Chapter 14: Species Movement to Analog Climates

Dataset Overview

This dataset identifies areas that may facilitate species movement across western North America as species track changing climate conditions (figure 14.1). The dataset was produced with a connectivity model based on electrical circuit theory and considers climate analogs, landscape permeability, and species dispersal capacities (see chapter glossary for definitions of terms).

Data Access:

Figure 14.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • This dataset could be used to help prioritize land areas for conservation based on the objective of maximizing species’ abilities to track changing climatic conditions. This research shows that incorporating future climate projections highlights some areas that will be important for species movement that may not otherwise emerge as important if species movement is modeled only based on human modification of the landscape.

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:

  • This dataset cannot be used to represent species movement for individual species. For long-range planning, it should be noted that this dataset did not incorporate likely future changes to landscape permeability due to changes in human modification of the landscape.

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:

Littlefield, C. E., C. Carroll, B. H. McRae, J. L. Michalak, and J. J. Lawler. 2017. Connecting today’s climates to future climate analogs to facilitate movement of species under climate change. Conservation Biology 31:1397–1408.

Dataset documentation link: (subscription or fee required)

Data access:

The dataset can be downloaded from:

The dataset is not available for interactive online map viewing.

Metadata access:

Formal metadata is not available for this dataset.

Dataset corresponding author:
Caitlin Littlefield
Department of Forest Management, College of Forestry and Conservation, University of Montana
[email protected]

Data type category (as defined in the Introduction to this guidebook): Climate, landscape connectivity

Species or ecosystems represented:

This dataset does not represent any individual species or ecosystems

Units of mapped values:

Potential species’ movements, available with scaled or unscaled units. Unscaled units represent current flow in amps. Scaled units have been scaled from 0-1, where higher values indicate greater potential for species’ movements.

Range of mapped values:

The range of unscaled mapped values varies depending on the climate model used and inclusion/exclusion of landscape permeability. Mapped values that have been scaled from 0-1 are also available for each climate model. All mapped values should be interpreted with regard to the relative importance of specific areas for potential movement.

Spatial data type: a raster dataset (grid)

Data file format(s): GeoTiff (.tif)

Spatial resolution: 1 km

Geographic coordinate system: World Geodetic System (WGS) 1984

Projected coordinate system: World Geodetic System (WGS) 1984 Lambert Conformal Conic

Spatial extent: Regional (western North America)

Dataset truncation: The dataset is truncated at non-ecological borders along the eastern and southern edges, which limits the geographic extent to western North America.

Time period represented: Future (later than 2020)

Future time period(s) represented: End-of-century (2071 to 2100)

Baseline time period (against which future conditions were compared): 1961 to 1990.

Methods overview:

First, climate analogs were identified by comparing historic (1961-1990) to future (2071-2100) climate conditions. Then, connectivity between climate analogs was modeled by considering landscape permeability and species dispersal capacities (Littlefield et al., 2017). 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: Connectivity models

This dataset employed the following specific models: Circuitscape (McRae et al., 2016; Shah and Mohapatra, 2013)

Major input data sources for this dataset included:

Current land use, historical climate observations or models, future climate projections, biologically-informed dispersal and climate analog similarity parameters

This dataset used the following general circulation models (GCMs):


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: RCP 8.5

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):

The study that produced this dataset relied on climate data from the ClimateNA version 5.10 software package, which had been downscaled from 4 km to 1 km (Wang et al., 2016).

The mapped values of the dataset may be interpreted as follows:

High values indicate important movement routes for species to track changing climate conditions, with human modification largely determining the underlying permeability of the landscape and thus which parts of the landscape species may traverse. Low values indicate areas that may be relatively less important for climate-induced movements.

Representations of key concepts in climate-change ecology:

The dataset identifies important movement routes for species to track suitable climatic conditions, which may be a critical adaptive response under climate change. Greater connections between future climate analogs (indicated by higher values of current in this dataset) suggest areas may be especially important in supporting the adaptive capacity of species that are migrating to track favorable climate conditions. The dataset may be related to climate-change resilience in that greater movement potential could help populations rebound after disturbances such as droughts. The dataset also relates to climate-change sensitivity in that areas with greater movement potential might be less sensitive to climate-change impacts on ecosystems, which help mitigate overall climate-change vulnerability.

As explained in chapter 13, the ability of species to move across landscapes in response to climate change is one component of adaptive capacity, which can also include species’ abilities to persist in place or to use newly favorable nearby habitats. This dataset can help assess constraints to adaptive capacity because areas of more intensive human land use are generally represented as less permeable and so are less likely to be identified as important migration areas. Another important determinant of adaptive capacity—species’ dispersal abilities—is also incorporated into this dataset. All else equal, species that can disperse rapidly over long distances may be better able to adapt to climate change by shifting their ranges than are species with limited dispersal.

This dataset involves the following assumptions, simplifications, and caveats:

Pathways between future climate analogs did not account for intervening climate conditions or physical barriers (e.g., mountains) that could, in reality, limit species movement. This probably caused the models to overestimate species movement in some locations. In addition, the model did not incorporate fine-scale diversity in climate that could provide microclimate refugia to some species. Also, human modification was represented as static even though it will likely change over time.

Although this dataset was informed by climate niches and dispersal abilities for many species, it does not represent individual species and may fail to represent certain types of species for which there are limited data available.

This dataset is less meaningful at the inland edges of the study area because species movement into and out of the study area was not modeled. The dataset is also less meaningful in locations with unusual climate gradients.

Quantification of uncertainty: This dataset does not include any quantification of uncertainty relating to the mapped values.

Field verification:

Field verification of the mapped values in this dataset was not possible because the dataset represents a future condition. Datasets representing the baseline time period were derived from downscaled climate grids from the ClimateNA dataset (Wang et al. 2016). Accuracy of the baseline climate variables in the ClimateNA dataset was assessed using monthly mean observations from 4,891 weather stations across North America, located predominantly in the United States; see table 2 in Wang et al. (2016).

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.

Littlefield, C. E., C. Carroll, B. H. McRae, J. L. Michalak, and J. J. Lawler. 2017. Connecting today’s climates to future climate analogs to facilitate movement of species under climate change. Conservation Biology 31:1397–1408.

McRae, B., K. Popper, A. Jones, M. Schindel, S. Buttrick, K. Hall, R. Unnasch, and J. Platt. 2016. Conserving nature’s stage: mapping omnidirectional connectivity for resilient terrestrial landscapes in the pacific northwest. The Nature Conservancy, Portland, OR.

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.

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.

Shah, V. and T. Mohapatra. 2013. Circuitscape 4 user guide. The Nature Conservancy, Arlington, VA.

Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One 11:e0156720.