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Record W3163985462 · doi:10.1002/ecs2.3530

Simulation modeling accounts for uncertainty while quantifying ecological effects of development alternatives

2021· article· en· W3163985462 on OpenAlexaff
Timothy J. Fullman, Benjamin K. Sullender, Matthew D. Cameron, Kyle Joly

Bibliographic record

VenueEcosphere · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsCanadian Parks and Wilderness Society
FundersWilburforce Foundation
KeywordsWildlifeEnvironmental resource managementHabitatEcologyEnvironmental scienceBiology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.184
GPT teacher head0.276
Teacher spread0.091 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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