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Influence of landscape pattern on breeding distribution and success in a threatened Alcid, the marbled murrelet: model transferability and management implications

2007· article· en· W2134638793 on OpenAlexafffundabout
Yuri Zharikov, David B. Lank, Fred Cooke

Bibliographic record

VenueJournal of Applied Ecology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsThreatened speciesEcologyNest (protein structural motif)GeographyHabitatOccupancyForagingFragmentation (computing)Biology

Abstract

fetched live from OpenAlex

Summary The marbled murrelet Brachyramphus marmoratus is a threatened Alcid nesting in old‐growth coastal forests from central California to Alaska. Logging has greatly reduced the amount and altered the pattern of the species’ nesting habitat. Landscape fragmentation effects on the breeding ecology of the species are poorly understood because of the inaccessibility of nest sites. Using radio‐telemetry, 157 marbled murrelet nests were located in two old‐growth areas in British Columbia, Canada, with different logging histories. Probable breeding success was estimated from nest attendance patterns by radio‐tagged parents. Information‐theoretic and hypothesis‐testing methods were used to model breeding distribution (used vs. random unknown sites) and success (successful vs. failed nests) within c. 50‐km radius extents at a scale of 2·3‐km radius landscapes. Intersite transferability of distribution models was tested. Breeding distribution was positively related with old‐growth patch proximity, edge density (natural and artificial) and contrast, proportion of landscape under old‐growth or core habitat, and interspersion of old‐growth patches; it was negatively related with coastal zone proximity and mean old‐growth patch size. Breeding success was negatively affected by the edge contrast, coast proximity and proportion of young forest, probably reflecting the distribution of nest predators. All distribution models discriminated well between used and random landscapes within the training area. Intersite model transferability was good for 50% of the models. The models less predictive of the training site (area under the curve 0·676–0·738) were more transferable, probably because at the training site, which had considerably less old‐growth nesting habitat (18% of extent) than the testing site (55%), breeding distribution was driven by a different subset of predictors. Synthesis and applications. Geographic information system (GIS) data were helpful in discriminating between landscapes known to be used by nesting marbled murrelets and those with unknown breeding status. Previous indirect inferences about landscape‐level effects on breeding distribution in the birds were shown to correspond with their true nesting distribution. Our results suggest that habitat fragmentation per se need not have a negative effect on the birds beyond that as a result of habitat loss, unless associated with an increased abundance of predators. Our results fine‐tune the existing habitat conservation guidelines by suggesting that protection of old‐growth forest adjacent to clearcuts is important. We provide a means for desktop classification of marbled murrelet landscapes. We advise the application of several different models, depending on the amount of remaining old‐growth forest, to evaluate the consistency of predictions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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.

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

Citations32
Published2007
Admission routes3
Has abstractyes

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