The cumulative effects of management on the population dynamics of the <scp>D</scp>ouble‐crested <scp>C</scp>ormorant <i><scp>P</scp>halacrocorax auritus</i> in the <scp>G</scp>reat <scp>L</scp>akes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wildlife species have been subject to control efforts throughout human history due to real or alleged human–wildlife conflicts. The Double‐crested C ormorant P halacrocorax auritus in the interior of N orth A merica is no exception, with recent population growth leading to increased conflicts and consequently the development of many control programmes. These control programmes are usually conducted at local scales, often with little or no effort to assess their cumulative effects at the population level. We attempted the first comprehensive assessment of the cumulative effects of control at various spatio‐temporal scales, focusing on 199 colonies of D ouble‐crested C ormorant monitored during a 29‐year period. Linear models were used to assess the relationship between colony‐specific growth rates and a set of candidate factors using an information‐theoretic approach. Colony‐level density‐dependent effects and local control efforts had the greatest influences on population growth. We detected a cumulative effect of management, whereby (i) the reduction in population growth rate was generally stronger when different control activities such as culling or egg oiling were combined, and (ii) past control operations tended to have a pervasive impact on growth rates, especially egg oiling and nest destruction, which negatively affected local recruitment. However, our results also suggest that catastrophic events and the culling of breeding adults that occurred at least 2 years previously could fuel subsequent recruitment or natural immigration from nearby colonies, for instance if the breeding success of remaining pairs was increased through a diminution of density‐dependent regulatory processes. Density‐dependence at the metapopulation level constituted a third source of regulation, as local growth rates were reduced with increasing number or proximity of active neighbouring colonies. We also found evidence that the culling of D ouble‐crested C ormorants wintering in the southeastern USA could negatively impact the population growth of individual breeding colonies in the G reat L akes, although further research integrating models of migratory connectivity is needed to reach more definitive conclusions. Finally, despite previous studies emphasizing its importance, the net effect of management‐induced dispersal appeared small at large spatial scales. We show that this can be explained in part by control strategies (e.g. spatially clustered operations). The continuation of Cormorant management efforts will provide an opportunity to refine the present assessment of the relative importance of density‐dependence, breeding vs. non‐breeding season management and dispersal, particularly if population models are coupled with monitoring programmes within an adaptive management framework.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 it