The Blueprint for Survival: The Blue Dasher Dragonfly as a Model for Urban Adaptation
Tolman, E. R.; Gamett, E.; Beatty, C. D.; Goodman, A.; Hahn, B.; Benischek, C.; Castillo, G.; Derdarian, E.; Fernandez-Juarez, S.; Gallafent, B.; Jenson, J.; Jordan, D.; Schneider, M.; Salazar, R.; Tamano, T.; Wei, M.; Idec, J.; Guralnick, R.; Ware, J. L.; Kohli, M. K.
Show abstract
Human alteration of natural environments and habitats is a major driver of species decline. However, a handful of species thrive in human altered environments. The biology, distribution, population structure, and molecular adaptations enabling certain species to thrive in human-altered habitats are not well understood. Here, we evaluate the population and functional genomics, ecological niche and distributions, and geometric morphometrics of the blue dasher (Pachydiplax longipennis), one of the most ubiquitously observed insects in human altered habitats. Using resequencing data we identify a number of genes involved with the success of the blue dasher in human altered habitats, including loci contributing to immune function and response to oxidative stress. Some genes related to these functions are found in regions of strong population structure, while others are not, potentially indicating both regional and widespread adaptations to urban environments within this species. Using one of the most robust locality datasets for any species to date, we also generate habitat suitability predictions which show that P. longipennis has spread with urbanization, suggesting humans have created suitable habitat for this species. These results complement morphological and genomic data showing P. longipennis (particularly East of the Rocky Mountains) has the capacity to rapidly disperse to newly suitable habitats. Given the shared barriers to colonizing an urban habitat, we expect that many of the adaptations we have identified in P. longipennis can be used for predicting what animals might succeed in urban habitats more generally.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Temperature accounts for the biodiversity of a hyperdiverse group of insects in urban Los Angeles 95%
- Species ecology explains the various spatial components of genetic diversity in tropical reef fishes 94%
- Light environment influences mating behaviours during the early stages of divergence in tropical butterflies 94%
Similar papers in this journal
- Coping with Pleistocene climatic fluctuations: demographic responses in remote endemic reef fishes 96%
- A tale of two shrimps - Speciation and demography of two sympatric shrimp species from hydrothermal vents 95%
- Complex genetic patterns and distribution limits mediated by native congeners of the worldwide invasive red-eared slider turtle 94%
Similar papers in this journal
- Are 150 km of open sea enough? Gene flow and population differentiation in a bat-pollinated columnar cactus 97%
- Comparison of genetic variation between rare and common congeners of Dipodomys with estimates of contemporary and historical effective population size 95%
- Historical biogeography supports Point Conception as the site of turnover between temperate East Pacific ichthyofaunas 94%
Similar papers in this journal
- Landscape and climatic features drive genetic differentiation processes in a South American coastal plant 96%
- Host-plant adaptation as a driver of incipient speciation in the fall armyworm (Spodoptera frugiperda) 94%
- Environmental differences explain subtle yet detectable genetic structure in a widespread pollinator 94%
Similar papers in this journal
- Panmixia across elevation in thermally sensitive Andean dung beetles 93%
- Extensive admixture among karst-obligate salamandersreveals evidence of recent divergence and gene exchangethrough aquifers 93%
- Evolutionary history of an Alpine archaeognath(Machilis pallida) - insights from different variant types 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.