Are human-induced changes good or bad to dynamic landscape connectivity?
Bibliographic record
Abstract
Land managers must find a compromise between rapidly changing landscapes and biodiversity conservation through ecological networks. Estimating ecological networks is a key approach to enhance or maintain functional connectivity by identifying the nodes and links of a graph, which represent habitats and their corresponding functional corridors, respectively. To understand the current state of biodiversity, it is necessary to consider dynamic landscape connectivity while relying on relevant land cover maps. Although a current land cover map is relatively easy to produce using existing data, this is challenging for past landscapes. Here we investigated the impact of changes in landscape connectivity in an urban landscape at a fine scale on the habitat availability of two bird species: the tree pipit Anthus trivialis and the short-toed treecreeper Certhia brachydactyla. These species, exhibiting different niche ecologies, have shown contrasting population trends at a medium-term scale. The occurrences of C. brachydactyla were better correlated with resistance values that maximise the use of corridors, whereas the occurrences of A. trivialis better fitted with intermediate resistance values. The statistical approach indirectly highlighted relevant information about the ecology the capacity of both species to use urban habitats. Landscape connectivity increased for both species over the 24-year study period and may have implications for local abundances, which could explain, at the national scale, the increase in populations of C. brachydactyla, but not the decrease in populations of A. trivialis. Thus, more attention must be paid on rural habitats and their associated species that are more impacted by human activities, but efforts could also be achieved in urban areas especially for highly corridor-dependent species. Studying dynamic landscape connectivity at a fine scale is essential for estimating past and future land cover changes and their associated impacts on ecological networks, to better reconcile human and biodiversity concerns in land management.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".