Accounting for Age Structure and Spatial Structure in Eco-Evolutionary Analyses of a Large, Mobile Vertebrate.

The idealized concept of a population is integral to ecology, evolutionary biology, and natural resource management. To make analyses tractable, most models adopt simplifying assumptions, which almost inevitably are violated by real species in nature. Here, we focus on both demographic and genetic estimates of effective population size per generation (Ne), the effective number of breeders per year (Nb), and Wright's neighborhood size (NS) for black bears (Ursus americanus) that are continuously distributed in the northern lower peninsula of Michigan, United States. We illustrate practical application of recently developed methods to account for violations of 2 common, simplifying assumptions about populations: 1) reproduction occurs in discrete generations and 2) mating occurs randomly among all individuals. We use a 9-year harvest dataset of >3300 individuals, together with genetic determination of 221 parent-offspring pairs, to estimate male and female vital rates, including age-specific survival, age-specific fecundity, and age-specific variance in fecundity (for which empirical data are rare). We find strong evidence for overdispersed variance in reproductive success of same-age individuals in both sexes, and we show that constraints on litter size have a strong influence on results. We also estimate that another life-history trait that is often ignored (skip breeding by females) has a relatively modest influence, reducing Nb by 9% and increasing Ne by 3%. We conclude that isolation by distance depresses genetic estimates of Nb, which implicitly assume a randomly mating population. Estimated demographic NS (100, based on parent-offspring dispersal) was similar to genetic NS (85, based on regression of genetic distance and geographic distance), indicating that the >36000 km2 study area includes about 4-5 black-bear neighborhoods. Results from this expansive data set provide important insight into effects of violating assumptions when estimating evolutionary parameters for long-lived, free-ranging species. In conjunction with recently developed analytical methodology, the ready availability of nonlethal DNA sampling methods and the ability to rapidly and cheaply survey many thousands of molecular markers should facilitate eco-evolutionary studies like this for many more species in nature.

Detecting black bear source-sink dynamics using individual-based genetic graphs.

Source-sink dynamics affects population connectivity, spatial genetic structure and population viability for many species. We introduce a novel approach that uses individual-based genetic graphs to identify source-sink areas within a continuously distributed population of black bears (Ursus americanus) in the northern lower peninsula (NLP) of Michigan, USA. Black bear harvest samples (n = 569, from 2002, 2006 and 2010) were genotyped at 12 microsatellite loci and locations were compared across years to identify areas of consistent occupancy over time. We compared graph metrics estimated for a genetic model with metrics from 10 ecological models to identify ecological factors that were associated with sources and sinks. We identified 62 source nodes, 16 of which represent important source areas (net flux > 0.7) and 79 sink nodes. Source strength was significantly correlated with bear local harvest density (a proxy for bear density) and habitat suitability. Additionally, resampling simulations showed our approach is robust to potential sampling bias from uneven sample dispersion. Findings demonstrate black bears in the NLP exhibit asymmetric gene flow, and individual-based genetic graphs can characterize source-sink dynamics in continuously distributed species in the absence of discrete habitat patches. Our findings warrant consideration of undetected source-sink dynamics and their implications on harvest management of game species.

Spatial genetic structure and landscape connectivity in black bears: Investigating the significance of using different land cover datasets and classifications in landscape genetics analyses.

Landscape genetic analyses allow detection of fine-scale spatial genetic structure (SGS) and quantification of effects of landscape features on gene flow and connectivity. Typically, analyses require generation of resistance surfaces. These surfaces characteristically take the form of a grid with cells that are coded to represent the degree to which landscape or environmental features promote or inhibit animal movement. How accurately resistance surfaces predict association between the landscape and movement is determined in large part by (a) the landscape features used, (b) the resistance values assigned to features, and (c) how accurately resistance surfaces represent landscape permeability. Our objective was to evaluate the performance of resistance surfaces generated using two publicly available land cover datasets that varied in how accurately they represent the actual landscape. We genotyped 365 individuals from a large black bear population (Ursus americanus) in the Northern Lower Peninsula (NLP) of Michigan, USA at 12 microsatellite loci, and evaluated the relationship between gene flow and landscape features using two different land cover datasets. We investigated the relative importance of land cover classification and accuracy on landscape resistance model performance. We detected local spatial genetic structure in Michigan's NLP black bears and found roads and land cover were significantly correlated with genetic distance. We observed similarities in model performance when different land cover datasets were used despite 21% dissimilarity in classification between the two land cover datasets. However, we did find the performance of land cover models to predict genetic distance was dependent on the way the land cover was defined. Models in which land cover was finely defined (i.e., eight land cover classes) outperformed models where land cover was defined more coarsely (i.e., habitat/non-habitat or forest/non-forest). Our results show that landscape genetic researchers should carefully consider how land cover classification changes inference in landscape genetic studies.

Range-wide phylogeographic analysis of the spotted frog complex (Rana luteiventris and Rana pretiosa) in northwestern North America.

The dynamic geological and climatic history of northwestern North America has made it a focal region for phylogeography. We conducted a range-wide phylogeographic analysis of the spotted frog complex (Rana luteiventris and Rana pretiosa) across its range in northwestern North America to understand its evolutionary history and the distribution of clades to inform conservation of R. pretiosa and Great Basin R. luteiventris, candidates for listing under the US Endangered Species Act. Mitochondrial DNA sequence data from a segment of the cytochrome b gene were obtained from 308 R. luteiventris and R. pretiosa from 96 sites. Phylogenetic analysis revealed one main R. pretiosa clade and three main R. luteiventris clades, two of which overlapped in southeastern Oregon. The three R. luteiventris clades were separated from each other by high levels of sequence divergence (average of 4.75-4.97%). Two divergent clades were also uncovered within the Great Basin. Low genetic variation in R. pretiosa and the southeastern Oregon clade of R. luteiventris suggests concern about their vulnerability to extinction.

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

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.

The conservation genetics juggling act: integrating genetics and ecology, science and policy.

The field of conservation genetics, when properly implemented, is a constant juggling act integrating molecular genetics, ecology, and demography with applied aspects concerning managing declining species or implementing conservation laws and policies. This young field has grown substantially since the 1980s following the development of polymerase chain reaction and now into the genomics era. Our laboratory has 'grown up' with the field, having worked on these issues for over three decades. Our multidisciplinary approach entails understanding the behavior and ecology of species as well as the underlying processes that contribute to genetic viability. Taking this holistic approach provides a comprehensive understanding of factors that influence species persistence and evolutionary potential while considering annual challenges that occur throughout their life cycle. As a federal laboratory, we are often addressing the needs of the U.S. Fish and Wildlife Service in their efforts to list, de-list, or recover species. Nevertheless, there remains an overall communication gap between research geneticists and biologists who are charged with implementing their results. Therefore, we outline the need for a National Center for Small Population Biology to ameliorate this problem and provide organizations charged with making status decisions firmer ground from which to make their critical decisions.

Application of large-scale parentage analysis for investigating natal dispersal in highly vagile vertebrates: a case study of American black bears (Ursus americanus).

Understanding the factors that affect dispersal is a fundamental question in ecology and conservation biology, particularly as populations are faced with increasing anthropogenic impacts. Here we collected georeferenced genetic samples (n = 2,540) from three generations of black bears (Ursus americanus) harvested in a large (47,739 km2), geographically isolated population and used parentage analysis to identify mother-offspring dyads (n = 337). We quantified the effects of sex, age, habitat type and suitability, and local harvest density at the natal and settlement sites on the probability of natal dispersal, and on dispersal distances. Dispersal was male-biased (76% of males dispersed) but a small proportion (21%) of females also dispersed, and female dispersal distances (mean ± SE  =  48.9±7.7 km) were comparable to male dispersal distances (59.0±3.2 km). Dispersal probabilities and dispersal distances were greatest for bears in areas with high habitat suitability and low harvest density. The inverse relationship between dispersal and harvest density in black bears suggests that 1) intensive harvest promotes restricted dispersal, or 2) high black bear population density decreases the propensity to disperse. Multigenerational genetic data collected over large landscape scales can be a powerful means of characterizing dispersal patterns and causal associations with demographic and landscape features in wild populations of elusive and wide-ranging species.

Taxonomic considerations in listing subspecies under the U.S. Endangered Species Act.

The U.S. Endangered Species Act (ESA) allows listing of subspecies and other groupings below the rank of species. This provides the U.S. Fish and Wildlife Service and the National Marine Fisheries Service with a means to target the most critical unit in need of conservation. Although roughly one-quarter of listed taxa are subspecies, these management agencies are hindered by uncertainties about taxonomic standards during listing or delisting activities. In a review of taxonomic publications and societies, we found few subspecies lists and none that stated standardized criteria for determining subspecific taxa. Lack of criteria is attributed to a centuries-old debate over species and subspecies concepts. Nevertheless, the critical need to resolve this debate for ESA listings led us to propose that minimal biological criteria to define disjunct subspecies (legally or taxonomically) should include the discreteness and significance criteria of distinct population segments (as defined under the ESA). Our subspecies criteria are in stark contrast to that proposed by supporters of the phylogenetic species concept and provide a clear distinction between species and subspecies. Efforts to eliminate or reduce ambiguity associated with subspecies-level classifications will assist with ESA listing decisions. Thus, we urge professional taxonomic societies to publish and periodically update peer-reviewed species and subspecies lists. This effort must be paralleled throughout the world for efficient taxonomic conservation to take place.