Remnant forest in Costa Rican working landscapes fosters bird communities that are indistinguishable from protected areas
Bibliographic record
Abstract
Abstract The outcome of the ongoing biodiversity crisis depends on the capacity of the Earth’s wildlife to persist in working landscapes. Yet, the species that occupy working landscapes are often distinct from those in protected areas, with a large group of “sensitive species” thought to rarely venture into human‐dominated landscapes. As governments have committed to restoring degraded lands world‐wide, determining whether and how working landscapes can be restored to benefit sensitive species remains a major challenge. We surveyed Neotropical birds across Northwestern Costa Rica in protected areas, farms and forests embedded within working landscapes. We analysed community composition to understand how gradients of forest cover, fragmentation and regional precipitation determine how conserving (or restoring) tropical forests in working landscapes could safeguard entire communities, especially sensitive species with limited ranges. We found agricultural sites maintained relatively high bird diversity but hosted very distinct communities from those found in protected areas. The average range size of species found in agricultural communities was double the size of species in protected areas. However, high forest cover sites in working landscapes housed bird communities with small range sizes that were equivalent to those in nearby protected areas, despite being twice as fragmented and significantly more disturbed. The effect of local forest cover on bird composition was contingent on both landscape context and regional climate. When local forest cover increased in wetter regions and more forested landscapes, bird communities in working landscapes exhibited a stronger shift towards the assemblages found in protected areas. Specifically, we found that reforesting the wettest sites would increase similarity to protected areas fourfold compared to only a twofold increase in the driest sites. Synthesis and applications. Despite experiencing much more fragmentation and degradation than protected areas, forests in Costa Rican working landscapes can maintain bird communities that strongly resemble those found in protected areas. This suggests that conserving or restoring forests in working landscapes, particularly within wetter regions and already forested landscapes, may safeguard bird communities when creating protected areas is infeasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".