Chapter 18: Tree & Songbird Macrorefugia
This dataset maps spatial patterns of macrorefugia for trees and songbirds in North America (figure 18.1; see chapter glossary for definitions of terms). The dataset was created using species-distribution models for 324 trees and 268 songbirds, and results can be accessed for individual species or by species group. The study that produced this dataset also identified the climate and topographic characteristics that were associated with macrorefugia (Stralberg et al., 2018).
Potential conservation applications of this dataset could include the following:
- This dataset could be used to identify areas that may serve as macrorefugia for multiple bird and/or tree species in the future as climate conditions change. Given the spatial resolution of the dataset (10 km) and its focus on multiple species, it is probably most suitable for large-scale (i.e., regional) conservation planning, such as prioritizing land areas for future acquisition and protection. In some cases, the dataset may provide regional-scale information relevant to potential efforts to translocate species (assist their migration) in response to climate change.
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 and Canada.
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:
- In the context of identifying and prioritizing areas for future land acquisition and protection, this dataset does not consider current or future land-use patterns, economic feasibility of protecting new land areas, or the role of conservation priorities other than songbird and tree habitat (e.g., aquatic and wetland habitats, fish, amphibian, reptile, mammal, and invertebrate species). Importantly, this dataset does not represent locations of microrefugia (e.g., based on local variations in topography or soil) that may influence species responses to climate change. Also, because of the dataset spatial resolution and focus on multiple species, it may not be suitable for local conservation planning or application to a particular species.
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.
Stralberg, D., C. Carroll, J. H. Pedlar, C. B. Wilsey, D. W. McKenney, and S. E. Nielsen. 2018. Macrorefugia for North American trees and songbirds: climatic limiting factors and multi-scale topographic influences. Global Ecology and Biogeography 27:690–703.
Dataset documentation link:
https://doi.org/10.1111/geb.12731 (subscription or fee required)
The dataset can be downloaded from: https://adaptwest.databasin.org/pages/climatic-macrorefugia-for-trees-and-songbirds
The dataset can be viewed online at: https://adaptwest.databasin.org/maps/new#datasets=af2b472e8c6a4dcd8f9ff064d6ab0bb7
Formal metadata is available from: https://adaptwest.databasin.org/datasets/af2b472e8c6a4dcd8f9ff064d6ab0bb7/layers/a6139408117e445897a1152b1825c1d6/metadata/fgdc
Dataset corresponding author:
Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
Data type category (as defined in the Introduction to this guidebook): Animal habitat, climate, vegetation
Species or ecosystems represented: The macrorefugia indices presented in this dataset were created using species-distribution models for 324 individual tree and 268 songbird species and are thus intended to represent refugia potential for multiple species. Combined indices are available for four specific habitat-based groups of songbirds: forest, open woodland, grassland, and shrub, as well as for individual species.
Units of mapped values: unitless
Range of mapped values:
0 to 1.13 (songbird refugia index, mid-century)
0 to 0.98 (songbird refugia index, end-of-century)
0 to 1.24 (tree refugia index, mid-century)
0 to 1.22 (tree refugia index, end-of-century)
Spatial data type: a raster dataset (grid)
Data file format(s): GeoTiff (.tif)
Spatial resolution: 10 km
Geographic coordinate system: World Geodetic System (WGS) 1984
Projected coordinate system: World Geodetic System (WGS) 1984 Lambert Azimuthal Equal Area
Spatial extent: National (USA and most of Canada)
Dataset truncation: The dataset is truncated along the border between the United States and Mexico, and at the boundary between southern and high arctic ecoregions, as mapped by the Commission for Environmental Cooperation (1997).
Time period represented: Future
Future time period(s) represented: Mid-century (2041-2070), end-of-century (2071-2100)
Baseline time period (against which future conditions were compared): 1971–2000.
Species-distribution models for 324 trees and 268 songbirds were used to project locations of future presence for each species. Backward velocity for each species was calculated using these presence projections, as the distance in kilometers (km) from each future distribution pixel to the nearest current distribution pixel. This was done for each combination of species and global climate model (GCM) for two representative concentration pathways (RCPs). An equation was used to translate backward velocity into a refugia index based on hypothetical long-distance dispersal distributions (see equation 1 in the dataset citation). For each RCP, refugia index values from four GCMs were averaged to produce an average refugia index for each species. Then, in averaging across species to produce an overall (multi-species) refugia index, species were weighted based on the projected proportional change in the total area of their distributions. 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
This dataset employed the following specific models:
Species-distribution models for songbirds were from Distler et al. (2015) and were developed using boosted regression trees. Species-distribution models for trees were from McKenney et al. (2011) and were developed using Maxent (Phillips et al., 2006).
Major input data sources for this dataset included:
Species point locations (presence/absence for birds based on Breeding Bird survey; presence only for trees based on various sources), historical climate observations or models, future climate projections
This dataset used the following general circulation models (GCMs):
CanESM2, CESM1-CAM5, HadGEM2-ES, MIROC-ESM
Separate files are provided for each GCM and an ensemble across GCMs is also provided. 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 4.5, 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 climate model downscaling methods:
Downscaling of climate models was not conducted as part of the study that produced this dataset, however, downscaled climate projections were used as inputs. Information on downscaling methods that produced these inputs is available from McKenney et al. (2011).
The mapped values of the dataset may be interpreted as follows:
Higher values indicate greater potential for a location to serve as a future macrorefugium for multiple species (i.e., multiple species of songbirds or of trees). For an individual species, an index value of 1 indicates high-probability in situ refugia (i.e., all GCMs in agreement). Lower values indicate that ex situ refugia are farther away and thus more difficult for species to access in response to climate change. Multispecies index values are weighted by projected changes in climatic suitability for each species; i.e., climate-increasing species are down-weighted with respect to climate-decreasing species.
Representations of key concepts in climate-change ecology:
This dataset contributes to the growing body of work focused on identifying and managing climate-change refugia as one component of climate adaptation to conserve biodiversity.
The dataset is influenced by climate-change exposure because it is driven by projections of future climate change. The dataset is relevant to climate-change resistance and, inversely, climate-change vulnerability because areas with higher in situ or ex situ refugia potential for a species may promote that species’ ability to cope with or respond to climate change. The dataset is also relevant to climate-change adaptive capacity in that it represents the availability of ex situ refugia to which species might migrate in response to climate change.
While availability of both in situ or ex situ refugia have the potential to mitigate species’ vulnerability to climate change, these refugia types represent different potential options for species and may require different management considerations. Availability of in situ refugia, identified as areas of overlap between current and future suitable habitats, may enable species to persist within parts of their current ranges. These areas may be important for conservation and management, for example to mitigate potentially negative effects from invasive species or habitat degradation. Availability of ex situ refugia may need to be considered along with the distances to those refugia from the current species range as well as species dispersal abilities. In addition, areas that are identified as potential refugia for many species may be of heightened ecological and conservation importance.
This dataset involves the following assumptions, simplifications, and caveats:
The dataset resolution (10 km) was limited by the resolution of the input climate model projections; therefore finer-scale microrefugia cannot be identified with this dataset.
The study that produced the dataset indicated that the results (i.e., the maps of multi-species refugia index) were highly sensitive to the species information used as inputs, including the number and types of species represented, and the data quality (accuracy or reliability) of the species location data. In other words, if different groups of species had been used then the resulting maps of refugia index would likely be different. Imperfect knowledge of current species locations also affects the results. The dataset authors indicated lower confidence in the results for the northernmost portion of the study area (arctic and boreal regions of Alaska and Canada) due to sparse location information for species.
Because the dataset was produced using species-distribution models (SDMs), assumptions made by these models may have affected the resulting dataset. For example, the SDMs assumed that species are currently in equilibrium with climate, that correlations between species locations and climate are meaningful, and that interactions between species are captured by climate. In cases where an SDM more closely represents a species’ fundamental niche (the climate conditions for which it is suited) than its actual distribution, the dataset may overestimate refugia potential. In addition, human obstacles to species movements, differing migration rates among species, and differences among species in their ability to move through unsuitable climates in pursuit of more suitable climates were not incorporated into the production of the dataset.
Quantification of uncertainty:
The multispecies refugia index values were compared across GCMs and RCPs. In general, refugia index values were similar across the four GCMs used as indicated by pairwise correlations and the standard deviation of values across the four GCMs. Refugia index values were higher under RCP 4.5 compared to RCP 8.5 but showed similar geographic patterns.
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 calculate the refugia indices were validated by Distler et al. (2015) using historical species observations from 1980 to 1999 (see table 1 and appendix S2 in Distler et al., 2015). Input tree species-distribution models are described in McKenney et al. (2007 and 2011). Climate variables used as inputs to these models were validated using withheld datasets (McKenney et al., 2007). Tree-species occurrence datasets used as inputs to these models were screened by comparison to range maps from Little (1971 and 1977).
Prior to dataset publication, peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).
Commission for Environmental Cooperation. 1997. Ecological Regions of North America: Toward a Common Perspective. Commission for Environmental Cooperation, Montreal, Canada.
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.
Little, E. 1971. Atlas of United States Trees, vol. 1: Conifers and important hardwoods. Washington (DC): U.S. Department of Agriculture. Miscellaneous publication no. 1146.
Little, E. 1977. Atlas of United States trees, vol. 4: Minor eastern hardwoods. Washington (DC): U.S. Department of Agriculture. Miscellaneous publication no. 1342.
McKenney, D., J. Pedlar, R. Rood, and D. Price. 2011. Revisiting projected shifts in the climate envelopes of North American trees using updated general circulation models. Global Change Biology 17:2720–2730.
McKenney, D., J. Pedlar, K. Lawrence, K. Campbell, and M. Hutchinson. 2007. Potential impacts of climate change on the distribution of North American trees. BioScience 57: 939–948.
Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231–259.
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.
Stralberg, D., C. Carroll, J. H. Pedlar, C. B. Wilsey, D. W. Mckenney, and S. E. Nielsen. 2018. Macrorefugia for North American trees and songbirds: climatic limiting factors and multi-scale topographic influences. Global Ecology and Biogeography 27:690–703.