Kim T. Scribner

Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis

Measures of genetic structure among individuals or populations collected at different spatial locations across a landscape are commonly used as surrogate measures of functional (i.e. demographic or genetic) connectivity. In order to understand how landscape characteristics influence functional connectivity, resistance surfaces are typically created in a raster GIS environment. These resistance surfaces represent hypothesized relationships between landscape features and gene flow, and are based on underlying biological functions such as relative abundance or movement probabilities in different land cover types. The biggest challenge for calculating resistance surfaces is assignment of resistance values to different landscape features. Here, we first identify study objectives that are consistent with the use of resistance surfaces and critically review the various approaches that have been used to parameterize resistance surfaces and select optimal models in landscape genetics. We then discuss the biological assumptions and considerations that influence analyses using resistance surfaces, such as the relationship between gene flow and dispersal, how habitat suitability may influence animal movement, and how resistance surfaces can be translated into estimates of functional landscape connectivity. Finally, we outline novel approaches for creating optimal resistance surfaces using either simulation or computational methods, as well as alternatives to resistance surfaces (e.g. network and buffered paths). These approaches have the potential to improve landscape genetic analyses, but they also create new challenges. We conclude that no single way of using resistance surfaces is appropriate for every situation. We suggest that researchers carefully consider objectives, important biological assumptions and available parameterization and validation techniques when planning landscape genetic studies.

Considering spatial and temporal scale in landscape-genetic studies of gene flow

Landscape features exist at multiple spatial and temporal scales, and these naturally affect spatial genetic structure and our ability to make inferences about gene flow. This article discusses how decisions about sampling of genotypes (including choices about analytical methods and genetic markers) should be driven by the scale of spatial genetic structure, the time frame that landscape features have existed in their current state, and all aspects of a species’ life history. Researchers should use caution when making inferences about gene flow, especially when the spatial extent of the study area is limited. The scale of sampling of the landscape introduces different features that may affect gene flow. Sampling grain should be smaller than the average home‐range size or dispersal distance of the study organism and, for raster data, existing research suggests that simplifying the thematic resolution into discrete classes may result in low power to detect effects on gene flow. Therefore, the methods used to characterize the landscape between sampling sites may be a primary determinant for the spatial scale at which analytical results are applicable, and the use of only one sampling scale for a particular statistical method may lead researchers to overlook important factors affecting gene flow. The particular analytical technique used to correlate landscape data and genetic data may also influence results; common landscape‐genetic methods may not be suitable for all study systems, particularly when the rate of landscape change is faster than can be resolved by common molecular markers.

Hybridization in freshwater fishes: a review of case studies and cytonuclear methods of biological inference

Interspecific hybridization occurs widelyacross a taxonomically diverse array of fishspecies. Multiple factors typically interactto effect the outcome of hybridization events. Human influences have been frequently cited ascontributing factors (nearly 50% of reviewedcase studies). Aquacultural activities,species introductions, and loss or alterationof habitats were frequently implicated. Wehighlight the utility of genetic markers andnovel methods of statistical analysis forinferring the extent, rate, direction, andlikely causes of hybridization. Emphasis isplaced on cytonuclear genetic systems. Wedemonstrate the utility of cytonuclear modelsfor hypothesis testing using empirical data.

Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field

Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non‐model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real‐world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non‐model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation.

Utility of computer simulations in landscape genetics

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space–time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio‐temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial–temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus‐specific spatial patterns due to micro‐scale environmental selection.

Patterns of invasion and colonization of the sea lamprey (Petromyzon marinus) in North America as revealed by microsatellite genotypes

Invasions by exotic organisms have had devastating affects on aquatic ecosystems, both ecologically and economically. One striking example of a successful invader that has dramatically affected fish community structure in freshwater lakes of North America is the sea lamprey (Petromyzon marinus). We used eight microsatellite loci and multiple analytical techniques to examine competing hypotheses concerning the origins and colonization history of sea lamprey (n = 741). Analyses were based on replicated invasive populations from Lakes Erie, Huron, Michigan, and Superior, populations of unknown origins from Lakes Ontario, Champlain, and Cayuga, and populations of anadromous putative progenitor populations in North America and Europe. Populations in recently colonized lakes were each established by few colonists through a series of genetic bottlenecks which resulted in lower allelic diversity in more recently established populations. The spatial genetic structure of invasive populations differed from that of native populations on the Atlantic coast, reflecting founder events and connectivity of invaded habitats. Anadromous populations were found to be panmictic (θP = 0.002; 95% CI = −0.003–0.006; P > 0.05). In contrast, there was significant genetic differentiation between populations in the lower and upper Great Lakes (θP = 0.007; P < 0.05; 95% CI = 0.003–0.009). Populations in Lakes Ontario, Champlain, and Cayuga are native. Alternative models that describe different routes and timing of colonization of freshwater habitats were examined using coalescent‐based analyses, and demonstrated that populations likely originated from natural migrations via the St Lawrence River.

Genetic variability and systematics of Gambusia in the southeastern United States.

Mosquitofish were sampled from 76 locations in 19 drainages in the southeastern United States. Thirteen polymorphic loci were resolved for fish from each location. Populations from eastern drainages had significantly higher levels of heterozygosity (H = 0.113) than those in western drainages (H = 0.055). Abrupt changes in allele frequencies for several loci occurred in the area of Mobile Bay, which corresponds with discontinuities previously reported for chromosomal and morphological data. Based on geographic patterns of allele frequencies, abrupt differentiation in local genetic constituency (DR = 0.443 between eastern Gambusia affinis holbrooki and G. a. afinis) and previously reported data on the existence of a reproductive barrier between the forms, the original taxonomic designations recognizing G. holbrooki and G. affinis as separate species are preferred. Populations in drainages west of Mobile Bay should be considered G. affinis, with those east of this divide being G. holbrooki.

Isolation and characterization of novel waterfowl microsatellite loci: cross‐species comparisons and research applications

are the subject of extensive research (e.g. see reviews in Batt et al. 1992), and are intensively managed (Nichols et al. 1995). Genetic studies utilizing allozyme electrophoresis and mitochondrial (mt)DNA have provided valuable information on waterfowl ecology and evolutionary history (Cooke & Buckley 1987). However, highly variable molecular genetic markers (e.g. multilocus minisatellites; Triggs et al. 1992) have not generally been identified for this group. Genetic markers vary greatly in evolutionary rates of change owing to heterogeneity in modes of inheritance, sequence organization, selective pressures, and rates of mutation and fixation (Burke et al. 1992). One class of markers which is receiving increasing attention are variable number of tandem repeat (VNTR) microsatellite loci (Bruford & Wayne 1993). In this study we present data from a series of novel microsatellite loci which were developed from spectacled eiders Somateria fischeri and greater white-fronted geese Anser albifrons. Optimal experimental conditions are described, and results from cross-species characterizations are presented in the hope of stimulating interest in the use of these markers in various ecological and evolutionary contexts. Two procedures were used for library construction and screening of microsatellites. Initial libraries were made using a modification of procedures described in Rassmann et al. (1991). Greater efficiency in terms of the number of positives identified and percentage of positives containing usable microsatellite motifs, was obtained by the construction of size-selected libraries using a modification of the library enrichment procedures described in Ostrander et al. (1992). Fifty-nine positive clones were identified from screenings based on colony or plaque hybridizations and were sequenced. Nineteen (32%) were found to contain simplesequence [CA]n (n = 11) or [GA]n (n = 8) regions. Seven microsatellite-containing clones were unusable due to a lack of sufficient flanking sequence in the cloned fragment, or were found to contain incomplete repeats (i.e. presence of single or multiple base-pair substitutions within the repeat region), or small repeat regions (< 10 repeat units). Twelve positive clones which contained flanking sequences of sufficient length were chosen to develop PCR primers. Characteristics of nine primer pairs are described below. For the other three clones, two primer pairs failed to produce single PCR products and one clone was identical to locus Sfiμ6. Primer sequences within flanking regions adjacent to microsatellite loci were developed using the software O L I G O (Rychlik & Rhoads 1989). Characteristics of nine PCR primers, amplification conditions, repeat motifs, and expected product sizes are detailed in Table 1. PCR conditions for each primer pair were optimized using S. fischeri and A. albifrons genomic DNAs using a 25-μL reaction mix consisting of 10 pmol of each primer, dNTPs at 200 μM each, 0.25 units Taq DNA polymerase (Applied Biosystems) and PCR buffer [10 mM Tris HCl, pH 8.3, 1.5 mM MgCl2, 50 mM KCl, 10 μg/mL BSA, 0.0025% Tween20 (BioRad)]. Approximately 250 μL of blood was collected from all individuals from brachial or femoral blood vessels. Samples were stored in 1 mL of nonrefrigerated buffer (100 mM Tris, pH 8.0, 100 mM EDTA, 10 mM NaCl, 0.5% SDS) in the field. Samples were subsequently stored at –20 °C in the laboratory until analysed. DNA was extracted using standard Proteinase K, phenol–chloroform techniques (Sambrook et al. 1989) and resuspended in TE (10 mM Tris-HCl, pH 8.0, 1 mM EDTA). One-hundred nanograms of DNA were used for each PCR reaction. Waterfowl DNA samples were screened for variation using gamma-32P ATP end-labelled primers. One primer from each pair was end-labelled using T4 polynucleotide kinase according to manufacturers specifications (Pharmacia). PCR reactions for seven loci (Sfiμ1, Sfiμ2, Sfiμ3, Sfiμ4, Sfiμ6, Sfiμ7, Aalμ2) consisted of 30 cycles of 1 min denaturation at 94 °C, annealing at primer-specific temperatures (see Table 1) for 1 min, and elongation for PRIMER NOTE

Behavioural structuring of relatedness in the spotted hyena ( Crocuta crocuta ) suggests direct fitness benefits of clan-level cooperation

Spotted hyenas (Crocuta crocuta) are gregarious carnivores that live in multigenerational social groups, called clans, containing one to several matrilines. Members of multiple matrilines within a clan cooperate during dangerous interactions with inter‐ and intraspecific competitors. The evolution of cooperation may be influenced by relatedness between individuals, which in turn is influenced by reproductive skew and mate choice, dispersal and territorial behaviours. Behavioural data exist for spotted hyenas, but corresponding data on patterns of relatedness are unavailable; this lack of data makes it difficult to assess the relative importance of selection pressures favouring cooperative behaviour within and among groups. Therefore we conducted a longitudinal analysis of relatedness within a single large clan of spotted hyenas, as well as a cross‐sectional analysis of relatedness among hyenas from multiple clans. Within a clan, patterns of relatedness reflected known pedigree relationships, and relatedness was higher within than among matrilines, even across generations. Although mean within‐matriline relatedness varied among matrilines, it did not decline with matriline rank. On average, clan members were not related closely, due to high levels of male‐mediated gene flow among clans, and relatedness declined very slightly across clan borders. Low mean relatedness within clans suggests that spotted hyenas cooperate with unrelated clan‐mates against close paternal kin in other clans. Our data also suggest that spotted hyenas must derive large net direct fitness benefits from group living and cooperation.

Landscape genetics and the spatial distribution of chronic wasting disease

Predicting the spread of wildlife disease is critical for identifying populations at risk, targeting surveillance and designing proactive management programmes. We used a landscape genetics approach to identify landscape features that influenced gene flow and the distribution of chronic wasting disease (CWD) in Wisconsin white-tailed deer. CWD prevalence was negatively correlated with genetic differentiation of study area deer from deer in the area of disease origin (core-area). Genetic differentiation was greatest, and CWD prevalence lowest, in areas separated from the core-area by the Wisconsin River, indicating that this river reduced deer gene flow and probably disease spread. Features of the landscape that influence host dispersal and spatial patterns of disease can be identified based on host spatial genetic structure. Landscape genetics may be used to predict high-risk populations based on their genetic connection to infected populations and to target disease surveillance, control and preventative activities.