The use and misuse of regression models in landscape genetic analyses

A Landscape Genetics Approach Reveals Species-Specific Connectivity Patterns for Stream Insects in Fragmented Habitats

Dispersal is a critical process in ecology and evolution, shaping global biodiversity patterns. In stream habitats, which often exist within diverse and fragmented landscapes, dispersal ensures population connectivity and survival. For aquatic insects in particular, landscape features may significantly influence the degree of genetic connectivity among populations. Thus, understanding connectivity drivers in such populations is essential for the conservation and management of streams. We conducted a landscape genetic study using mitochondrial DNA (mtDNA) and genome-wide single nucleotide polymorphism (SNP) markers to assess the functional connectivity of stream insects in a fragmented pasture-dominated landscape. We focused on three species with terrestrial winged adults: the mayfly Coloburiscus humeralis, the stonefly Zelandobius confusus, and the caddisfly Hydropsyche fimbriata. We observed significant spatial genetic structure at larger geographical distances (populations separated by ~30 and 170 km). However, the effects of landscape factors, which were assessed at fine spatial scales, varied among species: for C. humeralis SNP data, genetic differentiation was weakly correlated with land cover, suggesting greater population connectivity within stream channels protected by forested riparian zones compared to fragmented streams; for Z. confusus, widespread gene flow indicated high dispersal potential across forested and pasture land; while overland dispersal was reduced for H. fimbriata (potentially due to local habitat features), this did not seem to hinder broader population connectivity. Our results emphasise the importance of assessing landscape features when evaluating population connectivity in stream riparian zones, which can greatly benefit stream management efforts through an enhanced understanding of connectivity dynamics.

What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA

Modelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi-generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo-genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation-by-Resistance (IBR) patterns from either Isolation-by-Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross-validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross-validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.

Genetic connectivity of wolverines in western North America

Wolverine distribution contracted along the southern periphery of its range in North America during the 19th and 20th centuries due primarily to human influences. This history, along with low densities, sensitivity to climate change, and concerns about connectivity among fragmented habitats spurred the recent US federal listing of threatened status and special concern status in Canada. To help inform large scale landscape connectivity, we collected 882 genetic samples genotyped at 19 microsatellite loci. We employed multiple statistical models to assess the landscape factors (terrain complexity, human disturbance, forest configuration, and climate) associated with wolverine genetic connectivity across 2.2 million km2 of southwestern Canada and the northwestern contiguous United States. Genetic similarity (positive spatial autocorrelation) of wolverines was detected up to 555 km and a high-to-low gradient of genetic diversity occurred from north-to-south. Landscape genetics analyses confirmed that wolverine genetic connectivity has been negatively influenced by human disturbance at broad scales and positively influenced by forest cover and snow persistence at fine- and broad-scales, respectively. This information applied across large landscapes can be used to guide management actions with the goal of maintaining or restoring population connectivity.

Testing concordance and conflict in spatial replication of landscape genetics inferences

The degree to which landscape genetics findings can be extrapolated to different areas of a species range is poorly understood. Here, we used a broadly distributed ectothermic lizard (Sceloporus occidentalis, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses with a total of 119 individuals in five areas within the Sierra Nevada mountain range. Genetic distances calculated from thousands of ddRAD markers were used to optimize landscape resistance surfaces and infer the effects of landscape and topographic features on genetic connectivity. Across study areas, we found a great deal of consistency in the primary environmental gradients impacting genetic connectivity, along with some site-specific differences, and a range in the proportion of genetic variance explained by environmental factors across study sites. High-elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High temperature seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Across study areas, canyon valleys were always implicated as facilitators to dispersal and key features linking populations and maintaining genetic connectivity, though the relative importance varied in different areas. We emphasize that spatial data layers are complex and multidimensional, and careful consideration of spatial data correlation structure and robust analytic frameworks will be critical to our continued understanding of spatial genetics processes.

Disentangling effects of dispersal, environment and anthropogenic barriers on functional connectivity in aquatic systems

Disentangling the roles of structural landscape factors and animal movement behaviour can present challenges for practitioners managing landscapes to maintain functional connectivity and achieve conservation goals. We used a landscape genetics approach to combine robust demographic, behavioural and genetic datasets with spatially explicit simulations to evaluate the effects of anthropogenic barriers (dams, culverts) and natural landscape resistance (gradient, elevation) affecting dispersal behaviour, genetic connectivity and genetic structure in a resident population of Westslope Cutthroat Trout (Oncorhynchus clarkii lewisi). Analyses based on 10 years of sampling effort revealed a pattern of restricted dispersal, and population genetics identified discrete population clusters between distal tributaries and the mainstem stream and no structure within the mainstem stream. Demogenetic simulations demonstrated that, for this population, the effects of existing anthropogenic barriers on population structure are redundant with effects of restricted dispersal associated with the underlying environmental resistance. Our approach provides an example of how extensive field sampling combined with landscape genetics can be incorporated into spatially explicit simulation modelling to explore how, together, movement ecology and landscape resistance can be used to inform decisions around restoration and connectivity.

Isolation by environment and its consequences for range shifts with global change: Landscape genomics of the invasive plant common tansy

Invasive species are a growing global economic and ecological problem. However, it is not well understood how environmental factors mediate invasive range expansion. In this study, we investigated the recent and rapid range expansion of common tansy across environmental gradients in Minnesota, USA. We densely sampled individuals across the expanding range and performed reduced representation sequencing to generate a dataset of 3071 polymorphic loci for 176 individuals. We used non-spatial and spatially explicit analyses to determine the relative influences of geographic distance and environmental variation on patterns of genomic variation. We found no evidence for isolation by distance but strong evidence for isolation by environment, indicating that environmental factors may have modulated patterns of range expansion. Land use classification and soils were particularly important variables related to population structure although they operated on different spatial scales; land use classification was related to broad-scale patterns and soils were related to fine-scale patterns. All analyses indicated a distinctive genetic cluster in the most recently invaded portion of the range. Individuals from the far northwestern range margin were separated from the remainder of the range by reduced migration, which was associated with environmental resistance. This portion of the range was invaded primarily in the last 15 years. Ecological niche models also indicated that this cluster was associated with the expansion of the niche. While invasion is often assumed to be primarily influenced by dispersal limitation, our results suggest that ongoing invasion and range shifts with climate change may be strongly affected by environmental heterogeneity.

Conserving Evolutionary Potential: Combining Landscape Genomics with Established Methods to Inform Plant Conservation

Biodiversity conservation requires conserving evolutionary potential-the capacity for wild populations to adapt. Understanding genetic diversity and evolutionary dynamics is critical for informing conservation decisions that enhance adaptability and persistence under environmental change. We review how emerging landscape genomic methods provide plant conservation programs with insights into evolutionary dynamics, including local adaptation and its environmental drivers. Landscape genomic approaches that explore relationships between genomic variation and environments complement rather than replace established population genomic and common garden approaches for assessing adaptive phenotypic variation, population structure, gene flow, and demography. Collectively, these approaches inform conservation actions, including genetic rescue, maladaptation prediction, and assisted gene flow. The greatest on-the-ground impacts from such studies will be realized when conservation practitioners are actively engaged in research and monitoring. Understanding the evolutionary dynamics shaping the genetic diversity of wild plant populations will inform plant conservation decisions that enhance the adaptability and persistence of species in an uncertain future.

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.

The burden of anthropogenic changes and mutation load in a critically endangered harrier from the Reunion biodiversity hotspot, Circus maillardi

Anthropogenic impact is causing the decline of a large proportion of species worldwide and reduces their genetic diversity. Island species typically have smaller ranges than continental species. As a consequence, island species are particularly liable to undergo population bottlenecks, giving rise to conservation challenges such as inbreeding and unmasking of deleterious genetic load. Such challenges call for more detailed assessments of the genetic make-up of threatened island populations. The Mascarene islands (Indian Ocean) present many prime examples, being unusual in having been pristine until first human arrival ~400 years ago, following which anthropogenic pressure was unusually intense. A threatened harrier (Circus maillardi) endemic to the westernmost island of the archipelago is a good example of the challenges faced by species that have declined to small population size following intense anthropogenic pressure. In this study, we use an extensive set of population genomic tools to quantify variation at near-neutral and coding loci, in order to test the historical impact of human activity on this species, and evaluate the species' (mal)adaptive potential. We observed low but significant genetic differentiation between populations on the West and North-East sides of the island, echoing observations in other endemic species. Inbreeding was significant, with a substantial fraction of samples being first or second-degree relatives. Historical effective population sizes have declined from ~3000 to 300 individuals in the past 1000 years, with a more recent drop ~100 years ago consistent with human activity. Based on our simulations and comparisons with a close relative (Circus melanoleucos), this demographic history may have allowed purging of the most deleterious variants but is unlikely to have allowed the purging of mildly deleterious variants. Our study shows how using relatively affordable methods can reveal the massive impact that human activity may have on the genetic diversity and adaptive potential of island populations, and calls for urgent action to closely monitor the reproductive success of such endemic populations, in association with genetic studies.

Quantifying uncertainty in inferences of landscape genetic resistance due to choice of individual-based genetic distance metric

Estimates of gene flow resulting from landscape resistance inferences frequently inform conservation management decision-making processes. Therefore, results must be robust across approaches and reflect real-world gene flow instead of methodological artefacts. Here, we tested the impact of 32 individual-based genetic distance metrics on the robustness and accuracy of landscape resistance modelling results. We analysed three empirical microsatellite datasets and 36 simulated datasets that varied in landscape resistance and genetic spatial autocorrelation. We used ResistanceGA to generate optimised multi-feature resistance surfaces for each of these datasets using 32 different genetic distance metrics. Results of the empirical dataset demonstrated that the choice of genetic distance metric can have strong impacts on inferred optimised resistance surfaces. Simulations showed accurate parametrisation of resistance surfaces across most genetic distance metrics only when a small number of environmental features was impacting gene flow. Landscape scenarios with many features impacting gene flow led to a generally poor recovery of true resistance surfaces. Simulation results also emphasise that choosing a genetic distance metric should not be based on marginal R2 -based model fit. Until more robust methods are available, resistance surfaces can be optimised with different genetic distance metrics and the convergence of results needs to be assessed via pairwise matrix correlations. Based on the results presented here, high correlation coefficients across different genetic distance categories likely indicate accurate inference of true landscape resistance. Most importantly, empirical results should be interpreted with great caution, especially when they appear counter-intuitive in light of the ecology of a species.

Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity

Understanding how human infrastructure and other landscape attributes affect genetic differentiation in animals is an important step for identifying and maintaining dispersal corridors for these species. We built upon recent advances in the field of landscape genetics by using an individual-based and multiscale approach to predict landscape-level genetic connectivity for grizzly bears (Ursus arctos) across ~100,000 km2 in Canada's southern Rocky Mountains. We used a genetic dataset with 1156 unique individuals genotyped at nine microsatellite loci to identify landscape characteristics that influence grizzly bear gene flow at multiple spatial scales and map predicted genetic connectivity through a matrix of rugged terrain, large protected areas, highways and a growing human footprint. Our corridor-based modelling approach used a machine learning algorithm that objectively parameterized landscape resistance, incorporated spatial cross validation and variable selection and explicitly accounted for isolation by distance. This approach avoided overfitting, discarded variables that did not improve model performance across withheld test datasets and spatial predictive capacity compared to random cross-validation. We found that across all spatial scales, geographic distance explained more variation in genetic differentiation in grizzly bears than landscape variables. Human footprint inhibited connectivity across all spatial scales, while open canopies inhibited connectivity at the broadest spatial scale. Our results highlight the negative effect of human footprint on genetic connectivity, provide strong evidence for using spatial cross-validation in landscape genetics analyses and show that multiscale analyses provide additional information on how landscape variables affect genetic differentiation.

Estimating resistance surfaces using gradient forest and allelic frequencies

Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the gradient forest approach to produce a resistance surface. Used in community ecology, gradient forest is an extension of random forest, and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resistance Gradient Forest (resGF) performance was compared to other published methods (maximum likelihood population effects model, random forest-based least-cost transect analysis and species distribution model). In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the gradient forest approach performed similarly to the other random forest-based approach using least-cost transect analysis but outperformed MLPE-based methods. Additionally, two worked examples are provided using two previously published data sets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and inform long-term biodiversity conservation strategies.

Both selection and drift drive the spatial pattern of adaptive genetic variation in a wild mammal

The major histocompatibility complex (MHC) has been intensively studied for the relative effects of different evolutionary forces in recent decades. Pathogen-mediated balancing selection is generally thought to explain the high polymorphism observed in MHC genes, but it is still unclear to what extent MHC diversity is shaped by selection relative to neutral drift. In this study, we genotyped MHC class II DRB genes and 15 neutral microsatellite loci across 26 geographic populations of European badgers (Meles meles) covering most of their geographic range. By comparing variation of microsatellite and diversity of MHC at different levels, we demonstrate that both balancing selection and drift have shaped the evolution of MHC genes. When only MHC allelic identity was investigated, the spatial pattern of MHC variation was similar to that of microsatellites. By contrast, when functional aspects of the MHC diversity (e.g., immunological supertypes) were considered, balancing selection appears to decrease genetic structuring across populations. Our comprehensive sampling and analytical approach enable us to conclude that the likely mechanisms of selection are heterozygote advantage and/or rare-allele advantage. This study is a clear demonstration of how both balancing selection and genetic drift simultaneously affect the evolution of MHC genes in a widely distributed wild mammal.

How ancient forest fragmentation and riparian connectivity generate high levels of genetic diversity in a microendemic Malagasy tree

Understanding landscape changes is central to predicting evolutionary trajectories and defining conservation practices. While human-driven deforestation is intense throughout Madagascar, exceptions in areas such as the Loky-Manambato region (north) raise questions regarding the causes and age of forest fragmentation. The Loky-Manambato region also harbours a rich and endemic flora, whose evolutionary origin remains poorly understood. We assessed the genetic diversity of an endangered microendemic Malagasy olive species (Noronhia spinifolia Hong-Wa) to better understand the vegetation dynamics in the Loky-Manambato region and its influence on past evolutionary processes. We characterized 72 individuals sampled across eight forests through nuclear and mitochondrial restriction-associated DNA sequencing data and chloroplast microsatellites. Combined population and landscape genetics analyses indicate that N. spinifolia diversity is largely explained by the current forest cover, highlighting a long-standing habitat mosaic in the region. This sustains a major and long-term role of riparian corridors in maintaining connectivity across these antique mosaic habitats, calling for the study of organismal interactions that promote gene flow.

Landscape genetics of a sub-alpine toad: climate change predicted to induce upward range shifts via asymmetrical migration corridors

Climate change is expected to have a major hydrological impact on the core breeding habitat and migration corridors of many amphibians in the twenty-first century. The Yosemite toad (Anaxyrus canorus) is a species of meadow-specializing amphibian endemic to the high-elevation Sierra Nevada Mountains of California. Despite living entirely on federal lands, it has recently faced severe extirpations, yet our understanding of climatic influences on population connectivity is limited. In this study, we used a previously published double-digest RADseq dataset along with numerous remotely sensed habitat features in a landscape genetics framework to answer two primary questions in Yosemite National Park: (1) Which fine-scale climate, topographic, soil, and vegetation features most facilitate meadow connectivity? (2) How is climate change predicted to influence both the magnitude and net asymmetry of genetic migration? We developed an approach for simultaneously modeling multiple toad migration paths, akin to circuit theory, except raw environmental features can be separately considered. Our workflow identified the most likely migration corridors between meadows and used the unique cubist machine learning approach to fit and forecast environmental models of connectivity. We identified the permuted modeling importance of numerous snowpack-related features, such as runoff and groundwater recharge. Our results highlight the importance of considering phylogeographic structure, and asymmetrical migration in landscape genetics. We predict an upward elevational shift for this already high-elevation species, as measured by the net vector of anticipated genetic movement, and a north-eastward shift in species distribution via the network of genetic migration corridors across the park.

Habitat-linked genetic variation supports microgeographic adaptive divergence in an island-endemic bird species

We investigated the potential mechanisms driving habitat-linked genetic divergence within a bird species endemic to a single 250 km² island. The island scrub-jay (Aphelocoma insularis) exhibits microgeographic divergence in bill morphology across pine-oak ecotones on Santa Cruz Island, California (USA) similar to adaptive differences described in mainland congeners over much larger geographic scales. To test whether individuals exhibit genetic differentiation related to habitat type and divergence in bill length, we genotyped over 3,000 single nucleotide polymorphisms (SNPs) in 123 adult island scrub-jay males from across Santa Cruz Island using restriction site-associated DNA sequencing (RADseq). Neutral landscape genomic analyses revealed that genome-wide genetic differentiation was primarily related to geographic distance and differences in habitat composition. We also found 168 putatively adaptive loci associated with habitat type using multivariate redundancy analysis (RDA) while controlling for spatial effects. Finally, two genome-wide association analyses revealed a polygenic basis to variation in bill length with multiple loci detected in or near genes known to affect bill morphology in other birds. Our findings support the hypothesis that divergent selection at microgeographic scales can cause adaptive divergence in the presence of ongoing gene flow.

Assessment of drivers of spatial genetic variation of a ground-dwelling bird species and its implications for conservation

In modern wildlife ecology, spatial population genetic methods are becoming increasingly applied. Especially for animal species in fragmented landscapes, preservation of gene flow becomes a high priority target in order to restore genetic diversity and prevent local extinction. Within Central Europe, the Alps represent the core distribution area of the black grouse, Lyrurus tetrix. At its easternmost Alpine range, events of subpopulation extinction have already been documented in the past decades. Molecular data combined with spatial analyses can help to assess landscape effects on genetic variation and therefore can be informative for conservation management. Here, we addressed whether the genetic pattern of the easternmost Alpine black grouse metapopulation system is driven by isolation by distance or isolation by resistance. Correlative ecological niche modeling was used to assess geographic distances and landscape resistances. We then applied regression-based approaches combined with population genetic analyses based on microsatellite data to disentangle effects of isolation by distance and isolation by resistance among individuals and subpopulations. Although population genetic analyses revealed overall low levels of genetic differentiation, the ecological niche modeling showed subpopulations to be clearly delimited by habitat structures. Spatial genetic variation could be attributed to effects of isolation by distance among individuals and isolation by resistance among subpopulations, yet unknown effects might factor in. The easternmost subpopulation was the most differentiated, and at the same time, immigration was not detected; hence, its long-term survival might be threatened. Our study provides valuable insights into the spatial genetic variation of this small-scale metapopulation system of Alpine black grouse.

Towards a unified framework to study causality in Earth-life systems

There is considerable interest in better understanding how earth processes shape the generation and distribution of life on Earth. This question, at its heart, is one of causation. In this article I propose that at a regional level, earth processes can be thought of as behaving somewhat deterministically and may have an organized effect on the diversification and distribution of species. However, the study of how landscape features shape biology is challenged by pseudocongruent or collinear variables. I demonstrate that causal structures can be used to depict the cause–effect relationships between earth processes and biological patterns using recent examples from the literature about speciation and species richness in montane settings. This application shows that causal diagrams can be used to better decipher the details of causal relationships by motivating new hypotheses. Additionally, the abstraction of this knowledge into structural equation metamodels can be used to formulate theory about relationships within Earth–life systems more broadly. Causal structures are a natural point of collaboration between biologists and Earth scientists, and their use can mitigate against the risk of misassigning causality within studies. My goal is that by applying causal theory through application of causal structures, we can build a systems‐level understanding of what landscape features or earth processes most shape the distribution and diversification of species, what types of organisms are most affected, and why.

Microbial Pathogenicity in Space

After a less dynamic period, space exploration is now booming. There has been a sharp increase in the number of current missions and also of those being planned for the near future. Microorganisms will be an inevitable component of these missions, mostly because they hitchhike, either attached to space technology, like spaceships or spacesuits, to organic matter and even to us (human microbiome), or to other life forms we carry on our missions. Basically, we never travel alone. Therefore, we need to have a clear understanding of how dangerous our "travel buddies" can be; given that, during space missions, our access to medical assistance and medical drugs will be very limited. Do we explore space together with pathogenic microorganisms? Do our hitchhikers adapt to the space conditions, as well as we do? Do they become pathogenic during that adaptation process? The current review intends to better clarify these questions in order to facilitate future activities in space. More technological advances are needed to guarantee the success of all missions and assure the reduction of any possible health and environmental risks for the astronauts and for the locations being explored.