Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol.

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Genetic structure and landscape effects on gene flow in the Neotropical lizard Norops brasiliensis (Squamata: Dactyloidae).

One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g., geographic distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard species Norops brasiliensis (Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation in N. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets.

One metric or many? Refining the analytical framework of landscape resistance estimation in individual-based landscape genetic analyses.

One of the allures of landscape genetics is the ability to leverage pairwise genetic distance metrics to infer how landscape features promote or constrain gene flow (i.e. landscape resistance surfaces). Critically, properly parameterized landscape resistance surfaces are foundational to applied conservation and management decisions. As such, there has been considerable effort expended assessing methods and metrics to estimate landscape resistance from genetic data (Balkenhol et al., Ecography, 32, 2009, 818; Peterman et al., Landsc. Ecol., 34, 2019, 2197; Shirk et al., Mol. Ecol. Resour., 17, 2017, 1308; Shirk et al., Mol. Ecol. Resour., 18, 2018, 55). Nonetheless, a primary challenge to assessing the effects of landscapes on gene flow is in the estimation of landscape resistance values, and this problem becomes increasingly challenging as more landscape features or land cover classes are considered. It quickly becomes infeasible to adequately assess the potential parameter space through manual or systematic assignment of resistance values. The development of ResistanceGA (Peterman, Methods Ecol. Evol., 9, 2018, 1638) provided a framework for using genetic algorithms to optimize landscape resistance values and identify the best statistical relationship between pairwise effective distances and genetic distances. ResistanceGA has seen extensive use in both population- and individual-based landscape genetic analyses. However, there has been relatively limited assessment of ResistanceGA's ability to identify the landscape features affecting gene flow (but see Peterman et al., Landsc. Ecol., 34, 2019, 2197; Winiarski et al., Mol. Ecol. Resour., 20, 2020, 1583) or the sensitivity of ResistanceGA results to the choice of genetic distance metric used. In the current issue of Molecular Ecology Resources, Beninde et al. (2023) aim to address these knowledge gaps by examining the impact of individual-based genetic distance measures on landscape genetic inference.

Demography and space-use of Eastern Red-backed Salamanders (Plethodon cinereus) between mature and successional forests.

Space-use and demographic processes are critical to the persistence of populations across space and time. Despite their importance, estimates of these processes are often derived from a limited number of populations spanning broad habitat or environmental gradients. With increasing appreciation of the role fine-scale environmental variation in microgeographic adaptation, there is a need and value to assessing within-site variation in space-use and demographic patterns. In this study, we analyze 3 years of spatial capture-recapture data on the Eastern Red-backed Salamander collected from a mixed-use deciduous forest site in central Ohio, USA. Study plots were situated in both a mature forest stand and successional forest stand separated by <100-m distance. Our results showed that salamander density was reduced on successional plots, which corresponded with greater distance between nearest neighbors, less overlap in core use areas, greater space-use, and greater shifts in activity centers when compared to salamanders occupying the mature habitat. By contrast, individual growth rates of salamanders occupying the successional forest were significantly greater than salamanders in the mature forest. These estimates result in successional plot salamanders reaching maturity more than 1 year earlier than salamanders on the mature forest plots and increasing their estimated lifetime fecundity by as much as 43%. The patterns we observed in space-use and individual growth are likely the result of density-dependent processes, potentially reflecting differences in resource availability or quality. Our study highlights how fine-scale, within-site variation can shape population demographics. As research into the demographic and population consequences of climate change and habitat loss and alteration continue, future research should take care to acknowledge the role that fine-scale variation may play, especially for abiotically sensitive organisms with limited vagility.

Inferring population connectivity in eastern massasauga rattlesnakes (Sistrurus catenatus) using landscape genetics.

Assessing the environmental factors that influence the ability of a threatened species to move through a landscape can be used to identify conservation actions that connect isolated populations. However, direct observations of species' movement are often limited, making the development of alternate approaches necessary. Here we use landscape genetic analyses to assess the impact of landscape features on the movement of individuals between local populations of a threatened snake, the eastern massasauga rattlesnake (Sistrurus catenatus). We linked connectivity data with habitat information from two landscapes of similar size: a large region of unfragmented habitat and a previously studied fragmented landscape consisting of isolated patches of habitat. We used this analysis to identify features of the landscape where modification or acquisition would enhance population connectivity in the fragmented region. We found evidence that current connectivity was impacted by both contemporary land-cover features, especially roads, and inherent landscape features such as elevation. Next, we derived estimates of expected movement ability using a recently developed pedigree-based approach and least-cost paths through the unfragmented landscape. We then used our pedigree and resistance map to estimate resistance polygons of the potential extent for S. catenatus movement in the fragmented landscape. These polygons identify possible sites for future corridors connecting currently isolated populations in this landscape by linking the impact of future habitat modification or land acquisition to dispersal ability in this species. Overall, our study shows how modeling landscape resistance across differently fragmented landscapes can identify habitat features that affect contemporary movement in threatened species in fragmented landscapes and how this information can be used to guide mitigation actions whose goal is to connect isolated populations.

An evaluation of the Paycheck Protection Program using administrative payroll microdata.

The Paycheck Protection Program (PPP), a principal element of the fiscal stimulus enacted by Congress in response to the COVID-19 economic shock, was intended to assist small businesses to maintain employment and wages during the crisis, as well as cover other expenses. We use high-frequency administrative payroll data from ADP-one of the world's largest payroll processing firms-to estimate the causal effect of the PPP on the evolution of employment at PPP-eligible firms relative to PPP-ineligible firms, where eligibility is determined by industry-specific firm-size cutoffs. Our estimates indicate that the PPP boosted employment at eligible firms by between 2 percent to 5 percent at its peak effect around mid-May 2020. The boost to employment waned thereafter and ranged from no effect to a 3 percent boost at the end of 2020. Our estimates imply that employers retained an additional 3.6 million jobs as of mid-May 2020, and 1.4 million jobs at the end of 2020, as a consequence of PPP. The estimated cost per year of employment retained was $ 169 , 000 to $ 258 , 000 , equal to 3.4 to 5.2 times median earnings.

Prey-driven behavioral habitat use in a low-energy ambush predator.

Food acquisition is an important modulator of animal behavior and habitat selection that can affect fitness. Optimal foraging theory predicts that predators should select habitat patches to maximize their foraging success and net energy gain, likely achieved by targeting areas with high prey availability. However, it is debated whether prey availability drives fine-scale habitat selection for predators. We assessed whether an ambush predator, the timber rattlesnake (Crotalus horridus), exhibits optimal foraging site selection based on the spatial distribution and availability of prey. We used passive infrared camera trap detections of potential small mammal prey (Peromyscus spp., Tamias striatus, and Sciurus spp.) to generate variables of prey availability across the study area and used whether a snake was observed in a foraging location or not to model optimal foraging in timber rattlesnakes. Our models of small mammal spatial distributions broadly predicted that prey availability was greatest in mature deciduous forests, but T. striatus and Sciurus spp. exhibited greater spatial heterogeneity compared with Peromyscus spp. We found the spatial distribution of cumulative small mammal encounters (i.e., overall prey availability), rather than the distribution of any one species, to be highly predictive of snake foraging. Timber rattlesnakes appear to forage where the probability of encountering prey is greatest. Our study provides evidence for fine-scale optimal foraging in a low-energy, ambush predator and offers new insights into drivers of snake foraging and habitat selection.

Habitat connectivity and resource selection in an expanding bobcat (Lynx rufus) population.

Terrestrial carnivores are among the most imperiled species worldwide, yet some species are resilient and are recovering in human-dominated landscapes after decades or centuries of absence. Bobcat (Lynx rufus) populations were extirpated from much of Midwestern US in the mid-1800's, and are currently expanding and recolonizing their former range. In this study, we investigated multi-scale habitat selection for Ohio's expanding bobcat population, and examined habitat connectivity in order to evaluate the conduits for dispersal statewide. We used citizen observations collected between 1978 and 2019 and logistic regression to evaluate population-level habitat selection, and GPS telemetry data for 20 individuals collected between 2012 and 2014 and a distribution-weighted exponential Resource Selection Function to evaluate individual-level habitat selection within home ranges. At the population level, bobcats selected for higher amounts of forest and pasture (at a 50 km2 scale) and herbaceous vegetation (at 15-50 50 km2 scales), thus overall heterogeneous forested habitat. At individual (home range) level, bobcats selected for forested habitats with low road density and farther away from high traffic roads; they also showed weak selection for open habitat at the home range level. Male home ranges were significantly greater than female home ranges. Lastly, we used the population-level spatial outputs (i.e. habitat suitability map) to parameterize habitat connectivity models using circuit theory in the program Circuitscape. We tested three relationships between habitat suitability and resistance to movement and used a subset of data on potential dispersing individuals to evaluate which relationship performed best. All three relationships performed almost equally well, and we calculated a weighted averaged connectivity map as our final map. Habitat was highly permeable to movements between core areas of two genetically distinct subpopulations located in southeastern Ohio. We also identified potential dispersal corridors from the core areas to other regions of Ohio dominated by agriculture and suburban development via forested riparian corridors. Overall, our analysis offers new information on habitat selection and connectivity in a rebounding felid population and offers important ecological information for wildlife management strategies. We recommend that the suitability and connectivity models should be periodically updated until the population reaches an equilibrium, and be integrated with data from neighboring states for a comprehensive assessment of a conservation success story.

Multiscale assessment of functional connectivity: Landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida.

Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales of effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multiscale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multisurface multiscale LRS). We compared LRS where scale was selected using pseudo- and full optimization. Multisurface multiscale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multiscale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo-optimization. Multiscale LRS with large spatial scales (1200-1800 m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range-level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.

Abundance of montane salamanders over an elevational gradient.

Climate change is expected to systematically alter the distribution and population dynamics of species around the world. The effects are expected to be particularly strong at high latitudes and elevations, and for ectothermic species with small ranges and limited movement potential, such as salamanders in the southern Appalachian Mountains. In this study, we sought to establish baseline abundance estimates for plethodontid salamanders (family: Plethodontidae) over an elevational gradient in Great Smoky Mountains National Park. In addition to generating these baseline data for multiple species, we describe methods for surveying salamanders that allow for meaningful comparisons over time by separating observation and ecological processes generating the data. We found that Plethodon jordani had a mid-elevation peak (1,500 m) in abundance and Desmognathus wrighti increased in abundance with elevation up to the highest areas of the park (2025 m), whereas Eurycea wilderae increased in abundance up to 1,600 m and then plateaued with increasing uncertainty. Litter depth, herbaceous ground cover, and proximity to stream were also important predictors of abundance (dependent upon species), whereas daily temperature, precipitation, ground cover, and humidity influenced detection rates. Our data provide some of the first minimally biased information for future studies to assess changes in the abundance and distribution of salamanders in this region. Understanding abundance patterns along with detailed baseline distributions will be critical for comparisons with future surveys to understand the population and community-level effects of climate change on montane salamanders.

Direct effects influence larval salamander size and density more than indirect effects.

Direct and indirect effects both influence population and community dynamics. The relative strengths of these pathways are often compared using experimental approaches, but their evaluation in situ has been less frequent. We examined how individual and aggregate impacts of direct and indirect effects of species densities, proxies for competition and predation pressure, and habitat variables influenced patterns of larval density and body size of ringed (Ambystoma annulatum) and spotted salamanders (A. maculatum). We surveyed > 150 ponds in Missouri, USA, from 2012 to 2014 to measure the density and body size of each focal species, the density of co-occurring pond food web members, and select habitat features. We used structural equation modeling to quantify the relative importance of direct and indirect pathways on both body size and larval density. Overall, both responses were explained through a combination of direct and indirect effects. However, the magnitudes of direct effects were often greater than indirect effects. Some of the direct and indirect relationships with larval salamander size and density were also consistent with results from experimental studies. Finally, total direct and indirect effects were often weaker due to habitat and density variables negating each other's impacts. Overall, our study shows that direct effects were equivalent to, or more important than, indirect effects. We also demonstrate that the effects stemming from individual relationships can sum to produce net patterns that are negligible in magnitude. Further work on direct and indirect effects with observational data are needed to examine their magnitudes in natural communities.

The use and misuse of regression models in landscape genetic analyses.

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression-based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression-like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.

Predicted alteration of surface activity as a consequence of climate change.

Wildlife are faced with numerous threats to survival, none more pressing than that of climate change. Understanding how species will respond behaviorally, physiologically, and demographically to a changing climate is a cornerstone of many contemporary ecological studies, especially for organisms, such as amphibians, whose persistence is closely tied to abiotic conditions. Activity is a useful parameter for understanding the effects of climate change because activity is directly linked to fitness as it dictates foraging times, energy budgets, and mating opportunities. However, activity can be challenging to measure directly, especially for secretive organisms like plethodontid salamanders, which only become surface active when conditions are cool and moist because of their anatomical and physiological restrictions. We estimated abiotic predictors of surface activity for the seven species of the Plethodon jordani complex. Five independent data sets collected from 2004 to 2017 were used to determine the parameters driving salamander surface activity in the present day, which were then used to predict potential activity changes over the next 80 yrs. Average active seasonal temperature and vapor pressure deficit were the strongest predictors of salamander surface activity and, without physiological or behavioral modifications, salamanders were predicted to exhibit a higher probability of surface activity during peak active season under future climate conditions. Temperatures during the active season likely do not exceed salamander thermal maxima to cause activity suppression and, until physiological limits are reached, future conditions may continue to increase activity. Our model is the first comprehensive field-based study to assess current and future surface activity probability. Our study provides insights into how a key behavior driving fitness may be affected by climate change.

Evaluation of the R package 'resistancega': A promising approach towards the accurate optimization of landscape resistance surfaces.

Understanding how landscape features affect gene flow has implications for numerous fields including molecular and evolutionary ecology. Despite this, modelling landscape resistance surfaces has remained a significant challenge. The R package resistancega was developed to provide a framework for optimizing landscape resistance surfaces. In this study, we assessed ResistanceGA's ability to recover the true resistance surface under a variety of scenarios, including when the underlying surface: (a) had different levels of spatial autocorrelation and (b) was transformed into a resistance surface using different functional transformations. These scenarios were evaluated with regard to varying sample size and varying levels of variance in the measure of genetic distance. We also assessed the ability of ResistanceGA to identify the true resistance surface among alternative correlated surfaces. In univariate simulations, correlation between the true and optimized resistance surfaces remained high with increased variance in genetic distance, but only when sample size was moderate to high (≥50). Model selection error was also driven by sample size with low type I error when simulations had moderate to high sample sizes, even with moderate to high variance in genetic distance and correlated alternative surfaces. ResistanceGA also performed well in multivariate simulationsbut had more difficulty identifying the true data generating surfaces when genetic data were simulated using an agent-based approach (especially with individual-based genetic data). Overall, our simulations highlight the ability of ResistanceGA to accurately optimize resistance surfaces but also underscore challenges in optimizing landscape resistance surfaces, especially with highly stochastic individual-based data.

Multiscale resistant kernel surfaces derived from inferred gene flow: An application with vernal pool breeding salamanders.

The importance of assessing spatial data at multiple scales when modelling species-environment relationships has been highlighted by several empirical studies. However, no landscape genetics studies have optimized landscape resistance surfaces by evaluating relevant spatial predictors at multiple spatial scales. Here, we model multiscale/layer landscape resistance surfaces to estimate resistance to inferred gene flow for two vernal pool breeding salamander species, spotted (Ambystoma maculatum) and marbled (A. opacum) salamanders. Multiscale resistance surface models outperformed spatial layers modelled at their original spatial scale. A resistance surface with forest land cover at a 500-m Gaussian kernel bandwidth and normalized vegetation index at a 100-m Gaussian kernel bandwidth was the top optimized resistance surface for A. maculatum, while a resistance surface with traffic rate and topographic curvature, both at a 500-m Gaussian kernel bandwidth, was the top optimized resistance surface for A. opacum. Species-specific resistant kernels were fit at all vernal pools in our study area with the optimized multiscale/layer resistance surface controlling kernel spread. Vernal pools were then evaluated and scored based on surrounding upland habitat (local score) and connectivity with other vernal pools on the landscape, with resistant kernels driving vernal pool connectivity scores. As expected, vernal pools that scored highest were in areas within forested habitats and with high vernal pool densities and low species-specific landscape resistance. Our findings highlight the success of using a novel analytical approach in a multiscale framework with applications beyond vernal pool amphibian conservation.

Climate drives community-wide divergence within species over a limited spatial scale: evidence from an oceanic island.

Geographic isolation substantially contributes to species endemism on oceanic islands when speciation involves the colonisation of a new island. However, less is understood about the drivers of speciation within islands. What is lacking is a general understanding of the geographic scale of gene flow limitation within islands, and thus the spatial scale and drivers of geographical speciation within insular contexts. Using a community of beetle species, we show that when dispersal ability and climate tolerance are restricted, microclimatic variation over distances of only a few kilometres can maintain strong geographic isolation extending back several millions of years. Further to this, we demonstrate congruent diversification with gene flow across species, mediated by Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact. The unprecedented scale of parallel species responses to a common environmental driver for evolutionary change has profound consequences for understanding past and future species responses to climate variation.

Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists.

BACKGROUND: Rare or narrowly endemic organisms are difficult to monitor and conserve when their total distribution and habitat preferences are incompletely known. One method employed in determining distributions of these organisms is species distribution modeling (SDM). METHODS: Using two species of narrowly endemic burrowing crayfish species as our study organisms, we sought to ground validate Maxent, a commonly used program to conduct SDMs. We used fine scale (30 m) resolution rasters of pertinent habitat variables collected from historical museum records in 2014. We then ground validated the Maxent model in 2015 by randomly and equally sampling the output from the model. RESULTS: The Maxent models for both species of crayfish showed positive relationships between predicted relative occurrence rate and crayfish burrow abundance in both a Receiver Operating Characteristic and generalized linear model approach. The ground validation of Maxent led us to new populations and range extensions of both species of crayfish. DISCUSSION: We conclude that Maxent is a suitable tool for the discovery of new populations of narrowly endemic, rare habitat specialists and our technique may be used for other rare, endemic organisms.

The influence of breeding phenology on the genetic structure of four pond-breeding salamanders.

Understanding metapopulation dynamics requires knowledge about local population dynamics and movement in both space and time. Most genetic metapopulation studies use one or two study species across the same landscape to infer population dynamics; however, using multiple co-occurring species allows for testing of hypotheses related to different life history strategies. We used genetic data to study dispersal, as measured by gene flow, in three ambystomatid salamanders (Ambystoma annulatum, A. maculatum, and A. opacum) and the Central Newt (Notophthalmus viridescens louisianensis) on the same landscape in Missouri, USA. While all four salamander species are forest dependent organisms that require fishless ponds to reproduce, they differ in breeding phenology and spatial distribution on the landscape. We use these differences in life history and distribution to address the following questions: (1) Are there species-level differences in the observed patterns of genetic diversity and genetic structure? and (2) Is dispersal influenced by landscape resistance? We detected two genetic clusters in A. annulatum and A. opacum on our landscape; both species breed in the fall and larvae overwinter in ponds. In contrast, no structure was evident in A. maculatum and N. v. louisianensis, species that breed during the spring. Tests for isolation by distance were significant for the three ambystomatids but not for N. v. louisianensis. Landscape resistance also contributed to genetic differentiation for all four species. Our results suggest species-level differences in dispersal ability and breeding phenology are driving observed patterns of genetic differentiation. From an evolutionary standpoint, the observed differences in dispersal distances and genetic structure between fall breeding and spring breeding species may be a result of the trade-off between larval period length and size at metamorphosis which in turn may influence the long-term viability of the metapopulation. Thus, it is important to consider life history differences among closely related and ecologically similar species when making management decisions.

Landscape genetic analyses reveal fine-scale effects of forest fragmentation in an insular tropical bird.

Within the framework of landscape genetics, resistance surface modelling is particularly relevant to explicitly test competing hypotheses about landscape effects on gene flow. To investigate how fragmentation of tropical forest affects population connectivity in a forest specialist bird species, we optimized resistance surfaces without a priori specification, using least-cost (LCP) or resistance (IBR) distances. We implemented a two-step procedure in order (i) to objectively define the landscape thematic resolution (level of detail in classification scheme to describe landscape variables) and spatial extent (area within the landscape boundaries) and then (ii) to test the relative role of several landscape features (elevation, roads, land cover) in genetic differentiation in the Plumbeous Warbler (Setophaga plumbea). We detected a small-scale reduction of gene flow mainly driven by land cover, with a negative impact of the nonforest matrix on landscape functional connectivity. However, matrix components did not equally constrain gene flow, as their conductivity increased with increasing structural similarity with forest habitat: urban areas and meadows had the highest resistance values whereas agricultural areas had intermediate resistance values. Our results revealed a higher performance of IBR compared to LCP in explaining gene flow, reflecting suboptimal movements across this human-modified landscape, challenging the common use of LCP to design habitat corridors and advocating for a broader use of circuit theory modelling. Finally, our results emphasize the need for an objective definition of landscape scales (landscape extent and thematic resolution) and highlight potential pitfalls associated with parameterization of resistance surfaces.

Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages.

In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adult Ambystoma maculatum from five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (F ST and D C ) and isolation-by-distance (IBD) among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when using D C , the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.