Pervasive gaps in Amazonian ecological research.

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%-18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost.

Giants of the Amazon: How does environmental variation drive the diversity patterns of large trees?

For more than three decades, major efforts in sampling and analyzing tree diversity in South America have focused almost exclusively on trees with stems of at least 10 and 2.5 cm diameter, showing highest species diversity in the wetter western and northern Amazon forests. By contrast, little attention has been paid to patterns and drivers of diversity in the largest canopy and emergent trees, which is surprising given these have dominant ecological functions. Here, we use a machine learning approach to quantify the importance of environmental factors and apply it to generate spatial predictions of the species diversity of all trees (dbh ≥ 10 cm) and for very large trees (dbh ≥ 70 cm) using data from 243 forest plots (108,450 trees and 2832 species) distributed across different forest types and biogeographic regions of the Brazilian Amazon. The diversity of large trees and of all trees was significantly associated with three environmental factors, but in contrasting ways across regions and forest types. Environmental variables associated with disturbances, for example, the lightning flash rate and wind speed, as well as the fraction of photosynthetically active radiation, tend to govern the diversity of large trees. Upland rainforests in the Guiana Shield and Roraima regions had a high diversity of large trees. By contrast, variables associated with resources tend to govern tree diversity in general. Places such as the province of Imeri and the northern portion of the province of Madeira stand out for their high diversity of species in general. Climatic and topographic stability and functional adaptation mechanisms promote ideal conditions for species diversity. Finally, we mapped general patterns of tree species diversity in the Brazilian Amazon, which differ substantially depending on size class.

Flood-pulse disturbances as a threat for long-living Amazonian trees.

The long-lived tree species Eschweilera tenuifolia (O. Berg) Miers is characteristic of oligotrophic Amazonian black-water floodplain forests (igapó), seasonally inundated up to 10 months per year, often forming monodominant stands. We investigated E. tenuifolia' growth and mortality patterns in undisturbed (Jaú National Park - JNP) and disturbed igapós (Uatumã Sustainable Development Reserve - USDR, downstream of the Balbina hydroelectric dam). We analysed age-diameter relationships, basal area increment (BAI) through 5-cm diameter classes, growth changes and growth ratios preceding death, BAI clustering, BAI ratio, and dated the individual year of death (14 C). Growth and mortality patterns were then related to climatic or anthropogenic disturbances. Results were similar for both populations for estimated maximum ages (JNP, 466 yr; USDR, 498 yr, except for one USDR tree with an estimated age of 820 yr) and slightly different for mean diameter increment (JNP: 2.04 mm; USDR: 2.28 mm). Living trees from JNP showed altered growth post-1975 and sparse tree mortality occurred at various times, possibly induced by extreme hydroclimatic events. In contrast with the JNP, abrupt growth changes and massive mortality occurred in the USDR after the dam construction began (1983). Even more than 30 yr after dam construction, flood-pulse alteration continues to affect both growth and mortality of E. tenuifolia. Besides its vulnerability to anthropogenic disturbances, this species is also susceptible to long-lasting dry and wet periods induced by climatic events, the combination of both processes may cause its local and regional extinction.