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Record W4389730424 · doi:10.1139/facets-2023-0108

Integrative adaptive management to address interactions between biological invasions and protected area connectivity: a Canadian perspective

2023· article· en· W4389730424 on OpenAlexafffundvenueabout
Stuart W. Livingstone, Josie Hughes, Richard Pither, Marie‐Josée Fortin

Bibliographic record

VenueFACETS · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsThe Scarborough HospitalEnvironment and Climate Change CanadaUniversity of Toronto
FundersPalo Alto Medical FoundationEnvironment and Climate Change CanadaUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsAdaptive managementProtected areaEnvironmental resource managementComputer sciencePerspective (graphical)Landscape connectivityEnvironmental planningGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Expanding and creating protected area networks has become a central pillar of global conservation planning. In the management and design of protected area networks, we must consider not only the positive aspects of landscape connectivity but also how that connectivity may facilitate the spread of invasive species, a challenge that has become known as the connectivity conundrum. Here, we review key considerations for landscape connectivity planning for protected area networks, focusing on interactions between network connectivity and the management of invasive species. We propose an integrative adaptive management framework for protected area network planning with five main elements, including monitoring, budgeting considerations, risk assessment, inter-organizational coordination, and local engagement. Protected area planners can address the dynamic aspects of the connectivity conundrum through collaborative and integrative adaptive management planning.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.093
GPT teacher head0.286
Teacher spread0.193 · 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 designObservational
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

Citations5
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueFACETSSame topicWildlife-Road Interactions and ConservationFrench-language works237,207