a reference the Holocene, a geological period that started about 11,600 years ago, when climate change caused Amazonian tree communities to expand their distribution southward.

“It took them 3,000 years to advance nearly 100 kilometers (62 miles). Man-induced climate change is happening now, and trees will be unable to move in 30 years or so more than 300 kilometers (186 miles), the distance to which the most suitable climates may be in relation with the current distribution areas [by 2050]. Not to mention that deforested areas [caused by agribusiness and other human development] make it even more difficult for trees to move forward once that barrier has been created,” Gomes said.

Tree species found in the lower half of the Amazon — such as the Protium altissimum (Aubl.) Marchand, the second most abundant in the biome, for example — may face a serious threat of extinction as the species suffers losses of up to 50 percent of its suitable distribution area. While Eperua falcata Aubl ., common in the Guiana Shield, could lose up to 63 percent of its suitable distribution area.

Agreement with previous studies

Carlos Nobre, a respected Brazilian climatologist and senior researcher at the University of São Paulo, along with Thomas Lovejoy, a U.S. ecologist at George Mason University, have estimated that deforestation and climate change combined could cause a large part of the Amazon biome to experience a rainforest to savanna conversion tipping point when around 20 to 25 percent of the biome is deforested.

Nobre commented on the new study: “The results are quite credible as projections of the synergistic effect of climate change, due to global warming, and deforestation, on the distribution of species in the Amazon. In our studies, we looked at the rainforest as a biome, not species by species, as in their study, and we analyzed the risks of change in the type of vegetation — that is, the forest being replaced by a savanna.”

However, “The results of these two kinds of analysis are similar: higher impacts in the south and east of the Amazon, while the forest would remain in the west,” Nobre said. “The study strongly reinforces our projections and recommendations for a development model with zero deforestation or, better still, the restoration of large deforested areas” in the Amazon.

Paulo Brando, an ecologist at the Woods Hole Research Center in Massachusetts, USA, noted that “the division of the Amazon in the middle is not necessarily a surprise. Previous studies, such as Duffy et al. 2015 , have shown that the Eastern Amazon could be[come] drier and hotter with climate change, while the west would be more rainy. That said, the results are of immense importance for the conservation of the rainforest.

“With the reduction of deforestation, Brazil could avoid the loss of occupation areas of hundreds of species. Without a global effort to stabilize the climate, however, some species would still be threatened. Thus, reducing deforestation would help not only to stabilize the climate but also to prevent habitat loss,” Brando concluded.

(Un)protected areas

The new research also highlights the crucial role of the currently existing network of protected areas and indigenous lands in the Amazon. Those areas can help buffer the loss of species diversity against future impacts.

“Even though [trees inside protected areas] are not totally immune to climate change, our models show that forests outside protected areas can lose up to a third more species,” said Gomes. “That’s why the preservation [of conserved areas] and the creation of corridors between them are so necessary, allowing biological dispersion and the migration of animals. Otherwise there will be only forest patches left in the Amazon.”

Vieira, of the Museu Paraense (linked with the Brazilian Ministry of Science, Technology, Innovation and Communications), said that it’s not difficult to imagine the most pessimistic study scenario becoming reality, given the trouble the world’s nations are having in achieving their carbon reduction goals as expressed in the 2015 Paris Agreement, as well as the difficulty Brazil has had in controlling deforestation, first under the Temer administration and now under the Bolsonaro administration .

June 2019 deforestation in the Brazilian Amazon increased by 88 percent  as compared to the same month in 2018, and while this result is preliminary many analysts fear that 2019 could show a significant annual rise in deforestation.

However, the nation isn’t responding by curbing deforestation, rather its policies appear to be encouraging the opposite. In May, Brazilian Environment Minister Ricardo Salles proposed a reevaluation of all 334 federal conservation units , with an eye on reducing the size of some, and abolishing others. More recently he declared without offering any scientific evidence that Brazil has already reached “zero relative deforestation,” that is, deforestation in the Amazon represents, according to him, “0.002 percent of the biome.” Later he said the correct number is 0.16 percent.

Vieira responded: “There is a political scenario unfavorable to the environment in the country [of Brazil], with budget cuts in the environmental and scientific areas, reduction of operations in environmental control and inspection, setbacks in environmental legislation, freezing of indigenous lands demarcations and the threat of opening [indigenous reserves] to mining activities.

“To save the Amazon from destruction, initiatives should consider an integrated vision of the region, long-term public policies, respect for environmental legislation, as well as for indigenous territories and peoples, and there should be strong economic pressure to restrict the sale of products from deforested areas and protected areas of the Amazon,” Vieira concluded.

Gomes, V. H. F., Vieira, I. C. G., Salomão, R.P., & ter Steege, H. (2019). Amazonian tree species threatened by deforestation and climate change . Nature Climate Change , 9(7), 547-553.

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Avian ecological succession in the Amazon: A long-term case study following experimental deforestation

Affiliations.

• 1 Biological Dynamics of Forest Fragments Project Instituto Nacional de Pesquisas da Amazônia (INPA) Manaus Brazil.
• 2 School of Renewable Natural Resources Louisiana State University and Louisiana State University AgCenter Baton Rouge LA USA.
• 3 Coleções Zoológicas - INPA Manaus Brazil.
• 4 Centre for Tropical Environmental and Sustainability Science College of Science and Engineering James Cook University Cairns Qld Australia.
• PMID: 31938486
• PMCID: PMC6953691
• DOI: 10.1002/ece3.5822

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 nonprimary 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.

Keywords: Amazonia; Neotropics; colonization; deforestation; ecological species invasions; land‐use change; rain forest.

© 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

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Conflict of interest statement

None declared.

Study area, showing the Biological…

Study area, showing the Biological Dynamics of Forest Fragments Project (BDFFP; represented by…

The number of observed (gray…

The number of observed (gray bars) and expected (empty bars) bird species per…

Results of land cover classification…

Results of land cover classification as closed‐canopy forest (primary forest or mature regrowth;…

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• Published: 01 September 2021

How deregulation, drought and increasing fire impact Amazonian biodiversity

• Xiao Feng   ORCID: orcid.org/0000-0003-4638-3927 1   na1 ,
• Cory Merow 2   na1 ,
• Zhihua Liu   ORCID: orcid.org/0000-0002-0086-5659 3   na1 ,
• Daniel S. Park   ORCID: orcid.org/0000-0003-2783-530X 4 , 5   na1 ,
• Patrick R. Roehrdanz   ORCID: orcid.org/0000-0003-4047-5011 6   na1 ,
• Brian Maitner   ORCID: orcid.org/0000-0002-2118-9880 2   na1 ,
• Erica A. Newman 7 , 8   na1 ,
• Brad L. Boyle 7 , 9 ,
• Aaron Lien 8 , 10 ,
• Joseph R. Burger 7 , 8 , 11 ,
• Mathias M. Pires 12 ,
• Paulo M. Brando   ORCID: orcid.org/0000-0001-8952-7025 13 , 14 , 15 ,
• Mark B. Bush   ORCID: orcid.org/0000-0001-6894-8613 16 ,
• Crystal N. H. McMichael   ORCID: orcid.org/0000-0002-1064-1499 17 ,
• Danilo M. Neves 18 ,
• Efthymios I. Nikolopoulos 19 ,
• Scott R. Saleska 7 ,
• Lee Hannah 6 ,
• David D. Breshears   ORCID: orcid.org/0000-0001-6601-0058 10 ,
• Tom P. Evans   ORCID: orcid.org/0000-0003-4591-1011 20 ,
• José R. Soto 10 ,
• Kacey C. Ernst 21 &
• Brian J. Enquist 7 , 22   na1  

Nature volume  597 ,  pages 516–521 ( 2021 ) Cite this article

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• Biodiversity
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Biodiversity contributes to the ecological and climatic stability of the Amazon Basin 1 , 2 , but is increasingly threatened by deforestation and fire 3 , 4 . Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079–189,755 km 2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3–85.2% of species that are listed as threatened in this region 5 . The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253–10,343 km 2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

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Data availability.

The plant occurrences from the BIEN database are accessible using the RBIEN package ( https://github.com/bmaitner/RBIEN ). The climatic data are accessible from http://worldclim.org and the soil data are available from http://soilgrids.org . MODIS active fire and burned area products are available at http://modis-fire.umd.edu . The MODIS Vegetation Continuous Fields data are publicly available from https://lpdaac.usgs.gov/products/mod44bv006/ . The annual forest loss layers are available from http://earthenginepartners.appspot.com/science-2013-global-forest . The plant range maps are accessible at https://github.com/shandongfx/paper_Amazon_biodiversity_2021 . The vertebrate range maps are available from https://www.iucnredlist.org/resources/spatial-data-download . The SPEI data are available from SPEI Global Drought Monitor ( https://spei.csic.es/map ).

Code availability

The code to process the remote-sensing data is available at https://github.com/shandongfx/paper_Amazon_biodiversity_2021 .

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Acknowledgements

We acknowledge the herbaria that contributed data to this work: HA, FCO, MFU, UNEX, VDB, ASDM, BPI, BRI, CLF, L, LPB, AD, TAES, FEN, FHO, A, ANSM, BCMEX, RB, TRH, AAH, ACOR, AJOU, UI, AK, ALCB, AKPM, EA, AAU, ALU, AMES, AMNH, AMO, ANA, GH, ARAN, ARM, AS, CICY, ASU, BAI, AUT, B, BA, BAA, BAB, BACP, BAF, BAL, COCA, BARC, BBS, BC, BCN, BCRU, BEREA, BG, BH, BIO, BISH, SEV, BLA, BM, MJG, BOL, CVRD, BOLV, BONN, BOUM, BR, BREM, BRLU, BSB, BUT, C, CAMU, CAN, CANB, CAS, CAY, CBG, CBM, CEN, CEPEC, CESJ, CHR, ENCB, CHRB, CIIDIR, CIMI, CLEMS, COA, COAH, COFC, CP, COL, COLO, CONC, CORD, CPAP, CPUN, CR, CRAI, FURB, CU, CRP, CS, CSU, CTES, CTESN, CUZ, DAO, HB, DAV, DLF, DNA, DS, DUKE, DUSS, E, HUA, EAC, ECU, EIF, EIU, GI, GLM, GMNHJ, K, GOET, GUA, EKY, EMMA, HUAZ, ERA, ESA, F, FAA, FAU, UVIC, FI, GZU, H, FLAS, FLOR, HCIB, FR, FTG, FUEL, G, GB, GDA, HPL, GENT, GEO, HUAA, HUJ, CGE, HAL, HAM, IAC, HAMAB, HAS, HAST, IB, HASU, HBG, IBUG, HBR, IEB, HGI, HIP, IBGE, ICEL, ICN, ILL, SF, NWOSU, HO, HRCB, HRP, HSS, HU, HUAL, HUEFS, HUEM, HUSA, HUT, IAA, HYO, IAN, ILLS, IPRN, FCQ, ABH, BAFC, BBB, INPA, IPA, BO, NAS, INB, INEGI, INM, MW, EAN, IZTA, ISKW, ISC, GAT, IBSC, UCSB, ISU, IZAC, JBAG, JE, SD, JUA, JYV, KIEL, ECON, TOYA, MPN, USF, TALL, RELC, CATA, AQP, KMN, KMNH, KOR, KPM, KSTC, LAGU, UESC, GRA, IBK, KTU, KU, PSU, KYO, LA, LOMA, SUU, UNITEC, NAC, IEA, LAE, LAF, GMDRC, LCR, LD, LE, LEB, LI, LIL, LINN, AV, HUCP, MBML, FAUC, CNH, MACF, CATIE, LTB, LISI, LISU, MEXU, LL, LOJA, LP, LPAG, MGC, LPD, LPS, IRVC, MICH, JOTR, LSU, LBG, WOLL, LTR, MNHN, CDBI, LYJB, LISC, MOL, DBG, AWH, NH, HSC, LMS, MELU, NZFRI, M, MA, UU, UBT, CSUSB, MAF, MAK, MB, KUN, MARY, MASS, MBK, MBM, UCSC, UCS, JBGP, OBI, BESA, LSUM, FULD, MCNS, ICESI, MEL, MEN, TUB, MERL, CGMS, FSU, MG, HIB, TRT, BABY, ETH, YAMA, SCFS, SACT, ER, JCT, JROH, SBBG, SAV, PDD, MIN, SJSU, MISS, PAMP, MNHM, SDSU, BOTU, MPU, MSB, MSC, CANU, SFV, RSA, CNS, JEPS, BKF, MSUN, CIB, VIT, MU, MUB, MVFA, SLPM, MVFQ, PGM, MVJB, MVM, MY, PASA, N, HGM, TAM, BOON, MHA, MARS, COI, CMM, NA, NCSC, ND, NU, NE, NHM, NHMC, NHT, UFMA, NLH, UFRJ, UFRN, UFS, ULS, UNL, US, NMNL, USP, NMR, NMSU, XAL, NSW, ZMT, BRIT, MO, NCU, NY, TEX, U, UNCC, NUM, O, OCLA, CHSC, LINC, CHAS, ODU, OKL, OKLA, CDA, OS, OSA, OSC, OSH, OULU, OXF, P, PACA, PAR, UPS, PE, PEL, SGO, PEUFR, PH, PKDC, SI, PMA, POM, PORT, PR, PRC, TRA, PRE, PY, QMEX, QCA, TROM, QCNE, QRS, UH, R, REG, RFA, RIOC, RM, RNG, RYU, S, SALA, SANT, SAPS, SASK, SBT, SEL, SING, SIU, SJRP, SMDB, SNM, SOM, SP, SRFA, SPF, STL, STU, SUVA, SVG, SZU, TAI, TAIF, TAMU, TAN, TEF, TENN, TEPB, TI, TKPM, TNS, TO, TU, TULS, UADY, UAM, UAS, UB, UC, UCR, UEC, UFG, UFMT, UFP, UGDA, UJAT, ULM, UME, UMO, UNA, UNM, UNR, UNSL, UPCB, UPNA, USAS, USJ, USM, USNC, USZ, UT, UTC, UTEP, UV, VAL, VEN, VMSL, VT, W, WAG, WII, WELT, WIS, WMNH, WS, WTU, WU, Z, ZSS, ZT, CUVC, AAS, AFS, BHCB, CHAM, FM, PERTH and SAN. X.F., D.S.P., E.A.N., A.L. and J.R.B. were supported by the University of Arizona Bridging Biodiversity and Conservation Science program. Z.L. was supported by NSFC (41922006) and K. C. Wong Education Foundation. The BIEN working group was supported by the National Center for Ecological Analysis and Synthesis, a centre funded by NSF EF-0553768 at the University of California, Santa Barbara, and the State of California. Additional support for the BIEN working group was provided by iPlant/Cyverse via NSF DBI-0735191. B.J.E., B.M. and C.M. were supported by NSF ABI-1565118. B.J.E. and C.M. were supported by NSF ABI-1565118 and NSF HDR-1934790. B.J.E., L.H. and P.R.R. were supported by the Global Environment Facility SPARC project grant (GEF-5810). D.D.B. was supported in part by NSF DEB-1824796 and NSF DEB-1550686. S.R.S. was supported by NSF DEB-1754803. X.F. and A.L. were partly supported by NSF DEB-1824796. B.J.E. and D.M.N. were supported by NSF DEB-1556651. M.M.P. is supported by the São Paulo Research Foundation (FAPESP), grant 2019/25478-7. D.M.N. was supported by Instituto Serrapilheira/Brazil (Serra-1912-32082). E.I.N. was supported by NSF HDR-1934712. We thank L. López-Hoffman and L. Baldwin for constructive comments.

Author information

These authors contributed equally: Xiao Feng, Cory Merow, Zhihua Liu, Daniel S. Park, Patrick R. Roehrdanz, Brian Maitner, Erica A. Newman, Brian J. Enquist

Authors and Affiliations

Department of Geography, Florida State University, Tallahassee, FL, USA

Eversource Energy Center and Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA

Cory Merow & Brian Maitner

CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

Department of Biological Sciences, Purdue University, West Lafayette, IN, USA

Daniel S. Park

Purdue Center for Plant Biology, Purdue University, West Lafayette, IN, USA

The Moore Center for Science, Conservation International, Arlington, VA, USA

Patrick R. Roehrdanz & Lee Hannah

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

Erica A. Newman, Brad L. Boyle, Joseph R. Burger, Scott R. Saleska & Brian J. Enquist

Arizona Institutes for Resilience, University of Arizona, Tucson, AZ, USA

Erica A. Newman, Aaron Lien & Joseph R. Burger

Hardner & Gullison Associates, Amherst, NH, USA

Brad L. Boyle

School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

Aaron Lien, David D. Breshears & José R. Soto

Department of Biology, University of Kentucky, Lexington, KY, USA

Joseph R. Burger

Departamento de Biologia Animal, Universidade Estadual de Campinas, Campinas, Brazil

Mathias M. Pires

Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

Paulo M. Brando

Woodwell Climate Research Center, Falmouth, MA, USA

Instituto de Pesquisa Ambiental da Amazônia (IPAM), Brasilia, Brazil

Insitute for Global Ecology, Florida Institute of Technology, Melbourne, FL, USA

Mark B. Bush

Department of Ecosystem and Landscape Dynamics, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands

Crystal N. H. McMichael

Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil

Danilo M. Neves

Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, FL, USA

Efthymios I. Nikolopoulos

School of Geography, Development and Environment, University of Arizona, Tucson, AZ, USA

Tom P. Evans

Department of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ, USA

Kacey C. Ernst

The Santa Fe Institute, Santa Fe, NM, USA

Brian J. Enquist

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Contributions

X.F. conceived the idea, which was refined by discussion with D.S.P., C.M., B.M., P.R.R., E.A.N., B.L.B., A.L., J.R.B., D.D.B., J.R.S., K.C.E. and B.J.E.; X.F. and Z.L. processed the remote-sensing data; C.M., X.F., B.M., B.L.B., D.S.P. and B.J.E. conducted the analyses of plant data; P.R.R., C.M., B.M., X.F. and D.S.P. conducted the analyses of vertebrate data; X.F., C.M., S.R.S. and E.A.N. processed the drought data; D.S.P., X.F., C.M., P.R.R. and B.M. designed the illustrations with help from B.J.E., D.D.B., K.C.E. and E.A.N.; E.A.N., X.F., and D.S.P. conducted the statistical analyses with help from B.J.E.; X.F., B.J.E., B.M., A.L., J.R.B., D.S.P., C.M., E.A.N., Z.L. and P.R.R. wrote the original draft; all authors contributed to interpreting the results and the editing of manuscript drafts. B.J.E., C.M., K.C.E. and D.D.B. led to the acquisition of the financial support for the project. X.F., C.M., B.M., D.S.P., P.R.R., Z.L., E.A.N. and B.J.E. contributed equally to data, analyses and writing.

Corresponding author

Correspondence to Xiao Feng .

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Competing interests.

The authors declare no competing interests.

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Extended data figures and tables

Extended data fig. 1 fire-impacted forest and forest loss in the amazon basin..

a – h , Visualization of fire-impacted forest ( a , b ), forest loss without fire ( c , d ), fire-impacted forest with forest loss ( e , f ), and fire-impacted forest without forest loss ( g , h ) in the Amazon Basin based on MODIS burned area (left panels) and active fire (right panels). Data in a – d are resampled from the 500m (MODIS burned area) or 1 km (MODIS active fire) to 10 km resolution using mean function and thresholded at 0.01 to illustrate the temporal dynamics. Black represents non-forested areas masked out from this study. The cumulative fire-impacted forest is classified into two categories: fire-impacted forest with forest loss ( e , f ) and fire-impacted forest without forest loss ( g , h ). Data in e – h are resampled to 10 km using mean function to illustrate the cumulative percentages of impacts.

Extended Data Fig. 2 Scatter plot of species’ range impacted by fire.

Scatter plot of species’ range size in Amazon forest (x-axis) and percentage of total range impacted by fire (red) and forest loss without fire (black) up to 2019 for plants (left panel) and vertebrates (right panel).

Extended Data Fig. 3 Density plot of species’ cumulative range impacted by fire.

Density plot of species’ cumulative range impacted by fire. The different colours represent years 2001-2019. The x-axis is log10 transformed.

Extended Data Fig. 4 Summary of forest impacts in the Amazon Basin.

Areas of forest impact in the Amazon Basin estimated from MODIS burned area (top) and MODIS active fire (bottom).

Extended Data Fig. 5 Cumulative impacts on biodiversity in the Amazon Basin.

Cumulative effects of forest loss without fire on biodiversity in the Amazon rainforest. In the left panels, the black and grey shading represent the cumulative forest loss without fire based on MODIS burned area and MODIS active fire, respectively. Coloured areas represent the lower and upper bounds of cumulative numbers of a , plant and c , vertebrate species’ ranges impacted. Right panels depict the relationships between the cumulative forest loss without fire (based on MODIS burned area) and cumulative number of b , plant and d , vertebrate species. Coloured lines represent predicted values of an ordinary least squares linear regression and grey bands define the two-sided 95% confidence interval (two-sided, p values = 0.00). The silhouette of the tree is from http://phylopic.org/ ; silhouette of the monkey is courtesy of Mathias M. Pires.

Extended Data Fig. 6 Fire-impacted forest in Brazil.

Newly fire-impacted forest in Brazil (based on MODIS active fire). a shows the area of fire-impacted forest not explained by drought conditions. Different colours represent years from different policy regimes: pre-regulations in light red (mean value in dark red), regulation in grey (mean value in black dashed line), and 2019 in blue. The y-axis represents the difference between actual area and area predicted by drought conditions calibrated by data from regulation years ( Methods ). A positive value on the y-axis represents more area than expected, using the regulation years as a baseline. b shows a scatter plot of newly fire-impacted forest in Brazil and drought conditions (SPEI); The lines represent the ordinary least squares linear regression between fire-impacted forest and drought conditions for pre-regulation (red) and regulation (black) respectively.

Extended Data Fig. 7 Fire-impacted forest in different countries.

The contribution (0–1) of different countries to the newly fire-impacted forest each year based on MODIS active fire (top) and MODIS burned area (bottom).

Extended Data Figure 8 Impacts of fire on forest and biodiversity in Brazil.

a , Newly fire-impacted forest, b , new range impact on plants and c , new range impacts on vertebrate species in Brazil each year (based on MODIS active fire) that are not predicted by drought conditions. The colours represent three policy regimes: pre-regulation in red, regulation in grey and 2019 in blue. The y-axis represents the difference between actual value (area or range impacted by fire) and the values predicted by drought conditions calibrated by data from regulation years ( Methods ). A positive value on the y-axis represents more area or range impacted by fire than the expectation using the regulation years as a baseline. The dotted lines represent a smooth curve fitted to the values based on the loess method.

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Feng, X., Merow, C., Liu, Z. et al. How deregulation, drought and increasing fire impact Amazonian biodiversity. Nature 597 , 516–521 (2021). https://doi.org/10.1038/s41586-021-03876-7

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DOI : https://doi.org/10.1038/s41586-021-03876-7

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Motivation.  Scientists have identified a number of factors to explain Amazonian deforestation. This project examines another driver that has not been adequately studied, namely that of contentious social interactions and conflict over access to land resources on forest cover, referred to here as Contentious Land Change (CLC). Land conflict, globally associated with agrarian reform, has waxed and waned in the Brazilian Amazon over the past three decades, a period that witnessed high rates of deforestation and hundreds of land conflict related fatalities.

The nexus between land conflict and natural resource impact is a widespread phenomenon. Transnational social movements like the Movement of the Landless Rural Workers (MST) and La Via Campesina have recently stimulated the rural poor to question access to land, often limited for institutional and historical reasons at odds with social equity. This has motivated a rural constituency that aggressively seeks more dignified, farm-based livelihoods. As a consequence, contention over land has seized headlines throughout the Global South, with notable movements in South American, Southeast Asia and Africa. 

Our examination of CLC focuses on the Brazil-Nut Polygon, or BNP of southeastern Amazonia (Figure 1). We employ a spatial analytic model using panel data at the property level generated from unique sources, including (1) a multi-decadal satellite image series covering 27 years; (2) cadastral maps for 180 large holdings (4,886 ha average) and agrarian reform settlements,; and (3) a newspaper archive detailing land conflicts unfolding at property scale for the same period as the satellite imagery (~8,000 pages). This information enables the systematic evaluation of deforestation associated with the social inequalities driving land conflict in the Amazon Basin. 

Conceptual Framework . This study dispenses with the LCS presumption that landholdings are managed by unitary decision-makers acting in the interest of market production. Often, deforestation results from contentious interactions between powerful elites and the rural poor, groups with differing motivations and agricultural practices.  An important component of land change in forest frontiers results as land managers deforest in order to engage in agricultural activities, a process we refer to as agronomic land change, or ALC. Nevertheless, the presence of land conflict alters this dynamic by introducing new behaviors and social processes that do not conform to a static, rational actor model. Changes in land cover in contentious settings stem from a variety of actions taken by competing claimants. These include preemptive deforestation in anticipation of seizure by alternative claimants, excessive land clearance meant to stake a claim, the use of fire as a conflict weapon, and resource mining (valuable hardwoods, soils) in advance of expected dispossession .

For Amazonia, we hypothesize that deforestation reflects both ALC and CLC, and that CLC adds to the total amount of deforestation than would otherwise occur under ALC. This is depicted in Figure 2 for two hypothetical holdings of 4,000 ha. One holding experiences CLC and the other does not. Both follow a linear land clearance pattern at first, a property “life cycle”. Divergence sets in at t=2, as direct action land reform (DALR) participants contest one of the holdings, thereby precipitating CLC. Property control then passes back and forth between DALR participants and the initial occupant, creating a series of management intervals (e.g., t=4 → t=5, t=5 → t=6, etc.). The property without contention experiences ALC until 2,000 ha are cleared, or half the holding, in accordance with federal law that mandates a “Legal Reserve” of 50 percent. The property in contention shows more LC once DALR has initiated (at t=2), and a higher amount of long-run, end-of-period deforestation (at t=10). 

We conducted a series of regression analyses, including OLS and fixed-effects in a panel environment, where each property (n = 180) has complete observations for each year (n = 27). The panel data, described above, were integrated at the property level using a digital cadastral map of 180 large landholdings created from the BNP paper map.

The main hypotheses and results:

(H1) land conflict augments deforestation magnitudes,such that:

(H1a) deforestation is greater by the end of study period on properties that have had more stability in control. SUPPORTED. Property stability as the years with only largeholder control increase, 80.57 ha more forest per year 

(H1b) property expropriation and settlement formation increases deforestation, reflecting CLC, compared to after when ALC is the dominant driver; SUPPORTED. By the end of the decadal analysis period, properties with CLC present 25.76 ha more deforestation per conflict event

(H2) that deforestation rates are greater during periods of conflict; and SUPPORTED.  All independent variables affect deforestation, and deforestation rates are significantly .

(H3) expropriated properties with agrarian reform settlements experience less deforestation than would otherwise be the case given more certain control by smallholder land managers. SUPPORTED.Expropriation and settlement formation increase deforestation, with 58.95 ha more deforestation each year after expropriation occurs.

Last Modified: 04/12/2017 Modified by: Cynthia S Simmons

Please report errors in award information by writing to: [email protected] .

Case Study: Losing the Rainforest, One Tree at a Time

Studying change over time.

Everything changes over time — including people! You might not see a change in yourself from one day to the next, but if you look at a picture of yourself as a baby you can see how much you have changed. Earth's surface changes, too. Some changes are small (a boulder rolls down a hill), or happen quickly (lava flows down the side of a volcano), and we can see a difference from one day to the next. Other changes are too large or too slow to see right away, so we take pictures over time and compare the present to the past, just like you looking at your baby pictures. Information, including pictures, that is collected over time is called "time series" information.

In this chapter, Synthetic Aperture Radar (SAR) satellite images are used to study change in the Amazon rainforest. There are three great reasons for using SAR images to look at the rainforest. First, the rainforest is a cloudy place! Regular cameras cannot take a picture through clouds, but SAR can see right through those clouds – clouds are invisible to SAR. Second, the rainforest is huge! If you were to visit the rainforest in person, it would seem endless. We need to study the "big picture" to tell what changes have occurred, and satellite images give us the biggest picture of all. Third, satellite images are taken of exactly the same places each time the satellite circles Earth, so images from different years match up. You will be able to compare SAR images of the exact same area from three different years to measure how much forest area was lost from a part of the Amazon rainforest between 1994 and 1996. Consider this: Using this technique measures how much forest was lost in just three years. People have been cutting rainforest trees for a long time, with no sign of stopping. What do you think that means for the forest?

Deforesting the Rainforest

Why should anyone care about the Amazon rainforest in Brazil, anyway? Brazil is a long way away, for sure. But today the Amazon River is the source of 20% of all fresh water on Earth, and the Amazon rainforest contains one-third of Earth's remaining rainforests. About 60% of the entire Amazonian rainforest is located in Brazil, and that rainforest affects everybody on our planet — including you!

Almost all living organisms, including you and me, breathe air and need oxygen. The rainforest plants produce about 20% of Earth's oxygen, and absorb almost 20% of the carbon dioxide we release annually by burning fossil fuels 1 . The Amazon is home to some incredible animals, including jaguars, poison dart frogs, scarlet macaws, howler monkeys, and millions of other species, including an estimated 30 million species of insects! Maybe you don't like bugs, but what about monkeys? Beautiful parrots? Big cats, or cool frogs?

Do you like tasty food? Many spices we use every day to make our food taste better come from the rainforest, including vanilla, coconut, cinnamon, and pepper. Chocolate comes from the rainforest, too! Let's talk about medicine. Have you or anyone you cared about ever been sick? Gone to the dentist? Everything from quinine, a medicine for malaria, to vincristine, a potent (really strong) cancer medicine, to novocaine, a local anesthetic (numbing agent) that your dentist uses, and lots more medicines come from rainforest plants. Only a small number of rainforest plants have been tested for medicinal uses.

Question: Who knows how many special plants we lose every year because the rainforests are being cut down? Answer: No one knows. No one at all.

Brazil is not just big trees, lots of bugs, and wild animals. People live there, too! People that cut down rainforest trees are not just inconsiderate or destructive. They want the land for roads and mining and cattle ranches. They sell valuable hardwood trees, like teak, to other countries, including the United States. Poor farmers clear the land so they can grow vegetables to feed their families. People who are pro-development conflict with people, including indigenous (native) people, who want very much to save the rainforest. This is not a simple problem. To learn more about the Amazon rainforest, a great place to start is Raintree .

Forests and Climate Change

In their natural state, forests are a sink, or storehouse, of carbon dioxide (CO 2 ). In other words, they store more CO 2 than they release. According to Dr Pep Canadell, from the Global Carbon Project and CSIRO Marine and Atmospheric Research, deforestation accounts for approximately 20% of global emissions. This is more than the amount released by 13 years of fossil fuel burning.

Scientists estimate that 5.9 gigatons of CO 2 enters the atmosphere every year from deforestation. That destruction amounts to 50 million acres — or an area the size of England, Wales and Scotland felled annually. When tropical forests are cut–down and burned they release the CO 2 that they had stored back to the atmosphere. Therefore, protecting tropical rainforests is not only important to protect biodiversity it is an important tool in our efforts to mitigate climate change.

In this chapter, you obtain SAR satellite images of an area in the rainforest of northwest Brazil where deforestation has occurred and roads have been built. It is easy to see the roads — they look like fish bones in the satellite images. After exploring the images, use a scientific analysis software package, ImageJ, to analyze them and determine how much forest has been lost from this rainforest area in just three years.

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The Deforestation of the Amazon

A Case Study in Understanding Ecosystems and Their Value

By Philip Camill

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In this case study, students examine tropical deforestation in the Amazon from the perspective of three dominant stakeholders in the region: a peasant farmer, logger, and environmentalist. As part of the exercise, students perform a cost-benefit analysis of clearing a plot of tropical forest in the Amazon from the perspective of one of these stakeholder groups. Developed for a course in global change biology, this case could also be used in courses in general ecology, environmental science, environmental ethics, environmental policy, and environmental/ecological economics.

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• Understand the political, cultural, and economic history leading to tropical deforestation in Amazonia. Understand issues facing the major stakeholders in the Amazon.
• Understand the concern for such a large loss in biodiversity.
• Understand the concepts of market and non-market valuation of ecosystems, benefit-cost analysis, and opportunity cost.
• Perform a cost-benefit analysis of clearing a plot of tropical forest in the Amazon, from the perspective of a peasant farmer, logger, and environmentalist.
• Critically evaluate economic vs. ethical valuation of ecosystems.
• Appreciate the political, social, economic, and ecological complexity of tropical deforestation.
• Appreciate how difficult decisions must me made in the face of limited or nonexistent data.

Deforestation; Amazon; tropical forest; rainforest; ecosystem; biodiversity; bioprospecting; ecotourism; ecological economics; cost-benefit analysis; tropics; developing world; South America

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High school, Undergraduate lower division, Undergraduate upper division

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Ethics, Policy issues, Social issues, Social justice issues

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The Deforestation of the Amazon : A Case Study in Understanding Ecosystems and Their Value

• P. Camill , R. Althaus , +3 authors P. Soranno
• Published 1999
• Environmental Science
• Science and Engineering Ethics

3 Citations

Exploring the impact of gamified role-playing on climate change knowledge and nature relatedness: evidence from an online undergraduate course on environmental health, tropical deforestation: can property rights stem the tide, earthworms as a bioindicator of mercury pollution in an artisanal gold mining community, cachoeira do piriá, brazil, related papers.

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Large differences between observed and expected Ecuadorian deforestation from 2001 to 2009: a counterfactual simulation approach

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• Volume 24 , article number  94 , ( 2024 )

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Ensuring the integrity of the world’s forests is indispensable for mitigating climate change, combatting biodiversity loss, and protecting the livelihoods of rural communities. While many strategies have been developed to address deforestation across different geographic scales, measuring their impact against a fluctuating background of market-driven forest loss is notoriously challenging. In this article, we (1) asses deforestation in Ecuador using a dynamic, counterfactual baseline that excludes non-market factors, (2) identify periods of reduced and excess deforestation, and (3) assess the economic consequences of associated CO 2 emissions using the social cost of carbon metric. We construct a counterfactual market-forces-only reference scenario by simulating heterogeneous profit-seeking agents making satisficing land-use allocation decisions under uncertainty. The model simulates a reference scenario for 2001–2022, a period encompassing dollarization, the beginning of a constitution granting inalienable rights to nature, and the launch of the largest payments for ecosystem services program in Ecuador’s history. On this period, total deforestation was approximately 20% lower than expected in a market-forces-only scenario (9540 vs.12,000 km 2 ). The largest deviation occurred in 2001–2009, when observed deforestation was 43.6% lower than expected (3720 vs 6590 km 2 ). From 2010 onwards, deforestation appears to be market-driven. We assess the economic value of avoided CO 2 emissions at US $5.7 billion if the reduction is permanent, or US $3.1 billion considering a 1% risk of loss from 2022 onwards. We discuss contributing factors that likely shaped periods of reduced and excess deforestation and stress the need to use realistic baselines.

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Introduction

Global forest area has decreased by roughly 10% in the last 60 years, with the most serious losses occurring in the tropics (Hansen et al. 2013 ; Estoque et al. 2022 ). In addition to their intrinsic value, tropical forests account for more than half of Earth’s terrestrial biodiversity (FAO 2022 ), provide local ecosystem services to approximately 1.5 billion people (Lewis et al. 2015 ), and generate global benefits like carbon sequestration and climate stabilization (Fuss et al. 2021 ). Unfortunately, gross carbon loss from tropical forests is accelerating, with a doubling from 2001 to 2019 that appears to be driven mainly by agricultural expansion (Feng et al. 2022 ). South America is a leading contributor (Hansen et al. 2013 ; Feng et al. 2022 ).

The integrity of the world’s forests, tropical and otherwise, depends heavily on how we interact with and use them (Díaz et al. 2019 ; Purvis et al. 2019 ; IPCC 2022 ). In recent decades, various initiatives operating at international, regional, and local levels have been developed to combat deforestation and mitigate its impacts, such as the UN-REDD+ Program, LEAF Coalition, Zero-Deforestation agreements, and Ecuador’s Socio Bosque program (de Koning et al. 2011 ; Un-REDD programme 2016 ; Pasiecznik and Savenije 2017 ; LEAF Coalition 2023 ). However, these schemes often lack explicit criteria for monitoring and assessment, making it difficult to evaluate their effectiveness (Garrett et al. 2019 ). Even when systematic assessments are employed, such as the Monitoring, Reporting, and Verification (MRV) system used by REDD+ projects, they are typically based on static before-after comparisons of carbon stocks and emissions (IPCC 2022 ), an approach which neglects existing trends and shifting drivers of forest loss. Although it is well-established that using inappropriate baselines can significantly overestimate emissions reductions (West et al. 2020 ; West et al. 2023 ), defining sound “business-as-usual” baselines can be immensely challenging, especially on forest-relevant timescales. As a result, most assessments lack plausible counterfactual reference scenarios that clearly describe what would have happened in the absence of an intervention (Köthke et al. 2014 ; Bos et al. 2017 ; Gifford 2020 ).

Ecuador stands out as one of the world’s most fascinating case studies in forest and biodiversity protection (Coral et al. 2021 ). Situated at the biogeographic confluence of the Andes, the Amazon basin, and the Tumbes-Chocó-Magdalena hotspot (Iturralde‐Pólit et al. 2017 ), Ecuador is a megadiverse country (Kleemann et al. 2022 ) that for decades recorded the highest deforestation rates in South America, losing more than half of its forest area since the 1970s (Bilsborrow et al. 2004 ; Tapia-Armijos et al. 2015 ). Figure 1 compares the forest conditions in 1990 and in 2022 based on public data from the Ministerio del Ambiente, Agua y Transición Ecológica ( 2024 ). The figure illustrates a substantial reduction in the extent of natural forest cover over the 32-year period, as well as the spatial distribution of the forest loss. Deforestation hotspots are concentrated in the Ecuadorian Amazon basin, particularly along the so-called Troncal Amazónica , a major road that crosses the region from north to south. Additionally, two other affected areas were identified along the northern and southern borders in this region. Intense deforestation was also observed in the northern coastal region, while the remaining forests in the central and southern coastal areas, as well as in the western Andean mountain range, experience comparatively lower levels of deforestation. The country has a disproportionately high share of species on the IUCN red list (Rodrigues et al. 2014 ). Although logging, mining, and oil concessions all make significant contributions (Ojeda Luna et al. 2020 ; Sierra et al. 2021 ), agricultural expansion (pasture and croplands) remains the primary driver of deforestation and biodiversity loss in Ecuador (Mena et al. 2011 ; Knoke et al. 2016 ).

Land-use land-cover (LULC) map of Ecuador illustrating the status of natural forest in 1990 and 2022. Based on data from Environmental, Water and Ecological Transition Ecuadorian Ministry (Ministerio del Ambiente, Agua y Transición Ecológica 2024 )

Following overlapping periods of economic upheaval and nearly a decade (1997–2008) of decentralized natural resource management reform which occurred mainly at the canton (a spatial administrative unit similar to a county) level (Kauffman and Terry 2016 ), in 2008, Ecuador became the first country in the world to constitutionally guarantee specific, legally enforceable rights to nature (Asamblea constituyente del Ecuador 2008 ; Tanasescu 2013 ; Kotzé and Villavicencio Calzadilla 2017 ). These include the right to exist and persist (Article 71), to restoration (Article 72), and to provide environmental benefits to people and communities (Article 74) (Kotzé and Villavicencio Calzadilla 2017 ; Nieto Sanabria 2017 ).

Empowered by this last provision in particular, Ecuador’s Environmental Ministry swiftly implemented a program, Socio Bosque, with the dual goals of conservation and poverty alleviation (de Koning et al. 2011 ; Nieto Sanabria 2017 ). This program offers economic incentives to private owners and communities who willingly pledge to conserve and restore forest and páramo for at least 20 years (Vanacker et al. 2018 ). As of May 2023, nearly 17,000 km 2 have been enrolled, representing more than 6% of continental Ecuador’s total land area, and payments have been disbursed to some 120,000 beneficiaries (Ministerio del Ambiente, Agua y Transición Ecológica 2023 ).

Taken together, it is a remarkable story. A nation that suffered from an economic crisis and was unable to halt the decline of its natural forests decided to undertake a bold legal experiment to constitutionally recognize the rights of nature. The country succeeded in implementing it and immediately launched the largest-ever payments for ecosystem services program in its history. Despite garnering well-deserved attention from scholars, policymakers, and the media, relatively few efforts have been made to systematically quantify the aggregate impact of these changes on national-scale deforestation trends (but see Mohebalian and Aguilar 2016 ; Eguiguren et al. 2019 ; Fischer et al. 2021 ).

In this study, we apply a new method to assess deforestation trends in Ecuador against a dynamic counterfactual baseline, and assess the economic value associated with periods of excess (i.e. above-baseline) and reduced (below-baseline) deforestation using published estimates of the social cost of carbon (SCC). The baseline scenario is designed to reflect the level of deforestation attributable to market forces only (i.e. what would have occurred in a counterfactual scenario where external factors, such as policy interventions, are absent). Deviations between the market-forces-only baseline and observed deforestation levels are assumed to result from these external factors. We investigate the following questions:

Q1 Does the observed deforestation deviate from the market-oriented deforestation trajectory?

Q2 Are the deviations between observed and counterfactual deforestation associated with non-market factors at the country scale?

Q3 What is the economic value of CO 2 emissions associated with excess or reduced deforestation relative to the counterfactual market-forces-only baseline?

To construct a dynamic, counterfactual baseline, we adopt an approach recently introduced by Knoke et al. ( 2023 ) consisting of a behaviourally consistent land-use allocation model in which profit-seeking agents make satisficing decisions under future uncertainty. This approach is based on three assumptions: (a) forest loss is driven by the transformation of land use, mainly into agriculture; (b) decision-makers seek satisfactory and sufficient (“satisficing”), rather than maximal, profits; and (c) the individual expectations of land managers drive land-use allocation decisions. Building on heterogenous expectations of land managers about market prices, agricultural productivity, and corresponding profits, this approach establishes a suitable deforestation baseline for estimating the social value of excess and avoided deforestation.

Material and methods

We compare observed forest cover losses for Ecuador (2000–2022) from Global Forest Watch ( 2024 ) with simulated forest losses from a market-oriented counterfactual land-use allocation model. Because Ecuador’s forest cover is dominated by natural forest, we interpret any forest loss as a permanent removal, neglecting that some forests might re-establish naturally or be replanted on previously cleared lands (see Bos et al. 2017 for a similar assumption and Feng et al. 2022 for empirical support).

Background and expected deforestation trends

Between 1990 and 2000, Ecuador experienced a series of interlinked economic crises (Beckerman 2001 ) and political instability (Conaghan and La Torre 2008 ; Kauffman and Terry 2016 ), culminating in a strong currency depreciation and the adoption of the US dollar as the national currency (“dollarization”). Because currency depreciations and political instability tend to enhance deforestation in developing countries (Didia 1997 ; Arcand et al. 2008 ), it is reasonable to assume that this period featured higher deforestation levels than would expected from purely microeconomic land-use allocation decisions. Conversely, however, this period of national political instability created space for a series of local, canton-level natural resource management reforms, such as measures to combat deforestation in rural watersheds, which were pursued from 1997 to 2008. Kauffman and Terry ( 2016 ) showed that these reforms were attempted by 94 of Ecuador’s 221 cantons, collectively representing about 50% of the country’s area (see Fig. 1 in Kauffman and Terry 2016 ). Combined with the currency stabilization from 2000 onwards, the reforms may have caused reduced deforestation.

In 2006, the election of president Rafael Correa marked a turning point in Ecuador’s political landscape (Conaghan and La Torre 2008 ). Among other things, it opened the door to ratifying a new constitution granting inalienable rights to nature and establishing the Socio Bosque conservation program, both in 2008 (Krause and Loft 2013 ). Intuitively, one would expect these developments to exert a downward pressure on deforestation rates. However, the 2007–2020 period featured a novel combination of large-scale conservation incentives alongside an expansion of “neo-extractivist” activities (Coral et al. 2021 ), opposing trends that may have counteracted one another.

Counterfactual land-use allocation to model expected deforestation

We adopted an approach to counterfactual land-use allocation modelling recently introduced by Knoke et al. ( 2023 ), which aims to support evaluations of broad country-scale non-market drivers of deforestation by simulating plausible reference scenarios corresponding to deforestation levels that would be expected in the absence of any policy intervention (i.e. if land-use allocation decisions were driven exclusively by microeconomic factors). The broad country-level counterfactual deforestation simulation is non-spatial. We simulate land-use allocation decisions (including forest conversion to alternative LULC types) for hypothetical regions of Ecuador, assuming groups of agents with heterogeneous expectations concerning economic profits and uncertainties. These simulations are likely to be really independent from observed deforestation and would thus be well-suitable as reference scenarios. We start with the premise that land-use allocation processes at the regional level, even when agents act individually, are often influenced by suggestions and advice from others. Salas-Molina et al. ( 2023 ) have recently described such simulation approaches in general. Our model is parsimonious in that it uses only input information on agricultural productivity and market prices and costs. The possible variation of the expectations of different land managers is simulated based on random Monte Carlo simulations, assuming that individual land managers perceive future productivities and profits differently. The random process assigns more or less pessimistic profit expectations to more or less risk-averse land managers, so that decision-makers’ heterogeneity is represented. The core principle of our simulation uses a distance function expressing the current economic dissatisfaction of the individual members of a group of agents. We minimize this function across multiple heterogeneous expectations concerning the current and future land-use profits by simulating an appropriate land-use re-allocation.

To address such decision processes, including multiple decision-making agents at the regional scale, we generated counterfactual reference scenarios by simulating a large number of land-use allocation decisions made by heterogeneous groups of agents responding to microeconomic signals. The difference between the empirical (actually observed) deforestation levels and the reference (i.e. simulated counterfactual baseline) represents reduced or excess deforestation, which we use as a basis for calculating the economic value of both avoided and additional carbon emissions, using published estimates for SCC (see Supplementary Methods 4.2 and Supplementary Table 8 in Knoke et al. 2023 ). We used an emission factor of 108 Mg C hectare −1 (derived from data in Harris et al. 2012 ).

In this application, agents represent land manager groups making satisficing decisions under uncertainty in response to subjective profit expectations. Uncertainty encompasses volatility of market prices, productivity fluctuations, and potential crop losses caused by natural hazards and sudden political changes. In our model, Ecuadorian land managers would consider multiple possible future profits. Our approach implies satisficing decisions, which means that land managers gradually improve their current land-use profits by seeking a compromise land-use allocation that promises “sufficient” outcome levels in all possible future states of the world. To achieve this, an individual land manager would consider two types of information: (1) expected profits under the current land-use allocation and (2) the best achievable profits expected under a different allocation (for multiple possible future profit scenarios). The potential for improvement is represented by the difference between (2) and (1), which shows the degree of land manager dissatisfaction with current profits. In contrast to a previous non-stochastic model (Knoke et al. 2020 ), we use a wide range of stochastic profit scenarios to account for the heterogeneity of individual expectations (Grêt-Regamey et al. 2019 ). This implies that not all land managers would contribute equally to LULC changes, but mainly those who perceive the greatest potential for improving their profits (i.e. those who experience the largest dissatisfaction with their current profits). Individual land managers are clustered into groups to represent potential regional heterogeneity, and we assume that such groups arrange their land-use allocation to minimize their maximum dissatisfaction across multiple future profit expectations over time, considering their heterogeneous profit expectations. Individual land managers’ subjective economic expectations are represented as profit scenarios \(p\) , consisting of a profit vector for seven LULC types generated using Monte Carlo simulation (i.e. random draws from regionally simulated profit probability distributions).

where \({R}_{\dots . p}\) represents the random profits of a specific LULC type under profit scenario \(p\) in a given period, \(E\left({R}_{\dots .}\right)\) denotes the mathematical expectation value of the profit expressed in US$ per hectare, the variable \(u\) is a multiplier to obtain the upper interval limit ( \(u=1\) ), while \(sd\) represents the standard deviation of the profits, \({r}_{\#}\) a uniformly distributed random number ( \(0\le {r}_{\#}\le 1\) ), and \(l\) acts as second multiplier to obtain lower interval limit ( \(l=2)\) .

The standard deviations \(sd\) were derived empirically as root means square errors (RMSE) of regression trend lines, parameterized using FAO data on gross production values. The decisions made by landowner groups are satisficing because they do not aim to maximize profits under expected conditions but instead seek a satisfactory and sufficient land-use allocation even under worst-case conditions across multiple future expectations. Satisficing allocations are identified using a modified version of the robust optimization approach described by Knoke et al. ( 2020 ), adjusted to incorporate heterogeneous and stochastic profit expectations. Each land manager group’s objective function minimizes a measure we call dissatisfaction \({D}_{p}\) , defined as the difference between the highest potential profit \({R}_{p}^{*}\) ($ ha −1 ) under assumed best-case conditions in one specific profit scenario \(p\) , and the assumed actually achievable profit obtained through a given future LULC composition \({R}_{p}\) , being \({R}_{p*}\) the lowest individually assumed profit ($ ha −1 )

\({R}_{p}\) is obtained as follows,

with \({N}_{Ti}+{Df}_{Ti}+{P}_{Ti}+{C}_{Ti}+{H}_{Ti}+{S}_{Ti}+{O}_{Ti}=1\) (negative area proportions excluded)

where \({N}_{Ti}{R}_{Np}\) represents the future proportion of natural forest multiplied by the profits of natural forest in profit scenario \(p\) , subject to the condition \({0\le N}_{Ti}\le N\) , where \(N\) denotes the current proportion of natural forest. \({Df}_{Ti}{R}_{Dp}\) describes the future proportion of deforested areas (forest conversion to agriculture) multiplied by the profits of deforestation in profit scenario \(p\) ( \({0\le Df}_{Ti}\le N\) ). \({P}_{Ti}{R}_{Pp}\) is the future proportion of pasture multiplied by the profits of pasture in profit scenario \(p\) , \({C}_{Ti}{R}_{Cp}\) is the future proportion of cropland (annual crops) times profits of cropland in profit scenario \(p\) , \({H}_{Ti}{R}_{Hp}\) stands for the future proportion of permanent crops times profits of permanent crops in profit scenario \(p\) , \({S}_{Ti}{R}_{Sp}\) denotes the future proportion of forest plantations multiplied by the profits of forest plantations in profit scenario \(p\) , and \({O}_{Ti}{R}_{Op}\) signifies the future proportion of other land times the profits of other land (profits are assumed to be zero for other land-use types).

Because each land manager-member in a group is assigned several random profit scenarios, their dissatisfaction with a given land allocation also varies. The simulation-optimization model selects a target land-use portfolio that minimizes the maximum dissatisfaction \({D}_{p}\) for the group as a whole. The current land-use allocation is then incrementally adjusted over the course of a 30-year time horizon to approach the target portfolio. Agents consider allocations across seven LULC types, including natural (i.e. non-plantation) forest \({N}_{Ti}\) . Conversions away from natural forests \({N}_{Ti}\) are counted as forest loss \({L}_{i}\) . To obtain overall reference deforestation levels \({L}_{t}\) , we average \({L}_{i}\) across all 50 groups for each year of the simulation.

where \({L}_{i}\) represents the area of natural forest lost for land manager group \(i\) (ha year −1 ), \(A\) stands for the total land area of a country (ha), and \({N}_{Ti}\) signifies the desired future proportion of natural forest after \(T\) years, with the condition \({0\le N}_{Ti}\le N\) , being \(N\) the current proportion of natural forest (with \(0\le N\le 1\) ). \(k\) represents a factor that takes into account for the planning horizon ( \(k=1\div T\) ), and \(T\) is the planning horizon, assumed to be 30 years ( \(T=30\) ).

For each year of the study period, we simulated decisions by \(n=50\) land manager groups, each consisting of individual members, who are differentiated from one another by the profit scenarios they have been assigned.

After calibrating the model using historical FAO deforestation data for 1990–2000 (FAO Statistics 2022 ), we compared model predictions with empirical forest loss data for the study period to identify deviations between observed deforestation levels and the reference levels that would be expected to arise from microeconomic decision-making alone (see Knoke et al. 2023 and their supplementary information for more details).

As the calibration period coincided with a period of pronounced political and currency instability in Ecuador, our model was calibrated conservatively. Tables 1 and 3 provide the upper and lower ranges of profit expectations (numbers with standard deviation as units) and the coefficients of variation used for deforestation and natural forest areas that we obtained as a result of calibration.

Economic valuation using the social cost of carbon

In the final step, we analyzed deviations between observed and expected deforestation as additional or avoided carbon emissions, which we valued economically using published SCC estimates. Following Groom and Venmans ( 2023 ), we define SCC as the present value of the social cost of climate damages associated with an additional tonne of emitted CO 2 :

where \({D}_{t}\) is the temperature-dependent marginal damage in USD associated with an additional unit of CO 2 emitted and \(r\) is a constant discount rate. We adopted conservative \({SCC}_{t}\) estimates from the United States Government ( 2021 ) for \(r=0.03\) and assume \({D}_{t}\) to increase at a constant annual rate of \(0.0202\) (United States Government 2021 ); see Knoke et al. ( 2023 ) and their supplementary material for details. We calculate the present value of avoided emissions (Groom and Venmans 2023 ) over time horizon T as:

where \({E}_{t}\) represents the social benefit (cost) associated with reduced (excess) deforestation, and \({S}_{t}\) represents the aggregate avoided (additional) emissions, assumed to stabilize at the end of T :

We account for the climate benefits associated with delayed (rather than absolutely avoided) emissions by assuming a constant annual probability \(\rho\) (here, 1%) that carbon stocks will revert to their initial values after T :

Although the social value of avoided emissions accrues at the global level, the opportunity costs associated with retaining natural forest rather than converting it to more profitable (typically agricultural) LULC are typically incurred by individual landowners. However, these opportunity costs vary substantially over the study period due to market fluctuations. We calculate their cumulative present value as:

where \({S}_{t}^{\Delta L}\) is the cumulative change in the total forest area and \({O}_{t}\) represent the time-dependent land opportunity costs.

\({\Delta L}_{t}\) represents the deviation in observed deforestation relative to the simulated market-only counterfactual reference level. As with climate benefits, we consider non-permanence by accounting for a constant annual probability that opportunity costs will revert to the reference level after the study period, using the same parameter values as above:

Depending on the period considered, the land opportunity costs ranged between 137 and 153 US$ per hectare per year (Table 2 ).

Data sources

Data for observed forest losses were obtained from Global Forest Watch ( 2024 ) and FAOSTAT (FAO Statistics 2022 ). FAO-documented data were used to represent observed forest losses for the calibration of the counterfactual model (1990–2000), as remotely sensed forest losses were not available for this period. Profit data, used as input in the land-use allocation model, were derived from gross production values published in FAOSTAT and from previously published materials (Table 3 ).

Gross profits were calculated by dividing gross production values (constant 2014–2016 US$) by the area harvested for crops or area of LULC type (milk and meat). For forest LULC types, we used the timber volume harvested per hectare multiplied by the price derived from export value for naturally regenerating forests, or used modelled volumes (Knoke et al. 2014 ) multiplied by timber prices adopted from the Global Forest Products model for planted forests (Buongiorno 2003 ). Variation coefficients, \(vc\) , were adopted to obtain standard deviations of profits, \(sd.\) The variation coefficient was built on the RMSE of fitted trend lines, \(vc=rmse/\overline{R }\cdot\;100.\) The LULC category “Deforestation” had the same coefficients as “Permanent meadows & pastures” but a different \(vc\) (Table 3 ). Following Knoke et al. ( 2023 , supplementary material) we assumed that the upfront profits from clearing the natural forest were high enough to finance the new establishment of new pasture.

Simulated counterfactual deforestation rate

The overall average annual deforestation rate for 1990–2022 predicted by the counterfactual market-oriented land-use allocation model was 0.47%, with a maximum of 0.58% p.a. in the period 2005–2009 (a period with very high crop prices) and a minimum of 0.35% p.a. in the periods 2010–2014 and 2015–2022 (a period of declining crop prices). In this section, we use random 5-year periods to convey overall overview of deforestation (Table 3 ).

The frequency distribution of simulated counterfactual deforestation rates followed a negative exponential distribution (Fig. 2 ) with a median deforestation rate of 0.32% p.a. The average simulated forest loss without the influence of non-market factors is 605 km 2 per annum in the period 1990–2022, including the calibration period (1990–2000). For 2001–2022, we obtained an expected deforestation reference level of 545 km 2 p.a., while the observed deforestation was 434 km 2 p.a.

Frequency distribution of simulated deforestation rates by l class . l class is the midpoint of the loss class, shown on the x -axis. The power law behind the distribution of the deforestation rates is described by a trendline with the following coefficients: N = 2090.1 exp (− 2.288 l class ). N is the frequency of the deforestation rates per loss class

Deforestation trends

We calibrate our non-spatial model conservatively to stablish reference deforestation levels slightly below those reported by the FAO (Fig. 3 ). These reference levels capture natural forest losses that would have been expected to occur in the absence of external factors like policy interventions.

Observed and reference forest cover losses. Observed forest losses refer to tree cover losses (Global Forest Watch 2024 ). Forest losses from the Food and Agriculture Organization of the United Nations (FAO Statistics 2022 ) are compared and were used for calibration (1990–1999) in the absence of remotely sensed data. Light orange shadow represents the calibration period and light blue represents the assessed period. Dashed lines mean the beginning of dollarization (blue) and Socio Bosque program (grey)

The results show large negative deviations between observed and expected counterfactual deforestation for 2001–2009 (Fig. 3 ). This period overlaps with canton-level “reforms of natural resource management” (1997–2008) and starts 1 year after the currency change (“dollarization” in 2000). Our counterfactual market-only scenario thus predicts a trend change of forest losses from 2000 to 2009 in which much less deforestation actually occurred than expected. From 2005 to 2009 in particular, high crop prices led our counterfactual model to predict accelerating deforestation, a trend that did not materialize on the ground. When crop prices declined from 2010 onwards, the observed and expected deforestation do not show systematic deviations, suggesting a period with market-oriented deforestation.

Social value of the deforestation trend

The estimated cumulative avoided emissions from 2000 to 2010 are + 0.114 gigatonnes CO 2 (Fig. 4 a). To calculate the economic value of these avoided emissions, we use conservative SCC ranging from 30.1 (2000) to 44.1 US$ per Mg CO 2 emission (2019) (in 2015 US$ per tonne of CO 2 , suggested by United States Government 2016 ), following the calculations reported in Knoke et al. ( 2023 ). This assessment is based on below-baseline deforestation rates from 2001 to 2009, since observed deforestation closely paralleled the counterfactual baseline from 2010 until the end of the study period.

Social values of deforestation in a given year (2000–2009). a Estimated changes in CO 2eq emissions from observed minus counterfactual forest losses. b Associated impact on the social value

The social value of the climate benefits associated with such reduced CO 2 emissions was estimated at $5.7 billion (assuming the achieved emission reductions are permanent, Fig. 4 b) or $3.1 billion (assuming a risk of 1% p.a. of losing the achieved emission reductions after 2022). The foregone agricultural benefits incurred by conserving tropical forests to achieve the climate benefits were estimated at $1.4 billion. Based on these evaluations, we obtain a rough benefit-cost ratio of 2.2.

The comparison of observed deforestation with our simulated market-forces-only baseline shows a substantial conservation of Ecuadorian natural forests of a size of 2872 km 2 between 2001 and 2009. According to our model, if deforestation decisions had been purely market-driven in this period, this forest cover would have been cleared. This is a remarkable result, considering that this period followed one of Ecuador’s major crises in the late 1990s, during which the economy contracted and inflation surged by approximately 60% (Jokisch and Pribilsky 2002 ). Food prices in Ecuador increased substantially from 2000 to 2008 (Gilbert 2010 ), stimulating demand for agricultural land (Harding et al. 2021 ). During this period, our counterfactual simulations predicted accelerating deforestation in response to profit expectations alone, but this expectation was largely unrealized.

One interpretation might hold that the natural resource management reforms pursued by many Ecuadorian cantons between 1997 and 2008, potentially supported by higher currency stability as a consequence of the “dollarization” in 2000, may have been more effective than previously thought (Kauffman and Terry 2016 ).

Conversely, however, some evidence suggests that the dollarization of the national currency at a controversial rate of 25,000 sucres per dollar likely exacerbated already-high emigration rates, which ultimately led to approximately 4% of the population leaving Ecuador (Acosta et al. 2014 ). The period 2000–2004 was particularly difficult for the agricultural sector, as the shuttering of various financial institutions in the years leading up to dollarization created a credit crunch (Chuncho Juca et al. 2021 ). Thus, an alternative explanation might be that the unexpectedly low deforestation rate between 2001 and 2009 was less the result of intentional policy choices or natural resource management reforms than it was a byproduct of inter-connected social, financial, and demographic crises contributing to farm abandonment, rural outmigration, and constraints on the labour resources needed to carry out deforestation.

In January 2007, a new government promising to support conservation came to power (Acosta et al. 2014 ). Its initiatives included, inter alia , the recognition of the rights of nature (Martínez 2021 ), conservation incentives like the Socio Bosque program, and compensation programs for reduced oil exploration and exploitation (Moreano Venegas and Bayón, 2021 ). Insofar as the 2008 constitution presented a groundbreaking legal framework, its implementation has faced persistent challenges from powerful business interests and weak law enforcement (Kauffman and Martin 2017 ). Thus, the effectiveness of Ecuador’s post-2008 environmental policy portfolio is generally viewed as inconsistent.

The Socio Bosque program was launched in 2008 with the aim of conserving forests and native grasslands and improving the livelihoods of rural populations (de Koning et al. 2011 ). Even though the compensation paid to beneficiaries typically falls short of the estimated land opportunity cost, from 2008 to 2017, the Socio Bosque program placed 16,700 km 2 of forest, mangroves, and páramo under protection—more than any other program in the country (Ministerio del Ambiente de Ecuador 2019 ). For comparison, the National Park System ( Sistema Nacional de Areas Protegidas , SNAP) included just 6320 km 2 in the same period, while an additional 3800 km 2 were classified under the Protective Forests and Vegetation ( Bosques y Vegetación Protectores , BVP) category (Ministerio del Ambiente de Ecuador 2019 ). Thus, the Socio Bosque program was expected to strongly decrease deforestation.

Our analysis is at least initially consistent with that expectation. Due to increasing crop revenues, our counterfactual market-forces-only baseline predicts much higher deforestation levels than are observed from 2003 to 2008, a period of expanding natural resource management reform (Fig. 3 ). The divergence between expected and observed deforestation is especially prominent from 2008 to 2010, the first 2 years of the Socio Bosque program, with Ecuador losing 1000 km 2 less natural forest than expected. However, one can argue that this deforestation trend also appears to be partly influenced by the collapse of palm oil prices during 2008 and 2009, which brought serious problems to small producers in Ecuador (Potter 2011 ).

Unfortunately, this reduction in deforestation does not persist: observed and expected deforestation reconverge in 2010 and generally remain in agreement until the end of the study period, apart from two notable deforestation spikes in 2012 and 2017. This post-2010 reconvergence is driven mainly by a sharp decrease in the level of expected deforestation due to declining palm oil and banana revenues, rather than a sustained increase in observed deforestation (Fig. 3 ). Falling palm oil prices at this time placed significant economic pressure on small producers (Potter 2011 ; Castellanos-Navarrete et al. 2021 ), making the prospect of clearing forest for oil palm plantations substantially less attractive (Vijay et al. 2016 ) and reducing the simulated deforestation rate.

Thus, while the Socio Bosque program likely plays an important role in shaping forest cover in Ecuador, national-scale deforestation trends are unavoidably shaped by a confluence of factors whose interacting effects are difficult to disentangle. Our method only enables the identification of aggregate effects which must be critically analyzed alongside supporting evidence concerning to social, economic, and policy developments. The launch of the Socio Bosque program, for instance, coincided with the early stages of an important economic turnaround in Ecuador (The World Bank 2023 ), facilitated in part by a second oil boom (Cueva and Díaz 2022 ), as well as with a series of biofuel initiatives that increased the extent of palm oil plantations across a number of Latin American countries (Furumo and Aide 2017 ). Dynamics of this nature tend to increase the level of deforestation that would be expected in a market-forces-only scenario.

What about the anomalous spikes in observed deforestation that occurred in 2012 and 2017? The first of these spikes roughly coincides with the establishment of two new large-scale hydroelectric projects, the Coca Codo Sinclair Dam and the Sopladora Hydroelectric Power Plant, in 2010 and 2011, respectively (Vallejo et al. 2019 ). The construction of such megastructures typically exacerbates deforestation both directly (i.e. forest displaced by dams, reservoirs, roads, and transmission lines) and indirectly by stimulating activities associated with deforestation while increasing accessibility to remote forest areas (Marques Da Silva et al. 2018 ). Preliminary evidence suggests that Ecuador is unlikely to present a major exception to these well-documented patterns (Finer and Jenkins 2012 ; Vallejo et al. 2019 ; Llerena-Montoya et al. 2021 ).

The 2017 spike, in turn, coincides with the completion of both hydroelectric projects, widespread mortality in Amazon forests associated with a particularly strong El Niño event in 2015–2016 (Berenguer et al. 2021 ), and a legislative effort to loosen regulations regarding mining concessions in protected forests in particular (Roy et al. 2018 ). This deregulation effort directly contributed to a fourfold increase in the area subject to mining exploration (Vandegrift et al. 2018 ). The land category most affected by this regulatory change ( Bosques y Vegetación Protectores , BVP) was first codified the 1980s, and thus was in place for the entirety of our calibration period.

Other relevant factors implemented by the Ecuadorian government during the study period that may have influenced deforestation trends include:

Water funds

Three funds aiming to protect watersheds through integrated water management, FONAG, FONAPA, and FORAGUA, were inaugurated in 2000, 2008, and 2009, respectively. These projects coincided with the period of canton-level natural resource management reform (Kauffman and Terry 2016 ). As of 2019, they incorporated 4614 km 2 of natural forests and private, communal, or acquired lands designated for restoration (Earth Innovation Institute 2019 ).

Command and control policies

The most significant command-and-control anti-deforestation initiative in recent decades was probably the expansion of national park area under the National System of Protected Areas (Utreras et al. 2017 ). Over the course of study period, 33 protected areas were established, covering 8560 km 2 , with most protected areas located in the Amazon region (Ministerio del Ambiente, Agua y Transición Ecológica 2021 ).

Land tenure

Our study period also encompassed the implementation of two land tenure policies. First, the Land Plan (2009–2013) aimed to foster cooperative approach to land management. Second, the Land Adjudication and Mass Legalization Project (2014–2018) focused on acquiring, redistributing, and legalizing state, private, and vacant properties, with a particular emphasis on agricultural productivity (potentially exacerbating deforestation, see Tanner and Ratzke 2022 ).

Overall, our results show that even temporarily effective forest conversation has high social value in terms of avoiding climate-driven damages. The additional forest conservation achieved between 2001 and 2010, worth approximately $3.1 billion, easily outweighs the estimated land opportunity cost of $1.4 billion associated with conservation. The economic benefits of the additional carbon storage alone thus suggest a benefit-to-cost ratio of 2.1. Crucially, the social value of tropical forest is substantially higher than its carbon storage value (e.g. Franklin and Pindyck 2018 ).

Limitations and strengths

Assessing the reliability of our results means evaluating the plausibility of our simulated baseline scenario, the accuracy of observed deforestation data, and assumptions about the future status of newly harvested forest parcels. Because the baseline scenario is designed to capture something that is empirically unobservable—what would have happened under different circumstances—it is difficult to conclusively validate using traditional means, such as experimental controls. Instead, we demonstrate plausibility mainly by showing that the model is able to capture deforestation trends effectively by the model implementation under distinct social conditions and deforestation scales (Fig. 5 ). Although asserting that deforestation in the calibration period was shaped by market forces alone would be a step too far, the overall correlation between observed and simulated deforestation is rather high. The response of deforestation simulations to changes in crop prices (particularly banana and palm oil) is not only facially plausible but also accord well with past empirical work (Taheripour et al. 2019 ; Gaveau et al. 2022 ).

Behaviour of our counterfactual model implemented in countries under different social and political conditions, and thus deforestation scales (data for Brazil, DR Congo, and Indonesia from Knoke et al. 2023 )

Regarding the data used to capture observed deforestation, there was a technical change in the accuracy of the forest loss estimates from Global Forest Watch after 2015 (Weisse and Potapov 2021 ). However, we do not identify a systematic shift of observed deforestation levels when comparing the periods 2010–2014 and 2015–2022 that could systematically bias our results (Fig. 3 ). We note that we used forest loss data specifically, which includes canopy removals that are not necessarily permanent and which might be expected to regenerate. Empirically, however, most recent deforestation in Ecuador is for agriculture, which is rarely followed by a land-use change back to forest (Feng et al. 2022 ). In sum, we expect that our market-oriented reference scenario is plausible and that the observed deforestation levels are valid (because excluding non-market factors rather too high than too low).

Conclusions

Using counterfactual land-use modelling, we estimate the aggregate effect of non-market factors like policy reform and conservation initiatives on national-scale deforestation trajectories in Ecuador. During a study period that encompassed large political, economic, and social change—including the ratification of a constitution granting rights to nature and the introduction of the largest payments for ecosystem services program in Ecuador’s history—we identify substantial reductions in deforestation relative to a market-forces-only counterfactual. This suppressed deforestation appears to have spared some 2872 km 2 of forest—an area roughly equivalent to the Ecuadorian provinces of Santa Elena or Carchi—between 2000 and 2010. Depending on assumptions about the permanence of emissions reductions, this corresponds to a delayed or avoided social cost of carbon in the range of $3.1–$5.7 billion, easily outweighing the value of foregone agricultural production.

While our method only allows for the estimation of aggregate trends and not causal attributions to specific events or policy initiatives, it is plausible that policies such as the Socio Bosque program, which placed more surface area under protection than any other program in Ecuador, likely contributed to decreasing deforestation at least temporarily, even relative to a dynamic baseline. Moving forward, efforts to develop and refine mechanisms that reward land managers for their role in sustaining the high social value that tropical natural forests contribute to global society need further support. In conjunction with efforts to quantify the social value of the ecosystem services provided by tropical forests, research and policy should continue to develop strategies to foster the integration of local stakeholders into tropical land-use policy and decision-making.

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Calvas, B., Castro, L.M., Kindu, M. et al. Large differences between observed and expected Ecuadorian deforestation from 2001 to 2009: a counterfactual simulation approach. Reg Environ Change 24 , 94 (2024). https://doi.org/10.1007/s10113-024-02253-0

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How Does Deforestation Affect the Environment?

Forests, a vital component of life on Earth, cover approximately 31% of our planet’s land area . However, more than 75% of the Earth’s surface has been modified and degraded by human activities such as deforestation. Destroying forests alters weather patterns, destroys habitats, and negatively affects rural communities, leading to food insecurity and causing irreversible damage to entire ecosystems. So how does deforestation affect the environment and what threats does it pose to living species?

To answer the question of how deforestation affects the environment, it is important to look at why humans need forests in the first place. Deforestation is the purposeful cleaning of forest land for other uses. Among the main reasons for this damaging practice are agricultural expansion and cattle breeding as well as to obtain raw materials such as palm oil, a key ingredient in cosmetics and food products widely used around the world, and timber used for fuel, manufacturing, and infrastructure development. Studies show that 15,3 billion trees are chopped down every year and over the past 12,000 years, nearly 50% of the world’s trees have been purposefully cleared by humans. This practice threatens our environment, from altering the climate and various ecosystems to compromising the existence of millions of humans and animals.

You might also like: 10 Deforestation Facts You Should Know About

1. Effects on Climate Change

The scientific consensus on deforestation is that it intensifies climate change at a dramatic rate. The Global Forest Watch made it clear: protecting tropical rainforests is essential for achieving the climate goals of the Paris Agreement. Trees are known for their capacity to absorb carbon dioxide through photosynthesis. Healthy forests act as extremely valuable carbon sinks, with the Amazon rainforest being one of the world’s most important ones. However, deforestation is turning these sinks into huge net emitters , something that can have huge implications for slowing the pace of climate change and contributing to a steep rise in global temperatures. The current rate of rainforest-loss generated emissions is nearly 25% higher than those generated in the European Union and just slightly below US levels. Deforestation also increases the risk of uncontrollable wildfires because of humans burning vegetation. This, in turn, contributes to destroying forests, intensifying deforestation even more.

2. Effects on Soil Pollution and the Water Cycle

In addition to their role as carbon sinks, forests are a crucial component of the water cycle and have the all important function of preventing desertification. Cutting down trees can disrupt the cycle by decreasing precipitation and affecting river flow and water volume. In the case of the Amazon rainforest, research shows that at least 80% of its trees would be needed in order to keep the hydrological cycle going. With nearly 17% of the forest lost already, the Amazon is currently at its tipping point . Statistics show that deforestation in the tropics reduces precipitation over the Amazon by around 10% , or 138 millimeter, every year. In the South Asian Monsoon region, the reduction in rainfall is even higher, with around 18% less rain recorded in India in a single year.

Aside from their contribution to the water flow, trees help the land retain water and sustain forest life by supplying the soil with rich nutrients. Deforestation deprives the land of its cover, leaving the soil exposed to wind and rain. This makes soil vulnerable to being washed away, and prone to erosion. According to the World Wildlife Fund (WWF), as much as half of the world’s topsoil has been lost as a consequence of the nearly 4 million square miles of forest that have been lost since the beginning of the 20th century.

3. The Effects on Humans 

In answering the question of how does deforestation affect the environment, you may discover that in fact, it also has a direct impact on the human population. With the loss of trees and entire forests, homelands are also being destroyed in the process. Indigenous communities who live in forests and depend on them to sustain their life bear the brunt of impacts from deforestation. As their houses are destroyed and resources compromised, these tribes are forced to migrate elsewhere and find other ways to sustain themselves. The Amazon rainforest is home to over one million Indigenous people , mostly of Indian descent, divided into more than 400 indigenous tribes. They live in settled villages by the rivers, and grow and hunt their food. These “uncontacted” tribes live by the rules of nature but are becoming increasingly vulnerable to deforestation, which has forced many of them to migrate. While some of them move into areas occupied by other tribes, straining the land’s resources, others are forced to relocate to urban settings and completely change their way of living.

4. The Effects on Animals and Plants

Along with Indigenous tribes, animals are some of the biggest victims of deforestation. Forests around the world are home to more than 80% of all terrestrial animal, plant, and insect species . However, the rapid destruction of forests is contributing to a decline in biodiversity never seen before. The main effect of deforestation on animals and plants is the loss of their habitat. Many factors related to cutting down trees contribute to driving species to extinction. Through land erosion, the soil is depleted of its nutrients, a huge source of nourishment for animals and plants. Furthermore, many animal species are heavily reliant on specific plants and their fruits for food sources. When these resources are lost, animals become weaker, more vulnerable to diseases and often succumb to starvation. Another important role of trees is to regulate the temperature of forests and maintain it constant. When deforestation occurs, temperature variates more drastically from day to night and this extreme change can often prove fatal for many animal species.  

5. The Effects on Food Security

One last major effect of deforestation is its impact on food security through the loss of biodiversity. While food availability for Indigenous tribes and animals that live in forests is reduced in the process of deforestation, its effects on weather patterns and soil degradation also drastically decrease agricultural productivity. Populations located in the proximity of tropical forests are mostly impacted by the worsening trend. Indeed, millions of people living in these areas depend almost entirely on agriculture and are thus extremely vulnerable to the impact of deforestation on food security, struggling to grow enough food and prevent crops from damage. It has been shown that the deforestation of the Amazon contributes to a decline in pasture productivity of about 39% as well as a drop of soy yields of nearly 25% in over half of the Amazon region and of a staggering 60% in a third of the area.

You might also like: 12 Major Companies Responsible for Deforestation

Can We Halt Deforestation?

Knowing how deforestation affect the environment more than one way and its catastrophic effects on the planet, it is crucial that people around the world take action to mitigate its impact. This can be done on an individual level, for example by reducing meat consumption, going paperless and recycling products as much as possible, opting for natural products that do not contain ingredients such as palm oil and supporting organisations and sustainable companies that are committed to reducing this dangerous practice. On a governmental level, the consequences of deforestation can be mitigated by introducing policies that protect natural forests and regulate mining and logging operations as well as other operations that require the destruction of tree plantations.

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After historic floods, Brazil braces for severe drought

After torrential rains that sparked historic flooding in southern Brazil, the country expects a swing to severe drought in parts, the environment minister said Wednesday.

Brazil has been battered by a series of extreme weather events, most recently once-in-a-century flooding in the state of Rio Grande do Sul that left 172 people dead.

Environmental Minister Marina Silva said the flooding was due to a mash-up of natural phenomena such as El Niño and climate change, a double-whammy also seen elsewhere.

"We have the same thing happening in the Pantanal, the Amazon," she said, referring to climate extremes in one of the world's largest tropical wetlands, and its largest rainforest.

She said the northeastern Caatinga—a unique and biodiverse semi-arid biome—was "already experiencing moments of severe drought, and in the case of Rio Grande do Sul we are going to have severe drought ."

Silva, who was speaking at an event with President Luiz Inacio Lula da Silva, warned of more fires after record blazes in the first few months of the year.

A rapid study done by global scientists after the flooding in Rio Grande do Sul determined that climate change made the event twice as likely, with El Niño playing as big a role in the catastrophe.

El Niño, which alters rainfall patterns around the world, making parts more susceptible to torrential showers or drought, is currently weakening.

After a brief neutral period, La Niña—which can lead to drought conditions in parts of Latin America—is expected to return.

A renowned activist in her field, Silva returned to head the environment ministry and oversee Brazil's climate change policies when Lula returned to power in January 2023.

She had good news for Brazil's Cerrado region, a vast tropical savanna renowned for its rich biodiversity where deforestation soared 43 percent in 2023 while it halved in the Amazon.

Silva said that between January and May, deforestation had reduced 12.9 percent in the Cerrado but "it is too early to say that this is a lasting inflection in the curve."

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Living World - Amazon Case Study

The Amazon is the largest tropical rainforest on Earth. It sits within the Amazon River basin, covers some 40% of the South American continent and as you can see on the map below includes parts of eight South American countries: Brazil, Bolivia, Peru, Ecuador, Colombia, Venezuela, Guyana, and Suriname. The actual word “Amazon” comes from river.

Amazing Amazon facts; • It is home to 1000 species of bird and 60,000 species of plants • 10 million species of insects live in the Amazon • It is home to 20 million people, who use the wood, cut down trees for farms and for cattle. • It covers 2.1 million square miles of land • The Amazon is home to almost 20% of species on Earth • The UK and Ireland would fit into the Amazon 17 times!

The Amazon caught the public’s attention in the 1980s when a series of shocking news reports said that an area of rainforest the size of Belgium was being cut down and subsequently burnt every year. This deforestation has continued to the present day according to the Sao Paulo Space Research Centre. In 2005 they had lost 17% of Amazon rainforest or 650000 square kilometres. Their satellite data is also showing increased deforestation in parts of the Amazon. The process of deforestation The Amazon helps a Newly Emerging Economy(NEE), Brazil, to make money. They build roads into the forest, logging firms then go in and take out valuable hard woods such as mahogany and cedar, worth thousands of pounds in richer economies like Europe. Then farmers, often cattle ranchers from big companies, burn the rest to make way for cattle pasture. 75% of cleared areas are used in this way. This is clearly shown on the map on figure 22 in red. Many of the deforested areas follow roads and branch off from there.  Deforestation is also worse in the South and South East of the Amazon basin, closer to major centres of population in Brazil.

© WWF   Source  Used with permission.

The causes of deforestation 1. Subsistence and commercial farming – subsistence farming is where poor farmers occupy plots of the forest to grow food to feed themselves and their families. They clear forest and then burn it, hence the name slash and burn.  They grow crops until the soil is exhausted and then move on.  This contributes to deforestation but not as much as commercial farming (Farming to sell produce for a profit to retailers or food processing companies). The Brazilian region of Mato Grosso was affected by deforestation in the 1980s and 1990s. 43% of rainforest losses were in this region, and area almost ½ the size of France. It has been replaced by fields for grain and cattle. This has allowed Brazil to overtake Australia as the largest exporter of beef in the world. The land is also flat and easy to farm. It also has high temperatures and lots of rainfall.

2. Logging – This involves cutting down trees for sale as timber or pulp.  The timber is used to build homes, furniture, etc. and the pulp is used to make paper and paper products.  Logging can be either selective or clear cutting. Selective logging is selective because loggers choose only wood that is highly valued, such as mahogany. Clear-cutting is not selective.  Loggers are interested in all types of wood and therefore cut all of the trees down, thus clearing the forest, hence the name- clear-cutting.

3. Road building – trees are also clear for roads.  Roads are an essential way for the Brazilian government to allow development of the Amazon rainforest.  However, unless they are paved many of the roads are unusable during the wettest periods of the year.  The Trans Amazonian Highway has already opened up large parts of the forest and now a new road is going to be paved, the BR163 is a road that runs 1700km from Cuiaba to Santarem. The government planned to tarmac it making it a superhighway. This would make the untouched forest along the route more accessible and under threat from development.

4. Mineral extraction – forests are also cleared to make way for huge mines. The Brazilian part of the Amazon has mines that extract iron, manganese, nickel, tin, bauxite, beryllium, copper, lead, tungsten, zinc and gold! 

The Belo Monte dam site under construction, copyright  Used with the kind permission of Phil Clarke-Hill  - His website is amazing, click here to see it.

5. Energy development – This has focussed mainly on using Hydro Electric Power, and there are 150 new dams planned for the Amazon alone.  The dams create electricity as water is passed through huge pipes within them, where it turns a turbine which helps to generate the electricity.  The power in the Amazon is often used for mining.  Dams displace many people and the reservoirs they create flood large area of land, which would previously have been forest.  They also alter the hydrological cycle and trap huge quantities of sediment behind them. The huge Belo Monte dam started operating in April 2016 and will generate over 11,000 Mw of power.  A new scheme the 8,000-megawatt São Luiz do Tapajós dam has been held up because of the concerns over the impacts on the local Munduruku people.

6. Settlement & population growth – populations are growing within the Amazon forest and along with them settlements.  Many people are migrating to the forest looking for work associated with the natural wealth of this environment. Settlements like Parauapebas, an iron ore mining town, have grown rapidly, destroying forest and replacing it with a swath of shanty towns. The population has grown from 154,000 in 2010 to 220,000 in 2012. The Brazilian Amazon’s population grew by a massive 23% between 2000 and 2010, 11% above the national average.

Impacts of deforestation – economic development, soil erosion, contribution to climate change. • Every time forest is cleared species are lost – so we lose BIODIVERSITY • Climate Change - Burning the forest releases greenhouse gasses like CO2.  This contributes to the warming of our planet via climate change and global warming.  In addition, the loss of trees prevents CO2 being absorbed, making the problem worse. The Amazon also helps to drive the global atmospheric system. There is a lot of rainfall there and changes to the Amazon could disrupt the global system. • Economic development – Brazil has used the forests as a way to develop their country.  The forest has many natural riches that can be exploited.  In addition, Brazil has huge foreign debt and lots of poor people to feed, so they want to develop the forest. May Brazilians see deforestation as a way to help develop their country and improve people’s standard of living. • Soil erosion - the soils of the Amazon forest are not fertile and are quickly exhausted once the forest is cleared. The farmers now artificially fertilise the soil when in the past the nutrient cycle would have done this naturally.  In addition, the lack of forest cover means that soils are exposed to the rainfall.  This washes huge amounts of soil into rivers in the process of soil erosion.

NEXT TOPIC - Living World - Sustainable Forest Management

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