Machine‐learning‐based detection of adaptive divergence of the stream mayfly <i>Ephemera strigata</i> populations

Li, Bin; Yaegashi, Sakiko; Carvajal, Thaddeus M.; Gamboa, Maribet; Chiu, Ming‐Chih; Ren, Zongming; Watanabe, Kozo

doi:10.1002/ece3.6398

Cited by 2 publications

(2 citation statements)

References 79 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although inferences of local adaptation in terms of environmental heterogeneity are increasingly recognized in empirical studies (e.g. Li et al, 2020; Nukazawa et al, 2015; Reis et al, 2015), these inferences can only be plausible when environmentally mediated gene flow and genetic drift are considered realistically.…”

Section: Discussionmentioning

confidence: 99%

“…Although much attention has been paid to environmental influences on adaptive genetic divergences that are detected as outliers of hypothetical neutral processes (e.g. Li et al, 2020; Nukazawa et al, 2015; Reis et al, 2015), the mechanisms behind environmentally mediated, non‐adaptive processes (i.e. genetic drift and gene flow) are still not clear.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Chiu

Nukazawa

Resh

et al. 2022

Journal of Biogeography

Self Cite

View full text Add to dashboard Cite

Aim: Both gene flow and genetic drift can affect the genetic diversity of interacting populations. However, the influence of the complex evolutionary histories of species may be obscured by simplified assumptions about how these roles operate in nature. To examine this issue, we mechanistically explored the relative importance of environmentmediated gene flow and genetic drift in determining genetic structure and diversity.Location: Northeast Japan.Taxon: Caddisflies (Hydropsyche orientalis and Stenopsyche marmorata; Insecta: Trichoptera). Methods:We developed a novel Bayesian framework to evaluate the individual and joint effects of these two evolutionary processes that operate through environmental networks on genetic structuring of these populations. Modelling was performed using genetic information on the two sympatric species of stream-macroinvertebrates having aerial-dispersing adult stages. Genotyping data for samples from 41 and 30 locations were used at 98 and 162 neutral amplified fragment length polymorphism loci in H. orientalis and S. marmorata, respectively.Results: Environmental conditions and habitat quality can mediate gene flow by providing different levels of resistance to interpopulation dispersal. Similarly, genetic drift may be altered through variable environmental limitations on population size.Compared to the role of genetic drift, gene flow has a greater influence on the genetic diversity of both species examined. The elevated gene-flow levels generally increase the genetic diversity in S. marmorata, whereas no obvious trend is evident across the various degrees of gene flow in H. orientalis.Main Conclusions: Information on environmentally mediated gene flow and genetic drift, coupled with different dispersal strategies in habitat networks, may influence management efforts and success. Consequently, evolutionary processes that act individually or jointly in determining genetic structure are important in the effectiveness of management strategies used for restoration and conservation efforts.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Chiu

Nukazawa

Resh

et al. 2022

Journal of Biogeography

Self Cite

View full text Add to dashboard Cite

show abstract

Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits

Li,

Luo,

et al. 2023

Front. Ecol. Evol.

Self Cite

View full text Add to dashboard Cite

In the face of rapid environmental changes, understanding and monitoring biological traits and functional diversity are crucial for effective biomonitoring. However, when it comes to freshwater macroinvertebrates, a significant dearth of biological trait data poses a major challenge. In this opinion article, we put forward a machine-learning framework that incorporates phylogenetic conservatism and trait collinearity, aiming to provide a better vision for predicting macroinvertebrate traits in freshwater ecosystems. By adopting this proposed framework, we can advance biomonitoring efforts in freshwater ecosystems. Accurate predictions of macroinvertebrate traits enable us to assess functional diversity, identify environmental stressors, and monitor ecosystem health more effectively. This information is vital for making informed decisions regarding conservation and management strategies, especially in the context of rapidly changing environments.

show abstract

Machine‐learning‐based detection of adaptive divergence of the stream mayfly Ephemera strigata populations

Cited by 2 publications

References 79 publications

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits

Contact Info

Product

Resources

About