Threatened Birds in a Changing Mediterranean Wetland: Long-Term Trends and Climate-Driven Threats
Bibliographic record
Abstract
Understanding the impact of climate change on waterbirds, particularly those of conservation concern, is essential for their long-term management and effective conservation. In arid regions such as North Africa, wetlands of international importance have been affected by severe drought events, but their impact on waterbirds is still not well understood. Here, we assess the population dynamics of four emblematic resident species (Aythya nyroca, Marmaronetta angustirostris, Oxyura leucocephala, and Porphyrio porphyrio porphyrio) in a North African Ramsar site (Garaet Hadj Tahar marsh, Northeast Algeria), and determine the climatic variables (temperature, precipitation, and drought index) that best predict the change in abundance in wintering and breeding seasons. We used a long-term survey of regular counts during the wintering and breeding seasons of 2002–2019. The temporal trend of waterbird abundance differed between species and seasons. Species showed different sensitivities to different climatic variables at different time windows preceding the wintering and breeding seasons. We found that the population dynamics of the endangered O. leucocephala in the wintering and breeding seasons were best predicted with the drought index, whereas those of P. p. porphyrio were best correlated with maximum temperature. Population dynamics during the wintering and breeding season of the two other species were best explained with either maximum temperature, precipitation, average temperature, or drought. Species responded differently to warming and drought. The most endangered species (O. leucocephala) showed population declines in drier years, whereas less threatened species (A. nyroca, M. angustirostris, and P. p. porphyrio) exhibited either positive or negative correlations. The observed temporal increase in vegetation cover was positively correlated with the wintering population size of O. leucocephala, as well as the breeding population size of P. p. porphyrio and M. angustirostris. These findings highlight the urgent need for climate-adaptive conservation strategies to sustain wetland biodiversity and ecosystem resilience in the face of increasing climate stressors, aligning with the global sustainability goals for biodiversity conservation and ecosystem management. Our results suggest that future increases in temperatures and drought severity might threaten endangered waterbirds and benefit more common species in the region.
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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 | 0.001 |
| 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) | 0.010 | 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".