Exploratory analysis of multi-trait coadaptations in light of population history.

During the process of range expansion, populations encounter a variety of environments. They respond to the local environments by modifying their mutually interacting traits. Common approaches of landscape analysis include first focusing on the genes that undergo diversifying selection or directional selection in response to environmental variation.

Population structure of chum salmon and selection on the markers collected for stock identification.

Genetic stock identification (GSI) is a major management tool of Pacific salmon ( Spp.) that has provided rich genetic baseline data of allozymes, microsatellites, and single-nucleotide polymorphisms (SNPs) across the Pacific Rim. Here, we analyzed published data sets for adult chum salmon (), namely 10 microsatellites, 53 SNPs, and a mitochondrial DNA locus (mtDNA3, control region, and NADH-3 combined) in samples from 495 locations in the same distribution range ( = 61,813).

Plant community assembly in suburban vacant lots depends on earthmoving legacy, habitat connectivity, and current mowing frequency.

In suburban regions, vacant lots potentially offer significant opportunities for biodiversity conservation. Recently, in Japan, due to an economic recession, some previously developed lands have become vacant. Little is known, however, about the legacy of earlier earthmoving, which involves topsoil removal and ground leveling before residential construction, on plant community composition in such vacant lots.

Estimating range expansion of wildlife in heterogeneous landscapes: A spatially explicit state-space matrix model coupled with an improved numerical integration technique.

Dispersal as well as population growth is a key demographic process that determines population dynamics. However, determining the effects of environmental covariates on dispersal from spatial-temporal abundance proxy data is challenging owing to the complexity of model specification for directional dispersal permeability and the extremely high computational loads for numerical integration. In this paper, we present a case study estimating how environmental covariates affect the dispersal of Japanese sika deer by developing a spatially explicit state-space matrix model coupled with an improved numerical integration technique (Markov chain Monte Carlo with particle filters).