Recipient Organization
AUBURN UNIVERSITY
108 M. WHITE SMITH HALL
AUBURN,AL 36849
Performing Department
Foresty & Wildlife Sciences
Non Technical Summary
Understanding and managing biodiversity, which comprises all of the plants, animals, microbes, and other species in an area, is a critical aspect of ensuring our ecosystems function into the future. Typically, wild populations are considered stable, and biodiversity is conserved, when population size does not change from year to year. However, we do not completely understand the forces that influence population size changes. For example, we know that individuals that have low levels of genetic diversity will sometimes have fewer offspring (reduced fitness) than those with more genetic diversity, but sometimes environmental effects (e.g. lots of rainfall) also changes individual's fitness. My 5-year research plan includes several projects aimed at understanding how and when population fitness changes, what that means for endangered species, and how we can monitor these changes in large populations in the wild. These projects will leverage a combination of field work, whole genome resequencing, remote sensing, and modeling.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Goals / Objectives
Understanding patterns of biological diversity lies at the center of ecology and evolutionary biology. Typically, ecology has focused on the ways biological diversity has been maintained in various ecosystems whereas evolution has focused on the generation of biological diversity via changes in genetic diversity(Post and Palkovacs 2009). However, identifying the mechanisms by which ecology and evolution interact would reveal the drivers of individual differences in fitness, and understanding these mechanisms will allow us to make more robust predictions concerning how populations will react to changing environments. This development of fundamental knowledge- how populations interact and evolve on the landscape- has direct implications for the monitoring and management of species of conservation concern as well as those that are managed for recreational purposes. In this 5-year plan, I will outline my research goals for: 1) defining the mechanistic genetic and environmental underpinnings of individual fitness; 2) quantifying the magnitude of genetic determinants of fitness in an endangered species; and 3) developing necessary methodology for monitoring these determinants in animal populations at a landscape scale.
Project Methods
Part 1:Environmental and genetic effects on individual fitnessObjective 1.1 A: How does the architecture of genomic variation affect individual fitness?We will sequence whole genomes from multiple kangaroo rat individuals at low coverage (i.e., 5X). Specifically, we will sequence 12 pairs of full sibling females and their mates (N = 48). Following quality control measures and mapping our reads to an existing kangaroo rat genome assembly, we will identify the variable positions across the genome and use these sites to characterize the genomic architecture of the sequenced individuals. We will compare the distribution of ROH quantity and length to lifetime reproductive success. In addition, we will compare the genomic locations of ROHs between mated pairs across siblings.Objective 1.1 B: How does the local environment contribute to fitness differences among individuals?We will leverage remote sensing data to characterize the local environment across the population of kangaroo rats. By applying the tasseled cap transformation derived for cloud-free Landsat 7 scenes, we will calculate three orthogonal metrics--greenness, wetness, and brightness--at a resolution of 30 m across the focal population on a bi-weekly and seasonal basis. We will use greenness, which indicates the amount of vegetation covering a surface, and wetness, which provides an estimate of soil moisture content, as measures of resource availability for the granivorous kangaroo rats. In addition, brightness measures the amount of bare soil and will be used as an estimate the availability of open microhabitat.Objective 1.1 C: How does individual genomic variation interact with environmental factors to determine individual fitness?We will use the data collected in Objectives 1A and 1B to determine the relative effects of genetic and environmental variation on individual fitness across years using a series of regressions. After standardizing the predictor variables, we will use linear mixed models to compare the variability in fitness explained by genetic and environmental variables using R2and Cohen'sf2.Objective 1.2: How do interactions between genetic and environmental effects on fitness change when the strengths of relationships are altered?Using the relationships between genome variability and fitness and between environmental characteristics and fitness as well as the interactions between genetic and environmental effects on fitness, we will parameterize a forward-time, spatially-explicit, individual-based model to quantify how changes in genetic and environmental variables affect fitness. We will initiate each model run by assigning inbreeding coefficients to individuals and environmental variable values to cells in the model landscape. Each generation, individuals will be paired based on physical proximity and number of offspring produced by the pair will be determined by relatedness and local environmental characteristics. Across model runs, we will manipulate environmental heterogeneity and population parameters to determine how individual and population mean fitness values evolve in response to changes in these parameters.Part 2:Genetic limitations of recovery of an endangered speciesObjective 2.1: Quantify evolutionary population size and recent inbreedingWe will use low coverage whole genome sequencing data to estimate the population sizes over evolutionary time using pairwise sequentially Markovian coalescent (PSMC) models. We will compare the patterns of evolutionary population size in Steller sea lion populations to those estimated using data from all other Pinnipeds with existing reference and genome sequencing, and use these as the basis for estimates of contemporary inbreeding rates for each sampling site.Objective 2.2: Identify genomic variants related to fitnessWe will use pooled sequencing data to identify single variable points (SNPs) along the genome using the GATK pipeline and best practices(McKenna et al. 2010). We ultimately will use the identified SNPs to characterize the genomic diversity found in each sampling site and across each population. We will also identify signatures of differential selection that may be related to differential fitness across the range.Objective 2.3: Evaluate the potential effects of increased immigrationWe will use a series of forward time, agent-based models to understand the effects of migration on patterns of inbreeding and adaptation. We will use all of our parameter estimates to create a model of the evolution of neutral and non-neutral genes in the eastern and western populations when gene flow occurs at various rates. Importantly, our model will apply selection at a range of intensities in one or both populations, using the simulated non-neutral genes and considering a range of variable allele frequencies. We will track the evolution of fitness, as population fitness is the primary predictor of population recovery. We will also include a wide range of migration rates to consider how gene flow altered patterns of adaptation.Part 3. Monitoring wild populations at landscape-level scalesObjective 3.1: Genetic mark-capture-recapture in very large populationsWe will use fecal samples collected twice a year for two consecutive years during the spring and fall. Genetic material will be extracted from pellets and sequenced following standard reduced genomic representation protocols. After processing the genetic data (~10,000 SNP genotypes per individual), we will estimate herd abundance by first identifying unique individuals captured in our samples and then applying standard capture-recapture models to these data using program MARK. To evaluate genetic differentiation between the East and West subgroups, we will use the Bayesian analysis program Structure.Objective 3.2: Use of genetic data to monitor rare speciesWe will deploy a camera grid and tissue collection line for a single winter season. We will set 125 cameras, with a density of approximately one camera trap per 2 ha. We will evaluate all of the images captured using MLWIC, an artificial intelligence/machine learning program, and use an N-mixture model to estimate abundance. We will also extract DNA from the tissues, genotype all samples at ~10,000 SNPs using a reduced representation sequencing approach, and use these data to generate individual genotypes for each individual. These individual identifications will then be used to conduct a standard genetic mark-capture-recapture analysis to estimate population abundance.Objective 3.3: Systematically evaluate monitoring approaches for various life-history characteristicsTo determine the suitability of each of the methods identified in a comprehensive literature review in monitoring population trends of furbearers, we will develop a series of forward time, agent-based models. Our models will simulate furbearer populations inclusive of density and density-independent effects. Using these population models, we will test the ability of the identified trend and abundance estimation methods to accurately detect and quantify patterns of population changes.