Topic: Conserving genes for the future: Improving statistical approaches for genetic resource monitoring
Meeting dates: November 7-9, 2016
Location: NIMBioS at the University of Tennessee, Knoxville
Sean Hoban, Tree Conservation Biologist, Morton Arboretum, Lisle, IL
Michael W. Bruford, Biosciences, Cardiff Univ., Wales, UK
Louis Bernatchez, Biology, Univ. Laval, Canada
Erin Landguth, Computational Ecology Laboratory, Univ. of Montana
Objectives: Genetic variation underlies key ecological processes and economic activities, and is thus a collective resource for sustaining future species, ecosystems and human society. As biodiversity loss accelerates and environmental challenges mount, there is need for quantitative evaluation of the status and trends of genetic biodiversity. However, appropriate mathematical tools for this evaluation are lacking. Despite major recent advances in obtaining genetic data, current genetic metrics are piecemeal, may be incomparable across studies and data types, and are often collected and presented in an ad hoc manner.
There is an urgent need to conceive and develop standard, summary-level genetic indices that are robust, easily interpretable and tractable across diverse datasets. In this workshop we developed a framework for constructing these mathematical tools. Specifically, we: identified key attributes of successful indices in biodiversity science, surveyed and critiqued existing genetic metrics, and identified potential statistical approaches suitable for summarizing the highly dimensional nature of genetic data. We hope the workshop inspired several synthesis papers, new collaborations, and research funding proposals for developing, evaluating and distributing new genetic indices.
The inherent challenges of our aims require integration of mathematical and computational approaches with ecology, genetics, and biodiversity science. Our workshop was designed to help unlock the conservation potential of genomics research, and ensure that genetic and non-genetic factors are considered in quantitative fashion when evaluating and managing wild and cultivated species. More broadly, we laid a foundation for developing new theory and approaches for describing, quantifying, and interpreting the complex, multidimensional information contained in biodiversity datasets.
Presentations were available for viewing via live streaming during the workshop.
Summary Report. Genetic variation underlies key ecological processes and economic activities and is a collective resource for sustaining human society. As biodiversity loss accelerates and environmental challenges mount, there is need for quantitative evaluation of the status and trends of genetic biodiversity. However, appropriate mathematical tools for this evaluation are lacking, even as genetic datasets and genetic studies are larger and more complex than ever. Our group formed around the recognition that there is an urgent need to summarize and integrate genetic data in ways that are easily interpretable and tractable across diverse datasets. We also recognized that other fields of biodiversity science and ecological statistics may have solutions to this need or may face similar challenges. In this workshop we had 10 speakers, and critical discussion following the speakers, in which we identified key attributes of successful data analysis in biodiversity science, and surveyed and critiqued existing genetic metrics. As a large group we then brainstormed potential, feasible approaches for summarizing, representing and translating the highly dimensional nature of genetic data. We then formed five groups, each tackling a different approach at a different scale. Each group focused on different elements of the main topic, from theoretical to practical, from mathematical developments to review articles. Our workshop led to plans for synthesis papers, new collaborations, and research proposals for developing, evaluating and distributing new mathematical and statistical approaches. We accomplished our workshop aims: to help unlock the conservation potential of genomics research, ensure that genetic and non-genetic factors can be considered in quantitative fashion, and lay a foundation for new theory and approaches for describing, quantifying, and interpreting the complex, multidimensional information contained in biodiversity datasets. /p>
Flanagan SP, Forester BR, Latch EK, Aitken SN, Hoban S. 2017. Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evolutionary Applications. [Online]
Gaggiotti OE, Chao A, Peres‐Neto P, Chiu CH, Edwards C, Fortin MJ, Jost L, Richards CM, Selkoe KA. 2018. Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales. Evolutionary Applications. [Online]
Hunter ME, Hoban SM, Bruford MW, Segelbacher G, Bernatchez L. 2018. Next‐generation conservation genetics and biodiversity monitoring. Evolutionary Applications 11(7): 1029-34. [Online]
Jost L, Archer F, Flanagan S, Gaggiotti O, Hoban S, Latch E. Accepted 2017. Differentiation measures for conservation genetics. Evolutionary Applications. [Online]
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