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Record W6911967292 · doi:10.5281/zenodo.14681689

Velocity-based macrorefugia indices for Canadian tree species

2025· dataset· en· W6911967292 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of AlbertaNatural Resources CanadaUniversité LavalUniversity of British ColumbiaCanadian Forest Service
Fundersnot available
KeywordsTree (set theory)Index (typography)Climate changeFunction (biology)Distribution (mathematics)Projection (relational algebra)Extreme value theorySpecies distribution

Abstract

fetched live from OpenAlex

Velocity-based macrorefugia indices for Canadian tree species Data description Velocity-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.14662106 Macrorefugia 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_Z where: Spp = tree species seven-letter code (eastern)* or common name (western)** Type = backward or forward refugia X = baseline Y= 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 names Douglas-fir = Pseudotsuga menziesii Engelmann spruce = Picea engelmannii grand fir = Abies grandis lodgepole pine = Pinus contorta Pacific silver fir = Abies amabilis red alder = Alnus rubra Sitka spruce = Picea sitchensis subalpine fir = Abies lasiocarpa western hemlock = Tsuga heterophylla western redcedar = Thuja plicata References Carroll, 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.0140486 Hamann, 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.12736 Stralberg, 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.12731 Wang, T., & Campbell, E. (2024). Climate niche model projections for Canadian Tree species. https://climatena.ca/mapVersion Zhao, 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.010
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.257
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes2
Has abstractyes

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