Ecoregional projections
Climate model projections suggest major North American
biome shifts in response to anthropogenic climate change (Rehfeldt et al. 2012).
Such shifts could have profound influences on native flora and fauna, many of
which would have to move long distances to track their climatic niches. To
evaluate potential ecosystem changes at a somewhat finer scale, the
change in climate space was projected for level III ecoregions (Commission for Environmental
Cooperation 1997) as surrogates for multiple associated species and ecological
communities. First, a random forest model (Breiman 2001) was developed to predict
ecoregion class from bioclimatic variables (see Table 1 in pdf), using 1-km interpolated
climate data for the 1969-1990 normal period (Hamann et al. 2013), available at
http://adaptwest.databasin.org/pages/adaptwest-climatena.
This model was then used to project ecoregions onto
future mid-century (2041-2070) and end-of-century (2071-2100) climate
conditions. Climate projections were based on 1-km downscaled climate anomalies
(Wang et al. 2016) generated by an ensemble of 15 widely-used GCMs from the
Coupled Model Intercomparison Project, Phase 5 (CMIP5, Taylor et al. 2012),
available at
http://adaptwest.databasin.org.
Representative concentration pathway (RCP) 8.5 was used to represent the 21st
century conditions that are to be expected without dramatic reductions in
greenhouse gas emissions or technological fixes (Fuss et al. 2014). RCP 4.5 was also evaluated to represent a future in which significant emissions
reductions are achieved. The change in area was also calculated for each Level
III ecoregion (see Table 2 in pdf).
Results for RCP 4.5 and RCP 8.5 are shown below in Figures 1
and 2, respectively.
Figure 1.
Model-predicted (a) baseline, (b) mid-century, and (c) end-of-century changes in
North American ecoregions for RCP 4.5. Boreal, hemi-boreal, and western forested
regions are shown in green and blue-green shades; arctic ecoregions are in blue
shades; prairie/parkland ecoregions are in brown shades; and temperate forest
ecoregions are in yellow and orange shades (see Table 1 for full list of
ecoregions). Boreal ecoregions are also outlined in black.
Figure 2.
Model-predicted (a) baseline, (b) mid-century, and (c) end-of-century changes in
North American ecoregions for RCP 8.5. Boreal, hemi-boreal, and western forested
regions are shown in green and blue-green shades; arctic ecoregions are in blue
shades; prairie/parkland ecoregions are in brown shades; and temperate forest
ecoregions are in yellow and orange shades (see Table 1 for full list of
ecoregions). Boreal ecoregions are also outlined in black.
Ecoregional refugia
Climate-change refugia, or areas
of species persistence under climate change, may vary in proximity to a species’
current distribution, with major implications for their conservation value.
Thus, the concept of climate velocity—the speed at which an organisms must
migrate to keep pace with climate change—is useful to compare and evaluate
refugia. Using analog climate methods, both forward and backward velocity can be
calculated, providing complementary information about spatio-temporal responses
to climate change (Hamann et al. 2015). In particular, backward velocity
calculations can be used to identify areas of high potential refugium value for
a given time period and species or ecoregion (Stralberg et al. 2018). Refugia
for a given ecoregion represent areas where the climates of that ecoregion may
persist into the future.
Stralberg (2019) used random forest model projections of
future ecoregions described above (Stralberg 2018) to generate an index of climate-change refugia
potential for individual ecoregions, using the methods outlined in Stralberg et
al. (2018). The index ranges from 0 to 1, with values close to 1 indicating
overlap or very close proximity to the current mapped ecoregion, across multiple
climate models. Because the random forest algorithm is a classifier that assigns
an ecoregion class to every future pixel, it does not account for novel climates
that are not currently found in any North American ecoregion. Of course novelty
is relative and can be measured in many different ways. A multivariate environmental similarity surface (MESS) was calculated following Elith et al.
(2010) to generate an index of novelty for each future ecoregion (negative
values indicate dissimilarity). For mapping purposes, novel climates for each ecoregion, RCP, and time period were identified as those with values lower than the 1st percentile of dissimiarity values for the baseline periods.
Figure 3.
Maximum ecoregional refugia values for (a) RCP 4.5, 2050s, (b) RCP 4.5, 2080s,
(c) RCP 8.5, 2050s, and (d) RCP 8.5, 2080s. CEC Level III ecoregion boundaries
are overlaid.
Data can be downloaded or viewed below, and are also found on Zenodo.org as linked below. A document with links to maps in pdf format is also available here.
Cite the ecoregional projection data as: Stralberg, D. 2018. Climate-projected distributional shifts and refugia for North American ecoregions [Data set]. http://doi.org/10.5281/zenodo.1407176. Available at https://adaptwest.databasin.org.
Cite the ecoregional refugia data as: Stralberg, D. 2019. Velocity-based macrorefugia for North American ecoregions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2579337. Available at https://adaptwest.databasin.org.
Also see: Stralberg, D., A. Camfield, M. Carlson, C. Lauzon, N. K. S. Barker, A. Westwood, and F. K. A. Schmiegelow. 2019. Strategies for identifying priority areas for passerine conservation in Canada’s boreal forest. Avian Conservation and Ecology. https://doi.org/10.5751/ACE-01363-140113
Data files
|
Download
|
---|---|
Ecoregional projections, zip format (65 MB)
|
Link |
Ecoregional projections: supplemental information | Link |
Ecoregional refugia, 7-zip format (1.5 GB)
|
Link |
Ecoregional refugia, supplemental information | Link |
Individual rasters as Databasin map layers
|
|
Predicted ecoregions, current period | Map layer |
Predicted ecoregions, RCP 4.5, 2050s | Map layer |
Predicted ecoregions, RCP 4.5, 2080s | Map layer |
Predicted ecoregions, RCP 8.5, 2050s | Map layer |
Predicted ecoregions, RCP 8.5, 2080s | Map layer |
Data layers
-----------------
All data layers
are at 1-km resolution, with an extent of North America.
Ecoregional projections:
Filename format:
_predcurrent.tif
_pred_XXXXX_YYYY.tif
where:
XXXXX = Representative Concentration Pathway (rcp45 or rcp85)
YYYYY = Time period (2050s or 2080s)
See "ecoregion_lookup.csv" for ecoregion definitions and projected change summaries.
Ecoregional refugia:
Filename format:
_Zeco_curr.tif [Current
mapped ecoregion]
_Zpred_curr.tif [Current predicted ecoregion]
ZZrefugia_XXXXX_YYYY.tif [Ecoregional refugia index]
ZZnovel_XXXX_YYYY
[Dissimilarity index]
MaxRefugia_XXXX_YYYY [Maximum pixel-level refugia
value]
where:
Z = Ecoregion ID
XXXXX = Representative Concentration
Pathway (rcp45 or rcp85)
YYYYY = Time period (2050s or 2080s)
Supporting documents
--------------------
ecoregion_lookup.csv [Ecoregion
definitions and projected change summaries]
EcoregionLabels.pdf [Ecoregion
index map]
Projection information
----------------------
"+proj=lcc
+lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m
+no_defs"
----------------------
Projection LAMBERT
Spheroid GRS80
Units METERS
Zunits NO
Xshift 0.0
Yshift 0.0
Parameters
49 0 0.0
/* 1st standard parallel
77 0 0.0 /* 2nd standard parallel
-95 0 0.0 /*
central meridian
0 0 0.0 /* latitude of projection's origin
0.0 /* false
easting (meters)
0.0 /* false northing (meters)
REFERENCES
Breiman, L. 2001. Random Forests. Machine Learning
45:5-32.
Commission for Environmental Cooperation. 1997.
Ecological Regions of North America: Toward a Common Perspective, Montreal,
Canada.
Elith, J., M. Kearney, and S. Phillips. 2010. The art of
modelling range-shifting species. Methods in Ecology and Evolution 1:330-342.
Fuss, S., J. G. Canadell, G. P. Peters, M. Tavoni, R. M.
Andrew, P. Ciais, R. B. Jackson, C. D. Jones, F. Kraxner, N. Nakicenovic, C. Le
Quere, M. R. Raupach, A. Sharifi, P. Smith, and Y. Yamagata. 2014. Betting on
negative emissions. Nature Climate Change 4:850-853.
Hamann, A., T. Wang, D. L. Spittlehouse, and T. Q.
Murdock. 2013. A comprehensive, high-resolution database of historical and
projected climate surfaces for western North America. Bulletin of the American
Meteorological Society 94:1307-1309.
Hamann, A., D. Roberts, Q. Barber, C. Carroll, and S.
Nielsen. 2015. Velocity of climate change algorithms for guiding conservation
and management. Global Change Biology 21:997-1004.
Rehfeldt, G. E., N. L. Crookston, C. S enz-Romero, and E.
M. Campbell. 2012. North American vegetation model for land-use planning in a
changing climate: a solution to large classification problems. Ecological
Applications 22:119-141.
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.
https://doi.org/10.1111/geb.12731
Taylor, K. E., R. J. Stouffer, and G. A. Meehl. 2012. An
Overview of CMIP5 and the Experiment Design. Bulletin of the American
Meteorological Society 93:485-498.
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.