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Record W3083534214 · doi:10.1139/facets-2020-0018

Spatial distribution and conservation hotspots of mammals in Canada

2020· article· en· W3083534214 on OpenAlexaffvenueabout
Victor Cameron, Anna L. Hargreaves

Bibliographic record

VenueFACETS · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsMammalRange (aeronautics)Species richnessBiodiversityGeographyHabitatEcologyGrid cellDistribution (mathematics)BiologyGrid

Abstract

fetched live from OpenAlex

High-latitude countries often contain the polar range edge of species that are common farther south, potentially focusing national conservation efforts toward range-edge populations. The global conservation value of edge populations is controversial, but if they occur where biodiversity is high, there need not be trade-offs in protecting them. Using 152 of 158 terrestrial mammal species in Canada, we tested how species’ distributions relate to their national conservation status and total mammal richness. We found that half of “Canadian” mammals had <20% of their global range in Canada. National threat status was strongly associated with range area; mammals considered “at risk” had 42% smaller Canadian ranges than mammals considered secure. However, after accounting for range area, taxa with smaller proportions of their global range in Canada were not more likely to be considered at risk, suggesting edge populations are not inherently more vulnerable. When we calculated mammal diversity across Canada (100 × 100 km grid cells), we found that hotspots of at-risk or range-edge mammals were twice as species rich as nonhotspot cells, containing up to 44% of Canadian mammal diversity per grid cell. Our results suggest that protecting areas with the most at-risk or range-edge mammals would simultaneously protect habitat for many species currently deemed secure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.021
GPT teacher head0.207
Teacher spread0.186 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes3
Has abstractyes

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