Efforts to reduce deforestation to mitigate climate change and to conserve biodiversity are taking place on a global scale. While many studies have estimated the emissions occurring from deforestation, few studies have quantified the domestic and international drivers sustaining deforestation rates. In this study we establish the link between Brazilian deforestation and production of cattle and soybeans, and allocate emissions between 1990 and 2010 along the global supply chain to the countries that consume products dependent on Brazilian deforestation. We find that 30% of the carbon emissions associated with deforestation were exported from Brazil in the last decade, of which 29% were due to soybean production and 71% cattle ranching. The share exported is growing, with industrialized nations and emerging markets (especially Russia and China) greatly increasing imports. We find a correlation between exports (and hence global consumption) of Brazilian cattle and soybeans and emissions from deforestation. We conclude that trade is emerging as a key driver of deforestation in Brazil, and this may indirectly contribute to loss of the forests that industrialized countries are seeking to protect through international agreements.

The Center for International Forestry Research (CIFOR) has released an overview publication of its Sustainable Wetlands Adaptation and Mitigation Program (SWAMP), which outlines the project's role in informing national and international policy makers.

The publication underscores that carbon-rich mangroves and peatlands are high priorities in climate change adaptation and mitigation strategies throughout the world. SWAMP seeks to quantify greenhouse gas (GHG) emissions from intact and degraded wetlands in an effort to quantify carbon stocks in tropical wetlands worldwide and develop carbon dynamics modeling tools to support policy analyses.

Coastal wetlands can have exceptionally large carbon (C) stocks and their protection and restoration would constitute an effective mitigation strategy to climate change. Inclusion of coastal ecosystems in mitigation strategies requires quantification of carbon stocks in order to calculate emissions or sequestration through time. In this study, we quantified the ecosystem C stocks of coastal wetlands of the Sian Ka'an Biosphere Reserve (SKBR) in the Yucatan Peninsula, Mexico. We stratified the SKBR into different vegetation types (tall, medium and dwarf mangroves, and marshes), and examined relationships of environmental variables with C stocks. At nine sites within SKBR, we quantified ecosystem C stocks through measurement of above and belowground biomass, downed wood, and soil C. Additionally, we measured nitrogen (N) and phosphorus (P) from the soil and interstitial salinity. Tall mangroves had the highest C stocks (987±338 Mg ha-1) followed by medium mangroves (623±41 Mg ha-1), dwarf mangroves (381±52 Mg ha-1) and marshes (177±73 Mg ha-1). At all sites, soil C comprised the majority of the ecosystem C stocks (78–99%). Highest C stocks were measured in soils that were relatively low in salinity, high in P and low in N:P, suggesting that P limits C sequestration and accumulation potential. In this karstic area, coastal wetlands, especially mangroves, are important C stocks. At the landscape scale, the coastal wetlands of Sian Ka'an covering ˜172,176 ha may store 43.2 to 58.0 million Mg of C.

This toolbox supports an organization’s needs for developing, managing and reporting carbon projects at the national or project level. It provides an enterprise-wide solution of on-line tools for planning and implementing national forest inventory for carbon, development and management of carbon projects across all of your organization’s offices and units, and enterprise training and capacity-building. The Toolbox supports planning, tasking and implementation, and its distributed web-enabled approach allows managers in one office to communicate and interact with field offices and other offices or cooperators across the organization.  This structure and its secure login and workspace design allows verifiers and others to review the project data, providing a level of transparency and openness needed for most carbon projects today.

Forests in lowland Bolivia suffer from severe deforestation caused by different types of agents and land use activities. We identify three major proximate causes of deforestation. The largest share of deforestation is attributable to the expansion of mechanized agriculture, followed by cattle ranching and small-scale agriculture. We utilize a spatially explicit multinomial logit model to analyze the determinants of each of these proximate causes of deforestation between 1992 and 2004. We substantiate the quantitative insights with a qualitative analysis of historical processes that have shaped land use patterns in the Bolivian lowlands to date. Our results suggest that the expansion of mechanized agriculture occurs mainly in response to good access to export markets, fertile soil, and intermediate rainfall conditions. Increases in small-scale agriculture are mainly associated with a humid climate, fertile soil, and proximity to local markets. Forest conversion into pastures for cattle ranching occurs mostly irrespective of environmental determinants and can mainly be explained by access to local markets. Land use restrictions, such as protected areas, seem to prevent the expansion of mechanized agriculture but have little impact on the expansion of small-scale agriculture and cattle ranching. The analysis of future deforestation trends reveals possible hotspots of future expansion for each proximate cause and specifically highlights the possible opening of new frontiers for deforestation due to mechanized agriculture. Whereas the quantitative analysis effectively elucidates the spatial patterns of recent agricultural expansion, the interpretation of long-term historic drivers reveals that the timing and quantity of forest conversion are often triggered by political interventions and historical legacies.

Recognizing the importance of preserving biodiversity and ecosystem services, human society has established extensive protected area networks to conserve these resources in recent decades. Are protected areas working as expected? Empirical coarse-scale assessments of this question across large regions, or even globally, tend to answer “yes”, while fine-scale studies of individual protected areas often and repeatedly answer “no”. We conducted a first fine-scale analysis of Brazil’s extensive Amazonian protected area network (1.8 million km2) and have quantitatively estimated conservation effectiveness in light of changing human development pressures in the surrounding landscape. The overall network maintained intact forest cover for 98.6% of protected forest lands, largely agreeing with previous coarse-scale studies. However, detailed examination of 474 individual protected areas unveils a broad range of efficacy. Many protected areas (544,800 km2) experience default protection simply due to their remoteness. Many others (396,100 km2) have provided highly effective protection in the face of substantial human development pressure. Conversely, 12% (38) of protected areas have failed to protect the 27,300 km2 that they encompass, and another 7% (23) provide only marginal protection of 37,500 km2. Comprehensive landscape assessments of protected area networks, with frequent monitoring at scales matching the patterns of human-caused disturbances, are necessary to ensure the conservation effectiveness and long term survival of protected areas in rapidly changing landscapes. The methods presented here are globally adaptable to all forested protected areas.

This risk assessment projects the effects of eight forest diseases under two climate-change scenarios (warmer and drier, warmer and wetter). Examples are used to describe how various types of forest diseases may respond to environmental changes. Forest diseases discussed in this report include foliar diseases, Phytophthora diseases, stem rusts, canker diseases, dwarf mistletoes, root diseases, and yellow-cedar decline. The likelihood and consequences of increased damage to forests from each disease as a result of climate change are analyzed and assigned a risk value of high, moderate, or low. The risk value is based on available biological information and subjective judgment. Although results suggest that climate change will affect forest health, uncertainty arises regarding the degree of climate change that will occur; pathogen biology under changing climate; the effects of changing climate directly on the host; and the interactions between the pathogen, host, and climate.