Characterizing greater sage-grouse climate-driven maladaptation
Climate change will expose many species to novel extreme environmental conditions, that may test organisms’ ability to respond to environmental change. Local adaptation, when a species evolves to be more suited for its local environment, can be an indicator of whether a species is likely to persist in the rapidly changing environment. Habitat specialists, like the greater sage-grouse, have evolved to use resources within a relatively narrow ecological niche and may therefore be more likely to have genes that are locally adapted, a trait which may not be beneficial (or, maladaptive) if their local environments undergo rapid change. Indeed, there is some evidence that suggests an association between greater sage-grouse genetic variation and different climatic regimes. Understanding how the genetic-climate relationship is expected to be strained by climate change, or the degree of future maladaptation, is an important part of assessing conservation concern. We will leverage our current understanding of genetic-climate relationships and future climate predictions to characterize range-wide, climate-associated maladaptation for the greater sage-grouse.
Background
The rapidly changing climate is predicted to expose many species to novel environmental conditions. This could mean subtle shifts in the timing and duration of historical annual weather patterns but will also likely include more variable and extreme weather events. These environmental changes are expected to stress the local biota and test their ability to persist and adapt to new environments. Local adaptation can be an indicator of whether a species is likely to persist in the rapidly changing environment and how it may respond. Habitat specialists, like the greater sage-grouse, have evolved to utilize resources within a relatively narrow ecological niche and may therefore be more likely to have genes that are locally adapted. Indeed, there is evidence that genetic variation covaries with climate across the species range. We are currently investigating genes underlying greater sage-grouse adaptation with a range-wide genomic data set. Expanding on this work to understand how and where the species may be adapted to local climate is an important part of understanding adaptive capacity and thus conservation concern.
Research Objectives
We are expanding our investigation of greater sage-grouse climate-associated adaptive divergence to include the development of spatial predictions of adaptive similarity (also called the adaptive index) and predicted maladaptation (also called genomic offset) under multiple climate change scenarios based on WorldClim bioclimatic variables (Fick & Hijmans 2017). We will use existing range-wide whole-genome sequence data to characterize the relationship between genetic variation and climate with multiple statistical approaches.
Management Implications
The difference between the current and predicted adaptive indices at any location is a prediction of the degree to which the changing climate is expected to disrupt the observed adaptive relationship, or the genomic offset. This information can be used as part of a Resist-Accept-Direct (RAD) framework to help resource managers make decisions in the face of climate-related ecological change (Schuurman and others 2020). In this framework, areas with the lowest predicted genomic offset may be good candidates for protecting or improving habitat or population augmentation (Resist), areas with the highest predicted genomic offset may be poor candidates for significant management action (Accept), and areas where climate is expected to shift to become more favorable or adaptively similar could be good candidates for management interventions to facilitate change, like assisted migration (Direct). Predicted adaptive similarity could also be used to guide augmentations by identifying populations that are either similarly adapted and therefore the least likely to displace locally adapted genes in the recipient population or to identify populations currently adapted to the predicted future climate of a recipient population as a source of pre-adapted individuals to boost population fitness.
Beyond the RAD framework, these spatial data layers could be incorporated into existing monitoring frameworks and management strategies. An index of climate risk could be incorporated into the Targeted Annual Warning System (Coates and others 2021) and the Genetic Warning System (Zimmerman and others 2022), and areas predicted to be less at risk from climate change might be candidate areas for protection as part of defending and growing the core of sagebrush habitat (Doherty and others 2022). We will use our raster predictions to create additional spatial data products that illustrate how our index of climate risk can be used to guide decision making in practice.
Climate change will expose many species to novel extreme environmental conditions, that may test organisms’ ability to respond to environmental change. Local adaptation, when a species evolves to be more suited for its local environment, can be an indicator of whether a species is likely to persist in the rapidly changing environment. Habitat specialists, like the greater sage-grouse, have evolved to use resources within a relatively narrow ecological niche and may therefore be more likely to have genes that are locally adapted, a trait which may not be beneficial (or, maladaptive) if their local environments undergo rapid change. Indeed, there is some evidence that suggests an association between greater sage-grouse genetic variation and different climatic regimes. Understanding how the genetic-climate relationship is expected to be strained by climate change, or the degree of future maladaptation, is an important part of assessing conservation concern. We will leverage our current understanding of genetic-climate relationships and future climate predictions to characterize range-wide, climate-associated maladaptation for the greater sage-grouse.
Background
The rapidly changing climate is predicted to expose many species to novel environmental conditions. This could mean subtle shifts in the timing and duration of historical annual weather patterns but will also likely include more variable and extreme weather events. These environmental changes are expected to stress the local biota and test their ability to persist and adapt to new environments. Local adaptation can be an indicator of whether a species is likely to persist in the rapidly changing environment and how it may respond. Habitat specialists, like the greater sage-grouse, have evolved to utilize resources within a relatively narrow ecological niche and may therefore be more likely to have genes that are locally adapted. Indeed, there is evidence that genetic variation covaries with climate across the species range. We are currently investigating genes underlying greater sage-grouse adaptation with a range-wide genomic data set. Expanding on this work to understand how and where the species may be adapted to local climate is an important part of understanding adaptive capacity and thus conservation concern.
Research Objectives
We are expanding our investigation of greater sage-grouse climate-associated adaptive divergence to include the development of spatial predictions of adaptive similarity (also called the adaptive index) and predicted maladaptation (also called genomic offset) under multiple climate change scenarios based on WorldClim bioclimatic variables (Fick & Hijmans 2017). We will use existing range-wide whole-genome sequence data to characterize the relationship between genetic variation and climate with multiple statistical approaches.
Management Implications
The difference between the current and predicted adaptive indices at any location is a prediction of the degree to which the changing climate is expected to disrupt the observed adaptive relationship, or the genomic offset. This information can be used as part of a Resist-Accept-Direct (RAD) framework to help resource managers make decisions in the face of climate-related ecological change (Schuurman and others 2020). In this framework, areas with the lowest predicted genomic offset may be good candidates for protecting or improving habitat or population augmentation (Resist), areas with the highest predicted genomic offset may be poor candidates for significant management action (Accept), and areas where climate is expected to shift to become more favorable or adaptively similar could be good candidates for management interventions to facilitate change, like assisted migration (Direct). Predicted adaptive similarity could also be used to guide augmentations by identifying populations that are either similarly adapted and therefore the least likely to displace locally adapted genes in the recipient population or to identify populations currently adapted to the predicted future climate of a recipient population as a source of pre-adapted individuals to boost population fitness.
Beyond the RAD framework, these spatial data layers could be incorporated into existing monitoring frameworks and management strategies. An index of climate risk could be incorporated into the Targeted Annual Warning System (Coates and others 2021) and the Genetic Warning System (Zimmerman and others 2022), and areas predicted to be less at risk from climate change might be candidate areas for protection as part of defending and growing the core of sagebrush habitat (Doherty and others 2022). We will use our raster predictions to create additional spatial data products that illustrate how our index of climate risk can be used to guide decision making in practice.