Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.
By M. Fitzpatrick, Stephen R. Keller. Ecology Letters, 2015
621 citations recorded by the source corpus.
Subfields: Ecological Modeling, Genetics
View Paper: https://www.semanticscholar.org/paper/e9724921423fcb11057a2b4638e04470ba214531
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecological Modeling
- John Stanton-Geddes
- Andrew Storfer
- M. Whitlock
- Niko Balkenhol
- Nicholas Gotelli
- S. Heyden
- K. Selkoe
- Werner Ulrich
In Genetics
- John Stanton-Geddes
- Andrew Storfer
- M. Whitlock
- Stéphane Joost
- Sean M. Hoban
- Niko Balkenhol
- Konstantin V. Krutovsky
- D. Lowry
Citations in the corpus
- Climate change, genetic markers and species distribution modelling (2015, positive): “…the future, the combination of high-density molecular data (Santure et al., 2013) and phenotype data for ecologically important traits (Etterson, 2004) may allow for improved forecasts that take into account the potential for evolutionary change (Hancock et al., 2011; Fitzpatrick & Keller , 2015).”
- Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “…borrows from techniques that examine changes in species community composition through space, but instead assesses the effect of environmental gradients on changing allele frequencies using Generalized Dissimilarity Modeling (GDM) or Gradient Forest (GF) analysis (Fitzpatrick and Keller 2015).”
- Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, positive): “…to develop and test multivariate approaches that can detect covarying signals across multiple loci (e.g., Turchin et al. 2012; Berg and Coop 2014; Fitzpatrick and Keller 2014; Forester et al. 2015; Laporte et al. 2016), as it is still not known how frequently signals of local adaptation can be…”
- Potential and limits of exploitation of crop wild relatives for pea, lentil, and chickpea improvement (2020, methodology): “Newer methods without the biases of Fst have emerged or associations of SNPs with climatic variables and are available with improving power to detect SNP-environment associations (e.g., Baypass, bayscan, bayenv2, Bedassle, and Gradient forests) (Fitzpatrick & Keller, 2015).”