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Record W4410831308 · doi:10.1002/ecog.08006

Of all shapes and sizes: a theoretical framework for animal‐mediated terrestrial heterogeneity across scales

2025· article· en· W4410831308 on OpenAlexafffund
Kristy M. Ferraro, Janey R. Lienau

Bibliographic record

VenueEcography · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEcologyBiologyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

Animals redistribute elements throughout their lives by depositing wastes and carcasses. Growing evidence shows that these zoogeochemical processes enhance landscape diversity and heterogeneity worldwide. We provide a descriptive framework for understanding how direct animal depositions (i.e. fecal matter, urine, carcasses, and other body materials) contribute to element heterogeneity across scales, with particular focus on how a species' contributions differ relative to one another. In this framework, we identify mean body mass and population density as the main predictors of element heterogeneity. Secondary predictors, including population strategy, overabundance, habitat preference, elemental composition and predation, are nested within and influenced by body size and density. We then demonstrate how animals can play unique roles within communities, leading to multiscale patterns of elemental heterogeneity within an ecosystem. In doing so, we highlight the importance of studying zoogeochemistry through both an ecosystem ecology and community ecology lens. We illustrate our framework using three spatial scales of animal communities (100 cm 2 , 100 m 2 , 100 km 2 ) within an eastern temperate forest, considering both individual species traits and their community interactions at each scale. The community heterogeneity framework provides a theoretical understanding of how individual species and animal communities collectively drive element heterogeneity, allowing a predictive mechanism for the ecosystem contributions of animals across systems and scales.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.292

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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.

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

Citations5
Published2025
Admission routes2
Has abstractyes

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