Chapter 17: Spatial Priorities for Conserving Birds of the PNW

Dataset Overview

This dataset represents an integrated assessment of spatial priorities for conserving 27 bird species identified as species of greatest conservation need in Idaho, Oregon, and/or Washington (figure 17.1). Priority areas for conservation were identified using species-distribution models for several different climate scenarios and future time periods. This produced multiple data layers that were synthesized to produce an overall spatial prioritization layer to support bet-hedging in the context of uncertainties concerning how species will respond to climate change (see chapter glossary for definitions of terms).

Data Access: 

Figure 17.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • This dataset directly supports conservation of bird species of greatest conservation need in Washington, Oregon, and Idaho and can be used to compare areas on the landscape in terms of their conservation value for these 27 species. In addition, further value could be derived from the study that produced this dataset (Schuetz et al., 2015) by applying a similar approach to other conservation problems (e.g., other types of species). This study provides a generalized framework for dealing with uncertainty around possible species' responses to climate change that could be applied to create spatial prioritizations in a variety of other contexts.

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 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, a level-3 ecoregion (e.g., the North Cascades).

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

  • This dataset represents conservation priorities only for the 27 bird species considered. Therefore, it is likely that other (possibly competing) conservation priorities will also need to be considered, including conservation priorities for other bird species, for non-bird species, and conservation priorities based on "coarse-filter" approaches such as habitat connectivity and landscape diversity. In addition, the priorities in this dataset would need to be weighed against the costs of conservation. Because of the spatial resolution of the dataset (10 km), it is best used for discerning general regional patterns and cannot necessarily be used to guide small-scale conservation investments, which would require more high-resolution data.

Past or current conservation applications:

  • This dataset has not yet been used in any on-the-ground conservation applications to the knowledge of the authors of this chapter. However, Audubon North Carolina is using a similar analysis to prioritize conservation action and advocacy across their State (

Dataset citation:

Schuetz, J., G. Langham, C. Soykan, C. Wilsey, T. Auer, and C. Sanchez. 2015. Making spatial prioritizations robust to climate change uncertainties: a case study with North American birds. Ecological Applications 25:1819–1831.

National Audubon Society. 2015. Audubon’s birds and climate change report: a primer for practitioners. National Audubon Society, New York, NY. Contributors: Gary Langham, Justin Schuetz, Candan Soykan, Chad Wilsey, Tom Auer, Geoff LeBaron, Connie Sanchez, Trish Distler. Version 1.3.

Dataset documentation links: (open access) (open access)

Data access:

The dataset can be downloaded from:

The dataset is not available for interactive online map viewing.

Metadata access:

Formal metadata can be download from:

Dataset corresponding author:
Chad Wilsey
National Audubon Society

Data type category (as defined in the Introduction to this guidebook): Animal habitat, climate

Species or ecosystems represented:

This dataset focuses on 27 bird species: Bobolink; Brown Pelican; Burrowing Owl; Common Loon; Common Nighthawk; Ferruginous Hawk; Franklin's Gull; Grasshopper Sparrow; Horned Lark; Loggerhead Shrike; Long-billed Curlew; Marbled Murrelet; Mountain Quail; Olive-sided Flycatcher; Purple Martin; Red-necked Grebe; Sage Sparrow; Sage Thrasher; Sharp-tailed Grouse; Upland Sandpiper; Vesper Sparrow; Western Bluebird; White-breasted Nuthatch; White-headed Woodpecker; Yellow-billed Cuckoo; Great Gray Owl; Greater Sage-Grouse

Units of mapped values: unitless

Range of mapped values: 0 to 1

Spatial data type: a raster dataset (grid)

Data file format(s): Imagine image (.img)

Spatial resolution: 10 km

Geographic coordinate system: North American Datum of 1983

Projected coordinate system: North American Datum of 1983 Albers

Spatial extent: Continental (United States and Canada)

Dataset truncation: The dataset is truncated along the border between the United States and Mexico.

Time period represented: Future (later than 2020)

Future time period(s) represented: This dataset does not represent any single future time period. Instead, it integrates the following future time periods: 2020s (2010-2039), 2050s (2040-2069), and 2080s (2070-2099).

Baseline time period (against which future conditions were compared): 2000.

Methods overview:

Species-distribution models were constructed for each of the 27 bird species in this study (see section 3; (Distler et al., 2015; Langham et al., 2015; Schuetz et al., 2015). Species distributions were projected into the future for three time periods (2020s, 2050s, and 2080s) using three greenhouse-gas scenarios (B2, A1B, and A2), two seasons (summer and winter), and three different sets of assumptions (scenarios) about how species might respond to climate change (National Audubon Society, 2015). In the "suffer in place" scenario, species were sensitive to climate change but unable to effectively track changing climate by shifting their distributions. In the "track and move" scenario, species distributions tracked their preferred climates across the landscape. In the "adapt in place" scenario, species remained in place geographically and adapted to the changing climate conditions. For each combination of future time period, season, greenhouse-gas scenario, and species-response scenario, species richness (the total number of species predicted for each pixel) was calculated. A spatial prioritization was built for each of these scenarios using the Zonation software (Moilanen et al., 2009). Finally, the three prioritization grids for the three approaches were integrated to produce a "bet hedging" prioritization grid. 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: Species-distribution models; bioclimatic niche models

This dataset employed the following specific models: ZONATION software (Moilanen et al., 2009)

Major input data sources for this dataset included: Species ranges or point locations, historical climate observations or models, future climate projections

This dataset used the following general circulation models (GCMs): CCCMA-CGCM3.1T47, CSIRO-Mk3.0, IPSL-CM4, MPI-ECHAM5, NCAR-CCSM3.0, HadCM3, HadGEM1, NIES

An ensemble across GCMs is provided; 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: SRES A1B, SRES A2, SRES B2

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 did not involve downscaling of climate data, however, the study used downscaled climate data that were downscaled using the delta method as described by Ramirez‐Villegas and Jarvis (2010).

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

High values represent areas that are the greatest priorities for conservation based on an integration across multiple future time periods, seasons, climate scenarios, and scenarios of species' responses to climate change.

Representations of key concepts in climate-change ecology:

Climate-change exposure is represented indirectly in this dataset through the downscaled climate models used in the species-distribution modeling process. Climate sensitivity and adaptive capacity of bird species is more difficult to assess, for example because (a) species may vary widely in their tolerance of changing climate and their abilities to cope with or adapt to environmental change; (b) projected climate changes may be outside the historical range of variation such that adequate data do not exist to assess species’ responses to future climate changes; and (c) adaptive capacity may be constrained by a range of factors, such as habitat degradation, invasive species, and changing disturbance regimes. The study that produced this dataset attempted to account for these sources of uncertainty (and others) using a scenario approach representing multiple possibilities for how species might respond to climate change (i.e., "suffer in place", "track and move", and "adapt in place"). Scenario-based approaches can be particularly useful in the context of multiple forms of uncertainty and can allow scientists and managers to evaluate a range of possible future outcomes and related management options.

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

The spatial prioritization depicted in this dataset was produced using species-distribution models that were informed by bioclimatic variables that primarily represented temperature and precipitation. These species-distribution models did not incorporate current or future land use or land cover into projections of species distributions. Nor did they model inter-species interactions such as competition between species, avoidance of predators, or the biological effects of pathogens or parasites. The relatively coarse spatial resolution (10 km) also means that microclimatic effects on species distributions were not accounted for.

In addition, the study that produced this dataset considered three possible species responses to climate change ("suffer in place", "track and move", and "adapt in place"). In reality, other species responses may be possible, including intermediary responses that incorporate some components of these three named responses. It is also possible that responses will vary based on geography, habitat, time, or other factors. For example, "adapt in place" might be a plausible response to moderate climate change but become nonviable once a certain threshold of climate change has been surpassed, in which case "suffer in place" would become the new response.

Quantification of uncertainty:

Uncertainty regarding the intermediate variables in the analysis (i.e., species distributions, species richness projections) were not directly quantified. However, Schuetz et al. (2015) demonstrated that biological uncertainty (such as the alternative sets of assumptions about species’ responses to climate change included in this prioritization) was considerably greater than the uncertainty represented by greenhouse-gas scenarios. Also, the study that produced this dataset examined a number of factors that could contribute to uncertainty in conservation decision-making. For example, a metric of conservation efficiency was developed that represents how well a spatial prioritization for an individual species is represented by a prioritization for many species. In addition, prioritizations were compared across the three approaches to highlight areas where the three approaches most closely agreed and areas where they disagreed.

Field verification:

Field verification of the mapped values in this dataset was not possible because the dataset represents a future condition. Bird species-distribution models used as inputs to generate spatial priorities were evaluated by Distler et al. (2015) and Langham et al. (2015) using historical species observations from 1980 to 1999; see table 1 and appendix S2 in Distler et al. (2015) and appendices S8 and S9 in Langham et al (2015).

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

Distler, T., J. G. Schuetz, and J. Vel. 2015. Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. Journal of Biogeography 42:976–988.

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

Langham, G. M., J. G. Schuetz, T. Distler, C. U. Soykan, and C. Wilsey. 2015. Conservation status of North American birds in the face of future climate change. PLoS ONE 10:e0135350.

Moilanen, A., H. Kujala, and J. Leathwick. 2009. The zonation framework and software for conservation prioritization. Pages 196–210 in A. Moilanen, K. Wilson, and H. Possingham, editors. Spatial conservation prioritization: quantitative methods and computational tools. Oxford University Press, Oxford, UK.

National Audubon Society. 2015. Audubon’s birds and climate change report: a primer for practitioners. National Audubon Society, New York, NY. Contributors: Gary Langham, Justin Schuetz, Candan Soykan, Chad Wilsey, Tom Auer, Geoff LeBaron, Connie Sanchez, Trish Distler. Version 1.3.

Ramirez‐Villegas, J., and A. Jarvis. 2010. Downscaling global circulation model outputs: the delta method decision and policy analysis working paper No. 1. International Center for Tropical Agriculture, Cali, Colombia.

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.

Schuetz, J., G. Langham, C. Soykan, C. Wilsey, T. Auer, and C. Sanchez. 2015. Making spatial prioritizations robust to climate change uncertainties: a case study with North American birds. Ecological Applications 25:1819–1831.