Misuse of bird digital distribution maps creates reversed spatial diversity patterns in the Amazon

Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions

Reliable maps of species distributions are fundamental for biodiversity research and conservation. The International Union for Conservation of Nature (IUCN) range maps are widely recognized as authoritative representations of species' geographic limits, yet they might not always align with actual occurrence data. In recent area of habitat (AOH) maps, areas that are not habitat have been removed from IUCN ranges to reduce commission errors, but their concordance with actual species occurrence also remains untested. We tested concordance between occurrences recorded in camera trap surveys and predicted occurrences from the IUCN and AOH maps for 510 medium- to large-bodied mammalian species in 80 camera trap sampling areas. Across all areas, cameras detected only 39% of species expected to occur based on IUCN ranges and AOH maps; 85% of the IUCN only mismatches occurred within 200 km of range edges. Only 4% of species occurrences were detected by cameras outside IUCN ranges. The probability of mismatches between cameras and the IUCN range was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya and in areas with shorter canopy forests. Our findings suggest that range and AOH maps rarely underrepresent areas where species occur, but they may more often overrepresent ranges by including areas where a species may be absent, particularly at range edges. We suggest that combining range maps with data from ground-based biodiversity sensors, such as camera traps, provides a richer knowledge base for conservation mapping and planning.

Response of Understory Avifauna to Annual Flooding of Amazonian Floodplain Forests

The annual flooding in the Amazon basin transforms the understory of floodplain forests into an aquatic environment. However, a great number of non-aquatic bird species occupy the understory and midstory of these forests. In general, these birds are thought to be sedentary and territorial, but the way they adapt to this dramatic seasonal transformation has never been described in detail. In this study, we describe avifaunal strategies to cope with seasonal flooding in the lower Purus region, central Amazonia, Brazil. We conducted focal observations of five insectivorous species occupying the lowest forest strata in two types of floodplain forest (black- and whitewater) during the low- and high-water seasons. For each observation, the height of the bird above the substrate (ground or water), its vertical position in the forest, and vegetation density around the bird were noted. All species remained present in the floodplain forests during the two seasons and were not recorded in adjacent unflooded (terra firme) forest. In general, birds migrated vertically to higher forest strata and most species (three of the five) occupied similar vegetation densities independent of water level. Despite the tendency of all species to rise in relative vertical position at high water, there was a reduction in height above substrate for four of the five species, suggesting that their position relative to water was not an important microhabitat element for them. Responses were similar in the two floodplain forest types. It is likely that the decrease in available space during the flood, combined with similar vertical displacement in arthropods, leads to increased prey density for understory insectivorous birds and permits year-round territoriality without major habitat shifts.

Spatial Congruence Analysis (SCAN): A method for detecting biogeographical patterns based on species range congruences

Species with congruent geographical distributions, potentially caused by common historical and ecological spatial processes, constitute biogeographical units called chorotypes. Nevertheless, the degree of spatial range congruence characterizing these groups of species is rarely used as an explicit parameter. Methods conceived for the identification of patterns of shared ranges often suffer from scale bias associated with the use of grids, or the incapacity to describe the full complexity of patterns, from core areas of high spatial congruence, to long gradients of range distributions expanding from these core areas. Here, we propose a simple analytical method, Spatial Congruence Analysis (SCAN), which identifies chorotypes by mapping direct and indirect spatial relationships among species. Assessments are made under a referential value of congruence as an explicit numerical parameter. A one-layered network connects species (vertices) using pairwise spatial congruence estimates (edges). This network is then analyzed for each species, separately, by an algorithm which searches for spatial relationships to the reference species. The method was applied to two datasets: a simulated gradient of ranges and real distributions of birds. The simulated dataset showed that SCAN can describe gradients of distribution with a high level of detail. The bird dataset showed that only a small portion of range overlaps is biogeographically meaningful, and that there is a large variation in types of patterns that can be found with real distributions. Species analyzed separately may converge on similar or identical groups, may be nested in larger chorotypes, or may even generate overlapped patterns with no species in common. Chorotypes can vary from simple ones, composed by few highly congruent species, to complex, with numerous alternative component species and spatial configurations, which offer insights about possible processes driving these patterns in distinct degrees of spatial congruence. Metrics such as congruence, depth, richness, and ratio between common and total areas can be used to describe chorotypes in detail, allowing comparisons between patterns across regions and taxa.

The influence of riverine barriers, climate, and topography on the biogeographic regionalization of Amazonian anurans

We evaluated five non-mutually exclusive hypotheses driving the biogeographic regions of anuran species in the Amazonia. We overlaid extent-of-occurrence maps for anurans 50 × 50 km cells to generate a presence–absence matrix. This matrix was subjected to a cluster analysis to identify the pattern and number of biogeographic regions for the dataset. Then, we used multinomial logistic regression models and deviance partitioning to explore the relative importance of contemporary and historical climate variables, topographic complexity, riverine barriers and vegetation structure in explaining the biogeographic regions identified. We found seven biogeographic regions for anurans in the Amazonia. The major rivers in the Amazonia made the largest contribution to explaining the variability in anuran biogeographic regions, followed by climate variables and topography. The barrier effect seems to be strong for some rivers, such as the Amazon and Madeira, but other Amazonia rivers appear to not be effective barriers. Furthermore, climate and topographical variables provide an environmental gradient driving the species richness and anuran range-size distributions. Therefore, our results provide a spatially explicit framework that could be used to address conservation and management issues of anuran diversity for the largest tropical forests in the world.