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Record W4404160203 · doi:10.1111/1365-2656.14194

Space‐for‐time substitutions exaggerate urban bird–habitat ecological relationships

2024· article· en· W4404160203 on OpenAlexafffundabout
Harold N. Eyster, Kai M. A. Chan, M. Fletcher, Brian Beckage

Bibliographic record

VenueJournal of Animal Ecology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of British Columbia
FundersGund Institute for EnvironmentEnvironment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsEcologyHabitatGeographySpace (punctuation)BiologyComputer science

Abstract

fetched live from OpenAlex

North American bird abundance has declined by 29% over the last 50 years. These continental population dynamics interact with local landscape changes to affect local bird diversity. Mitigating local declines in cities is particularly significant because (a) such declines greatly impact human-bird relationships since most people live in cities and (b) cities provide levers to create bird-friendly habitat, such as managing yards and gardens, street trees, and urban parks. Yet, the potential for cities to modify habitats to mitigate broader bird declines remains unclear. Studies have been stymied by the difficulty of assembling mutidecadal habitat-bird population datasets. Instead, studies have substituted space for time (e.g. used habitat associations across space at one time point to project future species abundance due to changing land use), but this method may fail amidst nonstationary environments of the Anthropocene. Here, we test the validity of space-for-time substitutions for explaining changes in bird abundance in a North American city over the past two decades by examining the degree to which these changes are explainable by changes in local landcover at multiple spatial scales. Specifically, we use longitudinal urban bird surveys of Metro Vancouver, BC, Canada from 1997 and 2020; deep learning models of remote sensing data to classify contemporaneous landcover; out-of-sample prediction and boosted regression trees to identify multiple spatial scales of landcover that best explained bird abundance (i.e. optimal scale of effect for each species by each habitat); and Bayesian multispecies abundance models in Stan to determine relationships between changes in landcover and bird abundance. We found that total bird abundance declined by 26% over the last two decades. Landcover measured at both 50 m and optimal scales explained spatial variation in bird abundance, but only landcover at the optimal scale explained temporal changes, and only partially. These results suggest that space-for-time substitutions overemphasize habitat-bird ecological relationships, urban habitats only partially determine bird abundance, and measuring habitat at the appropriate scale is important for capturing the most relevant changes in landscapes.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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 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

Citations8
Published2024
Admission routes3
Has abstractyes

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