Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal-habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities

Use of secondary forests by understory birds in a fragmented landscape in central Amazonia

Rates of deforestation in the Brazilian Amazon have increased since 1991 and forecasts are not optimistic about the slowing of this process. Some authors believe that the Amazon may be experiencing a massive process of species extinction. However, the deforestation is accompanied by the expansion of secondary forests that are established in the abandoned areas. The trend is an increase in secondary forests cover, resulting in a mosaic of primary forest (FP) and fragments separated by an array of secondary forests (FS). In this scenario, the prediction of a massive extinction could be wrong if many species could survive in the secondary forests. To assess the importance of FS for the understory birds we sampled areas in regeneration and a continuous forest of a fragmented landscape. We conducted mist netting (24 nets/day) for six consecutive days/month, for 8 months (May-November) in 2009. Some forest species as do not seem to be adapted to the secondary forest environment and their occurrences are restricted to continuous forest environments. But most focal species showed no significant difference in apparent survival rates between the enviroments, suggesting that these species inhabit the secondary forest and the primary forest similarly. Because most of the matrix in fragmented landscapes are composed by secondary forests, such results highlights the conservation value that these habitats present in the long term. Thus, FS should be regarded as dynamic matrix that not only allows the movement of individuals but also function as habitat for many species typical of FP.Na Amazônia, as taxas de desmatamento crescem desde 1991 e as previsões não são otimistas quanto à desaceleração desse processo. A devastação da floresta é acompanhada de uma expansão de florestas secundárias (FS) que se estabelecem nas áreas abandonadas. A tendência é um aumento de florestas secundárias, resultando num mosaico de floresta contínua e fragmentos separados por uma matriz de FS. Nesse cenário, autores acreditam que a Amazônia pode passar por um processo massivo de extinção de espécies. Por outro lado, a previsão de um processo massivo de extinção pode ser equivocada, pois muitas espécies florestais poderiam sobreviver nas florestas secundárias. Para avaliar o valor das florestas secundárias para espécies florestais amostramos por oito meses com redes de neblina uma capoeira (FS) em regeneração e uma floresta primária (FP) de uma paisagem fragmentada. Algumas espécies não foram capturadas na capoeira e aparentemente evitam esse tipo de hábitat. No entanto, a maioria das espécies do grupo focal não apresentou diferença na sobrevivência aparente entre os ambientes, o que nos indica que estão habitando a capoeira e a floresta primária da mesma forma. Na realidade amazônica, onde grande parte da matriz é composta por floresta secundária, a matriz tem valor para conservação e deve ser analisada como um elemento dinâmico que não apenas permite a movimentação de indivíduos, mas também serve de hábitat para muitas espécies de floresta primária. Mas ressaltamos que é fundamental a preservação de áreas de floresta primária que servirão de fonte às florestas secundárias adjacentes

Data from: Composite measures of selection can improve the signal-to-noise ratio in genome scans

The growing wealth of genomic data is yielding new insights into the genetic basis of adaptation, but it also presents the challenge of extracting the relevant signal from multi-dimensional datasets. Different statistical approaches vary in their power to detect selection depending on the demographic history, type of selection, genetic architecture and experimental design. Here, we develop and evaluate new approaches for combining results from multiple tests, including multivariate distance measures and methods for combining P-values. We evaluate these methods on (i) simulated landscape genetic data analysed for differentiation outliers and genetic-environment associations and (ii) empirical genomic data analysed for selective sweeps within dog breeds for loci known to be selected for during domestication. We also introduce and evaluate how robust statistical algorithms can be used for parameter estimation in statistical genomics. On the simulated data, many of the composite measures performed well and had decreased variation in outcomes across many sampling designs. On the empirical dataset, methods based on combining P-values generally performed better with clearer signals of selection, higher significance of the signal, and in closer proximity to the known selected locus. Although robust algorithms could identify neutral loci in our simulations, they did not universally improve power to detect selection. Overall, a composite statistic that measured a robust multivariate distance from rank-based P-values performed the best. We found that composite measures of selection could improve the signal of selection in many cases, but they were not a panacea and their power is limited by the power of the univariate statistics they summarize. Since genome scans are widely used, improving inference for prioritizing candidate genes may be beneficial to medicine, agriculture, and breeding. Our results also have application to outlier detection in high-dimensional datasets and to combining results in meta-analyses in many disciplines. The compound measures we evaluate are implemented in the r package minotaur