Genetic monitoring as a promising tool for conservation and management

152

186

Intraspecific variation is a major component of biodiversity, yet it has received relatively little attention from governmental and nongovernmental organizations, especially with regard to conservation plans and the management of wild species. This omission is ill‐advised because phenotypic and genetic variations within and among populations can have dramatic effects on ecological and evolutionary processes, including responses to environmental change, the maintenance of species diversity, and ecological stability and resilience. At the same time, environmental changes associated with many human activities, such as land use and climate change, have dramatic and often negative impacts on intraspecific variation. We argue for the need for local, regional, and global programs to monitor intraspecific genetic variation. We suggest that such monitoring should include two main strategies: (i) intensive monitoring of multiple types of genetic variation in selected species and (ii) broad‐brush modeling for representative species for predicting changes in variation as a function of changes in population size and range extent. Overall, we call for collaborative efforts to initiate the urgently needed monitoring of intraspecific variation.

Section: Toward a Monitoring System For Intraspecific Variationmentioning

confidence: 99%

Understanding and monitoring the consequences of human impacts on intraspecific variation

Mimura

Yahara

Faith

et al. 2016

152

186

“…If adaptive genetic variants are identified, individuals with genotypes more likely to have higher fitness in local environments could be used in breeding, reinforcement, or reintroduction programs to help ensure success of those programs (He, Johansson, & Heath, 2016; Kelly & Phillips, 2016; Sgro et al., 2011). Managers could also monitor the frequency of these genetic variants over time to gauge the genetic health of a population, or to assess changes in allele frequencies following management interventions (Schwartz et al., 2007; Shafer et al., 2015). …”

Section: Introductionmentioning

confidence: 99%

Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation

Flanagan

Forester

Latch

et al. 2017

185

189

Identifying and monitoring locally adaptive genetic variation can have direct utility for conserving species at risk, especially when management may include actions such as translocations for restoration, genetic rescue, or assisted gene flow. However, genomic studies of local adaptation require careful planning to be successful, and in some cases may not be a worthwhile use of resources. Here, we offer an adaptive management framework to help conservation biologists and managers decide when genomics is likely to be effective in detecting local adaptation, and how to plan assessment and monitoring of adaptive variation to address conservation objectives. Studies of adaptive variation using genomic tools will inform conservation actions in many cases, including applications such as assisted gene flow and identifying conservation units. In others, assessing genetic diversity, inbreeding, and demographics using selectively neutral genetic markers may be most useful. And in some cases, local adaptation may be assessed more efficiently using alternative approaches such as common garden experiments. Here, we identify key considerations of genomics studies of locally adaptive variation, provide a road map for successful collaborations with genomics experts including key issues for study design and data analysis, and offer guidelines for interpreting and using results from genomic assessments to inform monitoring programs and conservation actions.

“…However, further inference about the effects of specific biological and ecological processes that shape SGS is often limited because sampling is typically conducted at a single point in time (Goetze, Andrews, Peijnenburg, Portner, & Norton, 2015). These “snapshots” of data only measure populations in their current state (Anderson et al., 2010; Martensen, Saura, & Fortin, 2017; Schwartz, Luikart, & Waples, 2007). Therefore, inferences can be problematic when studying long‐lived and iteroparous species such as black bears ( Ursus americanus ) that are sensitive to landscape features (i.e., land cover) that are expected to undergo future modification (Cushman, McKelvey, Hayden, & Schwartz, 2006; Cushman, Wasserman, Landguth, & Shirk, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Time series data are increasingly being applied to understand biological processes, most notably in the field of ecology and population genetics (Lindenmayer et al., 2012; Schwartz et al., 2007). A number of empirical studies have used temporal genetic data to contrast historical and contemporary genetic diversity (Wandeler, Hoeck, & Keller, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, there has been increasing interest to include genetic monitoring as an important component of management programs (De Barba et al., 2010; Rudnick, Katzner, Bragin, Rhodes, & Dewoody, 2005; Steele et al., 2013). Assessments of changes in genetic diversity provide a means to evaluate trends in connectivity, to infer demographic histories of populations, and to gauge loss of genetic diversity (Schwartz et al., 2007). However, implementing such studies is challenging because sampling the same population(s) at regular intervals is difficult despite the potential value for conservation and management.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

Draheim

Moore

Fortin

et al. 2018

Landscape genetic studies typically focus on the evolutionary processes that give rise to spatial patterns that are quantified at a single point in time. Although landscape change is widely recognized as a strong driver of microevolutionary processes, few landscape genetic studies have directly evaluated the change in spatial genetic structure (SGS) over time with concurrent changes in landscape pattern. We introduce a novel approach to analyze landscape genetic data through time. We demonstrate this approach using genotyped samples (n = 569) from a large black bear (Ursus americanus) population in Michigan (USA) that were harvested during 3 years (2002, 2006, and 2010). We identified areas that were consistently occupied over this 9‐year period and quantified temporal variation in SGS. Then, we evaluated alternative hypotheses about effects of changes in landscape features (e.g., deforestation or crop conversion) on fine‐scale SGS among years using spatial autoregressive modeling and model selection. Relative measures of landscape change such as magnitude of landscape change (i.e., number of patches changing from suitable to unsuitable states or vice versa), and during later periods, measures of fragmentation (i.e., patch aggregation and cohesion) were associated with change in SGS. Our results stress the importance of conducting time series studies for the conservation and management of wildlife inhabiting rapidly changing landscapes.