Chapter 15: Minimum Cumulative Exposure & Minimum Exposure Distance

Dataset Overview

This dataset includes five spatial data layers produced in one study. Minimum exposure distance (MED) represents the distance traveled by an organism that migrates in response to climate change, assuming that the organism does not necessarily travel in a straight line but instead travels so as to minimize its exposure to dissimilar climate (in this study, dissimilar temperature) along its route (see chapter glossary for definitions of terms). Minimum cumulative exposure (MCE) represents the total exposure to dissimilar climate experienced by that organism along its route (figure 15.1). Euclidean-distance velocity is a measure of climate-change velocity assuming an organism migrates in a straight line, whereas MED-based velocity measures that exposure along the (possibly non-linear) route taken to minimize exposure to dissimilar climate. Finally, the dataset includes a ratio of MED-based velocity to Euclidean-distance velocity.

Data Access:

Figure 15.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • A primary conservation application of this dataset concerns the ways in which land managers and conservation planners think about mountainous areas versus flat terrain. Previous studies have indicated that mountainous areas may serve as climate refugia because they contain steep climate gradients (i.e., large changes in climate over short distances, such as on steep mountain slopes). These steep climate gradients, it has been argued, will provide opportunities for species to find newly favorable climates nearby, without having to migrate long distances. This study raises an important additional consideration: although favorable climates may be located nearby (e.g., two adjacent mountain peaks with cold temperatures), they may not be easily accessible to migrating organisms if they are separated by inhospitable climate (e.g., a warm valley in between). Dobrowski and Parks (2016) argue that although mountainous areas may provide refugia in the near term (or under relatively mild climate-change scenarios), they will only be temporary "holdouts", meaning that they will eventually cease functioning as refugia once climate change exceeds the local climate gradients that mountains provide. Conversely, for flat areas that have previously been considered highly vulnerable to climate change because they contain little spatial variation in climate, an additional consideration raised in this study is that they may be relatively free of climate-based barriers to species movement, making it easier for species to move through flat landscapes.

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 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), the continental United States, the North American continent.

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]), a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest.

Use of the dataset in conservation applications may be limited by the following considerations:

  • This dataset cannot be used to evaluate climate-change vulnerability for individual species or ecosystems. Because this dataset does not incorporate information on species dispersal or barriers to species movement based on human land use, conservation practitioners should understand that actual experiences of organisms moving across landscapes in response to climate change may vary widely across species and, for some species, may not be well represented by these datasets.

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:

Dobrowski, S. Z., and S. A. Parks. 2016. Climate change velocity underestimates climate change exposure in mountainous regions. Nature Communications 7:1–8.

Dataset documentation link: (open access)

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 authors:
Solomon Dobrowski
Department of Forest Management, College of Forestry and Conservation, University of Montana
[email protected]

Sean Parks
Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station, USDA Forest Service
[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:

MED: kilometers (km)
MED-based velocity: km/year
Euclidean-distance velocity: km/year
Ratio of MED-based velocity to Euclidean-distance velocity: ratio

Range of mapped values:

MED: 0 to 6,401
MED-based velocity: 0 to 71.122
MCE: 0 to 32,706
Euclidean-distance velocity: 0 to 34.367
Ratio of MED-based velocity to Euclidean-distance velocity: 1 to 10.4296

Note: Pixels on islands and whose future climate analog is on an island are coded -9999 in the MED dataset and are coded -1 in all other datasets.

Spatial data type: a raster dataset (grid)

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

Spatial resolution: 5 km

Geographic coordinate system: World Geodetic System (WGS) 1984

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

Spatial extent: Continental (North America) excluding Central America and the Caribbean islands

Dataset truncation: The dataset is truncated along the border separating Mexico from Guatemala and Belize.

Time period represented: Future (later than 2020)

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

Baseline time period (against which future conditions were compared): 1981-2010.

Methods overview:

Using baseline and projected future values for mean annual temperature (MAT), climate analogs (destination pixels) were identified as any pixel with future MAT that is ±0.25 °C from the baseline MAT for the pixel of interest (source pixel). Least-cost modeling was then used to identify potential migration routes from source pixels to destination pixels. These potential migration routes are the paths that organisms could take to track changing climate conditions while minimizing the "cost" of their migration, where "cost" is conceptualized as the experience of travelling through pixels of unfavorable climate. MED is the length of these potential migration routes and MED-based velocity is MED divided by 90 years (the time between baseline year 1995 and future projections in 2085). MCE was calculated using an equation (see equation 3 in the dataset citation) that includes "cost", MED, and a penalty value based on climate dissimilarity. MED-based velocity for each pixel was compared to Euclidean-distance (straight-line) velocity using a ratio. 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: Least-cost models

This dataset employed the following specific models: raster, rgdal, and gdistance packages in the R software environment (van Etten, 2015)

Major input data sources for this dataset included: Historical climate observations or models, future climate projections

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


The dataset was produced using input climate data ensembles across these GCMs, therefore individual files for individual GCMs are not available. 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):

No climate downscaling was performed in this study. The mean-annual temperature input data had a resolution of 1 km, which was resampled to 5 km prior to analysis.

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

MED: high MED values indicate that organisms must travel long distances in order to reach locations with suitable climate in the future as they minimize their exposure to unsuitable climates along the way, i.e., exposure to temperatures that are too warm or too cold relative to the optimal temperature range that the organism is tracking. Conversely, low MED values indicate that suitable climates in the future are available nearby and/or without having to navigate around regions of unsuitable climate.

MED-based velocity: see interpretation of MED above. Higher MED-based velocity values mean that organisms will have to travel faster to "keep pace" with changing climate.

MCE: higher MCE values indicate that, over the course of their migration to track favorable climate as climate conditions change, organisms will be exposed to high levels of unsuitable climate; lower values represent lesser exposure to unsuitable climate along the migration route.

Euclidean-distance velocity: Higher values mean organisms will have to travel faster to "keep pace" with changing climate, assuming that they travel in straight lines.

Ratio of MED-based velocity to Euclidean-distance velocity: The higher this ratio, the greater the degree to which the migration route for an organism deviates from a straight line due to avoidance of unfavorable climate or open water.

Representations of key concepts in climate-change ecology:

The data layers in this dataset (MCE, MED and MED-based velocity, and Euclidean-distance velocity) represent various ways of conceptualizing climate-change vulnerability based on climate-change exposure (how rapidly climate conditions are predicted to change) and organisms' adaptive capacity (their ability to respond to climate change by tracking suitable climate conditions through time). For example, climate-change vulnerability may be especially great for species in areas with high velocity (because poorly dispersing species may not be able to "keep pace" with climate change) and/or in areas with high MCE (because species with narrow ranges of temperature tolerance would be subjected to possibly damaging levels of unsuitable temperature along migration routes).

Climate-change velocity is an important consideration in climate vulnerability assessments because it indicates the rate at which species would need to move to track favorable conditions. However, species may be forced to cross areas of less favorable conditions in their movement routes, as demonstrated by this dataset. While mountainous areas generally have relatively low velocity (because diverse microclimates are available nearby and can be reached by organisms traveling relatively short distances), these areas can have relatively high MCE values because organisms are exposed to unfavorable conditions as they travel to new microhabitats. Conversely, flat landscapes with high velocity generally have low MCE values because relatively homogenous climate conditions pose few barriers to species’ movements. Thus, MCE and MED in this dataset can be useful complements to climate-velocity datasets for evaluating constraints to species’ adaptive capacity.

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

The data layers in this dataset were all created using MAT to represent climate conditions, whereas actual climate conditions important to organisms may also include measures of moisture and other factors. In addition, MAT represents average temperature by definition, so this dataset does not account for exposure to temperature variability or extremes that may be important to organisms' experiences of climate change.

This dataset represents a "coarse-filter" approach to assessing climate vulnerability that does not apply to any individual species or differentiate among species. Thus it does not account for the likelihood that within a given geographic area, some species (those that can tolerate a wider range of temperatures) will actually experience lower MCE, lower MED, and lower velocity because more of their immediate surroundings will represent favorable climate conditions, whereas other species (those that are more sensitive to temperature) will experience greater MCE, MED, and velocity.

Although MED-based velocity and MCE can be used to evaluate how accessible a site is to migrating organisms, in practice that accessibility will also be influenced by non-climate landscape attributes (such as physical barriers and human land use) and the dispersal of organisms (the means by which they disperse, over what distances they are able to disperse, and the speed at which they do so).

Calculation of MCE and the velocity data layers in this dataset is sensitive to the spatial resolution of the input climate data and to several parameters used in least-cost modeling (see supplementary material in Dobrowski and Parks 2016).

Quantification of uncertainty:

Uncertainty was not quantified in the sense that the potential effects on MCE or MED owing to variability among GCMs or across greenhouse-gas scenarios was not calculated. However, the study that produced the dataset did involve a sensitivity analysis that examined the effects of the resolution of the climate data used as inputs and two parameters used in least-cost modeling (see supplementary material in Dobrowski and Parks, 2016).

Field verification:

Field verification of the mapped values in this dataset was not possible because the dataset represents a future condition. Mean annual temperature values for the baseline time period were obtained 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).

Dobrowski, S. Z., and S. A. Parks. 2016. Climate change velocity underestimates climate change exposure in mountainous regions. Nature Communications 7:1–8.

Knutti, R., and J. Sedláček. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373.

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.

van Etten, J. 2015. gdistance: Distances and routes on geographical grids. R package version 1.1-9.

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.