In response to ever-increasing anthropogenic changes to natural ecosystems, regional, national and international organizations have established guidelines for monitoring biological diversity. Most monitoring programs, however, do not take full advantage of the potential afforded by molecular genetic markers, which can provide information relevant to both ecological and evolutionary time frames, while costing less and being more sensitive and reliable than traditional monitoring approaches. As several molecular and computational approaches are relatively new, many technical and theoretical issues remain to be resolved. Here, we illustrate how DNA and population genetic data can provide valuable information, often unattainable via other approaches, for monitoring species of management, conservation and ecological interest.

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Genetic monitoring as a promising tool for conservation and management

The identification of management units (MUs) is central to the management of natural populations and is crucial for monitoring the effects of human activity upon species abundance. Here, we propose that the identification of MUs from population genetic data should be based upon the amount of genetic divergence at which populations become demographically independent instead of the current criterion that focuses on rejecting panmixia. MU status should only be assigned when the observed estimate of genetic divergence is significantly greater than a predefined threshold value. We emphasize the need for a demographic interpretation of estimates of genetic divergence given that it is often the dispersal rate of individuals that is the parameter of immediate interest to conservationists rather than the historical amount of gene flow.

Identification of management units using population genetic data

Comparison of population structure between studies can be difficult, because the value of the often-used FST-statistic depends on the amount of genetic variation within populations. Recently, a standardized measure of genetic differentiation was developed based on GST, which addressed this problem, though no method was provided to estimate this standardized measure without bias. Here I present a method to estimate a standardized measure of population differentiation based on the analysis of molecular variance framework. One advantage of the method is that it can be readily expanded to include different hierarchical levels in the tested population structure.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0014-3820.2006.tb01874.x

Using the amova framework to estimate a standardized genetic differentiation measure

Knowledge of statistical power is essential for sampling design and data evaluation when testing for genetic differentiation. Yet, such information is typically missing in studies of conservation and evolutionary genetics, most likely because of complex interactions between the many factors that affect power. powsim is a 32-bit Windows/DOS simulation-based computer program that estimates power (and α error) for chi-square and Fisher's exact tests when evaluating the hypothesis of genetic homogeneity. Optional combinations include the number of samples, sample sizes, number of loci and alleles, allele frequencies, and degree of differentiation (quantified as FST). powsim is available at http://www.zoologi.su.se/~ryman.

POWSIM: a computer program for assessing statistical power when testing for genetic differentiation

Large-scale exploitation of wild animals and plants through fishing, hunting and logging often depends on augmentation through releases of translocated or captively raised individuals. Such releases are performed worldwide in vast numbers. Augmentation can be demographically and economically beneficial but can also cause four types of adverse genetic change to wild populations: (1) loss of genetic variation, (2) loss of adaptations, (3) change of population composition, and (4) change of population structure. While adverse genetic impacts are recognized and documented in fisheries, little effort is devoted to actually monitoring them. In forestry and wildlife management, genetic risks associated with releases are largely neglected. We outline key features of programs to effectively monitor consequences of such releases on natural populations.

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Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals

Information on statistical power is critical when planning investigations and evaluating empirical data, but actual power estimates are rarely presented in population genetic studies. We used computer simulations to assess and evaluate power when testing for genetic differentiation at multiple loci through combining test statistics or P values obtained by four different statistical approaches, viz. Pearson's chi-square, the log-likelihood ratio G-test, Fisher's exact test, and an F(ST)-based permutation test. Factors considered in the comparisons include the number of samples, their size, and the number and type of genetic marker loci. It is shown that power for detecting divergence may be substantial for frequently used sample sizes and sets of markers, also at quite low levels of differentiation. The choice of statistical method may be critical, though. For multi-allelic loci such as microsatellites, combining exact P values using Fisher's method is robust and generally provides a high resolving power. In contrast, for few-allele loci (e.g. allozymes and single nucleotide polymorphisms) and when making pairwise sample comparisons, this approach may yield a remarkably low power. In such situations chi-square typically represents a better alternative. The G-test without Williams's correction frequently tends to provide an unduly high proportion of false significances, and results from this test should be interpreted with great care. Our results are not confined to population genetic analyses but applicable to contingency testing in general.

http://www.ufscar.br/~evolucao/TGE/ref12-3.pdf

Power for detecting genetic divergence: differences between statistical methods and marker loci.

The genetic relationships among 337 northern pike (Esox lucius) collected from the coastal zone of the central Baltic region and the Finnish islands of Aland were analysed using five microsatellite loci. Spatial structure was delineated using both traditional F-statistics and individually based approaches including spatial autocorrelation analysis. Our results indicate that the observed genotypic distribution is incompatible with that of a single, panmictic population. Isolation by distance appears important for shaping the genetic structure of pike in this region resulting in a largely continuous genetic change over the study area. Spatial autocorrelation analysis (Moran's I) of individual pairwise genotypic data show significant positive genetic correlation among pike collected within geographical distances of less than c. 100-150 km (genetic patch size). We suggest that the genetic patch size may be used as a preliminary basis for identifying management units for pike in the Baltic Sea.

Spatial genetic structure of northern pike (Esox lucius) in the Baltic Sea.

Programs for monitoring biological diversity over time are needed to detect changes that can constitute threats to biological resources. The convention on biological diversity regards effective monitoring as necessary to halt the ongoing erosion of biological variation, and such programs at the ecosystem and species levels are enforced in several countries. However, at the level of genetic biodiversity, little has been accomplished, and monitoring programs need to be developed. We define “conservation genetic monitoring” to imply the systematic, temporal study of genetic variation within particular species/populations with the aim to detect changes that indicate compromise or loss of such diversity. We also (i) identify basic starting points for conservation genetic monitoring, (ii) review the availability of such information using Sweden as an example, (iii) suggest categories of species for pilot monitoring programs, and (iv) identify some scientific and logistic issues that need to be addressed in the context of conservation genetic monitoring. We suggest that such programs are particularly warranted for species subject to large scale enhancement and harvest—operations that are known to potentially alter the genetic composition and reduce the variability of populations.

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Potentials for monitoring gene level biodiversity: using Sweden as an example

Introduction of alien species is a major threat to biological diversity. Although public attention typically focuses on the species level, guidelines from the Convention of Biological Diversity define alien species to include entities below species level. This inclusion recognizes that release of nonlocal populations of native species may also result in negative effects on biodiversity. In practice, little is known about the extent, degree of establishment, or the effects on natural gene pools of such releases. Existing information on the releases in Sweden shows that alien populations are spread to a great extent. The most commonly released species include brown trout, Atlantic salmon, Arctic char, common whitefish, Scots pine, Norway spruce, mallard duck, gray partridge, and pheasant. Although millions of forest trees, fish, and birds are released annually, poor documentation makes the geographic and genetic origin of these populations, as well as the sites where they have been released, largely unclear. We provide recommendations for urgently needed first steps relating to the risks and problems associated with release of alien populations.

https://bioone.org/journals/ambio-a-journal-of-the-human-environment/volume-35/issue-5/05-A-060R.1/Release-of-Alien-Populations-in-Sweden/10.1579/05-A-060R.1.pdf

Release of alien populations in Sweden.

Detecting population subdivision when apparent barriers to gene flow are lacking is important in evolutionary and conservation biology. Recent research indicates that intraspecific population complexity can be crucial for maintaining a species 0 evolutionary potential, productivity, and ecological role. We monitored the genetic relationships at 14 allozyme loci among *4,000 brown trout (Salmo trutta) collected during 19 years from two small intercon- nected mountain lakes (0.10 and 0.17 km 2 , respectively) in central Sweden. There were no allele frequency differences between the lakes. However, heterozygote deficiencies within lakes became obvious after a few years of monitoring. Detailed analyses were then carried out without a priori grouping of samples, revealing unexpected differentiation patterns: (i) the same two genetically distinct (FST C 0.10) populations occur sympatrically at about equal frequencies within both lakes, (ii) the genetic subdivision is not coupled with apparent phenotypical dichotomies, (iii) this cryptic structure remains stable over the two decades monitored, and (iv) the point estimates of effective population size are c. 120 and 190, respectively, indicating that genetic drift is important in this system. A subsample of 382 fish was also analyzed for seven microsatellites. The genetic pattern does not follow that of the allozymes, and in this subsample the presence of multiple populations would have gone unde- tected if only scoring microsatellites. Sympatric populations may be more common than anticipated, but difficult to detect when individuals cannot be grouped appropriately, or when markers or sample sizes are insufficient to provide adequate statistical power with approaches not requiring prior grouping.

/pdf/monitoring-reveals-two-genetically-distinct-brown-trout-1kemfqcupc.pdf

Anna Palmé

Papers

Power for detecting genetic divergence: differences between statistical methods and marker loci.

Spatial genetic structure of northern pike (Esox lucius) in the Baltic Sea.

Potentials for monitoring gene level biodiversity: using Sweden as an example

Release of alien populations in Sweden.

Monitoring reveals two genetically distinct brown trout populations remaining in stable sympatry over 20 years in tiny mountain lakes