Developing a population target for an overabundant ungulate for ecosystem restoration
Bibliographic record
Abstract
1. Ecosystem restoration typically focuses on re-establishing native plant communities with the hope of restoring ecological processes over the long term. In contrast, endangered species management usually focuses on short-term actions that directly affect population numbers. Here, we present an intermediate approach. We develop an ecologically based population target for the overabundant herbivore, moose Alces alces, with the goal of restoring a predator–prey system and thus preventing the extinction of the endangered ungulate, woodland caribou Rangifer tarandus. 2. Forest harvesting is a major factor contributing to increases in the number of moose, which in turn increases predator populations. Caribou populations decline as a result of increased predation representing a form of apparent competition between moose and caribou. This presents a unique conservation challenge as recovery of caribou through forest restoration would take decades, while the alternative of directly reducing predator numbers is a short-term solution. A third option is to reduce moose numbers to also maintain predators at low numbers, but the question is to what density should moose be reduced? 3. We created a statistically based target for moose abundance under conditions without forest harvesting by developing a habitat-based population model for moose under current conditions. We then calculated the habitat quality in the same area but under simulated ‘pristine’ conditions. We also evaluated three measures of ecological carrying capacity to determine the reliability of using current moose abundance to back-calculate numbers for the pristine landscape. 4. Our analysis suggests an 81·6% (71·0–89·9%, 95% CI) reduction in moose habitat quality under pristine conditions. All three measures indicated that moose numbers in the current landscape were near carrying capacity, suggesting that the current abundance could be used to approximate numbers for the pristine landscape and thus be used as an ecological target. 5. Synthesis and applications. There are few experimental tests designed to alleviate predator-mediated apparent competition by reducing overabundant prey. Our target will now be used in an adaptive management framework to evaluate the success of this recovery option for caribou, and inform whether this approach can be applied to other systems involving species endangerment from the apparent competition mechanism.
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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 | 0.001 | 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".