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Record W3017416710 · doi:10.1002/ecs2.3011

Reducing uncertainty in species management: forecasting secondary spread with expert opinion and mechanistic models

2020· article· en· W3017416710 on OpenAlexaff
Emily S. Chenery, David Drake, Nicholas E. Mandrak

Bibliographic record

VenueEcosphere · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsThe Scarborough HospitalFisheries and Oceans CanadaUniversity of Toronto
Fundersnot available
KeywordsWeightingRobustness (evolution)Expert opinionTaxonPoolingExpert elicitationEcologyPopulationAdaptive managementEnvironmental resource managementComputer scienceStatisticsBiologyArtificial intelligenceEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

Abstract Predicting the spatial and temporal dynamics of invasive species is critical for successful management intervention, yet substantial uncertainty exists about how species will interact with human pathways when introduced to new ecosystems. We demonstrate a novel approach for quantifying uncertainty when predicting the uptake, movement, and establishment of invasive species by combining mechanistic modeling of the spread process with expert opinion of the demographic factors that govern species performance. We demonstrate the utility of this approach using a case study involving the transfer potential of nonindigenous species ( NIS ) in the Laurentian Great Lakes basin ( GLB ). A survey using structured expert judgment was completed by 24 North American taxonomic experts, covering 60 species of NIS established in the GLB . Experts estimated species‐specific demographic parameters describing population growth and establishment potential, which were incorporated into an existing mechanistic model of human‐mediated spread via ballast water with species‐specific spread rates (number of ports or lakes invaded/year) as outputs. Expert judgments within each group varied widely, indicating that generalizable rates of spread across taxa are unlikely and highlighting the value of cross‐taxon comparisons. Most species were predicted to establish throughout the GLB within 10 yr, assuming status quo management conditions. Sensitivity analysis for expert performance‐based weighting demonstrated that most model outputs were insensitive to weighting (<1% difference over baseline) and shows the robustness of the joint model. Overall, the joint expert opinion and predictive modeling method demonstrates a novel means of handling sparse data when forecasting invasion dynamics. Divergent estimates resulted in a range of likely spread rates, but improved upon traditional best‐guess approaches. Incorporating joint methods into ecological decision‐making frameworks has clear implications for invasive species management but may also inform other ecological scenarios where data are scarce and conservation action is urgent.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.588
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.035
GPT teacher head0.216
Teacher spread0.181 · 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 teacher head, not a consensus.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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