ISOLATION BY RESISTANCE

Disentangling the contemporary and historical effects of landscape on the population genomic variation of two bird species restricted to the highland forest enclaves of northeastern Brazil

Investigating the impact of landscape features on patterns of genetic variation is crucial to understand spatially dependent evolutionary processes. Here, we assess the population genomic variation of two bird species (Conopophaga cearae and Sclerurus cearensis) through the Caatinga moist forest enclaves in northeastern Brazil. To infer the evolutionary dynamics of bird populations through the Late Quaternary, we used genome-wide polymorphism data obtained from double-digestion restriction-site-associated DNA sequencing (ddRADseq), and integrated population structure analyses, historical demography models, paleodistribution modeling, and landscape genetics analyses. We found the population differentiation among enclaves to be significantly related to the geographic distance and historical resistance across the rugged landscape. The climate changes at the end of the Pleistocene to the Holocene likely triggered synchronic population decline in all enclaves for both species. Our findings revealed that both geographic distance and historical connectivity through highlands are important factors that can explain the current patterns of genetic variation. Our results further suggest that levels of population differentiation and connectivity cannot be explained purely on the basis of contemporary environmental conditions. By combining historical demographic analyses and niche modeling predictions in a historical framework, we provide strong evidence that climate fluctuations of the Quaternary promoted population differentiation and a high degree of temporal synchrony among population size changes in both species.

The genetic structure and connectivity in two sympatric rodent species with different life histories are similarly affected by land use disturbances

The negative impact of habitat fragmentation due to human activities may be different in different species that co-exist in the same area, with consequences on the development of environmental protection plans. Here we aim at understanding the effects produced by different natural and anthropic landscape features on gene flow patterns in two sympatric species with different specializations, one generalist and one specialist, sampled in the same locations. We collected and genotyped 194 wood mice (generalist species) and 199 bank voles (specialist species) from 15 woodlands in a fragmented landscape characterized by different potential barriers to dispersal. Genetic variation and structure were analyzed in the two species, respectively. Effective migration surfaces, isolation-by-resistance (IBR) analysis, and regression with randomization were used to investigate isolation-by-distance (IBD) and the relative importance of land cover elements on gene flow. We observed similar patterns of heterozygosity and IBD for both species, but the bank vole showed higher genetic differences among geographic areas. The IBR analysis suggests that (i) connectivity is reduced in both species by urban areas but more strongly in the specialist bank vole; (ii) cultivated areas act as dispersal corridors in both species; (iii) woodlands appear to be an important factor in increasing connectivity in the bank vole, and less so in the wood mouse. The difference in dispersal abilities between a generalist and specialist species was reflected in the difference in genetic structure, despite extensive habitat changes due to human activities. The negative effects of fragmentation due to the process of urbanization were, at least partially, mitigated by another human product, i.e., cultivated terrains subdivided by hedgerows, and this was true for both species.

Genetic diversity pattern reveals the primary determinant of burcucumber (Sicyos angulatus L.) invasion in Korea

Biological invasion is a complex process associated with propagule pressure, dispersal ability, environmental constraints, and human interventions, which leave genetic signatures. The population genetics of an invasive species thus provides invaluable insights into the patterns of invasion. Burcucumber, one of the most detrimental weeds for soybean production in US, has recently colonized Korea and rapidly spread posing a great threat to the natural ecosystem. We aim to infer the determinants of the rapid burcucumber invasion by examining the genetic diversity, demography, and spread pattern with advanced genomic tools. We employed 2,696 genome-wide single-nucleotide polymorphisms to assess the level of diversity and the spatial pattern associated with the landscape factors and to infer the demographic changes of 24 populations (364 genotypes) across four major river basins with the east coastal streams in South Korea. Through the approximate Bayesian computation, we inferred the likely invasion scenario of burcucumber in Korea. The landscape genetics approach adopting the circuit theory and MaxEnt model was applied to determine the landscape contributors. Our data suggested that most populations have experienced population bottlenecks, which led to lowered within-population genetic diversity and inflated population divergences. Burcucumber colonization in Korea has strongly been affected by demographic bottlenecks and multiple introductions, whereas environmental factors were not the primary determinant of the invasion. Our work highlighted the significance of preventing secondary introductions, particularly for aggressive weedy plants such as the burcucumber.

habCluster: identifying the geographical boundary among intraspecific units using community detection algorithms in R

Conservation management for a species generally rests on intraspecific units, while identification of their geographic boundaries is necessary for the implementation. Intraspecific units can be discriminated using population genetic methods, yet an analytical approach is still lacking for detecting their geographic boundaries. Here, based on landscape connectivity, we present a raster-based geographical boundary delineation method, habCluster, using community detection algorithms. Community detection is a technique in graph theory used to identify clusters of highly connected nodes within a network. We assume that the habitat raster cells with better connections tend to form a continuous habitat patch than the others, thus making the range of an intraspecific unit. The method was tested on the gray wolf (Canis lupus) habitat in Europe and the giant panda (Ailuropoda melanoleuca) habitat in China. The habitat suitability index (HSI) maps for gray wolves and giant pandas were evaluated using species distribution models. Each cell in the HSI raster is treated as a node and directly connected with its eight neighbor cells. The edge weight between nodes is the reciprocal of the relative distance between the centers of the nodes weighted by the average of their HSI values. We implement habCluster using the R programming language with the inline C++ code to speed up the computing. We found that the boundaries of the clusters delineated using habCluster could serve as a good indicator of habitat patches. In the giant panda case, the clusters match generally well with nature reserves. habCluster can provide a spatial analysis basis for conservation management plans such as monitoring, translocation and reintroduction, and population structure research.

Integrating Landscape Connectivity and Natural-Anthropogenic Interaction to Understand Karst Vegetation Restoration: A Case Study of Guizhou Province, China

Because of implementation of ecological projects, the restoration of vegetation not only changes the typological composition and spatial structure of the landscape, but also improves the regional ecosystem function. The present study considered the effects of natural-anthropogenic factors and landscape connectivity on vegetation restoration. It also explored the impact and underlying mechanisms by which structural changes in landscape connectivity affect vegetation coverage in the karst region of Guizhou Province and provided a novel perspective for the maintenance of regional ecological security. We used morphological spatial pattern analysis (MSPA) and integrated valuation of ecosystem services and tradeoffs (InVEST) and circuit theory to identify ecological networks and explore the changes in landscape structure. We performed a Theil-Sen Median trend analysis and a Mann-Kendall (MK) trend test to determine spatiotemporal variations in vegetation coverage. We conducted a coupling analysis to discover correlations between the average cumulative current density (CCD) and the normalized difference vegetation index (NDVI) in various karst landform counties. We also implemented a geographical detector to detect the factors affecting the NDVI trend and disclose interactions among factors. The results showed that (1) Though the total area of forests and core areas was reduced, the ecological networks and landscape connectivity steadily improved. (2) Areas with improved vegetation coverage accounted for 77.77% of the total. By contrast, the degraded areas covered 14.28% while the remaining 7.95% was stable. (3) The relationships between the average CCD and the NDVI were inconsistent among various geomorphological counties. The counties with the highest proportions of karst landforms presented with negative correlations between the average CCD and the NDVI in 2005 and positive correlations between these parameters in 2018. (4) The NDVI trend was influenced by several factors. Of these, anthropogenic activity played a dominant role. Nevertheless, changes in landscape connectivity was also implicated. Attention should be paid to the impact of landscape connectivity on ecological restoration. The foregoing results indicated that the rocky desertification projects effectively improved landscape connectivity and vegetation coverage and provided a reference for developing policies establishing and maintaining ecological security of the karst ecosystem and coordinating sustainable development in this region.

Fast and flexible estimation of effective migration surfaces

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

Is There an Equivalence between Measures of Landscape Structural and Functional Connectivity for Plants in Conservation Assessments of the Cerrado?

Landscape connectivity can be assessed based on the physical connection (structural connectivity) or the maintenance of flow among habitats depending on the species (functional connectivity). The lack of empirical data on the dispersal capacity of species can lead to the use of simple structural measures. Comparisons between these approaches can improve decision-making processes for the conservation or restoration of habitats in fragmented landscapes, such as the Cerrado biome. This study aimed to understand the correspondence between the measures of landscape structural and functional connectivity for Cerrado plants. Three landscapes with cerradão patches in a pasture matrix were selected for the application of these metrics based on the functional connectivity of four profiles of plant dispersal capacity. The results showed divergent interpretations between the measures of landscape structural and functional connectivity, indicating that the assessment of biodiversity conservation and landscape connectivity is dependent on the set of metrics chosen. Structurally, the studied landscapes had the same number of cerradão patches but varied in optimal resource availability, isolation, heterogeneity, and aggregation. Functional connectivity was low for all profiles (based on the integral index of connectivity—IIC) and null for species with a low dispersal capacity (based on the connectance index—CONNECT), indicating that species with a medium- to long-distance dispersal capacity may be less affected by the history of losses and fragmentation of the Cerrado in the pasture matrix. The functional connectivity metrics used allowed a more robust analysis and, apparently, better reflected reality, but the lack of empirical data on dispersal capacity and the difficulty in choosing an indicator organism can limit their use in the management and planning of conservation and restoration areas.

Searching for Networks: Ecological Connectivity for Amphibians Under Climate Change

Ecological connectivity depends on key elements within the landscape, which can support ecological fluxes, species richness and long-term viability of a biological community. Landscape planning requires clear aims and quantitative approaches to identify which key elements can reinforce the spatial coherence of protected areas design. We aim to explore the probability of the ecological connectivity of forest remnants and amphibian species distributions for current and future climate scenarios across the Central Corridor of the Brazilian Atlantic Forest. Integrating amphibian conservation, climate change and ecological corridors, we design a landscape ranking based on graph and circuit theories. To identify the sensitivity of connected areas to climate-dependent changes, we use the Model for Interdisciplinary Research on Climate by means of simulations for 2080-2100, representing a moderated emission scenario within an optimistic context. Our findings indicate that more than 70% of forest connectivity loss by climate change may drastically reduce amphibian dispersal in this region. We show that high amphibian turnover rates tend to be greater in the north-eastern edges of the corridor across ensembles of forecasts. Our spatial analysis reveals a general pattern of low-conductance areas in landscape surface, yet with some well-connected patches suggesting potential ecological corridors. Atlantic Forest reserves are expected to be less effective in a near future. For improved conservation outcomes, we recommend some landscape paths with low resistance values across space and time. We highlight the importance of maintaining forest remnants in the southern Bahia region by drafting a blueprint for functional biodiversity corridors.

Habitat Loss Does Not Always Entail Negative Genetic Consequences

Although habitat loss has large, consistently negative effects on biodiversity, its genetic consequences are not yet fully understood. This is because measuring the genetic consequences of habitat loss requires accounting for major methodological limitations like the confounding effect of habitat fragmentation, historical processes underpinning genetic differentiation, time-lags between the onset of disturbances and genetic outcomes, and the need for large numbers of samples, genetic markers, and replicated landscapes to ensure sufficient statistical power. In this paper we overcame all these challenges to assess the genetic consequences of extreme habitat loss driven by mining in two herbs endemic to Amazonian savannas. Relying on genotyping-by-sequencing of hundreds of individuals collected across two mining landscapes, we identified thousands of neutral and independent single-nucleotide polymorphisms (SNPs) in each species and used these to evaluate population structure, genetic diversity, and gene flow. Since open-pit mining in our study region rarely involves habitat fragmentation, we were able to assess the independent effect of habitat loss. We also accounted for the underlying population structure when assessing landscape effects on genetic diversity and gene flow, examined the sensitivity of our analyses to the resolution of spatial data, and used annual species and cross-year analyses to minimize and quantify possible time-lag effects. We found that both species are remarkably resilient, as genetic diversity and gene flow patterns were unaffected by habitat loss. Whereas historical habitat amount was found to influence inbreeding; heterozygosity and inbreeding were not affected by habitat loss in either species, and gene flow was mainly influenced by geographic distance, pre-mining land cover, and local climate. Our study demonstrates that it is not possible to generalize about the genetic consequences of habitat loss, and implies that future conservation efforts need to consider species-specific genetic information.

A landscape genetic analysis of swamp rabbits (Sylvilagus aquaticus) suggests forest canopy cover enhances gene flow in an agricultural matrix

Habitat loss and fragmentation pose a continued and immediate threat to wildlife and create a persistent need for ecological information at the landscape scale to guide conservation efforts. Landscape features influence population connectivity for many species and genetic analyses can be employed to determine which of these features are most important. Because population connectivity through dispersal is important to the persistence of swamp rabbits (Sylvilagus aquaticus (Bachman, 1837)) at the northern edge of their range, we used a landscape genetic approach to relate gene flow to landscape features that may impact dispersal success. We tested resistance values for attributes of land cover, watercourse corridors, canopy cover, and roads and used causal modeling and redundancy analysis to relate these representations of landscapes to genetic distance for swamp rabbits in southern Illinois, USA. Models that included canopy cover had the strongest correlations with genetic distance and were supported by our methods whereas other models were not. We concluded that high tree canopy cover enhances gene flow and landscape connectivity for swamp rabbits in southern Illinois. Our study provides important empirical evidence that landscape variables may impact the habitat connectivity of swamp rabbits. Preserving dispersal routes for swamp rabbits should focus on improving canopy cover, in both bottomland and upland, to connect suitable habitat.

Geography and Environment Shape Landscape Genetics of Mediterranean Alpine Species Silene ciliata Poiret. (Caryophyllaceae)

The study of the drivers that shape spatial genetic structure across heterogeneous landscapes is one of the main approaches used to understand population dynamics and responses in changing environments. While the Isolation-by-Distance model (IBD) assumes that genetic differentiation increases among populations with geographical distance, the Isolation-by-Resistance model (IBR) also considers geographical barriers and other landscape features that impede gene flow. On the other hand, the Isolation-by-Environment model (IBE) explains genetic differentiation through environmental differences between populations. Although spatial genetic studies have increased significantly in recent years, plants from alpine ecosystems are highly underrepresented, even though they are great suitable systems to disentangle the role of the different factors that structure genetic variation across environmental gradients. Here, we studied the spatial genetic structure of the Mediterranean alpine specialist Silene ciliata across its southernmost distribution limit. We sampled three populations across an altitudinal gradient from 1850 to 2400 m, and we replicated this sample over three mountain ranges aligned across an E-W axis in the central part of the Iberian Peninsula. We genotyped 20 individuals per population based on eight microsatellite markers and used different landscape genetic tools to infer the role of topographic and environmental factors in shaping observed patterns along the altitudinal gradient. We found a significant genetic structure among the studied Silene ciliata populations which was related to the orography and E-W configuration of the mountain ranges. IBD pattern arose as the main factor shaping population genetic differentiation. Geographical barriers between mountain ranges also affected the spatial genetic structure (IBR pattern). Although environmental variables had a significant effect on population genetic diversity parameters, no IBE pattern was found on genetic structure. Our study reveals that IBD was the driver that best explained the genetic structure, whereas environmental factors also played a role in determining genetic diversity values of this dominant plant of Mediterranean alpine environments.

Simulating Pattern-Process Relationships to Validate Landscape Genetic Models

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all plausible alternative hypotheses. In this paper, rather than infer the process of gene flow from an observed genetic pattern, we simulate gene flow and determine if the simulated genetic pattern is related to the observed empirical genetic pattern. This is a form of inverse modeling and can be used to independently validate a landscape genetic model. In this study, we used this approach to validate a model of landscape resistance based on elevation, landcover, and roads that was previously related to genetic isolation among mountain goats (Oreamnos americanus) inhabiting the Cascade Range, Washington (USA). The strong relationship between the empirical and simulated patterns of genetic isolation we observed provides independent validation of the resistance model and demonstrates the utility of this approach in supporting landscape genetic inferences.