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EFFECT OF HABITAT FRAGMENTATION ON THE EXTINCTION THRESHOLD: A SYNTHESIS<sup>*</sup>

2002· article· en· W1968121043 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEcological Applications · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHabitat fragmentationExtinction (optical mineralogy)Fragmentation (computing)Habitat destructionExtinction debtHabitatEcologyPopulationLocal extinctionPopulation sizeEnvironmental scienceBiological dispersalBiologyDemography

Abstract

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I reviewed and reconciled predictions of four models on the effect of habitat fragmentation on the population extinction threshold, and I compared these predictions to results from empirical studies. All four models predict that habitat fragmentation can, under some conditions, increase the extinction threshold such that, in more fragmented landscapes, more habitat is required for population persistence. However, empirical studies have shown both positive and negative effects of habitat fragmentation on population abundance and distribution with about equal frequency, suggesting that the models lack some important process(es). The two colonization–extinction (CE) models predict that fragmentation can increase the extinction threshold by up to 60–80%; i.e., the amount of habitat required for persistence can shift from <5% of the landscape to >80% of the landscape, with a shift from completely clumped to completely fragmented habitat. The other two models (birth–immigration–death–emigration, or BIDE models) predict much smaller potential effects of fragmentation on the extinction threshold, of no more than a 10–20% shift in the amount of habitat required for persistence. This difference has important implications for conservation. If fragmentation can have a large effect on the extinction threshold, then alteration of habitat pattern (independent of habitat amount) can be an effective tool for conservation. On the other hand, if the effects of fragmentation on the extinction threshold are small, then this is a limited option. I suggest that the difference in model predictions results from differences in the mechanisms by which the models produce the extinction threshold. In the CE models, the threshold occurs by an assumed reduction in colonization rate with decreasing habitat amount. In the BIDE models, loss of habitat is assumed to increase the proportion of the population that spends time in the matrix, where reproduction is not possible and the mortality rate is assumed to be higher (than in breeding habitat). Habitat loss therefore decreases the overall reproduction rate and increases the overall mortality rate on the landscape. I hypothesize that this imposes a constraint on the potential for habitat fragmentation to mitigate effects of habitat loss in BIDE models. To date, empirical studies of the independent effects of habitat loss and fragmentation suggest that habitat loss has a much larger effect than habitat fragmentation on the distribution and abundance of birds, supporting the BIDE model prediction, at least for this taxon.

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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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.997

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)0.0130.003

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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