Melissa Burger NRS 509 December 2014 Applying GIS and remote sensing to landscape genetics and genome size research

Transcription

1 Melissa Burger NRS 509 December 2014 Applying GIS and remote sensing to landscape genetics and genome size research Genetic and genomic research are at the forefront of scientific technological advancement. As these techniques to analyze genetic information have improved over time, their costs have decreased dramatically, data are analyzed more rapidly, and results are more readily available and accessible to a wider number of researchers and agencies. This has led to an influx of genetic data throughout many fields including molecular biology, epidemiology, and ecology. Novel applications of this genetic data have been crucial in understanding species distributions and range shifts and the management of invasive species, pests, and diseases. The incorporation of this genetic information with GIS and remote sensing proves to be a promising avenue to examine how both biotic and abiotic factors can influence community structure. It is also an exciting approach to understand species range shifts in response to global environmental change. Many landscape genetics studies use genetic distance to understand current and historical movement of species. Genetic distance is a useful tool to estimate gene flow between populations (Fresia et al 2014). These data on genetic distance are often overlaid with land cover data, climate data, and digital elevation models (Fresia et al 2014; Paulson and Martin 2014). This overlay can examine how abiotic factors are influencing populations. LiDAR and other remote sensing techniques can also be used to create species distribution models (Paulson and Martin 2014). Together this information shows where a species currently exists, estimates possible migration routes to new areas, and allows for predictions on species range shifts under future environmental conditions. Maps and other visuals created from these data are more accessible to a broader audience which allows the general public and management and conservation agencies to see how genetic diversity can impact the spread of invasive species and diseases (Manel and Holderegger 2014). Incorporation of GIS and landscape genetics into invasion ecology and epidemiology will be crucial to predict and prevent the spread of unwanted species. Techniques for genome size quantification have been standardized and provide a rapid tool to predict invasiveness (Knight and Ackerly 2002). Genome size is negatively correlated with invasive success (Knight and Ackerly 2002), so understanding genome size variation across spatial scales will be important to predict range shifts and spread of invasive species. Polyploid species tend to be more phenotypically plastic and this increases their invasion success (Scaldaferro et al 2012). Many studies show relationships between environmental factors and both genome size and ploidy (Knight and Ackerly 2002; Scaldaferro 2012). Spatial examination of genome size and ploidy level information may show new patterns in both native and invasive species. These species distribution maps can be compared with climate maps and digital elevation models to see how abiotic factors may select for genetic characteristics (Knight and Ackerly 2002). These studies should be coupled with experimental research to see how

2 environmental variables affect the survival and fitness of invasive species. Global climate change modelling is also important in relation to genome size and ploidy level analyses. Another promising aspect of landscape genetics is the study of phylogeography. Phylogeography deals with the spatial patterns of species and historical data to infer species relatedness and speciation events (Chan et al 2011). This allows evolutionary processes to be incorporated into the field of landscape genetics. Since gene flow, or lack thereof, can cause speciation events, understanding physical and climatic barriers to species migration is useful when constructing phylogenetic trees (Chan et al 2011). More research in this field will create a better understanding of biodiversity and increase our knowledge of the tree of life. Statistical analysis of these types of large, complex data sets in landscape genetics are often difficult. Mantel tests have been used in many studies to statistically compare genetic distance matrices with geographic matrices (Manel and Holderegger 2013). Although this technique is widely used in genetics studies, new techniques to analyze these data are needed. The abiotic variables are often not independent, so the Mantel tests are not an accurate comparison between the variables (Manel and Holderegger 2013). Since statistical examination of the data are crucial for interpretation, new techniques should be explored. Since large-genome species are more influenced by environmental variables, quantile regression has been suggested as a viable technique to understand genome size variation across environmental gradients (Knight and Ackerly 2002). Another limitation to the use of GIS and remote sensing in the field of landscape genetics is budgetary concerns. Since ecological interactions are very complex, many of these studies require data collection across multiple geographic scales. Precision at fine scales and continentwide data collection of aerial imagery are both costly endeavors that are essential for accurate data collection and interpretation. An additional cost to be considered is collecting data on multiple species with a system. In order for management agencies to understand potential impacts from restoration projects, multiple species should be considered. For example, physical barriers may be constructed in an area where water flow and high genetic diversity suggest migration of an aquatic invasive species in a new area (Paulson and Martin 2014). Studies should be done to understand how reducing the connectivity between these areas also affects native species. Landscape genetics, phylogeography, ploidy level determination, and genome size quantification will all be essential tools under global environmental change. As temperatures and sea levels continue to rise, habitats degrade, and landscapes become more fragmented, predictive models will be the best tools for management agencies. Some success at using landscape genetics to reduce invasive species has worked at small scales (Paulson and Martin 2014), but more in-depth analyses will be needed to create management strategies to incorporate multiple species and to model range shifts over a larger geographic space (Manel and Holderegger 2014). New GIS toolboxes like the Genetics Landscapes GIS Toolbox released in 2010 will need to be available as more genetic data accumulates. Landscape genetics is an evolving field and new techniques will increase our understanding of species community structure and species distributions both past and present.

3 Annotated Bibliography: Chan, L. M., et al Integrating statistical genetic and geospatial methods brings new power to phylogeography. Molecular Phylogenetics and Evolution 59(2): This paper is an overview of the incorporation of GIS techniques in the field of phylogeography. Phylogeography seeks to understand genetic and geographic distributions and how events in the past may have altered current species ranges and population structures. GPS, climate, and landscape data are critical in this field to map species distributions and their fundamental versus realized niches. Dispersal corridors can be predicted by looking at species distribution models, physical barriers, and potential habitats that are suitable for the given species. Chan et al 2011 compared species distribution models of four species in southwestern North America for three different time periods: the last glacial maximum, mid-holocene, and present day. These data were converted to binary and the data from each interval were overlaid to show how populations have shifted and migrated over time. This information, coupled with genetic distance data, is essential to predict the best model to explain species divergence across phylogenetic trees. This integration of GIS and phylogeography can give valuable insight into the environmental and evolutionary processes that influence speciation. Fresia, P., et al Applying spatial analysis of genetic and environmental data to predict connection corridors to the New World screwworm populations in South America. Acta Tropica 138: S34-S41. New World screwworms can devastate agricultural industries through their impacts on livestock health. This research team used GIS to map current screwworm populations and predict the potential spread throughout South America. Since genetic distance estimates the genetic divergence between these populations, it is critical to understand the underlying genetic components to determine how screwworms have dispersed in the past and where gene flow may have occurred. GRASS-GIS was used to create habitat suitability maps based on climatic variables, to map landscape barriers that may affect migration, and to examine genetic distance between populations. The information was used to predict connection corridors which show where populations may migrate over time. It was then used to create maps of the most likely areas to be affected by screwworm infestations. This research has implications for the control and management of screwworms and other livestock pests. This application of GIS in genetics studies could be extended to other studies that seek to map and predict spread of both invasive species and diseases. Knight, C. A. and D. D. Ackerly Variation in nuclear DNA content across environmental gradients: a quantile regression analysis. Ecology Letters 5(1): This study sought to examine how environmental factors could select for genome size within populations. GIS was used to examine environmental variables, including temperature and precipitation, within the ranges of 401 plants in California. A digital elevation model of the state was used to create species range maps in ArcView GIS software to compare with climate data. These data were collected by National Oceanographic and Atmospheric Administration weather stations. Along with genome size data collected from the DNA C-value data base, the species range and climate maps were used to examine genome size structure across wide

4 latitudinal and environmental ranges in California. Large-genome species were uncommon at the highest and lowest sections of the temperature range and were most common at average temperatures, suggesting a unimodal distribution of genome size. This paper was one of the first to suggest that extreme environmental conditions select against large-genome species. Although experimental studies may be able to begin to address selective forces on genome size, the integration of GIS and molecular techniques will be crucial to conduct large-scale field surveys to compare genome size and environmental conditions in the field. Manel, S. and R. Holderegger Ten years of landscape genetics. Trends in Ecology & Evolution 28(10): This paper is a review of the history of landscape genetics over the last decade. Genetic diversity, gene flow between populations, and adaptation to particular climates over time have been the key components of landscape genetics. By overlaying genetic data and landscape data to create a visual representation of concepts and conservation strategies, the final product is more easily understood by both management agencies and the general public. This facilitates the dissemination of crucial information to a broader audience. Future research in this field is predicted to focus on how global environmental change and anthropological disturbances will impact genetic diversity and, ultimately, extinction. Urban landscapes are currently understudied in this field even though they may be home to many important species. Manel and Holderegger 2013 suggest that modified approaches will be needed to study urban landscapes since they are more prone to sudden, large-scale disturbances and changes. Paulson, E. L. and A. P. Martin Discerning invasion history in an ephemerally connected system: landscape genetics of Procambarus clarkii in Ash Meadows, Nevada. Biological Invasions 16(8): This paper studied the invasion of red swamp crayfish, an invasive species in the Ash Meadows National Wildlife Refuge. Microsatellite and mdna were used to study genetic diversity in order to infer possible migration routes and determine if multiple introductions of the invader have occurred. Aerial imagery and LiDAR data were used to visualize different topographical features that may contain either possible dispersal routes or physical barriers that may reduce movements through specific areas. Three layers were used to create this image: waterways and drainages, water flow on a digital elevation model, and vegetation from aerial images. This project has implications for management in this refuge area. Understanding how an invasive species spreads can help management agencies construct obstacles to deter establishment in new areas. This research should be combined with similar studies on native species within the refuge. Since reducing connectivity between springs can have impacts on the community outside of the invasive species, it is critical to incorporate multiple species distribution maps with corridor connectivity to develop a successful management strategy. Peck, S. L Perspectives on why digital ecologies matter: Combining population genetics and ecologically informed agent-based models with GIS for managing dipteran livestock pests. Acta Tropica 138: S22-S25.

5 This paper describes ways by which management agencies can use GIS to control pests including tsete flies, old world screwworms, and new world screwworms. Peck 2014 offers agent-based models as a way to understand pest community structure across space and time. These agent-based models focus on independent organisms and analyze how they interact with biotic and abiotic variables. The agent-based models are paired with species distribution data, landscape information, and genetic distance to understand how livestock pests move and transmit disease. GIS technologies can be highly important instruments for management agencies in their ability to predict and prevent disease transmission. Since biotic interactions and community structure are complex, GIS can be helpful to examine underlying genetic components of disease ecology at multiple scales. This sort of research will be highly important as global climate change affects pest species distributions and influences disease transmission. This work also directly impacts the agricultural industry since many of these pests affect livestock. Scaldaferro, M., et al Geographical pattern and ploidy levels of the weed Solanum elaeagnifolium (Solanaceae) from Argentina. Genetic Resources and Crop Evolution 59(8): This paper combined GIS and genetic analysis to understand the distribution of a weedy plant, Solanum elaeagnifolium, in Argentina. DIVA-GIS was used to examine abiotic factors including elevation, precipitation, and temperature across the country. Elevation and precipitation maps were created to visualize the spatial patterns of diploids and polyploids. In addition to the common diploid, multiple polyploids were found. Diploids were found across Argentina while hexaploids were found in wet environments and tetraploids were found in dry environments. This suggests that different ploidy levels are favored under various environmental conditions. This study addresses an important area in genetics research; it focuses on the possible spread of an invasive species after polyploidization events. Since polyploid plants tend to be more phenotypically plastic, they can be highly successful invaders in stressful environments. Using GIS to map environmental conditions is an exciting approach to predict the spread of invasive species.