Large-scale Deforestation Research Articles

Regeneration of secondary forest following anthropogenic disturbance from 1985 to 2021 for Amazonas, Brazil.

Following old-growth forest loss and subsequent land abandonment, secondary forest grows throughout the Amazon biome. For Amazonas, agricultural colonization was unsuccessful in many regions, leading to the regeneration of secondary forest and carbon storage under favorable climate conditions. Herein, the extent of regeneration in Amazonas and its timescale are investigated, including a granular analysis of its 62 municipalities, based on the MapBiomas dataset from 1985 to 2021. By 2021, 10,495 km2 of secondary forest had grown, corresponding to 28% of the lost old-growth forest. After normalization for algorithmic differences, this estimate was 17%-38% lower than prior studies for Amazonas that used earlier versions of the MapBiomas dataset, indicating increased accuracy in landcover assignments for more current versions of the dataset. For the northeastern microregion, representing the 15 municipalities of economic and population dominance in Amazonas, the growth of secondary forest varied from 3.0% to 9.8% of the total land area. For the southern microregion, constituting seven municipalities adjacent to large-scale deforestation of Mato Grosso and Rondônia, regeneration of secondary forest constituted 0.4%-1.2% of the land area. For the remaining interior municipalities, the regeneration was 0.0%-1.9%. Among the municipalities, the median regeneration interval, corresponding to the duration between the loss of old-growth forest and the appearance of secondary forest, ranged from 2 to 7 years. The median regeneration intervals of the interior, northeastern, and southern microregions were 3, 4, and 5 years, respectively. Even as the secular trend of deforestation continues in the Amazon biome and encroaches into the southern border of Amazonas state, the results herein indicate a possible resiliency toward secondary forest for undisturbed land on a timescale of several years, at least for mixed pasture-forest landscapes of kilometer-scale heterogeneity and assuming that a favorable climate persists for regeneration even as global change occurs.

Global and regional hydrological impacts of global forest expansion

Abstract. Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes.

Decreased cloud cover partially offsets the cooling effects of surface albedo change due to deforestation

Biophysical processes of forests affect climate through the regulation of surface water and heat fluxes, which leads to further effects through the adjustment of clouds and water cycles. These indirect biophysical effects of forests on clouds and their radiative forcing are poorly understood but highly relevant in the context of large-scale deforestation or afforestation, respectively. Here, we provide evidence for local decreases in global low-level clouds and tropical high-level clouds from deforestation through both idealized deforestation simulations with climate models and from observations-driven reanalysis using space-for-time substitution. The decreased cloud cover can be explained by alterations in surface turbulent heat flux, which diminishes uplift and moisture to varying extents. Deforestation-induced reduction in cloud cover warms the climate, partially counteracting the cooling effects of increased surface albedo. The findings from idealized deforestation experiments and space-for-time substitution exhibit disparities, with global average offsets of, respectively, approximately 44% and 26%, suggesting the necessity for further constraints.

Deforestation-induced runoff changes dominated by forest-climate feedbacks.

Large-scale deforestation alters water availability through its direct effect on runoff generation and indirect effect through forest-climate feedbacks. However, these direct and indirect effects and their spatial variations are difficult to separate and poorly understood. Here, we develop an attribution framework that combines the Budyko theory and deforestation experiments with climate models, showing that widespread runoff reductions caused by the indirect effect of forest-climate feedbacks can largely offset the direct effect of reduced forest cover on runoff increases. The indirect effect dominates the hydrological responses to deforestation over 63% of deforested areas worldwide. This indirect effect arises from deforestation-induced reductions in precipitation and potential evapotranspiration, which decrease and increase runoff, respectively, leading to complex patterns of runoff responses. Our findings underscore the importance of forest-climate feedbacks for improved understanding and prediction of climate and hydrological changes caused by deforestation, with profound implications for sustainable management of forests and water resources.

Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India

The Khangchendzonga Biosphere Reserve (KBR) is located in the Eastern Himalayas and serves as a critical habitat for endemic species of flora and fauna, as well as playing a key role in carbon sequestration. The primary aim of this study was to map large-scale deforestation susceptibility zones in the mountainous region of KBR. The study area was divided into three zones: (a) Transition Zone, (b) Core Zone, and (c) Buffer Zone. This study utilized multiple remote sensing datasets acquired through the Google Earth Engine (GEE) platform, including precipitation, temperature, elevation, forest density, distance from rivers, NDVI, NDSI, distance from settlements, settlement density, distance from roads, and land use and land cover data. Additionally, the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods were employed to map deforestation susceptibility. To validate the proposed deforestation susceptibility, Hansen Global Forest Change (HGFC) data from 2001 to 2022 were used. Moreover, deforestation susceptibility was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metrics. Notably, our findings revealed significant declines in tree cover of 1.60%, 1.27%, and 0.89% in the Transition, Core, and Buffer Zones, respectively, during critical years (2009, 2011, 2019, 2020). These periods witnessed substantial deforestation, indicating a deteriorating condition of the reserve's forest cover. Although there were minor discrepancies in the results of the two methods, both highlighted the particular vulnerability of the transition zones in the eastern and southern regions of KBR. The comprehensive methodology employed in this research establishes an advanced spatial data infrastructure that is indispensable for immediate conservation planning and adaptive management strategies. The insights gleaned from this investigation hold substantial promise for guiding future restoration and conservation efforts aimed at enriching biodiversity and fortifying ecosystem services in this critical area.

Birhor Society and Culture: A Study on Particularly Vulnerable Tribal Groups of Jharkhand, India

A significant portion of the Indian population consists of tribal communities. The world's highest concentration of tribal people with diverse cultures is found exclusively in India. Tribal people are those who inhabit a specific region, have uniform cultural characteristics, and use basic technology. Tribes are recognized for their unique ethnic and cultural characteristics, but they are losing their unique identities as a result of massive and rapid urbanization and industrialization. Since the beginning of ethnographic research on Indian tribes, scientists have been interested in the unique livelihood practices of the Birhor, a particularly vulnerable tribal group (primitive tribal group) in Jharkhand. They have historically lived in forests and had a nomadic hunting and gathering lifestyle. Large-scale deforestation brought about by urbanization and industrialization has an impact on the entire culture and civilization. Their language and unique socio-cultural structure are undergoing rapid changes as a result of their increased exposure to modern culture through different government initiatives for tribal development, the modern educational system, etc. It is therefore necessary to investigate the evolving sociocultural patterns of the vulnerable "Birhor" tribe and to put appropriate strategies and programmes in place for the self-initiated preservation of indigenous tribal culture and identity. Keywords: Oral tradition, Social life, Economic life, Birhor, Jharkhand.

Employ Mathematical Modeling to Summarize, Analyze, And Predict the Relationship Between Carbon Dioxide and Temperature, Location, And Other Factors.

Fluctuations in the amount of greenhouse gases (GHGs) are affected by a variety of factors, both natural and artificial. Natural factors, such as volcanic eruptions and changes in the intensity of solar radiation, can cause fluctuations in greenhouse gases. However, in recent years, human activities have surpassed natural factors and have become a major driver of climate change. During the industrial revolution, the widespread use of fossil fuels, large-scale deforestation and emissions from factories caused irreversible damage to ecosystems. Rising global temperatures are destroying natural ecosystems around the world and are a potential threat to human survival. In this paper, we systematically examine and analyze the main sources of greenhouse gas emissions, explore the factors contributing to air pollution, and evaluate the specific impacts of these changes on global warming. We analyze the relationship between global warming and different factors (economic development, clean energy, and CO2 emissions) by using both one-way linear regression models and multiple linear regression models. A quadratic linear regression model shows that solar energy is the most promising renewable energy source. Meanwhile, using the global GDP as a criterion, we have shown that economic growth contributes to CO2 emissions. However, due to the governments having valued economic development more, the upward growth trend of the economic development curve is intensified, while the downward growth trend of the global average temperature is slowed down. Finally, we developed a multivariate linear regression model to evaluate whether using new energy sources could help mitigate global warming. Based on the previous analysis, we focus on the factor of CO2 emission. By using the linear regression model, we obtained the projected values of CO2 emissions, clean energy usage and global GDP in 2050, and the CO2 emissions in 2050 are 36.59439 billion tons, which meet the goal of carbon neutrality.

Do remnant forest patches provide microclimate buffering? A case study from Sri Lanka

Tropical forests have declined in extent considerably owing to large scale deforestation for providing land for multiple uses, and this has reduced their collective capacity to provide many important ecological functions. In the present study, conducted in the tropical island of Sri Lanka, we focus on remnant patches of two common and co-occurring vegetation types in the dry zone, Dry Mixed Evergreen Forest and Scrub Forest, to test (a) whether small remnant forest patches facilitate microclimate buffering and, if so, (b) whether the two forest types differ in terms of their potential to buffer the microclimate, and (c) whether the extent of canopy cover influences the microclimate buffering capacity. Co-occurring Grasslands were used as reference habitats. We used multiple temperature, relative humidity and vapour pressure deficit related metrics, and showed that Dry mixed Evergreen Forest and Scrub Forest had significant microclimate buffering potential, the latter to a lesser extent than the former (Dry mixed Evergreen Forest > Scrub Forest). These results are noteworthy, given that the patches of both forest types were relatively small (mean ± standard deviation: Dry Mixed Evergreen Forest = 0.63 ± 0.13 km2 and Scrub Forest = 0.10 ± 0.07 km2), intermixed and scattered across the same landscape. The study also showed that, after controlling for effects of patch size, the extent of canopy cover had a significant impact on most of the microclimate metrics. The impact of patch size on microclimate metrics was less pronounced. These findings highlight the need for conserving remnant forest patches, especially of the Dry Mixed Evergreen Forest, as refuges of biodiversity, particularly in the context of tropical forests in developing countries facing severe threats of deforestation.

Riverine sediment response to deforestation in the Amazon basin

Abstract. The Amazon experiences thousands of square kilometers of deforestation annually with recent rates increasing to levels unseen since the late 2000s. These increased rates of deforestation within the basin have led to changes in sediment concentration within its river systems, with potential impacts on ecological functioning, freshwater availability, and fluvial and coastal geomorphic processes. The relationship between deforestation and fluvial sediment dynamics in the Amazon has not been extensively studied using a basin-wide, comparative approach primarily due to lack of data. In this study, we utilize a novel remote-sensing-derived sediment concentration dataset to analyze the impact of deforestation from 2001 to 2020 on suspended sediment in large rivers (>50 m wide) across the Amazon River basin. These impacts are studied using a lag-based approach to quantify the spatiotemporal relationships between observed suspended sediment and changes in land cover over time. The results show that large-scale deforestation of the Amazon during the 2001–2020 period are associated with significant changes in sediment concentration in the eastern portion of the basin. In the heavily deforested eastern regions, the hydrogeomorphic response to deforestation occurs relatively rapidly (within a year), whereas the less disturbed western areas exhibit delays of 1 to 2 years before responses are observable. Moreover, we observe that deforestation must be substantial enough to overcome the collective influences of human activities and natural sediment variations to result in a discernible impact on sediment concentration in large rivers. In 69 % of Amazonian major tributary basins with an immediate response, more than 5 % of the basin was deforested during the 2001–2020 period, while in 85 % of basins with lagged responses, less than 5 % of the land was cleared. These findings suggest severe implications for future sediment dynamics across the Amazon if deforestation is to further expand into the basin.

ИННОВАЦИОННЫЕ СТРАТЕГИИ ФИНАНСИРОВАНИЯ ЗЕЛЕНОЙ ЭКОНОМИКИ: ЛЕСНЫЕ ОБЛИГАЦИИ

The sustainability of forest ecosystems is key to achieving the Sustainable Development Goals. However, the rate of deforestation and degradation of forest ecosystems is unprecedented, and the need for the investments needed to implement sustainable forest management mechanisms is substantial. The protection of natural forests is directly dependent on the availability of financial resources for sustainable forest management. At present, there is a lack of knowledge about innovative financing mechanisms for sustainable forest management that are suitable for implementation in developing markets where large-scale deforestation occurs. The article describes financial instruments that provide investment in forestry, analyzes the arguments in favor of investing in intensive forest management projects. Mechanisms for financing sustainable forest management programmes through the issuance of forest bonds were also considered. Forest bonds are a more appropriate financial instrument for financing sustainable forest management than traditional green bonds. A taxonomy is provided Projects implemented within the framework of intensive forest management programmes for which forest bonds can be issued. The purpose of this article is to develop academic research aimed at studying forest green bonds and, more generally, green market financing mechanisms for sustainable forest management.

Vegetation type and trail use interact to affect the magnitude and extent of recreational trail impacts on plant communities

Protected areas may prohibit large-scale deforestation and development, but still allow recreation via networks of roads and trails. Managers need to understand how the type of trail usage and the habitat the trail traverses influence the nature and extent of the trail impact. We measured the effect of trails on plant communities in a large, protected area in the southern Rocky Mountains of Alberta, Canada. We surveyed 118 transects adjacent to trails and 24 control transects at least 100 m from trails, recording the presence and abundance of all vascular plant species. We modelled changes in species richness, community composition, and the presence of exotic species as a function of trail type, vegetation type, and the distance from the trail edge. Overall, species richness increased with proximity to trails and community composition shifted significantly, with a greater likelihood of exotic species presence closer to trails. Heightened species richness and greater probability of exotic species presence extended a greater distance from off-highway vehicle trails than from footpaths, but only in shrubland and mixed forest vegetation. In addition, exotic species at higher elevations were most often associated with off-highway vehicle trails. Our study shows that the magnitude and extent of trail impacts on plant communities varies depending on trail type, vegetation type, and sometimes interactions between the two. The high frequency and intensity of off-highway vehicle trail use likely increases both propagule pressure and the severity of disturbance, while vegetation type determines light availability and hence invasibility. Managers can use this information to prioritize trail areas for exotic species monitoring and restrict high-intensity off-highway vehicle trails to less sensitive vegetation types at lower elevations.

DEFORESTATION DETECTION IN THE AMAZON WITH SENTINEL-1 SAR IMAGE TIME SERIES

Abstract. Deforestation has a significant impact on the environment, accelerating global warming and causing irreversible damage to ecosystems. Large-scale deforestation monitoring techniques still mostly rely on statistical approaches and traditional machine learning models applied to multi-spectral, optical satellite imagery and meta-data like land cover maps. However, clouds often obstruct observations of land in optical satellite imagery, especially in the tropics, which limits their effectiveness. Moreover, statistical approaches and traditional machine learning methods may not capture the wide range of underlying distributions in deforestation data due to limited model capacity. To overcome these drawbacks, we apply an attention-based neural network architecture that learns to detect deforestation end-to-end from time series of synthetic aperture radar (SAR) images. Sentinel-1 C-Band SAR data are mostly independent of the weather conditions and our trained neural network model generalizes across a wide range of deforestation patterns of Amazon forests. We curate a new dataset, called BraDD-S1TS, comprising approximately 25,000 image sequences for deforested and unchanged land throughout the Brazilian Amazon. We experimentally evaluate our method on this dataset and compare it to state-of-the-art approaches. We find it outperforms still-in-use methods by 13.7 percentage points in intersection over union (IoU). We make BraDD-S1TS publicly available along with this publication to serve as a novel testbed for comparing different deforestation detection methods in future studies.

Impacts on South America moisture transport under Amazon deforestation and 2 °C global warming

The increase in greenhouse gasses (GHG) anthropogenic emissions and deforestation over the last decades have led to many chemical and physical changes in the climate system, affecting the atmosphere's energy and water balance. A process that could be affected is the Amazonian moisture transport in the South American continent (including La Plata basin), which is crucial to the southeast Brazilian water regime. The focus of our research is on evaluating how local (i.e. Amazon deforestation) and global forcings (increase of atmospheric GHG concentration) may modify this moisture transport under climate change scenarios. We used two coupled land-atmosphere models forced by CMIP6 sea surface temperatures to simulate these processes for two scenarios: i) increase in carbon dioxide (CO2) — RCP8.5 atmospheric levels (00DEF), and ii) total Amazon deforestation simultaneous with atmospheric CO2 levels increased (100DEF). These scenarios were compared with a control simulation, set with a constant CO2 of 388 ppm and present-day Amazon Forest cover. The 30-year Specific Warming Level 2 (SWL2) index evaluated from the simulations is set to be reached 2 years earlier due to Amazon deforestation. A reduction in precipitation was observed in the Amazon basin (−3.1 mm·day−1) as well as in La Plata Basin (−0.5 mm·day−1) due to reductions in the Amazon evapotranspiration (−0.9 mm·day−1) through a stomatal conductance decrease (00DEF) and land cover change (100DEF). In addition, the income moisture transport decreased (22 %) in the northern La Plata basin in both scenarios and model experiments. Our results indicated a worse scenario than previously found in the region. Both Amazon and La Plata hydrological regimes are connected (moisture and energy transport), indicating that a large-scale Amazon deforestation will have additional climate, economic and social implications for South America.

Indonesia’s forest management progress: empirical analysis of environmental Kuznets curve

Purpose. High dependence on land resources is often the main cause of large-scale land conversion and deforestation in Indonesia. However, as a country vulnerable to climate change, Indonesia continues to increase its efforts to improve forest management and reduce deforestation. This is also in line with Indonesia’s commitment to reduce emission levels to achieve Net-Zero Emissions by 2060, where the forestry sector will be the base of this achievement. This study aims to measure Indonesia’s progress during 1970–2018, especially in reducing deforestation. With this study, we can see trends in forest management in developing countries, especially Indonesia. In addition, based on available data, we can see which factors are most closely related to Indonesia’s deforestation rate.
 Methodology / approach. This study uses a time-series data analysis with Error Correction Method (ECM). This ECM is used to detect the existence of the Environmental Kuznets Curve (EKC), which describes the relationship between environmental degradation and economic variables. In this study, EKC is used to assess further relationship pattern between the Gross Domestic Product (GDP) per capita and deforestation. The relation pattern between this GDP and deforestation is tested using quadratic and cubic models.
 Results. Under the quadratic model, a classic inverted U-shape EKC is detected in this study. It means that with economic growth and technological improvement, deforestation can be reduced. In other words, Indonesia has improved its forest management. However, as the N-shape relation was also detected in further cubic model simulation, it is also an indication that, at some point, Indonesia is also prone to increase its deforestation again. Another finding from the model is that the palm oil plantation area strongly relates to the deforested area. At the same time, the simulation detects a negative relationship between the horticulture cropland and the deforested area. The result may capture an insight into the forest transition period in Indonesia. In this transition, the government has started to slow down the speed at which forests are exploited in various ways.
 Originality / scientific novelty. Although the forestry sector is an important economic sector for Indonesia, there is still limited research on this topic, especially at the national level. This study uses more updated statistical information to better describe the Indonesian forestry condition. Moreover, the analysis is aligned with the Indonesian land policy by introducing additional variables from palm oil and crop areas. Moreover, the previous EKC assessments in Indonesia usually only use the quadratic model, which has some limitations in detecting other turning points and seeing the existence of N-shaped or inverted N-shaped EKC. This study also tries to add a bit on cubic model assessment to detect this N-shaped EKC in Indonesia.
 Practical value / implications. This study gives broader information based on the available data and statistics on the country’s deforestation situation and how the economic situation affects it. Policymakers can use the materials from this study to develop an effective forest management system throughout Indonesia.

Atmospheric CO2 inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions

Abstract. Tropical forests such as the Amazonian rainforests play an important role for climate, are large carbon stores and are a treasure of biodiversity. Amazonian forests have been exposed to large-scale deforestation and degradation for many decades. Deforestation declined between 2005 and 2012 but more recently has again increased with similar rates as in 2007–2008. The resulting forest fragments are exposed to substantially elevated temperatures in an already warming world. These temperature and land cover changes are expected to affect the forests, and an important diagnostic of their health and sensitivity to climate variation is their carbon balance. In a recent study based on CO2 atmospheric vertical profile observations between 2010 and 2018, and an air column budgeting technique used to estimate fluxes, we reported the Amazon region as a carbon source to the atmosphere, mainly due to fire emissions. Instead of an air column budgeting technique, we use an inverse of the global atmospheric transport model, TOMCAT, to assimilate CO2 observations from Amazon vertical profiles and global flask measurements. We thus estimate inter- and intra-annual variability in the carbon fluxes, trends over time and controls for the period of 2010–2018. This is the longest period covered by a Bayesian inversion of these atmospheric CO2 profile observations to date. Our analyses indicate that the Amazon is a small net source of carbon to the atmosphere (mean 2010–2018 = 0.13 ± 0.17 Pg C yr−1, where 0.17 is the 1σ uncertainty), with the majority of the emissions coming from the eastern region (77 % of total Amazon emissions). Fire is the primary driver of the Amazonian source (0.26 ± 0.13 Pg C yr−1), while forest carbon uptake removes around half of the fire emissions to the atmosphere (−0.13 ± 0.20 Pg C yr−1). The largest net carbon sink was observed in the western-central Amazon region (72 % of the fire emissions). We find larger carbon emissions during the extreme drought years (such as 2010, 2015 and 2016), correlated with increases in temperature, cumulative water deficit and burned area. Despite the increase in total carbon emissions during drought years, we do not observe a significant trend over time in our carbon total, fire and net biome exchange estimates between 2010 and 2018. Our analysis thus cannot provide clear evidence for a weakening of the carbon uptake by Amazonian tropical forests.

Fire may prevent future Amazon forest recovery after large-scale deforestation

The Amazon forest is regarded as a tipping element of the Earth system, susceptible to a regime change from tropical forest to savanna and grassland due to anthropogenic land use and climate change. Previous research highlighted the role of fire in amplifying irreversible large-scale Amazon die-back. However, large-scale feedback analyses which integrate the interplay of fire with climate and land-use change are currently lacking. To address this gap, here we applied the fire-enabled Potsdam Earth Model to examine these feedback mechanisms in the Amazon. By studying forest recovery after complete deforestation, we discovered that fire prevents regrowth across 56-82% of the potential natural forest area, contingent on atmospheric carbon dioxide levels. This emphasizes the significant contribution of fire to the irreversible transition, effectively locking the Amazon into a stable grassland state. Introducing fire dynamics into future assessments is vital for understanding climate and land-use impacts in the region.

Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments

Deforestation for agriculture is a key threat to global carbon stocks, biodiversity, and indigenous ways of life. In the absence of strong territorial governance, zero-deforestation commitments (ZDCs), corporate policies to decouple food production from deforestation, remain a central tool to combat this issue. Yet evidence on their effectiveness remains mixed and the mechanisms limiting effectiveness are poorly understood. To advance understanding of ZDCs’ potential at reducing deforestation, we developed the first spatially explicit estimates of farmers’ exposure to ZDC companies in the Brazilian Amazon cattle sector. Exposure was measured by determining the market share of ZDC firms from the first full year of ZDC adoption in 2010 until 2018. Our analysis evaluated how variation in this exposure influenced deforestation. We found the G4 Agreement, the most widespread and strongly implemented cattle ZDC, reduced cattle-driven deforestation by 7,000 ± 4,000 km2 (15 ± 8%) between 2010 and 2018. Additionally, had all firms adopted and implemented an effective ZDC, cattle-driven deforestation could have dropped by 24,000 ± 13,000 km2 (51 ± 28%). These results for the world’s principal deforestation hotspot suggests supply chain policies can substantially reduce deforestation. However, their effectiveness is contingent on widespread adoption and rigorous implementation, both of which are currently insufficient to prevent large scale deforestation. Increased adoption and implementation could be incentivized through greater pressure from the Brazilian government and import countries.

Agroforestry orchards support greater butterfly diversity than monoculture plantations in the tropics

Large-scale deforestation in the tropics, triggered by logging and subsequent agricultural monoculture has a significant adverse impact on biodiversity due to habitat degradation. Here, we measured the diversity of butterfly species in three agricultural landscapes, agroforestry orchards, oil palm, and rubber tree plantations. Butterfly species were counted at 127 sampling points over the course of a year using the point count method. We found that agroforestry orchards supported a greater number of butterfly species (74 species) compared to rubber tree (61 species) and oil palm plantations (54 species) which were dominated by generalist (73%) followed by forest specialists (27%). We found no significant difference of butterfly species composition between agroforestry orchards and rubber tree plantation, with both habitats associated with more butterfly species compared to oil palm plantations. This indicates butterflies were able to persist better in certain agricultural landscapes. GLMMs suggested that tree height, undergrowth coverage and height, and elevation determined butterfly diversity. Butterfly species richness was also influenced by season and landscape-level variables such as proximity to forest, mean NDVI, and habitat. Understanding the factors that contributed to butterfly species richness in an agroecosystem, stakeholders should consider management practices to improve biodiversity conservation such as ground vegetation management and retaining adjacent forest areas to enhance butterfly species richness. Furthermore, our findings suggest that agroforestry system should be considered to enhance biodiversity in agricultural landscapes.

A Worrying Future for River Flows in the Brazilian Cerrado Provoked by Land Use and Climate Changes

In this study, we assessed the impacts of land use and climate changes on the river flows of 81 watersheds within the Cerrado biome, Brazil, based on a comprehensive analysis of field and secondary data acquired between 1985 and 2018. Complementarily, we projected a future deforestation and climate scenario up to 2050 and predicted their impacts on surface water in the study area. We observed that direct impacts by large-scale deforestation oriented to the production of irrigated agricultural commodities have more significantly impacted river flows than climate changes. We estimated an average decrease of 8.7% and 6.7% in the streamflow due to deforestation and climate changes, respectively. Most of the observed changes (56.7%) were due to land use and land cover changes and occurred in recent decades. Climate and land use and land cover changes combined were responsible for a total surface water reduction of −19,718 m³/s within the Cerrado watersheds. By assuming the current deforestation rates, we predicted a total water reduction of 23,653 m³/s by 2050, equivalent to a decrease of 33.9% of the river flows in the study region. It will cause severe streamflow discontinuity in many rivers and strongly affect agricultural, electric power production, biodiversity, and water supply, especially during dry seasons in that region.

Social, Ecological and Economic Synergies of Forests for Sustainability Contradict Projects Involving Large-Scale Deforestation for Energy Production

Social, Ecological and Economic Synergies of Forests for Sustainability Contradict Projects Involving Large-Scale Deforestation for Energy Production