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Disturbance and habitat use: is edge more important than area?

2006· article· en· W2165558056 on OpenAlexaffabout
Alissa E. Moenting, Douglas W. Morris

Bibliographic record

VenueOikos · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsLakehead University
Fundersnot available
KeywordsForagingMicrotusPredationHabitatEcologyVoleForageDisturbance (geology)BiologyVegetation (pathology)Population

Abstract

fetched live from OpenAlex

In their efforts to maximize fitness while reducing the probability of dying, animals must decide which patches to forage in, when to forage, and how long to forage in each patch. Each decision will be modified by habitat and habitat disturbance. We evaluate the effects of habitat disturbance on foraging behaviour by imagining an initially homogeneous environment that is altered to create patches of different sizes. Disturbance increases predation risk, or otherwise alters patch profitability. Foragers can respond by changing their pattern of foraging, or by reducing their activity. We develop predictions for each scenario. We then test the predictions with data on the abundance and foraging activity of meadow voles ( Microtus pennsylvanicus ) in and around four sizes of circular disturbed patches. We created the patches by mowing vegetation in an abandoned hay field in northern Ontario, Canada. The treatments had no effect on vole density, and there was no consistent relationship between vole activity and distance from the edge of disturbed patches. Incidental predation of sunflower seeds, our measure of vole foraging behaviour, declined linearly with increasing patch circumference (edge). Seed consumption by meadow voles, and predation by voles on lower food levels, correlates with the length of edge habitat rather than with the area disturbed. Adaptive behaviour can thereby explain edge effects that, under current priorities emphasizing area, would appear at odds with conservation ecology.

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 score0.012
Threshold uncertainty score0.615

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)0.0010.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations23
Published2006
Admission routes2
Has abstractyes

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