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Python based GIS tools for landscape genetics: visualising genetic relatedness and measuring landscape connectivity: GIS tools for landscape genetics
Methods in Ecology and Evolution
Abstract
1. Landscape genetics is an area of research that can help to understand many spatial ecological processes, but requires significant interdisciplinary collaboration. Use of geographic information system (GIS) software is essential, but requires a degree of customisation that is often beyond the non-specialist.
2. To help address this, a series of Python script based GIS tools have been developed for use in landscape genetics studies.
3. The scripts convert files, visualise genetic relatedness, and measure landscape connectivity using least-cost path analysis. The scripts are housed in an ArcToolbox that is freely available along with the underlying Python code.
4. The Python scripts allow researchers to use more current software, provide the option of further development by the user community, and reduce the amount of time that would be spent developing common solutions.
... The analysis of spatial dynamics of sheep production in the country is important to understand development tendencies and production concentration, to aid in the elaboration of public policies to consolidate this activity. In this context, the use of geo-technologies have the potential to increase knowledge about sheep production (ESCUDERO et al, 2003;ETHERINGTON, 2011) contributing to the development of appropriate public policies for the expansion of production in the country. ...
... The analysis of spatial dynamics of sheep production in the country is important to understand development tendencies and production concentration, to aid in the elaboration of public policies to consolidate this activity. In this context, the use of geo-technologies have the potential to increase knowledge about sheep production (ESCUDERO et al, 2003;ETHERINGTON, 2011) contributing to the development of appropriate public policies for the expansion of production in the country. ...
The present study aims to evaluate the multitemporal dynamics of sheep production in Brazil, considering the Federal Government's official data from 1976 to 2010. Principal Component Analysis (PCA) and Self-Organizing Maps (SOFM) were used. PCA reduced data redundancy and the colour composition of the first three principal components evidenced temporal patterns. In the SOFM classification, a type of Artificial Neural Network (ANN) with non-supervised training, different dimensions of the Kohonen map were tested. The results obtained by the two methods are complementary, evidencing the development of sheep production in the country. The southern and northeast maintained the tradition of sheep production over the analyzed period. The municipalities in the Midwest showed production growth. The techniques used were effective in multitemporal analysis, providing a greater understanding of the dynamics of sheep production in Brazil and providing subsidies for the development of appropriate public policies for its expansion.
... This smoothing procedure produces a twodimensional geographical layer that can be easily visualised and customised in a Geographical Information System (GIS). Although this method may be of interest in other research fields, we will hereafter focus on spatial and landscape genetics, where pairwise metrics are widely used but flexible visualisation tools are still lacking for these measures (see Miller 2005;Vandergast et al. 2010;Etherington 2011;Petkova, Novembre & Stephens 2016). ...
... In such a situation, we still rely on exploratory analyses to identify environmental variables of interest and draw up hypotheses that can be further tested using appropriate sampling schemes (Kelling et al. 2009;Richardson et al. 2016). Although the visualisation of pairwise genetic measures may be an obvious first step in such an approach (Etherington 2011), there are still very few tools allowing visualisation of such measures along with environmental layers without, first, attributing features such as cost or resistance values to habitat types. Among those tools, Allele In Space (Miller 2005) and the GIS toolbox of Vandergast et al. (2010) offer the rare possibility to produce variation surfaces of genetic distances that can be mapped over landscape layers (Wood et al. 2013;Adams & Burg 2015). ...
Visualisation of spatial networks based on pairwise metrics such as (dis)similarity coefficients provides direct information on spatial organisation of biological systems. However, for large networks, graphical representations are often unreadable as nodes (samples), and edges (links between samples) strongly overlap. We present a new method, MAPI, allowing translation from spatial networks to variation surfaces.
MAPI relies on (i) a spatial network in which samples are linked by ellipses and (ii) a grid of hexagonal cells encompassing the study area. Pairwise metric values are attributed to ellipses and averaged within the cells they intersect. The resulting surface of variation can be displayed as a colour map in Geographical Information System ( GIS ), along with other relevant layers, such as land cover. The method also allows the identification of significant discontinuities in grid cell values through a nonparametric randomisation procedure.
The interest of MAPI is here demonstrated in the field of spatial and landscape genetics. Using simulated test data sets, as well as observed data from three biological models, we show that MAPI is (i) relatively insensitive to confounding effects resulting from isolation by distance (i.e. over‐structuring), (ii) efficient in detecting barriers when they are not too permeable to gene flow and, (iii) useful to explore relationships between spatial genetic patterns and landscape features.
MAPI is freely provided as a Postgre SQL /Post GIS data base extension allowing easy interaction with GIS or the r software and other programming languages. Although developed for spatial and landscape genetics, the method can also be useful to visualise spatial organisation from other kinds of data from which pairwise metrics can be computed.
... Through this study, we provide a basic, but replicable habitat suitability modeling procedure while demonstrating the utility of multiple openly accessible and downloadable habitat covariates available at a high resolution for the scale of the landscape and species in question. We further exhibit the utility of python-based tools [50], which can provide a user-friendly approach to the implement GIS to visualize genetic relatedness among individuals across a landscape. ...
... Because the manuscript has not yet been approved for publication by the U.S. Geological Survey (USGS), it does not represent any official USGS finding or policy. ArcGIS [50]. ...
The projected growth in human population, rapid urbanization, and expansion of structures like highways and canals pose a major threat to the future survival of wildlife, particularly large terrestrial mammals. In many cases, wild animal populations have been restricted to fragmented habitat islands due to anthropogenic developments, endangering them to local extinction. Current and future wildlife conservation and management strategies are leading to the implementation of mitigation measures such as creation of wildlife habitat corridors. In this light, novel and interdisciplinary research methods such as approaches in the field of landscape genetics are proving to be increasingly useful and necessary for assessing the status of wildlife populations and furthering efficacy of conservation programs and management efforts. In this 5-year research study, I review literature in the field of landscape genetics, highlighting studies and their applications toward wildlife conservation over the past decade (2005-2014). I then use a landscape genetic approach to understand the potential impact of natural and human-made barriers in and around the northern Sonoran Desert on one of the widest-ranging mammals in the world, the mountain lion (Puma concolor). I employ recently developed genetic tools to assess the current population genetic status of mountain lions in this region and Geographic Information Systems (GIS) tools to relate observations to landscape features through interpretive maps. I further investigate the utility of GIS and expert-based models in connectivity conservation and suggest validating them with information on genetic relatedness and functional connectivity among mountain lions. Lastly, in many parts of this document, I emphasize the use of these methods and data sharing in conservation planning as well as wildlife management.
... According to the calculation result of MaxEnt, the potential distribution map of species was obtained by loading the result and visualizing the suitable habitat grades in ArcGIS 10.6. The change in the area of potential distribution habitats and core distributional shifts of C. lanceolata in the future were analyzed using the SDM toolbox v2.5, written in Python language in ArcGIS 10.6 [28]. The statistics of the area mainly showed three types of changes: expansion, unchanged, and contraction. ...
Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection.
... We used geographic distribution measurement tools to fit the distribution range of the species to a single centroid (center) point and create a vector file to predict the migration trend of a species by tracking the changes in the centroid in different periods [29]. We drew the centroid shift map of potentially suitable areas for Pyrus of five species under different emission paths (SSP1.2-6, ...
Planting suitability determines the distribution and yield of crops in a given region which can be greatly affected by climate change. In recent years, many studies have shown that carbon dioxide fertilization effects increase the productivity of temperate deciduous fruit trees under a changing climate, but the potential risks to fruit tree planting caused by a reduction in suitable planting areas are rarely reported. In this study, Maxent was first used to investigate the spatial distribution of five Pyrus species in China, and the consistency between the actual production area and the modeled climatically suitable area under the current climatic conditions were determined. In addition, based on Coupled Model Intercomparison Project Phase 6, three climate models were used to simulate the change in suitable area and the migration trend for different species under different emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The results showed that the suitable area for pear was highly consistent with the actual main production area under current climate conditions. The potential planting areas of P. ussuriensis showed a downward trend under all emission paths from 2020 to 2100; other species showed a trend of increasing first and then decreasing or slowing down and this growth effect was the most obvious in 2020–2040. Except for P. pashia, other species showed a migration trend toward a high latitude, and the trend was more prominent under the high emission path. Our results emphasize the response difference between species to climate change, and the method of consistency analysis between suitable planting area and actual production regions cannot only evaluate the potential planting risk but also provide a reasonable idea for the accuracy test of the modeled results. This work has certain guiding and reference significance for the protection of pear germplasm resources and the prediction of yield.
... Under current and future climate circumstances, the SDM toolbox was used to calculate shifting trends in the area of suitable habitat based on Python, as well as changes in the centroid of the area of suitable habitat (Etherington, 2011). The toolbox is a Python-based GIS application (Brown et al., 2017). ...
Satyrium is an endangered and rare genus of plant that has various pharmacodynamic functions. In this study, optimized MaxEnt models were used in analyzing potential geographical distributions under current and future climatic conditions (the 2050s and 2070s) and dominant environmental variables influencing their geographic distribution. The results provided reference for implementation of long-term conservation and management approaches for the species. The results showed that the area of the total suitable habitat for Satyrium ciliatum (S. ciliatum) in China is 32.51 × 104 km2, the total suitable habitat area for Satyrium nepalense (S. nepalense) in China is 61.76 × 104 km2, and the area of the total suitable habitat for Satyrium yunnanense (S. yunnanense) in China is 89.73 × 104 km2 under current climatic conditions. The potential suitable habitat of Satyrium is mainly distributed in Southwest China. The major environmental variables influencing the geographical distribution of S. ciliatum were isothermality (bio3), temperature seasonality (bio4), and mean temperature of coldest quarter (bio11). Environmental variables such as isothermality (bio3), temperature seasonality (bio4), and precipitation of coldest quarter (bio19) affected the geographical distribution of S. nepalense; and environmental variables such as isothermality (bio3), temperature seasonality (bio4), and lower temperature of coldest month (bio6) affected the geographical distribution of S. yunnanense. The distribution range of Satyrium was extended as global warming increased, showing emissions of greenhouse gases with lower concentration (SSP1-2.6) and higher concentration (SSP5-8.5). According to the study, the distribution of suitable habitat will shift with a change to higher elevation areas and higher latitude areas in the future.
... The changes in the potential distribution area and suitable habitat distribution center of L. chinense and L. tulipifera in the future scenarios were mainly counted by the SDM toolbox in ArcGIS10.6. This toolkit is based on the Python language [22]. The statistics of the area showed three types of changing areas: expanding, unchanged, and shrinking. ...
Climate change has a significant impact on species population size and distribution, global biodiversity, and ecological status. The Liriodendron genus contains two species: Liriodendron chinense and Liriodendron tulipifera, both playing important roles in timber, medicinal, and landscape purposes. However, little is known about their population distribution characteristics and important climatic factors shaping their suitability. In this research, we used the geological record data, 19 climate components, MaxEnt, and ArcGIS to recreate and analyze the potential population distribution and their alterations of Liriodendron within the world beneath the current and future scenarios of RCP 2.6, RCP 4.5, and RCP 8.5 in 2050 and 2070. Our results showed that: Liriodendron is suitable to grow in subtropical monsoon climate areas, and that the climatic factor of precipitation of warmest quarter exerts the greatest impact on L. chinense, with a contribution rate of 57.6%. Additionally, we showed that the climatic factor of precipitation of the driest month exerts the greatest impact on L. tulipifera, with a contribution rate of 60.5%. Further analysis exhibited that low temperature and temperature fluctuations are major temperature factors affecting L. chinense and L. tulipifera, respectively. Therefore, we predicted that by the 2050s and 2070s, the areas of Liriodendron suitable habitats would increase first and then decrease in three scenarios; except the area of L. tulipifera suitable habitats under RCP8.5, which shows a slight increase. We then conclude that the Liriodendron suitable areas would shift to high latitudes due to global climate warming. The information gained from this study will provide a reference for developing forest cultivation, management, and conservation strategies for these two important tree species, and also a basis for subsequent biogeographic research.
... For example, the least-cost path may cover a short distance in Euclidean space, yet the ecological cost of moving across such a surface may be very high, resulting in high mortality or high energetic costs to the organism. To calculate least-cost path length and cost-weighted distance between sampling localities we ran Landscape Genetics Toolbox (Etherington 2011) for each of the ten resistance levels for each of the two hypotheses (pluvial lake and sagebrush). For each model we evaluated the proportion of the genetic variance explained using the effective distance. ...
ContextEnvironmental changes produce discontinuities in suitable habitat. However, drawing inference into the effects of these changes on contemporary genetic patterns is often difficult. Recent approaches for evaluating landscape resistance facilitate increased understanding of landscape effects on gene flow.Objectives
We investigated the effects of pluvial lakes and sagebrush cover on genetic connectivity for the pygmy rabbit (Brachylagus idahoensis), a sagebrush obligate species. We predicted that sagebrush-based surfaces would be more explanatory than pluvial lake surfaces. Furthermore, we predicted that habitat characteristics during the mid-Holocene would explain genetic differentiation better than those during the late-Pleistocene.Methods
We leveraged a genetic dataset to evaluate the explanatory power of landscape resistance surfaces. We generated resistance surfaces that represent varying degrees of resistance associated with pluvial lakes and sagebrush cover, then projected sagebrush distribution back to the mid-Holocene and late-Pleistocene.ResultsRepresentations based on sagebrush distribution were more explanatory than those based on pluvial lakes. Projections of sagebrush distribution back in time indicate concordance between genetic connectivity and mid-Holocene sagebrush distribution. Limited numbers of dispersal pathways were apparent among study regions, suggesting spatially restricted corridors of connectivity.Conclusions
We demonstrate that shifts in vegetative cover can shape contemporary patterns of genetic connectivity. By coupling testing of resistance surfaces with estimates of past vegetative change, we provide insights into the time scales over which genetic differentiation may occur. Given projections of future declines in sagebrush, maintaining sagebrush cover will be critical to population persistence of pygmy rabbits.
... For IBR, we generated a least-cost path distance matrix using the along-path cost of the LCP calculated in ArcMap 10.1. The along-path cost is the total sum of the friction values that characterize the LCP and allows for the testing of IBR between sample sites (Etherington, 2011 ...
Ecological, environmental, and geographic factors all influence genetic structure. Species with broad distributions are ideal systems because they cover a range of ecological and environmental conditions allowing us to test which components predict genetic structure. This study presents a novel, broad geographic approach using molecular markers, morphology, and habitat modeling to investigate rangewide and local barriers causing contemporary genetic differentiation within the geographical range of three white-crowned sparrow (Zonotrichia leucophrys) subspecies: Z. l. gambelii, Z. l. oriantha, and Z. l. pugetensis. Three types of genetic markers showed geographic distance between sampling sites, elevation, and ecosystem type are key factors contributing to population genetic structure. Microsatellite markers revealed white-crowned sparrows do not group by subspecies, but instead indicated four groupings at a rangewide scale and two groupings based on coniferous and deciduous ecosystems at a local scale. Our analyses of morphological variation also revealed habitat differences; sparrows from deciduous ecosystems are larger than individuals from coniferous ecosystems based on principal component analyses. Habitat modeling showed isolation by distance was prevalent in describing genetic structure, but isolation by resistance also had a small but significant influence. Not only do these findings have implications concerning the accuracy of subspecies delineations, they also highlight the critical role of local factors such as habitat in shaping contemporary population genetic structure of species with high dispersal ability.
... LCPs rely on excellent perception of the landscape by the organisms being modeled, which in our case might include pollinators of S. angularis such as bumblebees and honeybees that may optimize foraging paths (e.g., Kembro et al., 2019) and preferentially follow corridors connecting habitat patches (e.g., Cranmer et al., 2011). We calculated LCP using the 'least-cost paths' tool in the Landscape Genetics ArcToolbox (Etherington, 2011). To test for patterns of IBR, we evaluated the relationship between F ST and landscape distance for each resistance surface using maximum likelihood of population effects models (MLPE), a type of linear regression on distance matrices that use random effects to account for the pseudoreplication inherent in pairwise data (Clarke, Rothery, & Raybould, 2002;van Strien, Keller, & Holderegger, ...
Landscape heterogeneity can shape genetic structure and functional connectivity of populations. When this heterogeneity imposes variable costs of moving across the landscape, populations can be structured according to a pattern of ‘isolation by resistance’ (IBR). At the same time, divergent local environmental filters can limit gene flow, creating an alternative pattern of ‘isolation by environment’ (‘IBE’). Here, we evaluate IBR and IBE in the insect‐pollinated, biennial plant Sabatia angularis (L.) Pursh (Gentianaceae) across serpentine grasslands in the fragmented landscape of SE Pennsylvania, USA using ~4500 neutral SNP loci. Specifically, we test the extent to which radical alteration of the landscape matrix by humans has fundamentally altered the cost of movement, imprinting a pattern of IBR dictated by land use type and intensity, and the potential for IBE in relation to a gradient of heavy metal concentrations found in serpentine soil. We reveal a strong signal of IBR and a weak signal of IBE across sites, indicating the greater importance of the landscape matrix in shaping genetic structure of S. angularis populations in the study region. Based on Circuitscape and least cost path approaches, we find that both low‐ and high‐intensity urbanization resist gene flow by orders of magnitude greater than ‘natural’ habitats, although resistance to low‐intensity urbanization weakens at larger spatial scales. While cropland presents a substantially lower barrier than urban development, cumulative human land use surrounding populations predicts within‐population genetic diversity and inbreeding in S. angularis. Our results emphasize the role of forest buffers and corridors in facilitating gene flow between serpentine grassland patches and averting local extinction of plant populations.
... The geographic distance among neighbouring SGs (within sites in the real dataset) was calculated by the euclidean distance based on GPS coordinates using the Landscape Genetics Toolbox in ArcGis (Etherington 2011). ...
Many species are structured in social groups (SGs) where individuals exhibit complex mating strategies. Yet, most population genetic studies ignore SGs either treating them as small random-mating units or focusing on a higher hierarchical level (the population). Empirical studies acknowledging SGs have found an overall excess of heterozygotes within SGs and usually invoke inbreeding avoidance strategies to explain this finding. However, there is a lack of null models against which ecological theories can be tested and inbreeding avoidance quantified. Here, we investigate inbreeding (deviation from random mating) in an endangered forest-dwelling pair-living lemur species (Propithecus tattersalli). In particular, we measure the inbreeding coefficient (F IS) in empirical data at different scales: SGs, sampling sites and forest patches. We observe high excess of heterozygotes within SGs. The magnitude of this excess is highly dependent on the sampling scheme: while offspring are characterised by a high excess of heterozygotes (F IS < 0), the reproductive pair does not show dramatic departures from Hardy-Weinberg expectations. Moreover, the heterozygosity excess disappears at larger geographic scales (sites and forests). We use a modelling framework that incorporates details of the sifaka mating system but does not include active inbreeding avoidance mechanisms. The simulated data show that, although apparent "random mating" or even inbreeding may occur at the "population" level, outbreeding is maintained within SGs. Altogether our results suggest that social structure leads to high levels of outbreeding without the need for active inbreeding avoidance mechanisms. Thus, demonstrating and measuring the existence of active inbreeding avoidance mechanisms may be more difficult than usually assumed.
... Direct GIS applications in landscape models, such as customization in landscape connectivity model and classification of remote sensing rasterized images, are normally unavailable to most researchers (Etherington 2011). In our study, the cumulative landscape distance costs were inferred from unsupervised classifications of elevation values and spectral signatures of landscover. ...
Genetic diversity is inherently a spatial process of stochastic and directional evolutionary forces, as well as the interactions between such forces with the underlying environment. Combined, these factors may operate simultaneously and are challenging to understand, particularly in highly-mobile species or species whose genetic properties have dependence on or influence from human activities. In this dissertation, I employed molecular technologies and spatial analyses to examine genetic diversity and structure in Red Junglefowl (Gallus gallus), an important agricultural species. My aim was to understand how spatial processes affect genetic diversity and how landscape patterns may influence population structure at microgeographic scales. I screened 212 wild Red Junglefowl sampled across diverse habitats in South Central Vietnam with two genomic tools. First, amplified fragment length polymorphism (AFLP) surveyed genome-wide neutral variation. Second, a single nucleotide polymorphism (SNP) panel interrogated 84 sites spanning the entire 242 Kb major histocompatibility complex (MHC) B-locus. Analyses of 289 neutral AFLP markers identified a metapopulation structure. Red Junglefowl in all sampled localities displayed high degree of interspecific-population differentiation (overall FST = 0.1028) with no evidence of contemporary long-distance genetic exchanges especially across the major barrier Annamite Mountain Range. Fine-scale spatial landscape models detected substantial intraspecific genetic subdivision to distances as low as 5 km. The magnitude of spatial neutral variation of the ground-dwelling pheasants, however, showed no causative relationship between landscape features of landcover and topography. After screening 398 chromosomes, 310 unique MHC haplotypes (77.89%) were identified. Comparison to 17 lines of domestic chickens also screened with the SNP panel indicated that wild Red Junglefowl have extraordinarily high haplotypic diversity. The vast majority of variation in MHC haplotypes (94.51%) occurred within individuals while genetic differentiation between populations was negligible (overall FST = 0.0083). Likely augmented by recombination, the B-locus also exhibited a few areas of strong linkage suggesting perhaps concerted evolution against a common pathogen. Overall, the results suggest the spatial pattern of MHC is adaptive and under the influence of balancing selection. Neutral markers reflect demographic processes and movements of the Red Junglefowl. I conclude that wild populations of Red Junglefowl in Vietnam represent one of the richest resources of natural genomic variation. Both neutral and adaptive genetic diversity should be equally considered in a spatial research framework for future management of animal genetic diversity, including application to agricultural stock improvement.
... (Xuan, 2007). Os scripts Python permitem que os pesquisadores usem softwares mais atuais, proporcionem a opção de maior desenvolvimento pela comunidade de usuários e reduzam a quantidade de tempo que seria gasto desenvolvendo soluções comuns (Etherington, 2011). Para se otimizar produtos que tem que ser gerados apenas com mudanças de certos valores e variáveis é de suma importância o uso do Python para criação de novas rotinas que permitam a otimização do trabalho técnico e tempo. ...
Agriculture is established as an engine of the Brazilian economy, thus constantly requiring updates on the decision-making tools. Geographic Information Systems (GIS) are increasingly established as a primary support for analysis in the field, both because of the high potential in data representation and in the implementation of new solutions. This thesis aims to apply the Python Programming Language, commonly used among GIS developers, to analyze agricultural variables among ordinary users, who need a logical sequence for a fast spatial representation of data. In addition to a brief overview of the current scenario and the importance of agriculture in the context, it was also shown through the creation of codes and logical sequencing of the steps, a process from the insertion of data to its exit as a thematic map of the variable chosen. In the course of this thesis, several maps show the outputs generated through the Python scripts, which were finally organized with an addin named Agro as the final product. To validate the tool, data from the Baixio do Irecê Irrigation Perimeter in the state of Bahia, Brazil, were used as a new area for agricultural exploration and of great visibility for the rulers. It is also initiated a reflection of the wide range of possibilities that can be introduced through the implementation of new tools through Python as well as improvements that can be implemented in the codes and processes shown here.
... Besides that, easy-touse in Python makes this language an excellent choice to create powerful modern software [18]. Etherington has developed tools in Python for file conversion in his research for use in landscape genetics [19]. In addition, many published papers were related to Python for various purposes and their usage. ...
To retrieve part of data from huge .html files and convert it to .csv files respectively is a long time work if done manually. Consequently, this work needs an automation tool. This paper discuss developing automation tools based on Python language. Utilizing Python’s code makes the tools very small in line of code but powerful. It successfully retrieves part of data as needed. The tools have successfully been tested to other case study.
... Nowadays, GIS is an irreplaceable tool for investigating tasks related to spatially distributed information, including the entering and saving of source information, the efficient processing of spa-tial data, visual and geostatistical analysis, and the preparation of various output cartographic and other documents [3,4]. ...
Under current conditions the progressively increasing anthropogenic pressure on land resources and significant changes in the structure of land tenure require the creation and immediate implementation of a system for surveillance and inspection the use and condition of lands in order to identify changes on-time, to asses them prevent and eliminate the consequences of negative processes occurring on the land tenure territory. The task of effective use of land is not properly performed due to the lack of reliable information about its condition and use. Such information can be obtained through the creation and operation of the ongoing system of the land condition state monitoring based on geoinformation technologies. This study is directed at determining the influence and correlation of the landscape spatial development with elements of the territory organization using the criterion of agricultural crops productivity with geographic information system (GIS).
The necessity of the analysis is connected with the detection of cyclicality, regularity, the influence direction of the territory organization elements on the landscape spatial development, which will make it possible to apply the most cost-effective and environmentally sound ways of using particular areas.
... Besides that, easy-touse in Python makes this language an excellent choice to create powerful modern software [18]. Etherington has developed tools in Python for file conversion in his research for use in landscape genetics [19]. In addition, many published papers were related to Python for various purposes and their usage. ...
Halstead metric is one of software quality measurement technique. Some studies call it as Primitive and/or Classic Software Metric, although some researchers find vagueness of it. This paper utilizing Halstead metric to measuring the quality of various versions of Statcato software. Measurements are performed in each class of each version of Statcato software. Python programming used to facilitate this. This paper find that in terms of Halstead metric, this software seems stable during its lifecycle. So, the quality of the newer versions of Statcato are better in terms of stability of Halstead metrics than the earlier version, although its feature is increasing.
... Suitability values of each species (ranging from 0 to 1, with higher values indicating higher suitability) were reversed to assign resistance values. The least-cost path of resistance for each pairwise combination of sample sites, for each species, was calculated using the Landscape Genetics Toolbox (Etherington, 2011). These resistance values were also used to build a habitat connectivity graph (see Section 2.6 below). ...
Due to their static nature, protected areas (PAs) are vulnerable to global change, and resident species will likely need to colonize new sites and exchange migrants to sustain viable local populations. Alpine habitats often have a high level of protection, yet extensive environmental heterogeneity and the limited dispersal ability of many endemic species makes it unclear whether PA networks provide sufficient connectivity to protect vulnerable species. We assess landscape connectivity in the European alpine PA network by combining measures of habitat and genetic connectivity using community landscape genetics approaches. Examining 27 plant species, we compare levels of genetic diversity in PA and non-PA sites, and rank non-PA sites for their potential value in facilitating genetic and habitat connectivity, as well as preserving species richness in 893 alpine plants. Non-PA sites do not significantly enhance overall levels of genetic variability across species. However, spatial genetic turnover (allele frequency variation across space) is influenced by geographical and environmental distance, suggesting that genetic connectivity, and by extension landscape connectivity, is impacted by gaps in the PA network. A subset of non-PA sites, when measured for habitat connectivity, genetic connectivity and species richness using spatial graphs, substantially increase landscape connectivity for alpine plants, although there are discrepancies among metrics in ranking sites. We provide the first example of the evaluation and prediction of new PAs including levels of intraspecific genetic diversity for a whole community. This has significance for the management and extension of the European alpine network, especially in identifying valuable unprotected sites.
... Using natal-site collection coordinates, we determined the migration path between each natal site and the Pacific Ocean with the Landscape Genetics arctoolbox (Etherington, 2011) (Bourret et al., 2013;Hecht et al., 2015;Matala, Ackerman, Campbell, & Narum, 2014;Meeuwig, Guy, Kalinowski, & Fredenberg, 2010;Narum, Zendt, Graves, & Sharp, 2008). We chose to incorporate a breadth of both frequently and seldom used landscape variables that are categorized into three broad categories that potentially influence adaptation: temperature-related, precipitationrelated and topography-related. ...
Organisms typically show evidence of adaptation to features within their local environment. However, many species undergo long-distance dispersal or migration across larger geographical regions that consist of highly heterogeneous habitats. Therefore, selection may influence adaptive genetic variation associated with landscape features at residing sites and along migration routes in migratory species. We tested for genomic adaptation to landscape features at natal spawning sites and along migration paths to the ocean of anadromous steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin. Results from multivariate ordination, gene-environment association, and outlier analyses using 24,526 single-nucleotide polymorphisms (SNPs) provided evidence that adaptive allele frequencies were more commonly associated with landscape features along migration paths than features at natal sites (91.8% versus 8.2% of adaptive loci, respectively). Among the 45 landscape variables tested, migration distance to the ocean and mean annual precipitation along migration paths were significantly associated with adaptive genetic variation in three distinct genetic groups. Additionally, variables such as minimum migration water temperature and mean migration slope were significant only in inland stocks of steelhead that migrate up to 1200 km farther than those near the coast, indicating regional differences in migratory selective pressures. This study provides novel approaches for investigating migratory corridors and some of the first evidence that environment along migration paths can lead to substantial divergent selection. Consequently, our approach to understand genetic adaptation to migration conditions can be applied to other migratory species when migration or dispersal paths are generally known. This article is protected by copyright. All rights reserved.
... For all ten resistance maps, we assessed least-cost distances between the sampling location of all individuals using the Landscape Genetics Gis Toolbox (Etherington, 2011) in ArcGis 10 (ESRI, 2009) and calculated the pairwise matrix of CIRCUITSCAPE resistance distance with the software CIRCUITSCAPE 4.0.4 (McRae, Dickson, Keitt, & Shah, 2008). ...
... To identify biologically relevant dispersal distances for each individual identified as a disperser, we constructed least-cost paths (LCPs) among assigned and sampled sites using the Landscape Genetics Toolbox developed for ARCGIS 9.3 (Etherington 2011). This requires a base layer indicating the relative cost of each landcover type. ...
Dispersal is the central mechanism that determines connectivity between populations yet few studies connect the mechanisms of movement with realized dispersal in natural populations. To make such a link, we assessed how physiological variation among individuals predicted dispersal in natural populations of unisexual (all‐female) and sexual Ambystoma salamanders on the same fragmented landscape in Ohio.
Specifically, we assessed variation in a trait that influences long‐distance animal movement (locomotor endurance) and determined whether variation in endurance matched patterns of realized dispersal assessed using genetic assignment tests. A possible mechanism for why unisexuals would have lower locomotor endurance than a sympatric sexual species ( Ambystoma texanum ) is the potential energetic cost of evolutionarily mismatched mitochondrial and nuclear genomes within polyploid unisexuals.
We found that sexuals walked four times farther than unisexuals during treadmill endurance trials that mimic the locomotor endurance required for dispersal.
We then applied landscape genetic methods to identify dispersed adults and quantify realized dispersal. We show that the differences in locomotor endurance between unisexual and sexual salamanders scale to realized dispersal: dispersing sexual individuals travelled approximately twice the distance between presumed natal wetlands and the site of capture compared to dispersing unisexuals.
This study links variation in individual performance in terms of endurance with realized dispersal in the field and suggests a potential mechanism (physiological limitation due to mitonuclear mismatch) for the reduced endurance of unisexual individuals relative to sexual individuals although we discuss other possible explanations.
The differences in dispersal between these two types of salamanders also informs our understanding of sexual/unisexual coexistence by suggesting that unisexuals are at a competitive disadvantage in terms of colonization ability under a extinction‐colonization model of coexistence.
A lay summary is available for this article.
... The null model of isolation by distance (IBD) was initially measured by calculating all logarithmic pairwise geographic Euclidean distances. Additionally, landscape resistance and wolfpredation risk between the three groups of individuals (all, migratory and sedentary) were estimated using least-cost path (LCP RSF and LCP PRR , respectively) analyses on each resistance surface using the Landscape Genetics ArcToolbox (Etherington, 2011) The influence of spatial separation (IBD), habitat suitability (LCP RSF ), predation risk (LCP PRR ) and hypothesized human barriers to movement (IBB Roads , IBB Cutblocks IBB Linear Features ) on the genetic and relatedness distances among caribou was tested using simple Mantel tests (Smouse, Long, & Sokal, 1986) in ZT 1.1 (Bonnet & Van de Peer, 2002) under 100,000 permutations. We then used partial Mantel tests following the original causal modelling framework, which has a higher power to detect landscape influences on genetic structure in an individual-based analysis Cushman, McKelvey, Hayden, & Schwartz, 2006). ...
Ecosystem fragmentation and habitat loss have been the focus of landscape management due to restrictions on contemporary connectivity and dispersal of populations. Here, we used an individual approach to determine the drivers of genetic differentiation in caribou of the Canadian Rockies. We modelled the effects of isolation by distance, landscape resistance and predation risk and evaluated the consequences of individual migratory behaviour (seasonally migratory vs. sedentary) on gene flow in this threatened species. We applied distance‐based and reciprocal causal modelling approaches, testing alternative hypotheses on the effects of geographic, topographic, environmental and local population‐specific variables on genetic differentiation and relatedness among individuals. Overall, gene flow was restricted to neighbouring local populations, with spatial coordinates, local population size, groups and elevation explaining connectivity among individuals. Landscape resistance, geographic distances and predation risk were correlated with genetic distances, with correlations threefold higher for sedentary than for migratory caribou. As local caribou populations are increasingly isolated, our results indicate the need to address genetic connectivity, especially for populations with individuals displaying different migratory behaviours, whilst maintaining quality habitat both within and across the ranges of threatened populations.
... We created LCPs using the Landscape Genetics ARC-TOOLBOX (Etherington 2011) and determined Euclidean distances between all localities within a cluster using the Spider Diagrams ArcToolbox. To account for the Table 1 Landscape variables used for landscape genetic analyses. ...
Species’ geographic range limits are most often not demarcated by obvious dispersal barriers. Poor quality habitat at the edge of a species’ range can prevent range expansion by preventing outward migration or through reducing adaptive potential resulting from decreased genetic diversity. We identified habitat variables that constrain gene flow across the entire geographic range of an endemic salamander (Ambystoma barbouri) in the eastern United States, and we tested whether increased resistance resulting from these variables provides cryptic dispersal barriers at the range edges. Using polymorphic microsatellite loci, we first identified three genetic clusters that are separated by the Ohio and Kentucky rivers. Through a combination of landscape genetic analyses and generalized dissimilarity modeling, we then classified variables that (1), restrict gene flow in each of the genetic clusters across the geographic distribution of A. barbouri and (2), become more common towards the peripheries of the distribution. A decrease in limestone availability and an increase in growing season precipitation were correlated with high resistance to gene flow across the range, and both became more common at the edges of the species’ distribution. However, other landscape variables were more important for explaining variation in gene flow rates in different portions of the range, such as increased mean annual temperature and frost-free period in the south versus growing season precipitation in the north. Taken together, these results suggest that there are both range-wide and regionally specific cryptic habitat barriers preventing geographic range expansion. Species’ geographic range limits are likely governed by a set of ecological and evolutionary factors, and our landscape genetic approach could be applied to gain additional insight in many systems. This article is protected by copyright. All rights reserved.
... This method calculates effective distances between population pairs on a resistance surface using a least-cost model, but also incorporates the environmental characteristics surrounding the path within a specified transect width (Van Strien et al., 2012). We first used least-cost path analysis as implemented in the 'Landscape genetics toolbox 1.2.3' in arcmap 10 (Etherington, 2011) to calculate paths with the lowest accumulative cost from a landscape raster, where each cell represents a cost to movement. As sea trout are mainly found in coastal shallow waters (Knutsen et al., 2001;Rikardsen et al., 2006) close to the estuary of their natal river (Jonsson & Jonsson, 2011), we tested for an influence of depth on potential sea trout dispersal trajectories (Least-Depth Path hypothesis: LDP). ...
... Both environments, Python and R, have seen an increasing amount of tools to deal with the integration of genetic and geographic data, mixing complete open source software and open source and proprietary software (Jombart and Ahmed, 2011;Gruber and Adamack, 2015; van Etten, 2012;Etherington, 2011;Brown, 2014;Dick et al., 2014). See Figure 3 for an explanation of the data flow in the proposed pipeline. ...
Sharing and reusing data in research is a welcome and encouraged practice since it maximises the scientific outcomes given limited financial, material and human resources. Interdisciplinary research is considered to benefit from this practice, uniting researchers and data from two or more disciplines to advance fundamental understanding or tackle problems whose solution is beyond the limit of an individual body of knowledge. Here we discuss the challenges of combining data across disciplines, focusing in particular on associating geographic location data with genetic data in the context of a project involving Crop Science and Geospatial Information Science disciplines. This project aims to improve understanding of how geographical, environmental and anthropocentric factors affect the genetic variation in a neglected and underutilised crop called Bambara groundnut.
... We also used the least-cost path (LCP) algorithm to determine whether the studied populations were isolated by landscape features. Calculations of least-cost path lengths and cost-distances were computed using the Landscape Genetics toolbox [31] implemented in the ArcGIS 10.3 software. We used the Corine Land Cover 2012 raster dataset (European Environmental Agency, available at http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012, ...
The trajectories of postglacial range expansions, the occurrence of lineage patches and the formation and maintenance of secondary contact between lineages may mostly reflect neutral demographic processes, including density blocking, that may leave long-lasting genetic signatures. However, a few studies have recently shown that climate may also play a role. We used red deer, a large, mobile herbivore that is assumed to be sensitive to climate change, to test hypotheses of possible selection on the mitochondrial DNA cytochrome b gene (mtDNA cytb) and competitive and/or density-blocking (using mtDNA control region). We searched for a possible link between the phylogeographic structure and abiotic climatic variables. Finally, we tested for isolation by distance and isolation by environment and assessed the impact of human-mediated translocations on the genetic structure of red deer. Our analysis of 30 red deer populations in Poland using the mtDNA control region (N = 357) and cytochrome b (N = 50) markers not only confirmed the presence of the Western and SouthEastern lineages of the species but also indicated the presence of a previously unnoticed, rare relic haplotype that grouped together C. e. italicus from Italy (the Mesola deer). No significant signs of positive selection were detected for the mtDNA cytb gene in the studied red deer. However, a significant signal for purifying selection was found in our study that may explain the narrowness of the contact zone because gene flow between the Western and SouthEastern lineages should drive relatively strong mito-nuclear incompatibilities. MtDNA control region differentiation among red deer populations in Poland correlated with different abiotic climatic variables. Strikingly, the southernmost ice sheet limits during the Elsterian was the most important factor, and it explained the largest amount of variation. However, neither isolation by distance (IBD) nor isolation by environment (IBE) were recorded, and a very limited impact of human translocations was evident. The above-mentioned results suggest that in contemporary red deer populations in Poland, the phylo-geographic pattern is well preserved, and long-term processes (density and/or competitive blocking) still play a major role.
... MWEasyDHM tool is user-friendly, free, and proficient which produces selectable multi-functional hydrological analysis. Similarly, a number of GIS tools are programmed by Etherington (2011) in the Python environment for landscape genetics researches. Tools are capable of transforming files, viewing genetic relatedness, and calculating landscape associations through leastcost path procedure. ...
Modelling and classification difficulties are fundamental issues in natural
hazard assessment. A geographic information system (GIS) is a domain that
requires users to use various tools to perform different types of spatial
modelling. Bivariate statistical analysis (BSA) assists in hazard modelling.
To perform this analysis, several calculations are required and the user has
to transfer data from one format to another. Most researchers perform these
calculations manually by using Microsoft Excel or other programs. This
process is time-consuming and carries a degree of uncertainty. The lack of
proper tools to implement BSA in a GIS environment prompted this study. In
this paper, a user-friendly tool, bivariate statistical modeler (BSM), for
BSA technique is proposed. Three popular BSA techniques, such as frequency
ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are
applied in the newly proposed ArcMAP tool. This tool is programmed in Python
and created by a simple graphical user interface (GUI), which facilitates the
improvement of model performance. The proposed tool implements BSA
automatically, thus allowing numerous variables to be examined. To validate
the capability and accuracy of this program, a pilot test area in Malaysia
is selected and all three models are tested by using the proposed program.
Area under curve (AUC) is used to measure the success rate and prediction rate.
Results demonstrate that the proposed program executes BSA with reasonable
accuracy. The proposed BSA tool can be used in numerous applications, such
as natural hazard, mineral potential, hydrological, and other engineering
and environmental applications.
... In addition, we obtained and reclassified a road-only resistance surface from the Michigan Geographic Data Library (Center for Geographic Information; electronic supplementary material, table S2). Pairwise least-cost distances were calculated using the landscape genetic toolbox in ARCGIS [40]. Least-cost analysis and node weights may be sensitive to relative cost values assigned to land cover types [41] and could potentially influence our results. ...
Source–sink dynamics affects population connectivity, spatial genetic structure and population viability for many species. We introduce a novel approach that uses individual-based genetic graphs to identify source–sink areas within a continuously distributed population of black bears (Ursus americanus) in the northern lower peninsula (NLP) of Michigan, USA. Black bear harvest samples (n ¼ 569, from 2002, 2006 and 2010) were genotyped at 12 microsatellite loci and locations were compared across years to identify areas of consistent occupancy over time. We compared graph metrics estimated for a genetic model with metrics from 10 ecological models to identify ecological factors that were associated with sources and sinks. We identified 62 source nodes, 16 of which represent important source areas (net flux. 0.7) and 79 sink nodes. Source strength was significantly correlated with bear local harvest density (a proxy for bear density) and habitat suit-ability. Additionally, resampling simulations showed our approach is robust to potential sampling bias from uneven sample dispersion. Findings demonstrate black bears in the NLP exhibit asymmetric gene flow, and individual-based genetic graphs can characterize source–sink dynamics in continuously distributed species in the absence of discrete habitat patches. Our findings warrant consideration of undetected source–sink dynamics and their implications on harvest management of game species.
... To further explore patterns of dispersal, the cost-distance between individuals within Tasmania and Victoria was generated using the ArcGIS 9.3.1 (ESRI, Redlands, CA) landscape genetics toolbox (Etherington 2010). Water and land were weighted to create least cost path distances among individuals. ...
... As outlined by [74], each pixel within 100 m of a barrier was assigned either the maximum environmental resistance value from the habitat model (permeable barriers) or assigned a resistance value four orders of magnitude greater than the maximum resistance value (low-permeable barriers). For each resistance surface, we used the landscape genetics toolbox [75] in ArcMap 10.0 (Environmental Systems Research Institute, Redlands, CA) to produce a pairwise matrix of least-cost distance paths among leks. We assessed the association between genetic distance and least-cost path for each resistance surface with Mantel tests calculated in the ecodist package [76] in R. ...
Background
Mating systems that reduce dispersal and lead to non-random mating might increase the potential for genetic structure to arise at fine geographic scales. Greater sage-grouse (Centrocercus urophasianus) have a lek-based mating system and exhibit high site fidelity and skewed mating ratios. We quantified population structure by analyzing variation at 27,866 single-nucleotide polymorphisms in 140 males from ten leks (within five lek complexes) occurring in a small geographic region in central Nevada. ResultsLek complexes, and to a lesser extent individual leks, formed statistically identifiable clusters in ordination analyses, providing evidence for fine-scale geographic genetic differentiation. Lek geography predicted genetic differentiation even at a small geographic scale, which could be sharpened by strong site fidelity. Relatedness was also higher among individuals within lek complexes (and leks), suggesting that reproductive skew, where few males participate in most of the successful matings, could also potentially contribute to genetic differentiation. Models incorporating a habitat resistance surface as a proxy for potentially reduced movement due to landscape features indicated that both geographic distance and habitat suitability (i.e. preferred habitat) predicted genetic structure, with no significant effect of man-made barriers to movement (i.e. power lines and roads). Finally, we illustrate how data sets containing fewer loci (<4000) had less statistical precision and failed to detect the full degree of genetic structure. Conclusion
Our results suggest that habitat features and lek site geography of sage-grouse shape fine scale genetic structure, and highlight how larger data sets can have increased precision and accuracy for quantifying ecologically relevant genetic structure over small geographic scales.
... Recently, inverted ENMs have been used to create a resistance surface (i.e., a friction layer), which is a more objective alternative to the use of expert knowledge. Thus, we converted the ENM layers to friction layers in SDM-TOOLBOX by simply inverting the suitability scores and calculated a stream resistance matrix for each pair of populations through stream branches based on the friction layers using Python scripts for ARCGIS described in Etherington (2011). Using this method, a path through highly suitable habitat is converted to a path of low dispersal cost. ...
In the face of global climate change, organisms may respond to temperature increases by shifting their ranges poleward or to higher altitudes. However, the direction of range shifts in riverine systems is less clear. Because rivers are dendritic networks, there is only one dispersal route from any given location to another. Thus, range shifts are only possible if branches are connected by suitable habitat, and stream-dwelling organisms can disperse through these branches. We used Cumberlandia monodonta (Bivalvia: Unionoida: Margaritiferidae) as a model species to investigate the effects of climate change on population connectivity because a majority of contemporary populations are panmictic. We combined ecological niche models (ENMs) with population genetic simulations to investigate the effects of climate change on population connectivity and genetic diversity of C. monodonta. The ENMs were constructed using bioclimatic and landscape data to project shifts in suitable habitat under future climate scenarios. We then used forward-time simulations to project potential changes in genetic diversity and population connectivity based on these range shifts. ENM results under current conditions indicated long stretches of highly suitable habitat in rivers where C. monodonta persists; populations in the upper Mississippi River remain connected by suitable habitat that does not impede gene flow. Future climate scenarios projected northward and headwater-ward range contraction and drastic declines in habitat suitability for most extant populations throughout the Mississippi River Basin. Simulations indicated that climate change would greatly reduce genetic diversity and connectivity across populations. Results suggest that a single, large population of C. monodonta will become further fragmented into smaller populations, each of which will be isolated and begin to differentiate genetically. Because C. monodonta is a widely distributed species and purely aquatic, our results suggest that persistence and connectivity of stream-dwelling organisms will be significantly altered in response to future climate change. This article is protected by copyright. All rights reserved.
... GIS spatial analysis highlights the spatial relationships among biological, physical and human landscape components [11]. In addition, the use of geo-referenced genetic data within a GIS has become a prerequisite for the spatial analysis of ecological processes [12]. Spatial analysis also can provide significant information on the diversity of species within a specific geographical area, so as to be able to evaluate the current conservation status of plant species and to prioritize areas for conservation [13]. ...
The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is crucial for managing and conserving genetic resources in forest tree species. By combining tools from population genetics, landscape ecology and spatial statistics, landscape genetics thus represents a powerful method for evaluating the geographic patterns of genetic resources at the population level. In this study, we explore the possibility of combining genetic diversity data, spatial statistic tools and GIS technologies to map the genetic divergence and diversity of 31 Castanea sativa populations collected in Spain, Italy, Greece, and Turkey. The IDW technique was used to interpolate the diversity values and divergence indices as expected hete-reozygosity (He), allelic richness (Rs), private allelic richness (PRs), and membership values (Q) of each population to different clusters. Genetic diversity maps and a synthetic map of the spatial genetic structure of European chestnut populations were produced. Spatial coincidences between landscape elements and statistically significant genetic discontinuities between populations were investigated. Evidence is provided of the significance of cartographic outputs produced in the study and on their usefulness in managing genetic resources.
... We therefore used ArcMap 10.3 (ESRI, Redlands, USA) to generate a resistance surface (based on a 30x30m grid) of the study area that gave a high cost value to water bodies. We then used the Landscape Genetics Toolbox [41] to calculate the length of the least-cost paths between the pre-defined populations (S1 Fig). These were then introduced as geographic distance in the regression analysis. ...
The extent of gene flow during the range expansion of non-native species influences the amount of genetic diversity retained in expanding populations. Here, we analyse the population genetic structure of the raccoon dog (Nyctereutes procyonoides) in north-eastern and central Europe. This invasive species is of management concern because it is highly susceptible to fox rabies and an important secondary host of the virus. We hypothesized that the large number of introduced animals and the species' dispersal capabilities led to high population connectivity and maintenance of genetic diversity throughout the invaded range. We genotyped 332 tissue samples from seven European countries using 16 microsatellite loci. Different algorithms identified three genetic clusters corresponding to Finland, Denmark and a large 'central' population that reached from introduction areas in western Russia to northern Germany. Cluster assignments provided evidence of long-distance dispersal. The results of an Approximate Bayesian Computation analysis supported a scenario of equal effective population sizes among different pre-defined populations in the large central cluster. Our results are in line with strong gene flow and secondary admixture between neighbouring demes leading to reduced genetic structuring, probably a result of its fairly rapid population expansion after introduction. The results presented here are remarkable in the sense that we identified a homogenous genetic cluster inhabiting an area stretching over more than 1500km. They are also relevant for disease management, as in the event of a significant rabies outbreak, there is a great risk of a rapid virus spread among raccoon dog populations.
... Fields and wetlands were tested as both barriers and facilitators. Least cost path analyses for each univariate model were run in ArcMap (v10; Environmental Science Research Institute, Redlands, USA) using the landscape genetics toolbox (v1.2.3; [42]). ...
Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists.
... (Phillips et al. 2006) to predict habitat suitability at each grid cell of the study area, with values ranging from 0 (unsuitable habitat) to 1 (fully suitable habitat). We then inverted the suitability values to create friction landscapes (i.e., cost values), and calculated pairwise environmental cost distances (i.e., least-cost paths) among our 13 populations using SDMtoolbox (Etherington 2011;Brown 2014) in ArcMap (ver. 10.2.2; ESRI, Redlands, CA, USA). ...
The causes of population differentiation can provide insight into the origins of early barriers to gene flow. Two key drivers of population differentiation are geographic distance and local adaptation to divergent selective environments. When reproductive isolation arises since some populations of a species are under selection to avoid hybridization while others are not, population differentiation and even speciation can result. Spadefoot toad populations Spea multiplicata that are sympatric with a congener have undergone reinforcement. This reinforcement has resulted not only in increased reproductive isolation from the congener, but also in the evolution of reproductive isolation from nearby and distant conspecific allopatric populations. We used multiple approaches to evaluate the contributions of geographic distance and divergent selective environments to population structure across this regional scale in S. multiplicata, based on genotypes from six nuclear microsatellite markers. We compared groups of populations varying in both geographic location and in the presence of a congener. Hierarchical F-statistics and results from cluster analyses and discriminant analyses of principal components all indicate that geographic distance is the stronger contributor to genetic differentiation among S. multiplicata populations at a regional scale. However, we found evidence that adaptation to divergent selective environments also contributes to population structure. Our findings highlight how variation in the balance of evolutionary forces acting across a species’ range can lead to variation in the relative contributions of geographic distance and local adaptation to population differentiation across different spatial scales.
... In addition, while LCP analysis assumes that individuals have sufficient knowledge to choose the optimal path, resistance distances are calculated in terms of random walk probabilities, assuming that all pathways influence movement decisions). LCP analysis is commonly used to inform corridor design and habitat connectivity for conservation, but models have been criticised for lack of sufficient biological or empirical foundation and high sensitivity to assignment of landscape resistance costs (reviewed in Swayer et al.2011).I used the Landscape Genetics tool(Etherington 2011) in ArcGIS v10 (ESRI) to calculate Euclidian distances between pairs of colonies and the length of the LCP as a factor of the cumulative cost of movement between colonies. Resistance distances for the IBR analysis were calculated between pairs of colonies using the program Circuitscape v3.5(McRae 2006, McRae et al. 2008. ...
Despite the importance of scale for understanding ecological processes and evolutionary patterns, conservation studies and management practices addressing multiple scales are rare. In this thesis I use multi-scale and multi-disciplinary approaches to study the ecological and conservation requirements of one of the rarest UK mammals, the grey long-eared bat, Plecotus austriacus, with a focus on the edge-of-range English population. Using ecological niche modelling and radio-tracking I show that the UK distribution of P. austriacus is primarily limited by unsuitable climatic conditions. At the fine-scale, habitat suitability is limited by the availability of unimproved grasslands, a habitat that has undergone a severe decline in the last century. Molecular and microscopic diet analysis techniques identify a diet primarily composed of lepidopterans of the family noctuidae, and a high dietary overlap with its sympatric cryptic species, P. auritus, but offer evidence of habitat resource partitioning. Genetic studies point to a distinct population structure across Europe, with a unique population in England, and unequal distribution of genetic diversity. Gene flow across the range is affected by geographical barriers and landscape resistance to movement. Combining ecological niche and demographic history modelling, I identify Iberia as the principal glacial refugium. Future predictions warn that this important area, which concentrates high proportions of genetic variability, will become climatically unsuitable by the end of the century, while range expansions are predicted at the north-western edge. I conclude that the English edge-of-range population is of high conservation concern due to evidence of recent decline, small effective size, fragmentation and habitat loss, and due to its potential role in facilitating range shifts under future climate change. Conservation efforts should focus on increasing landscape connectivity and managing P. austriacus populations within the wider ecosystem context.
... We calculated the least cost path (LCP) between all pairs of marten locations, for each cost surface, as the sum of the costs along the least cost path, with the Landscape Genetics extension for ArcGIS 9.3 (Etherington 2011, ESRI 2008. For all landscape cost surfaces, we tested several different cost-weighting schemes (i.e., assigned values ranging from 1 to 10, 1 to 100, or 1 to 1000) using Mantel tests (Supplement 3, Mantel 1967, Rayfield et al. 2010) and the proportion of shared alleles among marten. ...
Context
Barriers to dispersal influence the ability of individuals to expand into new areas and can ultimately define success of reintroduction programs. American marten (Martes americana) were reintroduced to the Upper Peninsula of Michigan, USA, from multiple, genetically differentiated source populations from 1955 to 1992. Previous research found multiple genetic clusters near release sites with little admixing, suggesting barriers to dispersal exist.
Objectives
We sought to identify whether the contact zones between genetic clusters coincided with landscape features hypothesized to influence M. americana dispersal. We also investigated whether the degree of landscape contiguity within each genetic cluster differed among clusters.
Methods
We mapped cluster boundaries in M. americana genetic assignment probabilities and used correlation length as a measure of landscape contiguity between genetic clusters. We then evaluated the effects of land cover and roads on spatial genetic structure using a spatial autoregressive model.
Results
We found that gene flow was facilitated by contiguous coniferous forest and low incidence of roads. However, the strength of those relationships varied by genetic cluster. Contact zones among some genetic clusters spatially coincided with areas of high road and low conifer contiguity, compared to within-clusters.
Conclusions
In contrast to landscape genetic analyses focused on identifying barriers to gene flow, we incorporated methods that are relatively novel in landscape genetics to quantify how landscape contiguity influences spatial genetic structure. Using this method we were able to identify landscape barriers to dispersal at the genetic cluster boundaries for a reintroduced species distributed continuously across the landscape.
... differently weighted friction values for each of the friction layers by reclassifying each layer into different weighted resistance layers (5, 10, 100 and 1000the results for different resistance values of the same feature tend to be very similar; Table 1). Using the friction layers, we then calculated the least-costpaths and Euclidian distances between 96 samples applying the Landscape Genetics Tools (Etherington, 2011). We processed all geodata and analysis in Arc-GIS 10.0 (ESRI) with spatial analytical extension tools. ...
Bayesian clustering methods are typically used to identify barriers to gene flow, but they are prone to deduce artificial subdivisions in a study population characterized by an isolation-by-distance pattern (IbD). Here we analysed the landscape genetic structure of a population of wild boars (Sus scrofa) from south-western Germany. Two clustering methods inferred the presence of the same genetic discontinuity. However, the population in question was characterized by a strong IbD pattern. While landscape-resistance modelling failed to identify landscape features that influenced wild boar movement, partial Mantel tests and multiple regression of distance matrices (MRDMs) suggested that the empirically inferred clusters were separated by a genuine barrier. When simulating random lines bisecting the study area, 60% of the unique barriers represented, according to partial Mantel tests and MRDMs, significant obstacles to gene flow. By contrast, the random-lines simulation showed that the boundaries of the inferred empirical clusters corresponded to the most important genetic discontinuity in the study area. Given the degree of habitat fragmentation separating the two empirical partitions, it is likely that the clustering programs correctly identified a barrier to gene flow. The differing results between the work published here and other studies suggest that it will be very difficult to draw general conclusions about habitat permeability in wild boar from individual studies.
... In the case of sibship groups with n individuals sampled on one side of the road and m individuals sampled on the other side of the road, and if n > m, 120 we assumed that there was a minimum of m crossing events and that the direction was from n to m. The spatial pattern of kinship inferred in Colony2 was spatially visualized using the option "Kinship Links" from Arctoolbox Landscape genetics 1.2.3 (Etherington 2011 Genetic differentiation between populations separated by the motorway was estimated using pairwise F ST estimates, computed using GenAlEx 6.5. Population genetic structure was estimated using the Bayesian clustering algorithms implemented in STRUCTURE 2.3.4 (Pritchard et al. 2000) and GENELAND 4.0.4 ...
Landscape functional connectivity of a species depends on its behaviour and movement ability and influences its persistence in human-modified landscapes. Nonetheless, the effects of functional connectivity loss by small forest carnivores are still poorly understood. This study combines movement, behaviour and genetic data to assess the effects of roads and habitat heterogeneity on the functional connectivity of the common genet in a mixed forest-agricultural landscape, in southern Portugal.
Specifically, we aim to reach the following goals: i) to assess the environmental factors influencing resting behaviour; ii) to clarify the impacts of monitoring frequency on the analysis of resting behaviour; iii) to use path-level analysis to model landscape functional connectivity at different perception scales and asses the main environmental factors influencing it; iv) to combine roadkills, radiotracking and genetic analysis to infer
crossing events and dispersal effectiveness. Tree hollows were the safest resting place, with prevalence on the wet season and allowed individuals to rest near roads, whenever necessary. Daily monitoring was the best sampling regime to characterize resting site behaviour. Landscape connectivity was favoured by large forest patches, and near riparian areas providing corridors within open agricultural land highly resistant to genet movement. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the buffering offered by crossing structures. Results from the combined sources of information suggested that the motorway allowed high gene flow, despite the roadkills and the constraints to adult movements. Low traffic volume, numerous crossing structures, ability of genets to overcome obstacles, high population sizes on both sides of the motorway, and the absence of territorial constraints to effective migration potentially originated the observed results. Our results give important clues to mitigate road effects on the landscape functional connectivity of small forest carnivores.
... This method calculates effective distances between population pairs on a resistance surface using a least-cost model, but also incorporates the environmental characteristics surrounding the path within a specified transect width (Van Strien et al., 2012). We first used least-cost path analysis as implemented in the 'Landscape genetics toolbox 1.2.3' in arcmap 10 (Etherington, 2011) to calculate paths with the lowest accumulative cost from a landscape raster, where each cell represents a cost to movement. As sea trout are mainly found in coastal shallow waters (Knutsen et al., 2001;Rikardsen et al., 2006) close to the estuary of their natal river (Jonsson & Jonsson, 2011), we tested for an influence of depth on potential sea trout dispersal trajectories (Least-Depth Path hypothesis: LDP). ...
Aim
Most animal species live in spatially and temporally heterogeneous landscapes where resources are unevenly distributed. Consequently, individuals must adapt their migratory behaviour to optimize feeding and breeding opportunities. Here, we assess the role of life‐history (anadromy) and seascape (a derivation of the concept of landscape) heterogeneity on the genetic connectivity of coastal brown trout populations ( Salmo trutta L., 1758).
Location
English Channel.
Methods
A causal modelling approach, combining geographical data with inferences on gene flow among 20 populations, deploying neutral microsatellite markers, was used to identify environmental factors influencing brown trout genetic dispersal between rivers. Alternative models of landscape permeability combining least‐cost path and transect analysis were employed. The model selection procedure was based on a multiple regression analysis on distance matrices analysis ( MRDM ) and a linear mixed modelling approach using maximum likelihood population effects ( MLPE ).
Results
Landscape genetic analyses showed a hierarchical genetic structure with two main genetic units corresponding to the eastern and western marine ecoregions of the English Channel delineated by the Cotentin peninsula. Within these two regions, a model, which included parameters for depth and seabed substrate habitat features, explained significantly more variance in genetic differentiation than simple isolation‐by‐distance models alone.
Main conclusions
The genetic structure of brown trout populations within the English Channel appears to be shaped by the spatial arrangement and quality (with respect to feeding opportunities) of marine habitats, which promotes a clinal variation in migratory behaviour, which in turn determines the level of gene flow among neighbouring streams. Genetic structure thus partly reflects microgeographical responses to spatial variation in foraging conditions at sea. These results show that the marine environment, rarely considered previously, has an important role in combination with variation in freshwater environments in the evolution of life history in migratory aquatic animals.
Context
Connectivity between habitat patches is a recognized conservation action to conserve biodiversity in a rapidly changing world. Resistance surfaces, a spatial representation of cost of movement across the landscape, are often the foundation for connectivity analyses but working with them can be daunting due to the diversity and complexity of software tools.
Objectives
We present an overview of the steps involved when working with resistance surfaces, identify tools that perform specific tasks, evaluate user experience with the tools, identify needs of the user community, and present some recommendations for users and developers.
Methods
We identified tools applicable at each of the three steps (i) preparing data, (ii) constructing and optimizing surfaces, and (iii) using resistance surfaces. We conducted an online survey of the connectivity user community to assess the popularity and experience with tools on five criteria and identified characteristics important in the selection of connectivity tools.
Results
We reviewed a total of 43 tools, of which 10 are useful for data preparation, 27 allow construction, and 30 tools that use resistance surfaces. A total of 148 survey participants working in 40 countries were familiar with 37 tools. Tools are ranked heterogeneously for the five criteria. Crucial avenues for future development of connectivity tools identified by respondents are incorporation of uncertainties, dynamic connectivity modelling, and automated parameter optimization.
Conclusions
Since resistance surfaces are used for a variety of applications, it is important that users are aware about the appropriate tools. We anticipate that future tools for connectivity research will incorporate more complex and biologically more realistic analytical approaches.
Cognitive science is an interdisciplinary scientific study of mind, and it is the scientific study of neurobiological process in the brain. Learning and memory depend upon the functioning of the neural network. Artificial Neural Network is evolved by modelling the brain and is applicable in various fields. Artificial intelligence is the development and use of computers to perform tasks that traditionally require human intelligence, such as visual perception, speech recognition, and language translation. The functioning of mirror neurons in human brain and their applications are very much pioneers in the progress of AI. Machine learning, shortly known as ML, is a kind of artificial intelligence (AI), and its algorithms are broadly classified into three divisions, namely, supervised learning, unsupervised learning, and reinforcement learning. Python is a high-level programming language that is very popular in the scientific community. All these concepts are discussed in this chapter with special reference to Python and its applications.
Analysis of genetic polymorphism is a powerful tool for epidemiological surveillance and research. Powerful inference from pathogen genetic variation, however, is often restrained by limited access to representative target DNA, especially in the study of obligate parasitic species for which ex vivo culture is resource-intensive or bias-prone. Modern sequence capture methods enable pathogen genetic variation to be analyzed directly from host/vector material but are often too complex and expensive for resource-poor settings where infectious diseases prevail. This study proposes a simple, cost-effective ‘genome-wide locus sequence typing’ (GLST) tool based on massive parallel amplification of information hotspots throughout the target pathogen genome. The multiplexed polymerase chain reaction amplifies hundreds of different, user-defined genetic targets in a single reaction tube, and subsequent agarose gel-based clean-up and barcoding completes library preparation at under 4 USD per sample. Our study generates a flexible GLST primer panel design workflow for Trypanosoma cruzi , the parasitic agent of Chagas disease. We successfully apply our 203-target GLST panel to direct, culture-free metagenomic extracts from triatomine vectors containing a minimum of 3.69 pg/μl T . cruzi DNA and further elaborate on method performance by sequencing GLST libraries from T . cruzi reference clones representing discrete typing units (DTUs) TcI, TcIII, TcIV, TcV and TcVI. The 780 SNP sites we identify in the sample set repeatably distinguish parasites infecting sympatric vectors and detect correlations between genetic and geographic distances at regional (< 150 km) as well as continental scales. The markers also clearly separate TcI, TcIII, TcIV and TcV + TcVI and appear to distinguish multiclonal infections within TcI. We discuss the advantages, limitations and prospects of our method across a spectrum of epidemiological research.
Agricultural ecosystems are spatially and temporally dynamic systems that support wildlife populations in addition to food production for humans. The composition and configuration of land cover within agricultural systems, which often varies seasonally or annually, can have profound impacts on biodiversity. Specifically, landscape structure can influence animal dispersal and distribution. Depending on species-specific ecology, dispersal may be restricted, resulting in smaller, reproductively isolated populations. Few studies have characterized the spatial genetic structure of small mammal species in agricultural landscapes, despite their importance to crop pest management and food webs within agroecosystems. We characterized the spatial genetic structure of 2 co-distributed Peromyscus species (Peromyscus leucopus and Peromyscus maniculatus bairdii), and quantified relationships between spatial genetic structure based on interindividual relatedness resolved from 10 microsatellite loci, and landscape features hypothesized to influence dispersal. We found significant spatial autocorrelation in interindividual relatedness over interindividual distances of 1,800 m for P. leucopus and 300 m for P. maniculatus bairdii. We also identified distinct genetic clusters of interbreeding individuals for both species. Spatial genetic structure of P. leucopus was significantly related to the distribution of roads, indicating that highly fragmented landscapes could negatively affect dispersal and gene flow. Given the large and growing footprint of agriculture globally, it is important that studies of the effects that landscape features have on dispersal of wildlife species include human-dominated landscapes.
Agricultural intensification in tropical landscapes poses a new threat to the ability of biological corridors to maintain functional connectivity for native species. We use a landscape genetics approach to evaluate impacts of expanding pineapple plantations on two widespread and abundant frugivorous bats in a biological corridor in Costa Rica. We hypothesize that the larger, more mobile Artibeus jamaicensis will be less impacted by pineapple than the smaller Carollia castanea. In 2012 and 2013, we sampled 735 bats in 26 remnant forest patches surrounded by different proportions of forest, pasture, crops, and pineapple. We used 10 microsatellite loci for A. jamaicensis and 16 microsatellite loci for C. castanea to estimate genetic diversity and gene flow. Canonical correspondence analyses indicate that land cover type surrounding patches has no impact on genetic diversity of A. jamaicensis. However, for C. castanea, both percentage forest and pineapple surrounding patches explained a significant proportion of the variation in genetic diversity. Least-cost transect analyses (LCTA) and pairwise G"st suggest that for A. jamaicensis, pineapple is more permeable to gene flow than expected, while as expected, forest is the most permeable land cover for gene flow of C. castanea. For both species, LCTA indicate that development may play a role in inhibiting gene flow. The current study answers the call for landscape genetic research focused on tropical and agricultural landscapes, highlights the value of comparative landscape genetics in biological corridor design and management, and is one of the few studies of biological corridors in any ecosystem to implement a genetic approach to test corridor efficacy. This article is protected by copyright. All rights reserved.
Predicting the response of endemic species to urbanization has emerged as a fundamental challenge in 21st century conservation biology. The factors that underlie population declines of reptiles are particularly nebulous, as these are often the least understood class of vertebrates in a given community. In this study, we assess correlations between feeding ecology and phenotypic traits of the Lesser Antillean endemic Dutch leaf-toed gecko, Phyllodactylus martini, along an urban gradient in the Caribbean island of Curaçao. There has been a marked decline of this species in developed habitats associated with the invasive tropical house gecko Hemidactylus mabouia. We find a correlation between aspects of locomotor morphology and prey in undeveloped habitats that is absent in developed habitats. Analyses of stomach contents further suggest that Phyllodactylus martini alters primary prey items in developed areas. However, changes in prey promote the overlap in foraging niches between Phyllodactylus martini and Hemidactylus mabouia, suggesting that direct resource competition is contributing to the decline of Phyllodactylus martini. In addition to competitive exclusion, we suggest that the urban extirpation of Phyllodactylus martini could also be attributed to a top-down control on population growth by Hemidactylus mabouia. Coloniza-tions of walls put Phyllodactylus martini in direct contact with Hemidactylus mabouia increasing the chances for predation events, as evidenced by our observation of a predation event on a Phyl-lodactylus martini juvenile by an adult Hemidactylus mabuoia. In total, our results add to a growing body of literature demonstrating the threat that invasive synanthropic reptiles pose to endemics that might otherwise be able to cope with increased urbanization pressures.
Major drainage divides (separating the water flow draining to different seas) may significantly affect the dispersal, distribution, and genetic structure of semiaquatic animals. Assessing this effect is important for the proper management of both endangered and invasive riparian species. Here, I determined the fine-scale impact of the drainage divide between the Baltic Sea and the Black Sea drainage basins on the genetic structure and dispersal of the Eurasian otter (Lutra lutra) in the Bieszczady Mountains (Poland and Slovakia). I investigated the genetic structure of the otter population and assessed 3 alternative dispersal models using 6 landscape genetics methods. The analyses were based on 48 individual genotypes obtained from 622 fecal and gland secretion samples collected in 2008–2011. Results indicate that the major drainage divide is not a barrier to gene flow for this population. This was established by analyses of population genetic structure and confirmed by analysis of the spatial distribution of samples originating from closely related individuals. In line with these findings, the best-supported dispersal model assumed that otters migrate through mountain passes, away from streams, thus revealing that they are able to cross a drainage divide. The genetic structure of the population studied exhibits an isolation-by-distance pattern; however, the locations of several repeatedly recorded or closely related individuals revealed the occurrence of long-distance movements. Confirmation of the high mobility of otters and their ability to cross a major drainage divide ridge suggest that landscape obstacles and discontinuity of river network are unlikely to stop otter dispersal, gene flow, and recolonization of new areas.
Exposure of individuals to novel environmental conditions generally favors locally adapted phenotypes and can influence the likelihood of successful dispersal or the success of translocation efforts. We used geometric morphometrics to characterize American marten (Martes americana) skull morphology for descendants of animals reintroduced to the Upper Peninsula of Michigan, USA (~44,000 km2) from genetically distinct source populations. Using univariate and multivariate analyses, we quantified associations between interindividual variation in skull shape and size, genealogical relationships, past introduction history, local harvest density of competitors, and contemporary landscape features we hypothesized would be related to diet. Effects of other sources of variation including shared ancestry (source population, geographic distance, and coancestry among descendants), sex, and age were also evaluated. Descendant skull shape was related to progenitor source population, sex, and age. In contrast to strong associations between spatial genetic structure based on neutral molecular markers and measures of landscape effects on dispersal, variation in skull shape among descendants was not associated with geographic distance or landscape features. Our study addressed a critical issue regarding a widely used conservation prescription. Specifically, when individuals are transplanted to reestablish extirpated species, do their descendants retain the features of the source population(s), or do they adapt to local conditions? Marten skull shape following reintroduction events is most likely determined by a combination of differences in morphology of source populations and geographic variation in habitat, rather than being influenced by a single factor.
Impacts of climate change on habitats and especially on the Natura 2000 network of the European Union have proven difficult to estimate. In a multistep approach this study has developed a methodology to assess the impacts of climate change on habitat coherence, using the example of two habitat types ("dry heathlands" and "oak-hornbeam forests") defined by the Habitats Directive. Potential future distribution of these habitat types has been projected for three time steps during the 21st century. Subsequently, an omnidirectional dispersal model is applied under consideration of costs to reach individual habitats in order to identify the potentially reachable areas. Finally, habitat coherence is calculated for a graph-based index for both habitat types and for biogeographical scenarios (with and without realised dispersal). The study demonstrates a feasible combination of environmental envelope models with graph theory. Spatially explicit information required for the evaluation and prioritisation of conservation areas can be derived. The availability of maps with sequences of likely habitat development allows not only for an early identification of negative trends but also to proactively identify options for the establishment of new conservation areas and for the target-oriented implementation of conservation management.
Abstract pathmatrixis a tool used to compute matrices of effective geographical distances among samples using a least-cost path algorithm. This program is dedicated to the study of the role of the environment on the spatial genetic structure of populations. Punctual locations (e.g. individuals) or zones encompassing sample data points (e.g. demes) are used in conjunction with a species-specific friction map representing the cost of movement through the landscape. Matrices of effective distances can then be exported to population genetic software to test, for example, for isolation by distance.pathmatrixis an extension to the geographical information system (GIS) softwarearcview3.x.
kingroup is an open source java program implementing a maximum likelihood approach to pedigree relationships reconstruction and kin group assignment. kingroup implements a new method (currently being performance tested) for reconstructing groups of kin that share a common relationship by estimating an overall likelihood for alternative partitions. A number of features found in kinship (Goodnight & Queller 1999) have also been implemented to make them available outside the Classic Macintosh OS platform for the first time.
Holderegger, Rolf Wagner, Helene H.
Landscape connectivity can have profound consequences for distribution and persistence of populations and metapopulations.
Evaluating functional connectivity of a landscape for a species requires a measure of dispersal rates through landscape elements
at a spatial scale sufficient to encompass movement capabilities of individuals over the entire landscape. We evaluated functional
connectivity for a rock-dwelling mammal, the mountain vizcacha (Lagidium viscacia), in northern Patagonia. Because of the strict association of mountain vizcachas with rocks, we hypothesized that connectivity
for this species would be influenced by geology. We used molecular genetic estimates of gene flow to test spatially explicit
models of connectivity created with GIS cost-distance analysis of landscape resistance to movement. We analyzed the spatial
arrangement of cliffs with join counts and local k-function analyses. We did not capture and genotype individuals, but sampled
at the population level through non-invasive collection of feces of mountain vizcachas. The model of landscape connectivity
for mountain vizcachas based on geology was corroborated by the pattern of genetic structure, supporting the hypothesis that
functional connectivity for mountain vizcachas is influenced by geology, particularly by the distribution of appropriate volcanic
rocks. Analysis of spatial arrangement of cliffs indicated that occupied cliffs are clustered and confirmed that rivers act
as barriers to dispersal for mountain vizcachas. Our methods could be used, within certain constraints, to study functional
landscape connectivity in other organisms, and may be particularly useful for cryptic or endangered species, or those that
are difficult or expensive to capture.
Landscape connectivity is important in designing corridor and reserve networks. Combining genetic distances among individuals
with least-cost path (LCP) modelling helps to correlate indirect measures of gene flow with landscape connectivity. Applicability
of LCP modelling, however, is reduced if knowledge on dispersal pathways or routes is lacking. Therefore, we integrated habitat
suitability modelling into LCP analysis to avoid the subjectivity common in LCP analyses lacking knowledge on dispersal pathways
or routes. We used presence-only data and ecological niche factor analysis to model habitat suitability for the spiny rat,
Niviventer coninga, in a fragmented landscape of western Taiwan. We adapted the resultant habitat suitability map for incorporation into LCP
analyses. Slightly increased Mantel correlations indicated that a class-weighted suitability map better explained genetic
distances among individuals than did geographical distances. The integration of habitat suitability modelling into LCP analysis
can thus generate information on distribution of suitable habitats, on potential routes of dispersal, for placement of corridors,
and evaluate landscape connectivity.
Landscape genetics is an emerging interdisciplinary field that combines methods and concepts from population genetics, landscape
ecology, and spatial statistics. The interest in landscape genetics is steadily increasing, and the field is evolving rapidly.
We here outline four major challenges for future landscape genetic research that were identified during an international landscape
genetics workshop. These challenges include (1) the identification of appropriate spatial and temporal scales; (2) current
analytical limitations; (3) the expansion of the current focus in landscape genetics; and (4) interdisciplinary communication
and education. Addressing these research challenges will greatly improve landscape genetic applications, and positively contribute
to the future growth of this promising field.
Abstract spagedi version 1.0 is a software primarily designed to characterize the spatial genetic structure of mapped individuals or populations using genotype data of codominant markers. It computes various statistics describing genetic relatedness or differentiation between individuals or populations by pairwise comparisons and tests their significance by appropriate numerical resampling. spagedi is useful for: (i) detecting isolation by distance within or among populations and estimating gene dispersal parameters; (ii) assessing genetic relatedness between individuals and its actual variance, a parameter of interest for marker based inferences of quantitative inheritance; (iii) assessing genetic differentiation among populations, including the case of haploids or autopolyploids.
The growing awareness of the adverse effects of habitat fragmentation on natural systems has resulted in a rapidly increasing number of actions to reduce current fragmentation of natural systems as well as a growing demand for tools to predict and evaluate the effect of changes in the landscape on connectivity in the natural world. Recent studies used ‘least-cost’ modelling (available as a toolbox in GIS-systems) to calculate ‘effective distance’, a measure for distance modified with the cost to move between habitat patches based on detailed geographical information on the landscape as well as behavioural aspects of the organisms studied. We applied the method to a virtual landscape and a small scaled agricultural system subject to different scenarios in a land re-allotment project. We discuss the importance of technical aspects and ecological assumption underlying this modelling method. The model is shown to be a flexible tool to model functional connectivity in the study of the relation between landscape and mobility of organisms as well as in scenario building and evaluation in wild life protection projects and applied land management projects. Since ‘effective distance’ has the same units as Euclidean distance (m), this effective distance may be a straightforward way to include landscape and behavioural aspects in other models which include distance as a measure for isolation. We show the importance of the ‘ecological’ quality of the input maps and the choice of relevant landscape features and resistance values.
Landscape genetics is an emerging interdisciplinary field that combines methods and concepts from population genetics, landscape ecology, and spatial statistics. The interest in landscape genetics is steadily increasing, and the field is evolving rapidly. We here outline four major challenges for future landscape genetic research that were identified during an international landscape genetics workshop. These challenges include (1) the identification of appropriate spatial and temporal scales; (2) current analytical limitations; (3) the expansion of the current focus in landscape genetics; and (4) interdisciplinary communication and education. Addressing these research challenges will greatly improve landscape genetic applications, and positively contribute to the future growth of this promising field
I reexamine the use of isolation by distance models as a basis for the estimation of demographic parameters from measures of population subdivision. To that aim, I first provide results for values of F-statistics in one-dimensional models and coalescence times in two-dimensional models, and make more precise earlier results for F-statistics in two-dimensional models and coalescence times in one-dimensional models. Based on these results, I propose a method of data analysis involving the regression of FST/(1-FST) estimates for pairs of subpopulations on geographic distance for populations along linear habitats or logarithm of distance for populations in two-dimensional habitats. This regression provides in principle an estimate of the product of population density and second moment of parental axial distance. In two cases where comparison to direct estimates is possible, the method proposed here is more satisfactory than previous indirect methods.
Functional connectivity is a key factor for the persistence of many specialist species in fragmented landscapes. However, connectivity estimates have rarely been validated by the observation of dispersal movements. In this study, we estimated functional connectivity of a real landscape by modelling dispersal for the endangered natterjack toad (Bufo calamita) using cost distance. Cost distance allows the evaluation of 'effective distances', which are distances corrected for the costs involved in moving between habitat patches in spatially explicit landscapes. We parameterized cost-distance models using the results of our previous experimental investigation of natterjack's movement behaviour. These model predictions (connectivity estimates from the GIS study) were then confronted to genetic-based dispersal rates between natterjack populations in the same landscape using Mantel tests. Dispersal rates between the populations were inferred from variation at six microsatellite loci. Based on these results, we conclude that matrix structure has a strong effect on dispersal rates. Moreover, we found that cost distances generated by habitat preferences explained dispersal rates better than did the Euclidian distances, or the connectivity estimate based on patch-specific resistances (patch viscosity). This study is a clear example of how landscape genetics can validate operational functional connectivity estimates.
Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term "landscape genetics", provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.
This paper examines the usage and measurement of “landscape connectivity” in 33 recent studies. Connectivity is defined as the degree to which a landscape facilitates or impedes movement of organisms among resource patches. However, connectivity is actually used in a variety of ways in the literature. This has led to confusion and lack of clarity related to (1) function vs structure, (2) patch isolation vs landscape connectivity and, (3) corridors vs connectivity. We suggest the term connectivity should be reserved for its original purpose. We highlight nine studies; these include modeling studies that actually measured connectivity in accordance with the definition, and empirical studies that measured key components of connectivity. We found that measurements of connectivity provide results that can be interpreted as recommending habitat fragmentation to enhance landscape connectivity. We discuss reasons for this misleading conclusion, and suggest a new way of quantifying connectivity, which avoids this problem. We also recommend a method for reducing sampling intensity in landscape-scale empirical studies of connectivity.
In molecular ecology the analysis of large microsatellite data sets is becoming increasingly popular. Here we introduce a new software tool, which is specifically designed to facilitate the analysis of large microsatellite data sets. All common microsatellite summary statistics and distances can be calculated. Furthermore, the microsatellite analyser (msa) software offers an improved method to deal with inbred samples (such as Drosophila isofemale lines). Executables are available for Windows and Macintosh computers.
Summary
1.
In much of the world, fauna has been adversely affected by human actions, including conversion
of forests to farmland, logging and regulation of river flows. Landscape genetics data can provide
information about dispersal and gene flow across the landscape, identifying barriers and facilitators
of gene flow. Landscapes of central Victoria, Australia, have been altered extensively in the last 160
years. Much vegetation has been cleared or degraded, and only forest patches of mainly re-growth
remain, yet some forest-dependent species like the yellow-footed antechinus
Antechinus flavipes
persist. The antechinus has good dispersal capabilities and is the only native, small, carnivorous
mammal on most floodplains. We use antechinus as a model to understand species persistence in
fragmented landscapes.
2.
We analysed variation at 11 microsatellite loci and the control region of mitochondrial DNA to
infer past and contemporary gene flow among
A. flavipes
populations. To explore genetic connectivity,
we used least-cost path methods, which assign different ‘friction’ costs to vegetation, cleared
land, roads and rivers.
3.
Populations from 11 forests formed six distinct genetic groups, and with few exceptions, animals
from nearby forests clustered together despite the intervening Murray River or farmland with only
narrow vegetation corridors between them.
4.
Genetic connectivity was aided by corridors of vegetation and inhibited by cleared land.
5.
Synthesis and applications.
Our approach, capitalizing on inferences on both historic and contemporary
gene flow, provides management agencies with key information on metapopulation
dynamics in landscapes. Rather than merely maintaining existing vegetation upon which this (and
many other) species depend, the genetic information also informs where future plantings should be
prioritized to facilitate demographic and genetic exchange among sub-populations of species.
Moreover, the decline in condition (‘health’) of riparian trees in this region must be reversed by
provision of flooding flows; otherwise, metapopulation dynamics will become even more disarticulated
than at present.
Better tools are needed to predict population connectivity in complex landscapes. ‘Least‐cost modelling’ is one commonly employed approach in which dispersal costs are assigned to distinct habitat types and the least‐costly dispersal paths among habitat patches are calculated using a geographical information system (GIS). Because adequate data on dispersal are usually lacking, dispersal costs are often assigned solely from expert opinion. Spatially explicit, high‐resolution genetic data may be used to infer variation in animal movements. We employ such an approach to estimate habitat‐specific migration rates and to develop least‐cost connectivity models for desert bighorn sheep Ovis canadensis nelsoni .
Bighorn sheep dispersal is thought to be affected by distance and topography. We incorporated both factors into least‐cost GIS models with different parameter values and estimated effective geographical distances among 26 populations. We assessed which model was correlated most strongly with gene flow estimates among those populations, while controlling for the effect of anthropogenic barriers. We used the best‐fitting model to (i) determine whether migration rates are higher over sloped terrain than flat terrain; (ii) predict probable movement corridors; (iii) predict which populations are connected by migration; and (iv) investigate how anthropogenic barriers and translocated populations have affected landscape connectivity.
Migration models were correlated most strongly with migration when areas of at least 10% slope had 1/10th the cost of areas of lower slope; thus, gene flow occurred over longer distances when ‘escape terrain’ was available. Optimal parameter values were consistent across two measures of gene flow and three methods for defining population polygons.
Anthropogenic barriers disrupted numerous corridors predicted to be high‐use dispersal routes, indicating priority areas for mitigation. However, population translocations have restored high‐use dispersal routes in several other areas. Known intermountain movements of bighorn sheep were largely consistent with predicted corridors.
Synthesis and applications. Population genetic data provided sufficient resolution to infer how landscape features influenced the behaviour of dispersing desert bighorn sheep. Anthropogenic barriers that block high‐use dispersal corridors should be mitigated, but population translocations may help maintain connectivity. We conclude that developing least‐cost models from similar empirical data could significantly improve the utility of these tools.
Empirical studies of landscape connectivity are limited by the difficulty of directly measuring animal movement. ‘Indirect’
approaches involving genetic analyses provide a complementary tool to ‘direct’ methods such as capture–recapture or radio-tracking.
Here the effect of landscape on dispersal was investigated in a forest-dwelling species, the American marten (Martes americana) using the genetic model of isolation by distance (IBD). This model assumes isotropic dispersal in a homogeneous environment
and is characterized by increasing genetic differentiation among individuals separated by increasing geographic distances.
The effect of landscape features on this genetic pattern was used to test for a departure from spatially homogeneous dispersal.
This study was conducted on two populations in homogeneous vs. heterogeneous habitat in a harvested boreal forest in Ontario
(Canada). A pattern of IBD was evidenced in the homogeneous landscape whereas no such pattern was found in the near-by harvested
forest. To test whether landscape structure may be accountable for this difference, we used effective distances that take
into account the effect of landscape features on marten movement instead of Euclidean distances in the model of isolation
by distance. Effective distances computed using least-cost modeling were better correlated to genetic distances in both landscapes,
thereby showing that the interaction between landscape features and dispersal in Martes americana may be detected through individual-based analyses of spatial genetic structure. However, the simplifying assumptions of genetic
models and the low proportions in genetic differentiation explained by these models may limit their utility in quantifying
the effect of landscape structure.
As the European badger (Meles meles) can be of conservation or management concern, it is important to have a good understanding of the species' dispersal ability. In particular, knowledge of landscape elements that affect dispersal can contribute to devising effective management strategies. However, the standard approach of using Bayesian clustering methods to correlate genetic discontinuities with landscape elements cannot easily be applied to this problem, as badger populations are often characterized by a strong confounding isolation-by-distance (IBD) pattern. We therefore developed a two-step method that compares the location of pairs of related badgers relative to a putative barrier and utilizes the expected spatial genetic structure characterized by IBD as a null model to test for the presence of a barrier. If a linear feature disrupts dispersal, the IBD pattern characterising pairs of individuals located on different sides of a putative barrier should differ significantly from the pattern obtained with pairs of individuals located on the same side. We used our new approach to assess the impact of rivers and roads of different sizes on badger dispersal in western England. We show that a large, wide river represented a barrier to badger dispersal and found evidence that a motorway may also restrict badger movement. Conversely, we did not find any evidence for small rivers and roads interfering with badger movement. One of the advantages of our approach is that potentially it can detect features that disrupt gene flow locally, without necessarily creating distinct identifiable genetic units.
In species affiliated with heterogeneous habitat, we expect gene flow to be restricted due to constraints placed on individual movement by habitat boundaries. This is likely to impact both individual dispersal and connectivity between populations. In this study, a GIS-based landscape genetics approach was used, in combination with fine-scale spatial autocorrelation analysis and the estimation of recent intersubpopulation migration rates, to infer patterns of dispersal and migration in the riparian-affiliated Pacific jumping mouse (Zapus trinotatus). A total of 228 individuals were sampled from nine subpopulations across a system of three rivers and genotyped at eight microsatellite loci. Significant spatial autocorrelation among individuals revealed a pattern of fine-scale spatial genetic structure indicative of limited dispersal. Geographical distances between pairwise subpopulations were defined following four criteria: (i) Euclidean distance, and three landscape-specific distances, (ii) river distance (distance travelled along the river only), (iii) overland distance (similar to Euclidean, but includes elevation), and (iv) habitat-path distance (a least-cost path distance that models movement along habitat pathways). Pairwise Mantel tests were used to test for a correlation between genetic distance and each of the geographical distances. Significant correlations were found between genetic distance and both the overland and habitat-path distances; however, the correlation with habitat-path distance was stronger. Lastly, estimates of recent migration rates revealed that migration occurs not only within drainages but also across large topographic barriers. These results suggest that patterns of dispersal and migration in Pacific jumping mice are largely determined by habitat connectivity.
Genetic isolation by distance and landscape connectivity in the American marten (Martes americana)
- T Broquet
- N Ray
- E Petit
- J M Fryxell
- F Burel
Broquet, T., Ray, N., Petit, E., Fryxell, J.M. & Burel, F. (2006) Genetic isolation by distance and landscape connectivity in the American marten (Martes
americana). Landscape Ecology, 21, 877–889.
Microsatellite Analyser (MSA): a platform independent analysis tool for large microsatellite data sets
- Dieringer
Genetic isolation by distance and landscape connectivity in the American marten (Martes americana)
- Broquet