Incorporating climate-change refugia and corridors in conservation planning

Climate-informed Conservation Priorities for North America

This page provides links to inputs and outputs from conservation prioritization analyses published in Stralberg et al. 2020 (full citation below). Based on recently developed indicators of climatic macrorefugia, environmental diversity, and corridors, we used the Zonation software (Moilanen et al. 2007, Biological Conservation 134:571-579) to conduct a systematic, climate-informed prioritization of conservation values across North America. We explicitly considered complementarity of multiple conservation objectives, capturing key niche-based temperature and moisture thresholds for 324 tree species and 268 songbird species. Results from the full scenario with all objectives and constraints included are shown in Figure 1 (Figure 3 in the paper). Results from eight component scenarios showing the influence of individual objectives are shown in Figure 2. Please see metadata document for additional information.

Conservation rankings were influenced most strongly by climate corridors and species-specific refugia layers. Although areas of high conservation value under climate change were partially aligned with existing protected areas, ~80% of areas within the top quintile of biome-level conservation values lack formal protection. Results from this study and application of our approach elsewhere can help improve the long-term value of conservation investments at multiple spatial scales.

Figure 1

Figure 1. Zonation conservation rankings from 0 (lowest) to 1 (highest) for the full scenario (all factors combined), with current protected areas overlaid (transparent orange). Major lakes and urban areas were excluded from analysis. Biome boundaries are indicated in black. 1 = Mediterranean California; 2 = North American Deserts; 3 = Great Plains; 4 = Northwestern Forested Mountains; 5 = Marine West Coast Forest; 6 = Southern Semiarid Highlands; 7 = Temperate Sierras; 8 = Tundra; 9 = Taiga; 10 = Hudson Plain; 11 = Northern Forests; 12 = Eastern Temperate Forests; 13 = Tropical Wet Forests.

Please cite the data as:

 

Stralberg D., Carroll C., & Nielsen S.E. (2020) Toward a climate-informed North American protected areas network: Incorporating climate-change refugia and corridors in conservation planning. Conservation Letters. https://doi.org/10.1111/conl.12712.

 

 

Data Products

These data have been prepared as part of the AdaptWest project and their development was funded by the Wilburforce Foundation.

1. Zonation inputs and outputs (133 MB)*

2. Zonation outputs only (30 MB)

3. Metadata   

4. Databasin layer of Zonation results (full scenario only, as shown in Fig. 1 above)

5. Databasin layer of Zonation results (all sceanarios, as shown in Fig. 2 below)

6. Databasin layer of Zonation input rasters*

*Note that these input rasters do not exactly match the maps shown in Figure 1 of the paper, which were re-scaled for mapping purposes. Inputs are based on the following previously-published climate exposure metrics, also available at adaptwest.databasin.org: 

Climate-type macrorefugia (based on backward climate velocity) 

Songbird and tree macrorefugia 

Environmental diversity 

Climate corridors

1.   

Figure 2

Figure 2. Zonation conservation rankings from 0 (lowest) to 1 (highest) for the full scenario (a) and scenarios omitting (b) climate-type macrorefugia, (c) songbird macrorefugia, (d) tree macrorefugia, (e) climate corridors, (f) environmental diversity, (g) biome-stratification, and (h) human development index (see Table 1 for more information). Major lakes and urban areas were excluded from analysis. Biome boundaries are indicated in black.

Table 1. Input files for each of the eight scenarios shown in Figure 2 (Figure S1 in the paper) are summarized here:

 

Settings file

Species file

Additional inputs

a. full scenario

 

settings_final_eco_squaredcostadj.dat

birdstreescorridorsvelocitymicro.spp

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

HFPsqr1.tif

mask2.tif

b. full scenario without climate-type macrorefugia

 

settings_final_eco_squaredcostadj.dat

birdstreescorridorsmicro.spp

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

HFPsqr1.tif

mask2.tif

c. full scenario without songbird macrorefugia

 

settings_final_eco_squaredcostadj.dat

treescorridorsvelocitymicro.spp

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

HFPsqr1.tif

mask2.tif

d. full scenario without tree macrorefugia

 

settings_final_eco_squaredcostadj.dat

birdscorridorsvelocitymicro.spp

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

HFPsqr1.tif

mask2.tif

e. full scenario without climate corridors

 

settings_final_eco_squaredcostadj.dat

birdstreesvelocitymicro.spp

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

HFPsqr1.tif

mask2.tif

f. full scenario without environmental diversity

 

settings_final_eco_squaredcostadj.dat

birdstreescorridorsvelocity.spp outputs

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

HFPsqr1.tif

mask2.tif

g. full scenario without biome stratification

 

settings_final_squaredcostadj.dat

birdstreescorridorsvelocitymicro.spp

HFPsqr1.tif

mask2.tif

h. full scenario without human development cost

settings_final_eco.dat

birdstreescorridorsvelocitymicro.spp

admu_descriptions.txt

SpeciesWeightsADMU.txt

ecoregions2.tif

mask2.tif