Simulation modeling accounts for uncertainty while quantifying ecological effects of development alternatives
Bibliographic record
Abstract
Abstract Wildlife management often involves trade‐offs between protecting species and allowing human activities and development. Ideally, these decisions are guided by scientific studies that quantify the impacts of proposed actions on the environment. However, critical information to assess impacts of proposed activities may be lacking, such as certainty in where actions will take place, which may hinder a robust impact assessment. To address this issue, we present the Development Impacts Analysis (DIA), which employs Monte Carlo simulation modeling to quantify the environmental consequences of proposed development scenarios, while accounting for uncertainty in the exact location of future development. We applied the DIA to five proposed oil leasing management scenarios under a revised management plan for the National Petroleum Reserve—Alaska. For each management scenario with differing levels of proposed development (“alternatives”), oil production pads and roads were randomly simulated in proportion to estimated undiscovered oil and following alternative‐specific restrictions. We assessed habitat displacement for two caribou ( Rangifer tarandus ) herds, eight shorebird species, and black brant ( Branta bernicla ) based on reported responses to development, repeating the process 100 times for each alternative. Some habitat loss was reported for each proposed alternative, but the amount of impact varied by alternative and species. One caribou herd and most bird species indicated greatest effects in the alternative with the least restrictions on development and lesser impacts under more protective alternatives. Our results emphasized the importance of considering spatial variation in development effects and species‐specific differences when evaluating management proposals. The DIA quantified potential impacts on a suite of species under proposed management alternatives, while accounting for uncertainty in where development will occur and providing confidence intervals on estimated impacts. This illustrates that uncertainty need not preclude management decisions about establishment of broad land use restrictions prior to submission of project‐level proposals but can instead be explicitly incorporated into decision making. While no single management approach will likely benefit all species, use of tools such as the DIA allows managers to quantify trade‐offs among species and pursue approaches that balance the needs of various taxa and other management objectives.
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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 | 0.000 |
| 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) | 0.001 | 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 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".