Bothwell Landscape Conservation Genetics Lab

Welcome! Our lab works at the intersection of population genetics, landscape ecology, and spatial statistical modeling to investigate how environmental and anthropogenic factors are influencing the distribution of species and genetic variation across the landscape.

Our primary goal is to support conservation genetic management of wild populations facing rapid global change, while also addressing fundamental questions in ecology and evolution. We focus on applied solutions for native foundation and keystone species. Identifying spatial conservation priorities for species of large effect serves as an umbrella conservation model, with the co-benefit of maximizing limited conservation resources to also protect the wealth of associated biodiversity and ecosystem services these species support.

Our research also aims to contribute theoretical advances to the fields of landscape genetics, community genetics, and macrosystems ecology by applying a "genes-to-ecosystems" approach to elucidate the role of fine-scale, micro-evolutionary processes in driving the emergence of landscape to continental-scale patterns (e.g., emergent properties of communities and ecosystems).

Current projects

Landscape connectivity and conservation genetics of ruffed grouse (GADNR)

Estimating population size and genetic diversity of the central Georgia black bear population (GADNR)

Genomics of drought adaptation in endangered Eucalyptus woodlands (Australian Research Council)  

 

Research Themes

Spatial conservation priorities for threatened species

Effective conservation requires maintenance of the processes that support species evolutionary potential and capacity to respond to change. Species exist within a landscape matrix that can either support or constrain movement and mating. Our work developing genetic connectivity models for threatened riparian forests (Bothwell et al. 2017) and landscape resistance models for African lion (Cushman et al. 2018) illustrates how genetic, geospatial, and movement data can be combined to identify environmental factors influencing gene flow and the nature of barriers to dispersal (e.g., natural, invasive competitors, land use change). Spatially explicit models can then be applied to identify locations of key dispersal corridors, conflict hotspots, and isolated populations that may be candidates for translocations and assisted gene flow. Genetically informed ecological niche models (gENMs, Bothwell et al. 2021) are another tool our lab has developed to identify evolutionarily significant management units (ESUs), for predicting vulnerabilities to global change, and locating core climate change refugia for spatial prioritization of conservation reserves. 

Government, conservation, and landcare partnerships are essential to our work. Our research aims to support the conservation needs and priorities of wildlife and land managers through collaboration at each stage of the research process to provide practical solutions to those best poised to translate them into action. If your organization is working to manage a species of concern and could benefit from such modeling work, please reach out to Dr. Bothwell to discuss potential opportunities for collaboration.

Mapping the adaptive landscape for climate resilient forest conservation and restoration 

In addition to landscape genetic modeling, our lab also employs common garden and greenhouse experiments to investigate the genetic basis of local adaptation. Our research uses a synergistic framework, the G x E x P trifecta, to identify, validate, and map ecologically important variation across the landscape. 

G x E: We use field collections, population genetic and geospatial data to develop landscape genetic models that test hypothesized drivers of micro-evolutionary processes (e.g., gene flow, local adaptation). Models are then used to map the geographic distribution of ecologically important variation across the landscape to assist managers in identifying ESUs, core conservation areas (e.g., high diversity, climate refugia), and connectivity corridors that are critical for maintaining gene flow and species’ capacity to adapt to environmental change. 

G x P: Putative adaptive relationships identified via modeling are then validated with replicated, controlled experiments using a combination of common garden and greenhouse trials coupled with genome-wide association studies (GWAS). Identification of ecologically important traits with high heritability can then be used to inform selection of tree stock to improve likelihood of reforestation success (e.g., matching drought-tolerant genotypes to low rainfall target sites). 

P x E: Experimental manipulations (e.g., growing trees in controlled climate chambers and provenance trials) are employed to test the effects of predicted climate change on traits of interest (e.g., growth, phenology, water use efficiency). Research uniting these approaches is a key goal for advancing the field of landscape genetics (Sork et al. 2013).

Ongoing work with collaborators at the Australian National University is investigating drought adaptation strategies in several native eucalyptus species. 

 

Improving model performance and predictive power

As G.P.E. Box famously stated, “All models are wrong, but some are useful.” Ecological niche and habitat suitability models are some of the best tools available for predicting future climate change impacts on wild populations and informing conservation management decision making, yet these models are necessarily simplifications of nature. An ongoing aim in our lab is working to reduce sources of uncertainty and building greater complexity into model predictions to better approximate reality. Two areas of focus are incorporating functional genomics and scaling relationships into predictive modeling frameworks. For example, using a large camera trap dataset and environmental GIS data, we used multi-scale optimization to determine at what scale various habitat features were being utilized by clouded leopards. Scale-optimized habitat data was then used as input for mapping core areas and identifying connectivity corridors necessary to support clouded leopard conservation across Southeast Asia (Macdonald, Bothwell et al. 2019).

 

Select Publications

Please visit Google Scholar for an updated list of publications.

Bothwell, HM, AR Keith, JB Hull, HF Cooper, LV Andrews, C Wehenkel, … GJ Allan (In review) Interspecific genetic relationships predict macrosystem community assembly patterns across southwestern North America. 

Ahrens, C, S Chen, H Bothwell, K Stuart, R Edwards, J Bragg (In review) Tansley Review: A genome-wide view of adaptation across the landscape. New Phytologist.

    

Chiaverini, L, DW Macdonald, HM Bothwell, AJ Hearn, SM Cheyne, I Haidir, … SA Cushman (2022) Multi-scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands. Animal Conservation, 25, 660-679.

López-Márquez, V, SA Cushman, J Templado, HY Wan, HM Bothwell, A Machordom (2021) Genetic connectivity of two marine gastropods in the Mediterranean Sea: Seascape genetics reveals species-specific oceanographic drivers of gene flow. Molecular Ecology, 30, 4608-4629.

Ahrens, CW, R Jordan, J Bragg, PA Harrison, T Hopley, HM Bothwell, … R Andrew (2021) Regarding the F-word: The effects of data Filtering on inferred genotype-environment associations. Molecular Ecology Resources, 21, 1460-1474.

       

Bothwell, HM, LM Evans, EI Hersch-Green, SA Woolbright, GJ Allan, TG Whitham (2021) Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts. Ecological Applications, 31, e02254. 

    

Hultine, KR, GJ Allan, D Blasini, HM Bothwell, A Cadmus, HF Cooper, … TG Whitham (2020) Adaptive capacity in the foundation tree species Populus fremontii: implications for resilience to climate change and non-native species invasion in the American Southwest. Conservation Physiology, 8, coaa061.

    

Macdonald, DW, L Chiaverini, HM Bothwell, Ż Kaszta, E Ash, … AJ Hearn (2020) Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation? Biodiversity and Conservation, 29, 4035-4057.

    

Whitham, TG, CA Gehring, HM Bothwell, HF Cooper, JB Hull, GJ Allan, … RK Bangert (2020) Using the Southwest Experimental Garden Array to enhance riparian restoration in response to global change: Identifying and deploying genotypes and populations for current and future environments.  In Riparian research and management: Past, present, future. Volume 2.  Gen. Tech. Rep. RMRS-GTR-411. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 63-79.

    

Macdonald, DW, *HM Bothwell, Z Kaszta, E Ash, G Bolongon, D Burnham, … SA Cushman (2019) Multi-scale modeling identifies spatial conservation priorities for mainland clouded leopards (Neofelis nebulosa). Diversity & Distributions, 25, 1639-1654. *Joint First Author

Murray, KD, JK Janes, A Jones, HM Bothwell, RL Andrew, JO Borevitz. (2019) Landscape drivers of genomic diversity and divergence in woodland Eucalyptus. Molecular Ecology, 28, 5232-5247.

     

López-Márquez, V, SA Cushman, J Templado, HY Wan, HM Bothwell, C Krushcel, … A Machordom. (2019) Seascape genetics and connectivity modelling for an endangered Mediterranean coral in the northern Ionian and Adriatic seas. Landscape Ecology, 34, 2649-2668.