Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation
Bibliographic record
Abstract
In this paper, we tested whether the spatial distribution of a given species in more or less fragmented and disturbed landscapes depends on the species habitat specialization. We studied 891 spatial replicates from the French Breeding Bird Survey (FBBS) monitored at least two years during 2001–2005, and two independent landscape databases measuring respectively landscape fragmentation and recent landscape disturbance on each FBBS replicate. We used a continuous habitat specialization index for the 105 most common bird species monitored by the FBBS. We further modelled the spatial variation in abundance of each species according to fragmentation and disturbance across FBBS replicates, accounting for habitat differences and spatial trends. We then tested whether more or less specialized species responded to landscape fragmentation and disturbance. We found that the more specialist a species, the more negative its spatial response to landscape fragmentation and disturbance. Although there was a very high variation around these tendencies indicating that there are many other drivers of species distribution, our results suggest that measuring specialization may be helpful in predicting which species are likely to thrive in human degraded landscapes. We also emphasize the need to consider both positive and negative species responses when assessing consequences of habitat change in communities.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".