The Amazon biome, spanning nine countries, has one of the highest rates of deforestation worldwide. This deforestation contributes to biodiversity loss, climate change, the spread of infectious diseases, and damage to rural and indigenous livelihoods. Hundreds of articles have been published on the topic of deforestation across Amazonia, yet there has been no recent synthesis of deforestation drivers and deforestation-control policy effectiveness in the region. Here we undertook the first systematic review of papers published between 2000 and 2021 that have causally linked proximate and underlying drivers and policies to deforestation outcomes in Amazonia. In the 155 articles that met our inclusion criteria, we find that causal research is concentrated in Brazil, and to a lesser degree Peru, Ecuador, and Bolivia. There has been little study of the Guianas, Venezuela or Colombia. Large- and small-scale agriculture linked to improved market access and high agricultural prices are frequently researched underlying drivers of deforestation across the heavily researched regions. In the Guianas research focuses on mining with little focus on underlying causes. Research on infrastructure expansion, mining, and oil extraction and on technological, sociocultural, and institutional factors remains sparse. Many public and private policies have been found to be effective in controlling deforestation across the biome, with protected areas and public policies standing out as particularly successful in slowing deforestation vis-à-vis supply chain approaches. Frontier age, land tenure, and policy interactions are key moderating factors affecting the outcomes of different underlying causes and policies. Our findings indicate a greater need for research on (i) additional deforestation drivers beyond agriculture and economic factors, (ii) the complex interactions between different drivers and deforestation control policies, (iii) causes underlying deforestation in low or new deforestation areas, and (iv) the dynamics between Amazonian subregions and countries. Understanding the extent and diversity of deforestation drivers and effectiveness of existing deforestation mitigation policies across Amazonia is a necessary first step toward designing policies to further reduce deforestation in the biome.

The dense, intact rainforests of the Guianas (northern Amazonia) are important for the regulation of the local and regional climate, but preclude easy access so a large data gap exists. The rainforest–savannah boundary may also be particularly susceptible to increasing pressures from ecosystem exploitation and climate change. It is important therefore to establish a baseline climate regime so that the impacts of any future change can be determined. Currently this area is not covered by the publicly available climate data sets, so interpolated data sets are not an accurate representation of the climate of this area, emphasizing the need for a more detailed analysis in this region. Here, we collate the available data sets and use these to derive maps of observed precipitation and temperature across the region. To overcome the limitations in the inadequate observational data sets, we also test and use the ECMWF ERA‐40 reanalysis data set for the period 1958–2001 at a ∼125 km² (1.125°) resolution. Mean differences (biases) and annual average spatial correlations are examined between modelled and observed time series comparing the seasonal cycles and the yearly, monthly and monthly anomaly time series. The results show that with the exception of sub‐grid resolution mountain environments, reanalysis data for the Guianas provide a consistent and relatively accurate spatial distribution of temperature. Precipitation is modelled less accurately, with best results for the average timings, length and severity of the dry periods. This study provides consistent and quantitative details on the spatial variations in seasonality, particularly in areas lacking observations, thereby advancing beyond previous studies. Precipitation is highly variable in the region so care must be taken when averaging modelled data over large geographical areas for comparisons with gridded data sets based on few observations. This is the first comprehensive study of the recent historical climate and its variability in this area. Copyright © 2011 The Authors. Journal compilation © 2011 The Royal Meteorological Society