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Record W4401518332 · doi:10.1111/ele.14491

The biogeochemical boomerang: Site fidelity creates nutritional hotspots that may promote recurrent calving site reuse

2024· article· en· W4401518332 on OpenAlexaff
Kristy M. Ferraro, Dara Albrecht, Jack G. Hendrix, Eric Vander Wal, Oswald J. Schmitz, Quinn M. R. Webber, Mark A. Bradford

Bibliographic record

VenueEcology Letters · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
FundersNational Science Foundation Graduate Research Fellowship ProgramAnimal Welfare Institute
KeywordsBiogeochemical cycleEcologyReuseEnvironmental scienceFidelityEnvironmental resource managementGeographyBiologyComputer science

Abstract

fetched live from OpenAlex

Animals interact with nutrient cycles by consuming and depositing nutrients, interactions studied separately in nutritional ecology and zoogeochemistry. Recent theoretical work bridges these disciplines, highlighting that animal-driven nutrient recycling could be crucial in helping animals meet their nutritional needs. When animals exhibit site fidelity, they consistently deposit nutrients, potentially improving vegetation quality. We investigated this potential feedback by analysing changes in forage nitrogen stocks following simulated caribou calving. We found that forage nitrogen stocks increased after 2 weeks and remained elevated after 1 year, a change due to increased forage quality, not quantity. We also developed a nutrient budget within calving grounds, demonstrating that natal fluid and calf carcasses contribute substantial nitrogen subsidies. We, thus, highlight a positive zoogeochemical feedback whereby nutrients deposited during calving become bioavailable during lactation and provide evidence that site fidelity creates a biogeochemical boomerang in which animals deposit nutrients that can be reused later.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.218
Teacher spread0.199 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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