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9% to 96.2%. Finally, we discuss the trade-offs and complexities of the decision-making process that drives SNP panel development, optimization, and testing.In wing-polymorphic insects, wing morphs differ not only in dispersal capability but also in life history traits because of trade-offs between flight capability and reproduction. When the fitness benefits and costs of producing wings differ between males and females, sex-specific trade-offs can result in sex differences in the frequency of long-winged individuals. Furthermore, the social environment during development affects sex differences in wing development, but few empirical tests of this phenomenon have been performed to date. Here, I used the wing-dimorphic water strider Tenagogerris euphrosyne to test how rearing density and sex ratio affect the sex-specific development of long-winged dispersing morphs (i.e., sex-specific macroptery). I also used a full-sib, split-family breeding design to assess genetic effects on density-dependent, sex-specific macroptery. I reared water strider nymphs at either high or low densities and measured their wing development. I found that long-winged morphs developed more frequently in males than in females when individuals were reared in a high-density environment. However, the frequency of long-winged morphs was not biased according to sex when individuals were reared in a low-density environment. In addition, full-sib males and females showed similar macroptery incidence rates at low nymphal density, whereas the macroptery incidence rates differed between full-sib males and females at high nymphal density. Thus complex gene-by-environment-by-sex interactions may explain the density-specific levels of sex bias in macroptery, although this interpretation should be treated with some caution. Overall, my study provides empirical evidence for density-specific, sex-biased wing development. My findings suggest that social factors as well as abiotic factors can be important in determining sex-biased wing development in insects.Darkness and low biomass make it challenging for animals to find and identify one another in the deep sea. While spatiotemporal variation in bioluminescence is thought to underlie mate recognition for some species, its role in conspecific recognition remains unclear. The deep-sea shrimp genus, Sergestes sensu lato (s.l.), is one group that is characterized by species-specific variation in light organ arrangement, providing us the opportunity to test whether organ variation permits recognition to the species level. To test this, we analyzed the visual capabilities of three species of Sergestes s.l. in order to (a) test for sexual dimorphism in eye-to-body size scaling relationships, (b) model the visual ranges (i.e., sighting distances) over which these shrimps can detect intraspecific bioluminescence, and (c) assess the maximum possible spatial resolution of the eyes of these shrimps to estimate their capacity to distinguish the light organs of each species. Our results showed that relative eye size scaled negatively with body length across species and without sexual dimorphism. Though the three species appear capable of detecting one another's bioluminescence over distances ranging from less then 1 to ~6 m, their limited spatial resolution suggests they cannot resolve light organ variation for the purpose of conspecific recognition. Our findings point to factors other than conspecific recognition (e.g., neutral drift, phenotypic constraint) that have led to the extensive diversification of light organs in Sergestes s.l and impart caution about interpreting ecological significance of visual characters based on the resolution of human vision. This work provides new insight into deep-sea animal interaction, supporting the idea that-at least for these mesopelagic shrimps-nonvisual signals may be required for conspecific recognition.Fish migrations are energetically costly, especially when moving between freshwater and saltwater, but are a viable strategy for Pacific salmon and trout (Oncorhynchus spp.) due to the advantageous resources available at various life stages. Anadromous steelhead (O. mykiss) migrate vast distances and exhibit variation for adult migration phenotypes that have a genetic basis at candidate genes known as greb1L and rock1. We examined the distribution of genetic variation at 13 candidate markers spanning greb1L, intergenic, and rock1 regions versus 226 neutral markers for 113 populations (n = 9,471) of steelhead from inland and coastal lineages in the Columbia River. Patterns of population structure with neutral markers reflected genetic similarity by geographic region as demonstrated in previous studies, but candidate markers clustered populations by genetic variation associated with adult migration timing. Mature alleles for late migration had the highest frequency overall in steelhead populations throughout the migration distance, temperature, isothermality, and annual precipitation. This study improves our understanding of the spatial distribution of genetic variation underlying adult migration timing in steelhead as well as associated environmental factors and has direct conservation and management implications.Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. [https://www.selleckchem.com/products/sp2509.html SP2509] Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km2 spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated (a) energy-water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences.