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Genetic Monitoring As a Promising Tool for Conservation and Management University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Publications, Agencies and Staff of the U.S. Department of Commerce U.S. Department of Commerce 9-2006 Genetic monitoring as a promising tool for conservation and management Michael K. Schwartz USDA Forest Service, [email protected] Gordon Luikart University of Montana, [email protected] Robin Waples NOAA, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/usdeptcommercepub Schwartz, Michael K.; Luikart, Gordon; and Waples, Robin, "Genetic monitoring as a promising tool for conservation and management" (2006). Publications, Agencies and Staff of the U.S. Department of Commerce. 482. https://digitalcommons.unl.edu/usdeptcommercepub/482 This Article is brought to you for free and open access by the U.S. Department of Commerce at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Publications, Agencies and Staff of the U.S. Department of Commerce by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Review TRENDS in Ecology and Evolution Vol.22 No.1 Genetic monitoring as a promising tool for conservation and management Michael K. Schwartz1, Gordon Luikart2,3 and Robin S. Waples4 1 USDA Forest Service, Rocky Mountain Research Station, 800 E. Beckwith Avenue, Missoula, MT 59801, USA 2 Center for Investigation of Biodiversity and Genetic Resources, University of Porto, Campus Agra`rio de Vaira˜ o 4485-661, Portugal 3 Division of Biological Sciences, University of Montana, Missoula, MT 59812, USA 4 National Marine Fisheries Service, Northwest Fisheries Science Center, 2725 Montlake Boulevard East, Seattle, WA 98112, USA In response to ever-increasing anthropogenic changes to markets [8]. We consider this an assessment because data natural ecosystems, regional, national and international from a seven-year period were pooled to provide a single organizations have established guidelines for point-estimate. A monitoring study of whale-meat markets monitoring biological diversity. Most monitoring would evaluate whether the fraction of protected species programs, however, do not take full advantage of the changes over time. potential afforded by molecular genetic markers, which We separate genetic monitoring into two categories: can provide information relevant to both ecological and Category I includes the use of diagnostic molecular evolutionary time frames, while costing less and being markers for traditional population monitoring through more sensitive and reliable than traditional monitoring the identification of individuals, populations, and species, approaches. As several molecular and computational whereas Category II includes the use of genetic markers to approaches are relatively new, many technical and monitor population genetic parameters (Figure 1). theoretical issues remain to be resolved. Here, we illustrate how DNA and population genetic data can Category I. Diagnostic molecular markers for traditional provide valuable information, often unattainable population monitoring via other approaches, for monitoring species of Diagnostic assays that use molecular markers can identify management, conservation and ecological interest. individuals, populations, species and other taxonomic levels. We distinguish methods using molecular markers Introduction to identify individuals (Figure 1, Category Ia) from those It is becoming increasingly important to monitor identifying species or other groups (genera, subspecies or unintended consequences of anthropogenic changes on populations; Category Ib). natural populations. Although many national and international organizations have established principles Category Ia. Identifying individuals and strategies for monitoring biological diversity [1–3], Molecular tags can be used in lieu of physical tags or little use is made of the benefits afforded by molecular natural markings to identify individuals. Data generated genetic markers. Meanwhile, new laboratory and statisti- from such tags are often used in traditional population cal techniques now enable the use of molecular markers for ecology models estimating abundance or vital rates. Below genetic monitoring of wild populations [4–7]. Although the we describe several metrics commonly monitored in wild term ‘genetic monitoring’ is becoming widely used through- populations that have profited from DNA-based methods. out the ecological, evolutionary, wildlife and conservation Abundance The most common metric used for monitor- fields, there is no consensus, and some confusion, regard- ing animal populations is abundance (Box 1). With rare ing its definition. To some, genetic monitoring is simply the species, abundance indices, such as the number of animals use of genetic data to study demography or more complex detected, have proven relatively inexpensive to obtain evolutionary and ecological processes, whereas to others it through non-invasive genetic sampling. DNA-based abun- implies systematic measurements of population genetic dance indices are often adjusted to remove biases asso- parameters over time. ciated with limited sample size or imperfect detection Here, we define genetic monitoring as quantifying rates. These adjustments can be accomplished by applying temporal changes in population genetic metrics or other asymptotic analyses or using models that account for population data generated using molecular markers. We covariates associated with capture, such as season, time distinguish monitoring, which must have a temporal of day, or habitat [9,10]. dimension, from assessment, which reflects a snapshot Abundance can also be estimated through of population characteristics at a single point in time. capture–mark–recapture (CMR) analyses (see Glossary). For instance, the term ‘molecular genetic monitoring’ Traditionally, this has required physical capture and has been used to refer to estimates of the proportion of marking of a sample, followed by release and subsequent whales from protected stocks that appeared in Asian recapture of a proportion of marked animals (reviewed in Ref. [11]). Because each individual has a unique, Corresponding author: Schwartz, M.K. ([email protected]). Available online 7 September 2006. naturally occurring genetic code (typically revealed with www.sciencedirect.com 0169-5347/$ – see front matter ß 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tree.2006.08.009 26 Review TRENDS in Ecology and Evolution Vol.22 No.1 Glossary post-release genetic monitoring is badly needed, but currently underused. This approach will be most Admixture: composite gene pool of F1,F2,...Fx individuals resulting from hybridization between two or more populations. compelling when genotypes of reintroduced animals can Approximate Bayesian computation (ABC): a statistical framework using be determined before release [18] and the fate of each simulation modeling to approximate the Bayesian posterior distribution individual tracked by the presence or absence of its of parameters of interest (e.g. Ne, Nm), often by using multiple summary statistics (e.g. He, A, FST). It is computationally quicker than full Bayesian genotype in subsequent, non-invasive samples. These data approaches, with minimal loss of accuracy and precision. can also be used for parentage analysis to monitor repro- Assignment method: any of several related statistical methods that use genetic information to ascertain population membership of individuals. ductive output. Barcoding: the use of short, standardized DNA sequences, typically from a mitochondrial gene, to identify and discover species quickly and easily. Category Ib. Identifying species and other groups Capture–mark–recapture (CMR): field sampling method used to estimate population size or vital rates (i.e. survival, recruitment, movement). Diagnostic genetic markers can be used to identify species Effective population size (Ne): the size of an ideal population that would have or other groups (e.g. genetically differentiated populations, the same rate of genetic change as the population under consideration. Ne subspecies, or genera) in addition to individuals. The influences the rate of loss of genetic variation, the efficiency of natural combination of non-invasive genetic sampling and novel selection and the accumulation of mutations. As a rough guideline, Ne approximates the number of breeding individuals producing offspring that live species identification tools enables the monitoring of to reproductive age. (i) changes in site occupancy or the geographical FST: standardized index of the distribution of genetic variation between populations on a scale between 0 (identical allele frequencies among range of a taxon; (ii) the presence of hybrids; and populations) and 1 (populations fixed for different alleles). (iii) the emergence of disease or invasive species Ftemporal: a standardized measure of change in allele frequency between two or (Figure 1). This category differs from similar approaches more samples collected at different times from the same population. Ftemporal is commonly used to estimate Ne. covered in Category II, where gene frequencies and Gametic (linkage) disequilibrium (LD): non-random association of alleles at assignment methods are used to assign individuals statis- two or more loci. tically to populations or species; here molecular markers Genome typing: simultaneous genotyping of tens or hundreds of loci from across the genome, which ideally includes mapped loci and different classes
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