Selecting simultaneous actions of different durations to optimally manage an ecological network
Bibliographic record
Abstract
Summary Species management requires decision‐making under uncertainty. Given a management objective and limited budget, managers need to decide what to do, and where and when to do it. A schedule of management actions that achieves the best performance is an optimal policy. A popular optimisation technique used to find optimal policies in ecology and conservation is stochastic dynamic programming (SDP). MostSDPapproaches can only accommodate actions of equal durations. However, in many situations, actions take time to implement or cannot change rapidly. Calculating the optimal policy of such problems is computationally demanding and becomes intractable for large problems. Here, we address the problem of implementing several actions of different durations simultaneously. We demonstrate analytically that synchronising actions and their durations provide upper and lower bounds of the optimal performance. These bounds provide a simple way to evaluate the performance of any policy, including rules of thumb. We apply this approach to the management of a dynamic ecological network ofAedes albopictus, an invasive mosquito that vectors human diseases. The objective is to prevent mosquitoes from colonising mainland Australia from the nearby Torres Straits Islands where managers must decide between management actions that differ in duration and effectiveness. We were unable to compute an optimal policy for more than eight islands out of 17, but obtained upper and lower bounds for up to 13 islands. These bounds are within 16% of an optimal policy. We used the bounds to recommend managing highly populated islands as a priority. Our approach calculates upper and lower bounds for the optimal policy by solving simpler problems that are guaranteed to perform better and worse than the optimal policy respectively. By providing bounds on the optimal solution, the performance of policies can be evaluated even if the optimal policy cannot be calculated. Our general approach can be replicated for problems where simultaneous actions of different durations need to be implemented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".