Chapter 19: Environmental Diversity Datasets for North America

Dataset Overview

This dataset includes several data layers that represent, in various ways, the diversity of environmental conditions across a landscape. These data layers include two metrics of topographic diversity: elevational diversity and heat-load index (HLI) diversity (see chapter glossary for definitions of terms). Land-facet diversity (figure 19.1) represents the variety of land-facet types, which are landscape areas classified according to landform, elevation, HLI, and soil order. Ecotypic diversity measures the variety of Ecological Land Units as defined by Sayre et al. (2014). Ecological Land Units represent unique combinations of landform, growing degree days, an aridity index, lithology (soil parent material), and land cover. The study that produced these data layers also evaluated current climate diversity and climate velocity, however, these data layers are not described in detail in this chapter.

Data Access:

Figure 19.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • This dataset has potential to help inform an evaluation of existing networks of protected areas and provide information relevant to decision-making for acquisition of new protected areas. For example, the study that produced these datasets (Carroll et al., 2017) found that the existing network of protected areas across North America was more effective in capturing diversity of elevation and HLI and somewhat less effective in capturing diversity of ecotypes and land facets. This was linked to the tendency for mountainous areas to be over-represented in protected area networks relative to lower-elevation plains. Also, this study found that the various environmental diversity data layers (diversity of elevation, HLI, ecotypes, and land-facets) produced noticeably different outcomes when they were used as inputs to identify optimal networks of protected areas. This implies that, from a conservation perspective, a variety of strategies for considering environmental diversity may need to be considered and conservation practitioners should not assume that one type of environmental diversity is necessarily representative of other types of environmental diversity.

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), 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]).

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

  • In the decision-making process leading up to the designation of new protected areas, many practical considerations (e.g., social, economic, administrative) are involved that this dataset does not address. Furthermore, this dataset fundamentally represents a "coarse-filter" approach, meaning that it does not represent any individual species and uses no species-specific information. Managers may wish to combine information from this dataset and similar datasets on environmental diversity with species-specific information where available, e.g., species range maps or habitat suitability assessments. Conservation decision-making may also be informed by ecological information and management priorities related to disturbance (e.g., fires or droughts), ecological restoration, and specialized or sensitive habitats (e.g., wetlands or riparian environments) that are not represented in these data layers.

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:

Carroll, C., T. Wang, D. R. Roberts, J. L. Michalak, J. J. Lawler, S. E. Nielsen, D. Stralberg, A. Hamann, and B. H. McRae. 2017. Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change. Global Change Biology 23:4508–4520.

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 author:
Carlos Carroll
Klamath Center for Conservation Research

Data type category (as defined in the Introduction to this guidebook): Topoedaphic

Species or ecosystems represented: This dataset does not represent any individual species or ecosystems.

Units of mapped values:

Elevational diversity: meters (m)
HLI diversity: unitless
Land-facet diversity: unitless; represents the Gini‐Simpson diversity index
Ecotypic diversity: unitless; represents the Gini‐Simpson diversity index

Range of mapped values:

Elevational diversity: 0 to 1,286.54
HLI diversity: 0 to 0.07087
Land-facet diversity: 0 to 0.97828
Ecotypic diversity: 0 to 0.99402

Spatial data type: a raster dataset (grid)

Data file format(s): ASCII (.asc)

Spatial resolution: 1 km

Geographic coordinate system: World Geodetic System (WGS) 1984

Projected coordinate system: World Geodetic System (WGS) 1984 Lambert Azimuthal Equal Area

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: Static (relatively unchanging over time)

Methods overview:

Elevation, slope, HLI, and topographic position index (TPI) were obtained from a digital elevation model (DEM) at 100-m resolution. Calculation of HLI is explained in McCune and Keon (2002); calculation of TPI is explained in Jenness (2006). Diversity of HLI and elevation were calculated by comparing all pairs of pixels within a 27-km moving window (spatial neighborhood around the pixel of interest). Landforms were classified based on categories of slope (flat, gentle, and steep) and TPI. Land-facet categories were derived from classifications of elevation (10 bins), HLI (warm, neutral, and cool), landforms, and 38 soil orders. Ecotypes--also known as ecological land units--were obtained from Sayre et al. (2014) and represent landscape categories based on climate, landform, lithology, and land-cover type. Diversity of land-facets and of ecotypes was calculated using the Gini-Simpson diversity index and a 27-km moving window. 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: Terrain or geomorphology models

Major input data sources for this dataset included:

Historical climate observations or models, digital elevation models (DEMs) or topography, soil characteristics, lithology

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

Elevational diversity: higher values indicate a greater diversity of nearby elevations and are generally associated with complex, rugged terrain; lower values are associated with flatter terrain.

HLI diversity: higher values indicate a greater diversity of nearby microclimates (e.g., cooler north-facing slopes, warmer south-facing slopes) and are generally found in rugged terrain; lower values indicate less microclimate diversity and are generally found in flatter terrain.

Land-facet diversity: higher values indicate a greater nearby variety of land facets (combinations of  elevation, HLI, landforms, and soil orders).

Ecotypic diversity: higher values indicate a greater nearby variety of ecotypes (combinations of climate, landform, lithology, and land-cover type).

Representations of key concepts in climate-change ecology:

This dataset relates to species’ adaptive capacities, specifically the ability of species to access newly suitable climates in areas within or nearby their historical ranges. Given comparable levels of climate-change exposure, areas with greater environmental diversity (as represented by the data layers in this dataset) are generally expected to have reduced vulnerability (due to greater adaptive capacity) for two main reasons. First, locations with high geophysical diversity are generally correlated with higher levels of biodiversity. Second, a high diversity of topographic conditions increases the likelihood that a variety of microclimatic conditions are present that could serve as refugia (Beier et al., 2015). In other words, areas with diverse environmental conditions are expected to increase the likelihood that species will be able to find and access newly suitable habitats as climate conditions change (Carroll et al., 2017).

These concepts illustrate the conservation idea known as “conserving the stage” or “conserving nature’s stage” (Beier et al., 2015). This approach to conservation in the face of climate change suggests that conserving a diverse set of physical environmental conditions (the “stage”) can be an effective “coarse-filter” approach to conserving many individual species (the “actors”). This approach is commonly juxtaposed with other “fine-filter” approaches that focus on individual species. The diversity of physical environmental conditions in an area has been quantified in different ways by different research teams, but generally incorporates information on landscape topography (e.g., shaded slopes versus sunny slopes), soil types, and geology.

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

Topographic data layers derived from DEMs (such as slope, TPI, and HLI) are sensitive to the spatial resolution of the input DEM. This study used a 100-m DEM as input, therefore finer-resolution topographic features could not be discerned and small-scale (e.g., 10-m or 30-m) topographic diversity was not accounted for, although it might be important to certain species' responses to climate change. Furthermore, complex patterns of microclimate and diversity of microclimates can arise from factors not included in these datasets. Such considerations include latitude, season, disturbance patterns and succession, formations of fog, dew, and frost, wind patterns and their potential to redistribute precipitation, and biological feedbacks that affect soil characteristics.

Quantification of uncertainty:

Uncertainty was not quantified for the individual data layers in this environmental-diversity dataset. Sources of uncertainty associated with these datasets include mapping accuracy of soils and lithology datasets, the definition and classification of landforms, and whether, in general, the quantified topoedaphic variables appropriately capture features important to biodiversity and climatic resilience. The study that produced these datasets did include a comparison among the different data layers, informed by: correlations among data layers, relationships to elevation, and the spatial conservation priorities that would be derived from each data layer. The study that produced these datasets also examined climate velocity (not covered in this guidebook chapter) and did quantify uncertainty for this velocity data layer regarding variability across climate models and greenhouse-gas scenarios.

Field verification:

Creation of this dataset did not involve any field verification of the mapped values.

Prior to dataset publication, peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).

Beier, P., M. L. Hunter, and M. Anderson. 2015. Special section: conserving nature’s stage. Conservation Biology 29:613–617.

Carroll, C., T. Wang, D. R. Roberts, J. L. Michalak, J. J. Lawler, S. E. Nielsen, D. Stralberg, A. Hamann, and B. H. McRae. 2017. Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change. Global Change Biology 23:4508–4520.

Jenness, J. 2006. Topographic position index (tip_jen.avx) extension for ArcView 3.x.

McCune, B., and D. Keon. 2002. Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13:603–606.

Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, and others. 2014. A new map of global ecological land units — an ecophysiographic stratification approach. Association of American Geographers, Washington, DC.