The Buckley lab combines modelling, field and lab collection of ecological and physiological data, and ecoinformatics to examine how biology (morphology, physiology, and life history) determines an organism’s ecological and evolutionary responses to environmental change. We integrate approaches from physiological ecology and evolution, population and community ecology, and biogeography. While we continue some work on lizards (NSF Macrosystems Biology project that’s wrapping up and grad student projects), our research focus has been shifting toward montane butterflies and grasshoppers in Colorado as they offer excellent historical records. These projects are part of an initiative (funded by an NSF Advances in Biological Informatics CAREER grant) to develop computational and visualization tools to translate climate change into biological responses.
Questions we’ve worked on recently include:
- How does local adaptation across a species’ range influence responses to climate change?
- How does thermoregulatory behavior alter the evolution of thermal tolerances and climate change impacts over the short and long term?
- How does thermal exposure and sensitivity vary across the life cycle and what are the implications for demography and distributions?
- What are the implications of developmental plasticity for phenology and demography in changing environments?
- What are the relative impacts of acute (extremes) and chronic (means) climate conditions on demography and distributions?
- How does climate variability influence plastic and evolutionary responses to climate change?
Our research approaches include:
Phenotype-based mechanistic niche models
Connecting phenotypes to the ecological and evolutionary consequences of climate change requires integrated models at physiological, performance and fitness levels. First, climate conditions, microclimatic structure and phenotypic traits determine patterns of body temperature and organismal energy and water balances. Second, these patterns can be integrated with thermal performance curves to predict rates of survival, development and reproduction. Third, these different fitness components can be combined to predict population demography and fitness. These “mechanistic niche models” can forecast climate change impacts from first principles of heat and energy balances. We aim to apply the models to consider the range implications of geographic trait variation, evolution, and biotic constraints. Field and lab work to document ecology and physiology are employed to parameterize and test the models.
Characterizing phenotypes and their functional significance
We tend to focus on morphological and physiological phenotypes as they relate to thermal sensitivity because the functional basis is well established. We characterize phenotypes in the field as well as in controlled conditions (laboratory growth chambers). Field and lab experiments are used to document the functional implications of phenotypes. We’re increasingly moving into work on the genetic basis of adaptation.
Hindcasting phenotypic, demographic, and distribution shifts
We are working on two resurvey projects in Colorado to test models predicting ecological and evolutionary responses to climate change. First, we (with Joel Kingsolver, UNC) are using historic data on species’ traits from museum specimens (0-100ya) and performance from lab and field studies (30-50ya) to assess phenotypic shifts for Rocky Mountain Colias butterflies and their influence on responses to recent climate changes. Overall, we are finding that environmental variability limits evolutionary responses.Second, we (with Kingsolver and Cesar Nufio, NSF and CU Boulder) are examining shifts in traits, phenology, abundance, and performance of grasshoppers along a Rocky Mountain elevation gradient (since initial surveys and specimen collection from 1930-1960) in response to recent climate change. The project aims to enable ecological forecasting in a community context. More details from CU Boulder.
Computational and visualization tools for translating environmental change into biological responses
We build computational and visualization tools to translate environmental change into organismal responses. Details on the TrEnCh project website.
A key challenge in raising public awareness of climate change is conveying the ecological and human impacts of a given (e.g., 3°C) temperature increase. Biophysical models compute heat budgets for organisms and enable translating environmental conditions into potential body temperatures. They can thus predict how warming alters thermal stress and performance. They can be integrated with demographic models to scale up to the population, community, and ecosystem impacts of climate change. Organisms interact with their environment at small spatial and short temporal scales, requiring downscaling of most available data describing current and projected future environments. The high temporal and spatial resolution data will enable quantifying organismal responses to both environmental means and variability. We are developing tools for visualizing the ecological impacts of climate change that will be disseminated through education and outreach programs.
Broad scale implications of physiology
We also use ecoinformatic approaches to examine how the evolution of physiological traits constrains broad-scale patterns of abundance, diversity, and species turnover.