The extent and intensity of impacts of multiple new dams in the Amazon basin on specific biological groups are potentially large, but still uncertain and need to be better understood. It is known that river disruption and regulation by dams may affect sediment supplies, river channel migration, floodplain dynamics, and, as a major adverse consequence, are likely to decrease or even suppress ecological connectivity among populations of aquatic organisms and organisms dependent upon seasonally flooded environments. This article complements our previous results by assessing the relationships between dams, our Dam Environmental Vulnerability Index (DEVI), and the biotic environments threatened by the effects of dams. Because of the cartographic representation of DEVI, it is a useful tool to compare the potential hydrophysical impacts of proposed dams in the Amazon basin with the spatial distribution of biological diversity. As the impact of Amazonian dams on the biota of both rivers and periodically flooded riparian environments is severe, DEVIs from different Amazonian tributary basins are contrasted with patterns of diversity and distribution of fish, flooded forest trees and bird species. There is a consistent relationship between higher DEVI values and the patterns of higher species richness and endemism in all three biological groups. An assessment of vulnerability at the scale of tributary basins, the assessment of biodiversity patterns related to DEVI, and the analysis of teleconnections at basin scale, demonstrate that recent construction of dams is affecting the biota of the Amazon basin. The evidence presented here predicts that, if currently planned dams are built without considering the balance between energy production and environmental conservation, their cumulative effects will increase drastically and represent a major threat to Amazonian biodiversity.

Although vast literature exists on the drivers of tropical deforestation and its ecological consequences, less is known about how patterns of forest fragmentation emerge in the first place. The purpose of this paper is to address this issue for the Brazilian portion of the Amazon basin by analyzing the social processes generative of five specific patterns, including rectangular, fishbone, radial, dendritic, and what we refer to as ‘the stem of the rose.’ We argue that forest fragmentation patterns in the Brazilian Amazon are largely determined by the types and arrival times of the agents who engage in land clearing. We also argue that the patterns manifest in the landscape by virtue of road construction and agricultural property formation, which often occur in tandem. We conclude by placing our discussion within legal and institutional contexts, and observe that fragmentation stemming from formal colonization projects is more consistent with biodiversity conservation than that associated with a laissez-faire occupation. However, erosive impacts may be greater in topographically sensitive areas.

The effects of global climate change on the intensity of tropical cyclones are yet to be fully understood due to the variety of factors that affect storm intensity, the limited time spans of existing records, and the diversity of metrics by which intensity is characterized. The 2017 North Atlantic hurricane season induced record-breaking economic losses and caused hundreds of fatalities, and for many represents a presage of what the future holds under warmer tropical sea surface temperatures. This article focuses on one such major hurricane, María, and answers the question of how this event compares to the historical record of tropical storms that have assailed the island of Puerto Rico since 1898. Comparisons relied on interpolated weather station total rainfall and maximum 24-h rainfall intensities. María proved to have the greatest 24-h rain intensities among all storms recorded in Puerto Rico, yielding maximum 24-h recurrence intervals greater than 250 years for about 8% of the island.

The role of individual tropical cyclones in mobilizing sediment by shallow landsliding has been studied widely in islands of the Pacific but hardly within the Insular Caribbean. An opportunity to conduct such a study materialized in 2017 when Hurricane María provoked over 70,000 landslides on the island of Puerto Rico. Through aerial photo interpretation and high‐resolution digital elevation models, this study provides an estimate of the net mobilization and delivery of sediment by shallow landslides in a wet subtropical and actively cultivated 43.8 km² watershed. Landslide mobilization and delivery are contextualized here in terms of other active sources of sediment and annual sediment delivery. Maximum hourly and 24‐h average rain intensities within the study watershed during Hurricane María were 68 and 10.2 mm h⁻¹, respectively, and these represent intensities with local recurrence intervals of less than 10 years. However, these rain intensities were sufficient to trigger 2,318 landslides that mobilized 230,510–436,330 Megagrams of sediment, an amount that is much greater than surface erosion contributions and represents the equivalent of 0.5–3.6 years of annual watershed‐scale sediment delivery. The high susceptibility to landsliding in this region of Puerto Rico is linked to the abundance of roads (∼21 km km⁻²), particularly those crisscrossing terrain with slopes ranging from 30° to 60°. Only 15% of these roads are within actively cultivated areas. The remaining roads represent the support infrastructure of abandoned coffee farms that still appear to be inducing a legacy of much geomorphological and watershed management relevance.

Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD⁺ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200–300 km². This translates into aboveground carbon emissions of 1.36 and 1.85 x 10⁶ tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.