Joint species movement modeling: how do traits influence movements?

Joint species distribution modeling has enabled researchers to move from species-level to community-level analyses, leading to statistically more efficient and ecologically more informative use of data. Here, we propose joint species movement modeling (JSMM) as an analogous approach that enables inferring both species- and community-level movement parameters from multispecies movement data. The species-level movement parameters are modeled as a function of species traits and phylogenetic relationships, allowing one to ask how species traits influence movements, and whether phylogenetically related species are similar in their movement behavior. We illustrate the modeling framework with two contrasting case studies: a stochastic redistribution model for direct observations of bird movements and a spatially structured diffusion model for capture-recapture data on moth movements. In both cases, the JSMM identified several traits that explain differences in movement behavior among species, such as movement rate increasing with body size in both birds and moths. We show with simulations that the JSMM approach increases precision of species-specific parameter estimates by borrowing information from other species that are closely related or have similar traits. The JSMM framework is applicable for many kinds of data, and it facilitates a mechanistic understanding of the causes and consequences of interspecific variation in movement behavior.

Unbiased probabilistic taxonomic classification for DNA barcoding.

MOTIVATION: When targeted to a barcoding region, high-throughput sequencing can be used to identify species or operational taxonomical units from environmental samples, and thus to study the diversity and structure of species communities. Although there are many methods which provide confidence scores for assigning taxonomic affiliations, it is not straightforward to translate these values to unbiased probabilities. We present a probabilistic method for taxonomical classification (PROTAX) of DNA sequences. Given a pre-defined taxonomical tree structure that is partially populated by reference sequences, PROTAX decomposes the probability of one to the set of all possible outcomes. PROTAX accounts for species that are present in the taxonomy but that do not have reference sequences, the possibility of unknown taxonomical units, as well as mislabeled reference sequences. PROTAX is based on a statistical multinomial regression model, and it can utilize any kind of sequence similarity measures or the outputs of other classifiers as predictors. RESULTS: We demonstrate the performance of PROTAX by using as predictors the output from BLAST, the phylogenetic classification software TIPP, and the RDP classifier. We show that PROTAX improves the predictions of the baseline implementations of TIPP and RDP classifiers, and that it is able to combine complementary information provided by BLAST and TIPP, resulting in accurate and unbiased classifications even with very challenging cases such as 50% mislabeling of reference sequences. AVAILABILITY AND IMPLEMENTATION: Perl/R implementation of PROTAX is available at http://www.helsinki.fi/science/metapop/Software.htm CONTACT: panu.somervuo@helsinki.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Local adaptation in a changing world: the roles of gene-flow, mutation, and sexual reproduction.

In spatially heterogeneous environments, the processes of gene flow, mutation, and sexual reproduction generate local genetic variation and thus provide material for local adaptation. On the other hand, these processes interchange maladapted for adapted genes and so, in each case, the net influence may be to reduce local adaptation. Previous work has indicated that this is the case in stable populations, yet it is less clear how the factors play out during population growth, and in the face of temporal environmental stochasticity. We address this issue with a spatially explicit, stochastic model. We find that dispersal, mutation, and sexual reproduction can all accelerate local adaptation in growing populations, although their respective roles may depend on the genetic make-up of the founding population. All three processes reduce local adaptation, however, in the long term, that is when population growth becomes balanced by density-dependent competition. These relationships are qualitatively maintained, although quantitatively reduced, if the resources are locally ephemeral. Our results suggest that species with high levels of local adaptation within their ranges may not be the same species that harbor potential for rapid local adaptation during population expansion.