Velocity-based macrorefugia indices for Canadian tree species

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Campbell, Elizabeth;Wang, Tongli;Raymundo Sanchez, Angeles Ana Paula;Stralberg, Diana

Description

Velocity-based macrorefugia indices for Canadian tree speciesData descriptionVelocity-based microrefugia metrics for 25 North American tree species (15 eastern, 10 western) were developed for three future time periods (2011-2040, 2041-2070, 2071-2100), four greenhouse gas emission scenarios (SSP1.26, SSP2.45, SSP3.70, and SSP5.85), and 13 global climate models (ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) based on species distribution model projections by Campell and Wang (2024) and using the approach described in Stralberg et al. (2018).Each species distribution projection was reclassified into presence/absence using the threshold suggested by Zhao et al. (2023).Backward and forward biotic velocity (Carroll et al., 2015) for each species was calculated using the nearest-analog velocity algorithm defined by Hamann et al. 2015 and applied to binary presence/absence rasters representing current and projected future distributions. Presence thresholds were based on mean probability of occurrence in the baseline period. To convert biotic velocity into an index of microrefugia ranging from 0 to 1, a distance-decay function was applied to the distance value at each pixel, i.e., the shortest distance from a projected future location to the current distribution. The distance-decay function was based on a fat-tailed distribution (c= 0.5, and alpha = 8333.33) parameterized to result in a mean migration rate of 500 m/year or 50 km/century (details in Stralberg et al. 2018). Refugia index values were calculated separately for each GCM and then averaged to produce an overall index.Code (updated from Stralberg et al. 2018) is available at: https://doi.org/10.5281/zenodo.14662106Macrorefugia indices are provided as GeoTIFFs with a 1-km resolution (East or West folders), and map images are provided in PNG format (refugiaTreeMaps folder). All data layers are in the Albers Conic Equal Area projection (EPSG: 102008).Files are named as follows:Spp_TypeRefugia_X_Y_Zwhere:Spp = tree species seven-letter code (eastern)* or common name (western)**Type = backward or forward refugiaX = baselineY= shared socioeconomic pathway (SSP1.26, SSP2.45, SSP3.70, and SSP5.85)Z = year (2040, 2070, 2100) *Eastern tree species codes:ABIEBAL = Abies balsamea (balsam fir)ACERRUB = Acer rubrum (red maple)ACERSAC = Acer saccharum (sugar maple)BETUALL = Betula alleghaniensis (yellow birch)FAGUGRA = Fagus grandifolia (American beech)LARILAR = Larix laricina (tamarack)PICEENE = Picea engelmannii (Engelmann spruce)PICEGLA = Picea glauca (white spruce)PICEMAR = Picea mariana (black spruce)PICERUB = Picea rubens (red spruce)PINUBAN = Pinus banksiana (jack pine)PINURES = Pinus resinosa (red pine)PINUSTR = Pinus strobus (eastern white pine)POPUTRE = Populus tremuloides (trembling aspen)THUJOCC = Thuja occidentalis (eastern white-cedar) **Western tree species comon namesDouglas-fir = Pseudotsuga menziesiiEngelmann spruce = Picea engelmanniigrand fir = Abies grandislodgepole pine = Pinus contortaPacific silver fir = Abies amabilisred alder = Alnus rubraSitka spruce = Picea sitchensissubalpine fir = Abies lasiocarpawestern hemlock = Tsuga heterophyllawestern redcedar = Thuja plicata  ReferencesCarroll, C., Lawler, J. J., Roberts, D. R., & Hamann, A. (2015). Biotic and Climatic Velocity Identify Contrasting Areas of Vulnerability to Climate Change. PLOS ONE, 10(10), e0140486. https://doi.org/10.1371/journal.pone.0140486Hamann, A., Roberts, D. R., Barber, Q. E., Carroll, C., & Nielsen, S. E. (2015). Velocity of climate change algorithms for guiding conservation and management. Global Change Biology, 21(2), 997–1004. https://doi.org/10.1111/gcb.12736Stralberg, D., Carroll, C., Pedlar, J. H., Wilsey, C. B., McKenney, D. W., & Nielsen, S. E. (2018). Macrorefugia for North American trees and songbirds: Climatic limiting factors and multi-scale topographic influences. Global Ecology and Biogeography, 27(6), 690–703. https://doi.org/10.1111/geb.12731Wang, T., & Campbell, E. (2024). Climate niche model projections for Canadian Tree species. https://climatena.ca/mapVersionZhao, Y., O’Neill, G.A., and Wang, T. (2023). Predicting fundamental climate niches of forest trees based on species occurrence data. Ecological indicators, 148. https://doi.org/10.1016/j.ecolind.2023.110072

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Mentions (0)

Metrics

Dataset Index

0.4

FAIR Score

73%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Public Health, Environmental and Occupational Health

Field

Medicine

Domain

Health Sciences

Confidence Score

36%

Source

Scholar Data Model

Normalization Factors

FT

63.46

CTw

1.00

MTw

1.00