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Record W2989673757 · doi:10.1002/jwmg.21788

Occupancy Patterns in a Reintroduced Fisher Population during Reestablishment

2019· article· en· W2989673757 on OpenAlexaboutno aff
Patricia J. Happe, Kurt J. Jenkins, Rebecca McCaffery, Jeffrey C. Lewis, Kristine L. Pilgrim, Michael K. Schwartz

Bibliographic record

VenueJournal of Wildlife Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsOccupancyGeographyWilderness areaPeninsulaPopulationHabitatEcologyWildernessLand coverThreatened speciesEnvironmental resource managementLand useForestryEnvironmental scienceBiologyArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT Monitoring population performance in the years following species reintroductions is key to assessing population restoration success and evaluating assumptions made in planning species restoration programs. From 2008–2010 we translocated 90 fishers (Pekania pennanti) from British Columbia, Canada, to Washington's Olympic Peninsula, USA, providing the opportunity to evaluate modeling assumptions used to identify the most suitable reintroduction areas in Washington and enhance understanding of fisher habitat associations in the late‐successional forest ecosystems in the coastal Pacific Northwest. From 2013–2016, we deployed 788 motion‐sensing cameras and hair (DNA)‐snaring devices distributed among 263 24‐km2 primary sampling units across the Olympic Peninsula. Our objectives were to determine whether occupancy patterns of the reestablishing population supported assumptions of the initial habitat assessment models, whether the population had expanded or shifted in distribution since the initial reintroductions, compare physical habitat attributes among land‐management designations, and determine whether the founding fishers had successfully reproduced. We predicted that site occupancy by fishers would be associated with landscapes characterized by high proportional coverage of dense forest canopies and medium‐sized and large trees, a diversity of stand structural classes, and area near the administrative boundary separating wilderness from more intensively managed forest lands. We detected fishers across designated wilderness, federal lands outside of wilderness, and other land designations in proportion to land availability on the Peninsula. We found negligible support for predictions that occupancy by fishers was associated with percent forest cover, tree‐size class, or structural class diversity. Rather, occupancy was strongly associated with lands near the wilderness boundary on both sides. We speculate that the boundary between wilderness and more intensively managed forest lands provided fishers with the most suitable prey in proximity to contiguous expanses of low‐ to mid‐elevation late‐successional forests that provided optimal resting, denning, and security values. Occupancy patterns shifted toward the west and south along a precipitation gradient during the study, indicating that population distribution had not yet stabilized 5–8 years following translocation. Genetic results indicated that ≥2 generations of fishers have been produced on the Peninsula. Annual occupancy rates across the Peninsula (0.08–0.24) were lower than in other previously studied and established fisher populations, indicating that not all habitat was fully occupied or that initial estimates of the extent of habitat was overestimated. The strong selection fishers exhibited for wilderness edge and weak selection against extensive forested wilderness areas suggested that habitat managers should strive for maintaining a suitable interspersion of required forest structures and biotic habitat components, such as prey resource availability. © 2019 The Wildlife Society.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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