Modeling both potential changes in climate and responses of species and habitats can increase certainty in management decisions by helping managers to understand the range of possible species and habitat responses under different alternative futures. Climate envelope modeling is one type of modeling that can be useful in understanding species and habitat responses to climate change because it identifies key links between drivers of change (e.g., climate) and relevant responses.
PROJECT COMPLETED
The Science Issue and Relevance: Climate change will accelerate threats that challenge our ability to restore, preserve, and protect natural ecosystems and the species that depend on them. Successful conservation strategies will require an understanding of climate change and the ability to predict how it will affect species and habitats at multiple scales. Modeling both potential changes in climate and responses of species and habitats can increase certainty in management decisions by helping managers to understand the range of possible species and habitat responses under different alternative futures. Climate envelope modeling is one type of modeling that can be useful in understanding species and habitat responses to climate change because it identifies key links between drivers of change (e.g., climate) and relevant responses. Climate envelope models describe relationships between species occurrences and bioclimate variables (temperature and precipitation) to define a species climate niche (envelope). Relationships derived from contemporary data can be projected to the future using estimates of anticipated climate change.
Methodology for Addressing the Issue: Climate envelope models are a subset of the more general family of species distribution models that correlate species occurrence or abundance with climate variables to make spatially-explicit predictions of potential distribution. The general approach involves five steps: 1) acquiring species occurrence data and subsequent partitioning into ‘training’ and ‘validation’ subsets; 2) testing for statistical associations between occurrence and climate in the training data set; 3) applying associations between occurrence and climate revealed in the training dataset to predict species distributions; 4) evaluating performance of model predictions using occurrences in the validation dataset; and 5) using the associations between occurrence and contemporary climate conditions to forecast the occurrence of species under future climate projections.
Future Steps: We plan to take the next step in model refinement by adding data on land cover to species models. This additional layer of information will increase the accuracy of our models, and allow users to evaluate the relative strength of climate versus non-climate factors on species distributions. By including both climate and land cover predictors of species distributions, our models come closer to modeling the true geographic range of species rather than a more general climate envelope. Our objectives are to: 1) Improve our existing climate envelope models for T&E species to include data describing contemporary land cover associations and produce revised contemporary distribution output; 2) Create models that forecast future shifts in natural land cover under two emissions scenarios and three global circulation models; and 3) Using output from objectives 1 and 2, forecast potential species responses to direct effects of climate change (altered precipitation and temperature) as well as indirect climate change effects (land cover shifts).
Below are publications associated with this project.
Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models
Comparing species distribution models constructed with different subsets of environmental predictors
Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models
Threatened and endangered subspecies with vulnerable ecological traits Also have high susceptibility to sea level rise and habitat fragmentation
Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States
Do bioclimate variables improve performance of climate envelope models?
- Overview
Modeling both potential changes in climate and responses of species and habitats can increase certainty in management decisions by helping managers to understand the range of possible species and habitat responses under different alternative futures. Climate envelope modeling is one type of modeling that can be useful in understanding species and habitat responses to climate change because it identifies key links between drivers of change (e.g., climate) and relevant responses.
Climate envelope modeling is one type of modeling that can be useful in understanding species and habitat responses to climate change. PROJECT COMPLETED
The Science Issue and Relevance: Climate change will accelerate threats that challenge our ability to restore, preserve, and protect natural ecosystems and the species that depend on them. Successful conservation strategies will require an understanding of climate change and the ability to predict how it will affect species and habitats at multiple scales. Modeling both potential changes in climate and responses of species and habitats can increase certainty in management decisions by helping managers to understand the range of possible species and habitat responses under different alternative futures. Climate envelope modeling is one type of modeling that can be useful in understanding species and habitat responses to climate change because it identifies key links between drivers of change (e.g., climate) and relevant responses. Climate envelope models describe relationships between species occurrences and bioclimate variables (temperature and precipitation) to define a species climate niche (envelope). Relationships derived from contemporary data can be projected to the future using estimates of anticipated climate change.
Climate change will accelerate threats that challenge our ability to restore, preserve, and protect natural ecosystems and the species that depend on them. Methodology for Addressing the Issue: Climate envelope models are a subset of the more general family of species distribution models that correlate species occurrence or abundance with climate variables to make spatially-explicit predictions of potential distribution. The general approach involves five steps: 1) acquiring species occurrence data and subsequent partitioning into ‘training’ and ‘validation’ subsets; 2) testing for statistical associations between occurrence and climate in the training data set; 3) applying associations between occurrence and climate revealed in the training dataset to predict species distributions; 4) evaluating performance of model predictions using occurrences in the validation dataset; and 5) using the associations between occurrence and contemporary climate conditions to forecast the occurrence of species under future climate projections.
Climate envelope models fall within the general family of species distribution models. Future Steps: We plan to take the next step in model refinement by adding data on land cover to species models. This additional layer of information will increase the accuracy of our models, and allow users to evaluate the relative strength of climate versus non-climate factors on species distributions. By including both climate and land cover predictors of species distributions, our models come closer to modeling the true geographic range of species rather than a more general climate envelope. Our objectives are to: 1) Improve our existing climate envelope models for T&E species to include data describing contemporary land cover associations and produce revised contemporary distribution output; 2) Create models that forecast future shifts in natural land cover under two emissions scenarios and three global circulation models; and 3) Using output from objectives 1 and 2, forecast potential species responses to direct effects of climate change (altered precipitation and temperature) as well as indirect climate change effects (land cover shifts).
- Publications
Below are publications associated with this project.
Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models
Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMsAuthorsJames I. Watling, Laura A. Brandt, David N. Bucklin, Ikuko Fujisaki, Frank J. Mazzotti, Stephanie S. Romañach, Carolina SperoterraComparing species distribution models constructed with different subsets of environmental predictors
Aim To assess the usefulness of combining climate predictors with additional types of environmental predictors in species distribution models for range-restricted species, using common correlative species distribution modelling approaches. Location Florida, USA Methods We used five different algorithms to create distribution models for 14 vertebrate species, using seven different predictor setAuthorsDavid N. Bucklin, Mathieu Basille, Allison M. Benscoter, Laura A. Brandt, Frank J. Mazzotti, Stephanie S. Romañach, Carolina Speroterra, James I. WatlingAssessing effects of variation in global climate data sets on spatial predictions from climate envelope models
Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role ofAuthorsStephanie S. Romañach, James I. Watling, Robert J. Fletcher, Carolina Speroterra, David N. Bucklin, Laura A. Brandt, Leonard G. Pearlstine, Yesenia Escribano, Frank J. MazzottiThreatened and endangered subspecies with vulnerable ecological traits Also have high susceptibility to sea level rise and habitat fragmentation
The presence of multiple interacting threats to biodiversity and the increasing rate of species extinction make it critical to prioritize management efforts on species and communities that maximize conservation success. We implemented a multi-step approach that coupled vulnerability assessments evaluating threats to Florida taxa such as climate change, sea-level rise, and habitat fragmentation witAuthorsAllison M. Benscoter, Joshua S. Reece, Reed F. Noss, Laura B. Brandt, Frank J. Mazzotti, Stephanie S. Romañach, James I. WatlingClimate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States
High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two differeAuthorsDavid N. Bucklin, James I. Watling, Carolina Speroterra, Laura A. Brandt, Frank J. Mazzotti, Stephanie S. RomañachDo bioclimate variables improve performance of climate envelope models?
Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables withAuthorsJames I. Watling, Stephanie S. Romañach, David N. Bucklin, Carolina Speroterra, Laura A. Brandt, Leonard G. Pearlstine, Frank J. Mazzotti