Ecological mechanisms explaining interactions within plant-hummingbird networks: morphological matching increases towards lower latitudes.

Interactions between species are influenced by different ecological mechanisms, such as morphological matching, phenological overlap and species abundances. How these mechanisms explain interaction frequencies across environmental gradients remains poorly understood. Consequently, we also know little about the mechanisms that drive the geographical patterns in network structure, such as complementary specialization and modularity. Here, we use data on morphologies, phenologies and abundances to explain interaction frequencies between hummingbirds and plants at a large geographical scale. For 24 quantitative networks sampled throughout the Americas, we found that the tendency of species to interact with morphologically matching partners contributed to specialized and modular network structures. Morphological matching best explained interaction frequencies in networks found closer to the equator and in areas with low-temperature seasonality. When comparing the three ecological mechanisms within networks, we found that both morphological matching and phenological overlap generally outperformed abundances in the explanation of interaction frequencies. Together, these findings provide insights into the ecological mechanisms that underlie geographical patterns in resource specialization. Notably, our results highlight morphological constraints on interactions as a potential explanation for increasing resource specialization towards lower latitudes.

Modelling Highly Biodiverse Areas in Brazil.

Traditional conservation techniques for mapping highly biodiverse areas assume there to be satisfactory knowledge about the geographic distribution of biodiversity. There are, however, large gaps in biological sampling and hence knowledge shortfalls. This problem is even more pronounced in the tropics. Indeed, the use of only a few taxonomic groups or environmental surrogates for modelling biodiversity is not viable in mega-diverse countries, such as Brazil. To overcome these limitations, we developed a comprehensive spatial model that includes phylogenetic information and other several biodiversity dimensions aimed at mapping areas with high relevance for biodiversity conservation. Our model applies a genetic algorithm tool for identifying the smallest possible region within a unique biota that contains the most number of species and phylogenetic diversity, as well as the highest endemicity and phylogenetic endemism. The model successfully pinpoints small highly biodiverse areas alongside regions with knowledge shortfalls where further sampling should be conducted. Our results suggest that conservation strategies should consider several taxonomic groups, the multiple dimensions of biodiversity, and associated sampling uncertainties.

Modelling highly biodiverse areas in Brazil

Traditional conservation techniques for mapping highly biodiverse areas assume there to be satisfactory knowledge about the geographic distribution of biodiversity. There are, however, large gaps in biological sampling and hence knowledge shortfalls. This problem is even more pronounced in the tropics. Indeed, the use of only a few taxonomic groups or environmental surrogates for modelling biodiversity is not viable in mega-diverse countries, such as Brazil. To overcome these limitations, we developed a comprehensive spatial model that includes phylogenetic information and other several biodiversity dimensions aimed at mapping areas with high relevance for biodiversity conservation. Our model applies a genetic algorithm tool for identifying the smallest possible region within a unique biota that contains the most number of species and phylogenetic diversity, as well as the highest endemicity and phylogenetic endemism. The model successfully pinpoints small highly biodiverse areas alongside regions with knowledge shortfalls where further sampling should be conducted. Our results suggest that conservation strategies should consider several taxonomic groups, the multiple dimensions of biodiversity, and associated sampling uncertainties.

Modelling highly biodiverse areas in Brazil

Traditional conservation techniques for mapping highly biodiverse areas assume there to be satisfactory knowledge about the geographic distribution of biodiversity. There are, however, large gaps in biological sampling and hence knowledge shortfalls. This problem is even more pronounced in the tropics. Indeed, the use of only a few taxonomic groups or environmental surrogates for modelling biodiversity is not viable in mega-diverse countries, such as Brazil. To overcome these limitations, we developed a comprehensive spatial model that includes phylogenetic information and other several biodiversity dimensions aimed at mapping areas with high relevance for biodiversity conservation. Our model applies a genetic algorithm tool for identifying the smallest possible region within a unique biota that contains the most number of species and phylogenetic diversity, as well as the highest endemicity and phylogenetic endemism. The model successfully pinpoints small highly biodiverse areas alongside regions with knowledge shortfalls where further sampling should be conducted. Our results suggest that conservation strategies should consider several taxonomic groups, the multiple dimensions of biodiversity, and associated sampling uncertainties.

Biogeography of Amazon birds: rivers limit species composition, but not areas of endemism.

Amazonian rivers are usually suggested as dispersal barriers, limiting biogeographic units. This is evident in a widely accepted Areas of Endemism (AoEs) hypothesis proposed for Amazonian birds. We empirically test this hypothesis based on quantitative analyses of species distribution. We compiled a database of bird species and subspecies distribution records, and used this dataset to identify AoEs through three different methods. Our results show that the currently accepted Amazonian AoEs are not consistent with areas identified, which were generally congruent among datasets and methods. Some Amazonian rivers represent limits of AoEs, but these areas are not congruent with those previously proposed. However, spatial variation in species composition is correlated with largest Amazonian rivers. Overall, the previously proposed Amazonian AoEs are not consistent with the evidence from bird distribution. However, the fact that major rivers coincide with breaks in species composition suggest they can act as dispersal barriers, though not necessarily for all bird taxa. This scenario indicates a more complex picture of the Amazonian bird distribution than previously imagined.

Barcoding Neotropical birds: assessing the impact of nonmonophyly in a highly diverse group.

In this study, we verified the power of DNA barcodes to discriminate Neotropical birds using Bayesian tree reconstructions of a total of 7404 COI sequences from 1521 species, including 55 Brazilian species with no previous barcode data. We found that 10.4% of species were nonmonophyletic, most likely due to inaccurate taxonomy, incomplete lineage sorting or hybridization. At least 0.5% of the sequences (2.5% of the sampled species) retrieved from GenBank were associated with database errors (poor-quality sequences, NuMTs, misidentification or unnoticed hybridization). Paraphyletic species (5.8% of the total) can be related to rapid speciation events leading to nonreciprocal monophyly between recently diverged sister species, or to absence of synapomorphies in the small COI region analysed. We also performed two series of genetic distance calculations under the K2P model for intraspecific and interspecific comparisons: the first included all COI sequences, and the second included only monophyletic taxa observed in the Bayesian trees. As expected, the mean and median pairwise distances were smaller for intraspecific than for interspecific comparisons. However, there was no precise 'barcode gap', which was shown to be larger in the monophyletic taxon data set than for the data from all species, as expected. Our results indicated that although database errors may explain some of the difficulties in the species discrimination of Neotropical birds, distance-based barcode assignment may also be compromised because of the high diversity of bird species and more complex speciation events in the Neotropics.