Performing Department
Ecosystem Science & Management
Non Technical Summary
The continued decrease in cost of genomic sequencing opens up new possibilities for using genetic information in wildlife management. Genes are a code (DNA) which are transcribed (copied into a slightly different language, RNA) and translated into proteins which then influence physiology and behavior in diverse ways. Differences in which genes are actually "expressed,"in other words, actively being translated into proteins, are the basis of differences between cell types within an individual and differences between individuals in immune response, stress, and developmental and physiolgocial states. In the past, genetic data was used to determine how variables at the landscape level affected the size, diversity, and connectedness of different populations. For example, populations that are cut off from others due to unsuitable habitat are more inbred and have less genetic diversity. Genetic sequencing technology has allowed us to make inferences about movement of individuals from one population to another and how habitat features affect this movement. Knowing not just the genetic code, but when and how it is being expressed in individuals across a landscape scale adds on a critical piece of information. Itcan tell us about the physiological state, development, and health of the individual being tested, often with very little stress to the animal. Therefore, this new techniqe called "landscape transcriptomics,"which we aim to develop, will allow us to connect landscape-level variables (such as stream temperature, forest canopy cover, and distance to roads) to individual fish physiology and health. This is difficult to do with other techniques; imagine trying to assess the physiological condition of several hundred fish spread across a watershed in Pennsylvania through traditional means. First, we will determine which landscape-level variables impact gene expression the most and in what ways, characterizing the genes identified based on their function. We will then use experimental streams to directly test connections between the variables we identified in our first study and gene expression. Finally, we will develop and extend statistical techniques to best use this data for management decisions.
Animal Health Component
0%
Research Effort Categories
Basic
100%
Applied
0%
Developmental
0%
Goals / Objectives
How does habitat connectivity and composition affect genetic diversity, health, and potential for genetic or plastic response to environmental change? Although "landscape genetics" uses DNA sequence variation to identifiy landscape features affecting genetic connectivity and diversity, it does a poor job of connecting genes, physiology, and landscape-level ecological proceseses. Transcriptomes, or snapshots of gene expression patterns, add key information about an organism's condition by quantifying variation in developmental and physiological states like age, immunity, and stress, as reflected by the expression of genes associated with those states. We will develop techniques we refer to as "landscape transcriptomics,"the use of whole-genome gene expression data from many individuals across the landscape to address issues important to wildlife management. Landscape transcriptomics could address similar questions as landscape genetics, but also tackle issues such as how landscape features affect stress and health of populations. We will specifically apply this novel methodology to managing brook trout in Pennsylvania;although it is also relevant to other issues, for example, understanding how organisms respond to rapidly changing environments, how invasive species become established in new habitats, and how bee populations respond to landscape-level differences in nutritional resources, parasites, and pesticides. In this proposal, we outline our five-year plan to 1) identify genes and pathway expressed in brook trout in response to landscape features linked to climate change and anthropogenic disruption, 2) validate landscape-transcriptome associations in a controlled experimental stream system, and 3) extend models developed specifically for fish landscape genetics to include transcriptome data.Objective 1: Use transcriptomics to identify genes and pathways expressed in response to landscape features linked to climate change and anthropogenic disruptionObjective 2: Validate landscape-transcriptome associations in a controlled experimental stream systemObjective 3: Extend the bidirectional geneflow in riverscapes (BGR) model to include transcriptomic data
Project Methods
Objective 1:We will collect brook trout from the Loyalsock Creek watershed which has been studied extensively in a previous landscape genetics study (White et al. 2020). As the only native trout in Pennsylvania, brook trout are an important fishery in Pennsylvania, but face a number of challenges. We will focus on ten populations in the Loyalsock Creek watershed that vary in summer water temperatures, a critical variable affecting brook trout health and persistence (Merriam et al. 2019). We will sample gill tissue which can be removed in a non-lethal manner and is intimately in contact with the environment, including environmental contaminants, stressors, and disease vectors. We will sample 12-15 individuals per population in May-June 2021, with the goal of sequencing ten from each population. Concurrently, information about environmental features will be collected, including stream slope, elevation, length, catchment area, canopy cover, distance to nearest road, and the presence of competitors. Water temperature loggers will be deployed at all sites in order to characterize thermal habitat. RNA will be extracted from samples for sequencing in August-September 2021. We will perform linear modeling to assess the importance of population and landscape features on gene expression patterns, using a gene-by-gene and a gene-network module approach (WGCNA, Lanfelder and Horvath 2008). We will repeat this experiment in 2022 with new populations to increase power and also to access how yearly variation may impact gene expression-landscape variable interactions.Objective 2:The observational nature of Objective 1 requires that we validate associations between landscape features and transcriptomes in an environment that controls for behavioral, chemical, and physical variables. Working with Dr. Nathaniel Hitt (USGS) and the Leetown Science Center's Experimental Stream Lab in May-August 2022, we will directly manipulate variables like temperature, food availability, and physical structure for cover and collect transcriptomic data before and after the manipulation. A split-plot design will be employed to allow efficient use of fish while allowing tests for interactive effects between variables. RNA will be extracted and sequenced in August-September 2022 and then analyzed. This experimental approach can establish a causal link between environmental variables and gene expression to support our field observations. We will also observe mortality rates and perform fish health and behavioral stress assessments to build predictive models for how gene expression relates to health, behavior, and fitness. We will repeat this experiment in 2023 to increase power and also to assess how yearly variation may impact gene expression.Objective 3:The "bidirectional geneflow in riverscapes model" or BGR model is an approach where connectivity between aquatic populations is modeled not by direct distance between populations, but rather considering distance via continuous waterways (White et al. 2020). This approach allowed novel inferences about connectivity of brook trout populations and the discovery that larger streams too warm to support brook trout during summer months are never-the-less acting as conduits of gene flow. The goal of this objective is to extend this model to work with transcriptomic data which violates many of the assumptions of models based on sequence variation. Ephraim Hanks, who developed the BGR model for genetic sequence data, will assist.