Chapter 16: Animal Species Turnover
Dataset Overview
This dataset represents future projections of species turnover of vertebrate animals in western North America (figure 16.1; see chapter glossary for definitions of terms). These species turnover projections were derived from habitat suitability models for 366 terrestrial animals, which in turn were derived from the combination of projections of climate suitability with projections of vegetation (biome) change. For each pixel in the study area, each species was projected to experience contraction, expansion, or stability in habitat suitability. The species turnover rate value was then calculated for each pixel, representing the cumulative result of the projected habitat suitability change for all 366 species. Pixels with high species turnover rate values represent areas that may experience a high degree of change in species representation, whereas low species turnover rates represent areas of relative projected stability. Two versions of the dataset are available, based on two future global circulation models (GCMs) - the Hadley CM3 GCM and CGCM 3.1 GCM.
Data Access:
- The CGCM 3.1 GCM version of the dataset can be downloaded here: https://databasin.org/datasets/85722cc00ad445d680b8d50f90eb1bca
- The Hadley CM3 GCM version of the dataset can be downloaded here: https://databasin.org/datasets/bbb18483b81f4b2c9cb36154f2605d48
Conservation Applications
Potential conservation applications of this dataset could include the following:
- This dataset can be combined with other related climate-change projections to anticipate the magnitude of climate-driven change in animal species composition for a given area. For example, the study that produced this dataset (Langdon and Lawler, 2015) also examined climate projections and biome-change projections to evaluate protected areas throughout western North America and explore possible management approaches for different predicted magnitudes of change (Langdon and Lawler, 2015). Continuation of current management practices may be appropriate for protected areas where species turnover is predicted to be low, whereas protected areas with greater predicted species turnover may require more active or innovative management approaches such as habitat improvements, increasing connectivity between protected areas and other habitat patches, and in some cases assisted migration.
Applicable scales for detailed spatial assessments:
- For conservation applications requiring detailed assessment of spatial variation of the dataset within a geographic boundary (such as a protected area), the following geographic scales may be most appropriate (see appendix 3 for more information).
- At the scale of: a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest, a level-3 ecoregion (e.g., the North Cascades), a single state (Washington, Oregon, or Idaho), a region (multiple states in the Pacific Northwest or in western North America).
Applicable scales for assessing general patterns:
- Due to spatial resolution, the dataset may not show detailed spatial variation at the following geographic scales, however, the dataset may be useful to assess general patterns or for comparison to other locations (see appendix 3 for more information).
- At the scale of: a small (< 1 km2) nature preserve, a state park or state wildlife area, a local watershed (12-digit hydrologic unit code [HUC-12]).
Use of the dataset in conservation applications may be limited by the following considerations:
- Because this dataset was generated using habitat suitability models that simply associate species presence or absence with environmental variables, this dataset does not represent many important and complex processes that may influence species changes over time. These include disturbances such as fires and droughts, interactions between species, parasite and pathogen dynamics, and differences among species in how they may disperse to new areas or persist in place despite changing climate conditions. Therefore, managers may need to consider locally relevant conditions such as these when using this dataset to help inform management decisions.
Past or current conservation applications:
- The dataset has not yet been used in any on-the-ground conservation applications to the knowledge of the authors of this chapter
Dataset citation:
Langdon, J., and J. Lawler. 2015. Assessing the impacts of projected climate change on biodiversity in the protected areas of western North America. Ecosphere 6:87.
Dataset documentation link:
https://doi.org/10.1890/ES14-00400.1 (open access)
Data access:
The CGCM 3.1 GCM version of the dataset can be downloaded from: https://databasin.org/datasets/85722cc00ad445d680b8d50f90eb1bca
The Hadley CM3 GCM version of the dataset can be downloaded from: https://databasin.org/datasets/bbb18483b81f4b2c9cb36154f2605d48
Metadata access: Formal metadata is not available for this dataset.
Dataset corresponding author:
Jesse Langdon
[email protected]
Data type category (as defined in the Introduction to this guidebook): Animal habitat, climate
Species or ecosystems represented: This dataset is an integrated assessment for 366 terrestrial animal species, including 12 amphibians, 237 birds, and 117 mammals.
Units of mapped values: unitless
Range of mapped values:
For the Hadley CM3 GCM: 0 to 0.985507
For the CGCM 3.1 GCM: 0 to 0.980583
Spatial data type: raster dataset (grid)
Data file format(s): GeoTiff (.tif)
Spatial resolution: 30 seconds
Geographic coordinate system: World Geodetic System (WGS) 1984
Spatial extent: Regional
Dataset truncation: The dataset is truncated along lines of latitude (northern and southern boundaries of the dataset) and longitude (eastern boundary of the dataset).
Time period represented: Future (later than 2020)
Future time period(s) represented: End-of-century (2070 to 2099).
Baseline time period (against which future conditions were compared): 1961 to 1990.
Methods overview:
Habitat suitability models were constructed for each of the animal species, and animal species presence and absence were correlated with 23 variables representing environmental conditions. For each species, habitat suitability was assessed using an approach that combined future climate suitability (from species-distribution models) with future vegetation (biome) suitability. Future biome suitability represented both (a) whether a biome was deemed suitable for a given species, and (b) whether that biome was projected to change in the future to another biome. These assessments produced spatial grids for each species that predicted contraction, expansion, or stability of habitat suitability for each pixel. Species turnover was calculated using these predictions. The equation for species turnover is available in Langdon and Lawler (2015). For more information, please consult the dataset citation listed in section 2 of this chapter.
This dataset relied on the following general types of models: Habitat suitability models; bioclimatic niche models
This dataset employed the following specific models: Vegetation change was assessed using biome maps from Rehfeldt et al. (2012).
Major input data sources for this dataset included:
Species ranges or point locations, historical climate observations or models, future climate projections, current and future projections of vegetation types (biomes)
This dataset used the following general circulation models (GCMs): CGCM 3.1, Hadley CM3
Separate files are provided for each GCM; no ensemble is provided across GCMs. More information about climate models is available in appendix 1. Detailed information about climate models, including model evaluation and comparison among models, is available from Randall et al. (2007) and Rupp et al. (2013).
This dataset used the following greenhouse-gas scenarios: SRES A2
More information about greenhouse-gas scenarios is available in appendix 2 and from Knutti and Sedláček (2013).
Creation of this dataset involved the following methods to change the spatial resolution of climate models (e.g. to downscale or resample climate models):
Climate data were downscaled using a geographic distance-weighted bilinear interpolation method.
The mapped values of the dataset may be interpreted as follows:
High values represent landscape areas where large changes are predicted in the species that will be present in the future under climate change. This can include both gain of new species migrating in from other areas and loss of species. Low values represent landscape areas where species composition is projected to remain largely stable.
Representations of key concepts in climate-change ecology:
Species turnover is one possible component of climate-change vulnerability, in that locations with high species turnover may demonstrate increased susceptibility of animal communities to climate change. Species turnover can reflect several interrelated processes. First, climate-change exposure helps shape species turnover because greater changes in climate are more likely to require greater numbers of species to shift their ranges in response, and possibly for those range shifts to cover greater geographic distances. Second, species sensitivity to climate change may also shape species turnover, because the breadth of climatic conditions tolerated by a species is reflected in the geographic scope of its historic and projected future distributions.
Species turnover incorporates both increases and decreases in climatic suitability for species. This dataset did not include metrics of species’ dispersal abilities and thus does not directly represent species presence or absence under future conditions. Dispersal abilities are an important component of adaptive capacity, because favorable climate alone does not ensure that a species will be present in an area in the future if that species is not able to effectively colonize that area. Along with changes in habitat suitability, information on species dispersal abilities could also be considered in order to anticipate species arrivals (i.e., species migrating into an area because its climate has become favorable) and departures (i.e., species migration out of an area to seek favorable climate elsewhere). While both arrivals and departures would affect the overall change in community structure in a given area, they may have different types of ecological implications and necessitate different management approaches. For example, if a keystone species moves away from an area due to climate change there could be cascading effects on other species in local food webs. New species arriving in an area may disrupt relationships between existing species, for example by competing for resources with existing species.
This dataset involves the following assumptions, simplifications, and caveats:
This dataset was created in part by using climate suitability models, which only assess the degree to which future climate conditions will be similar or different to climate conditions associated with a current species range; they do not account for many complex ways in which climate change could affect species or their habitats such as changing disturbance regimes (e.g., fires, droughts), changes in seasonal timing of life cycles, inter-species relationships, changes in parasites or pathogen dynamics, and many other factors that will likely shape species distributions in addition to climate. In addition, these models do not consider differences among species in the rates at which species will be able to disperse to new habitats or adapt in place to changing conditions. These models essentially assume equilibrium, meaning an assumption that a given species or vegetation type will exist in a given climate, not accounting for potential time lags in transitions from one vegetation type to another.
Quantification of uncertainty:
Uncertainty in projected species turnover was assessed by conducting the analysis using two GCMs and comparing the results.
Field verification:
Species presence and absence data used in climate suitability models were not independently field verified.
Prior to dataset publication, peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).
Knutti, R., and J. Sedláček. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373.
Langdon, J., and J. Lawler. 2015. Assessing the impacts of projected climate change on biodiversity in the protected areas of western North America. Ecosphere 6:87.
Randall, D., R. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, and et al. 2007. Cilmate models and their evaluation. in S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, and H. Miller, editors. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, NY.
Rehfeldt, G., N. Crookston, S. Saenz-Romero, and E. Campbell. 2012. North American vegetation model for land-use planning in a changing climate: a solution to large classification problems. Ecological Applications 22:119–141.
Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote. 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research 118: 884–907.