Predator‐mediated effects of severe drought associated with poor reproductive success of a seabird in a cross‐ecosystem cascade
Bibliographic record
Abstract
Despite the profound impacts of drought on terrestrial productivity in coastal arid ecosystems, only a few studies have addressed how drought can influence ecological cascades across ecosystem boundaries. In this study, we examine the consequences of rainfall pulses and drought that subsequently impact the breeding success of a threatened nocturnal seabird, the Scripps's Murrelet (Synthliboramphus scrippsi). On an island off the coast of southern California, the main cause of reduced nest success for one of their largest breeding colonies is egg predation by an endemic deer mouse (Peromyscus maniculatus elusus). Mice on the island have an opportunistic diet of primarily terrestrial sources, but drastic declines in terrestrial productivity from drought might be expected to increase their reliance on marine resources, including murrelet eggs. We compiled data on terrestrial and marine productivity between 1983 and 2013 to determine how conditions in these ecosystems influence murrelet nest success. We found that the severity of drought had the strongest negative impact on murrelet nest success. We calculated that the reduction in fecundity during drought years due to increased egg predation by mice was substantial enough to produce a declining population growth rate. Nest success was much higher under normal or high rainfall conditions, depending on whether oceanic conditions were favorable to murrelets. Therefore, the more frequent and severe drought that is projected for this region could lead to an increased risk of murrelet population decline on this island. Our study highlights the need for understanding how species interactions will change through the effects of increasing drought and altered rainfall regimes under global change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".