EFFECT OF HABITAT FRAGMENTATION ON THE EXTINCTION THRESHOLD: A SYNTHESIS<sup>*</sup>
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".