Prioritize diversity or declining species? Trade-offs and synergies in spatial planning for the conservation of migratory birds in the face of land cover change
Bibliographic record
Abstract
Abstract Stemming biodiversity loss requires strategic conservation guided by well-articulated and achievable targets, whether they be proactive (e.g., protect diverse places) or reactive (e.g., protect threatened species). Both types of targets can be effective, but there are trade-offs, especially for broadly-distributed ecosystems or taxa, such as migratory species, a group for which conservation has been challenged by limited knowledge of distributions throughout the annual cycle. We combined novel spatiotemporal distribution models with population trend data to first examine focal areas for the conservation of Neotropical migratory birds (n=112 species) during the non-breeding period in the Western Hemisphere based on a proactive approach (highest diversity) versus a reactive approach (strongest declines) to conservation. For the focal areas, we then assessed the extent of recent anthropogenic impact, protected area status and projected future changes in land cover using three shared socioeconomic pathways (Sustainability=SSP1, Business-as-usual=SSP2, Regional nationalism=SSP3). Spatial priorities were strikingly different when targeting areas of high species diversity, emphasizing southern Mexico and northern Central America, versus areas with more severe declines across species, emphasizing the Andean cordilleras of South America. Only a fraction of the non-breeding region (1.4%) met targets for diversity and decline, mostly in southern Central America. Current levels of protection were similar for the two targets. Areas prioritized to conserve high species diversity have experienced less recent anthropogenic impact than areas prioritized for decline but are predicted to experience more rapid land conversion to less suitable open, agricultural landscapes in the next three decades under both an SSP1 and SSP2 scenario. Only the SSP3 scenario projected similar conversion rates for the two targets. Our findings indicate how even within taxa, efficient conservation efforts will depend on the careful consideration of desired targets combined with reliable predictions about the locations and types of land cover change under alternative socioeconomic futures.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".