Overcoming confusion and stigma in habitat fragmentation research
Bibliographic record
Abstract
Anthropogenic habitat loss is widely recognized as a primary environmental concern. By contrast, debates on the effects of habitat fragmentation persist. To facilitate overcoming these debates, here we: (i) review the state of the literature on habitat fragmentation, finding widespread confusion and stigma; (ii) identify consequences of this for biodiversity conservation and ecosystem management; and (iii) suggest ways in which research can move forward to resolve these problems. Confusion is evident from the 25 most-cited fragmentation articles published between 2017 and 2021. These articles use five distinct concepts of habitat fragmentation, only one of which clearly distinguishes habitat fragmentation from habitat area and other factors ('fragmentation per se'). Stigmatization is evident from our new findings that fragmentation papers are more charged with negative sentiments when compared to papers from other subfields in the environmental sciences, and that fragmentation papers with more negative sentiments are cited more. While most empirical studies of habitat fragmentation per se find neutral or positive effects on species and biodiversity outcomes, which implies that small habitat patches have a high cumulative value, confusion and stigma in reporting and discussing such results have led to suboptimal habitat protection policy. For example, government agencies, conservation organizations, and land trusts impose minimum habitat patch sizes on habitat protection. Given the high cumulative value of small patches, such policies mean that many opportunities for conservation are being missed. Our review highlights the importance of reducing confusion and stigma in habitat fragmentation research. To this end, we propose implementing study designs in which multiple sample landscapes are selected across independent gradients of habitat amount and fragmentation, measured as patch density. We show that such designs are possible for forest habitat across Earth's biomes. As such study designs are adopted, and as language becomes more precise, we expect that confusion and stigma in habitat fragmentation research will dissipate. We also expect important breakthroughs in understanding the situations where effects of habitat fragmentation per se are neutral, positive, or negative, and the reasons for these differences. Ultimately this will improve efficacy of area-based conservation policies, to the benefit of biodiversity and people.
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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 | 0.250 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".