MétaCan
Menu
Back to cohort

Patterns of distribution, relative abundance, and microhabitat use of anurans in a boreal landscape influenced by fire and timber harvest

2001· article· en· W2545821477 on OpenAlexafffundvenueabout
Juanita Constible, Patrick T. Gregory, Bradley R. Anholt

Bibliographic record

VenueEcoscience · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTaigaGeographyEcologyBorealForestryUnderstoryDeciduousTransectHabitatAbundance (ecology)CanopyFire regimeEnvironmental scienceEcosystemBiology

Abstract

fetched live from OpenAlex

AbstractEcosystem management is a theoretical framework in which land managers attempt to approximate natural disturbance with harvesting practices. In the mixedwood boreal forest of northeastern Alberta, Alberta-Pacific Forest Industries Inc. alters cutblock size, structure, and distribution over the landscape to simulate fires, the dominant disturbance type. In 1997 and 1998, we sampled for Rana sylvatica (Le Conte) and Pseudacris triseriata maculata (Wied-Neuwied) near Owl River and Mariana Lake, Alberta, in undisturbed, harvested, and naturally burned landscapes. We compared patterns of distribution and relative abundance using transects, time-constrained lake margin searches, and opportunistic detections. In 1998, we characterized the understory, shrub layer, and canopy layer on each transect. We used stepwise logistic regression to describe microhabitat use by each species. We did not detect consistent differences between burned and logged areas. This may reflect pre-treatment variation in regional habitat. Our data suggest that the presence of R. sylvatica is related to deciduous leaf litter, and that both species may require extensive ground cover and moist soil conditions. Although the microhabitat descriptions we present can be used to plan future harvests, further work is required to determine the effectiveness of ecosystem management in the boreal forest.RésuméPour gérer de façon plus écologique les écosystèmes forestiers où l'on prélève la matière ligneuse, les aménagistes du territoire tentent d'y recréer de façon artificielle le régime des perturbations naturelles. Dans la forêt boréale mixte du Nord-Est de l'Alberta, la compagnie Alberta-Pacific Forest Industries Inc. récrée par ses pratiques de coupe forestière un paysage similaire à celui qui serait altéré par le feu, le principal agent perturbateur en présence. En 1997 et 1998, nous avons capturé les amphibiens Rana sylvatica (Le Conte) et Pseudacris triseriata maculata (Wied-Neuwied) près de la rivière Owl et du lac Mariana (Alberta), dans des paysages forestiers non perturbés, récoltés pour la matière ligneuse ou incendiés. Nous avons comparé les patrons de répartition et d'abondance relative des espèces le long de transects, en cherchant durant une période de temps précise les individus en bordure du lac ou au fur et à mesure de nos randonnées sur le terrain. En 1998, nous avons décrit le sous-bois, la strate végétale arbustive et la voûte forestière le long de chaque transect. Nous avons utilisé une régression logistique pour décrire le micro-habitat propre à chaque espèce. Nous n'avons pas détecté de différences importantes entre les paysages brûlés et soumis à la coupe. Nos données suggèrent que la présence de R. sylvatica est associée à une litière de feuilles d'arbres décidus et que les deux espèces ont besoin d'un couvre-sol étendu et de conditions humides. Les descriptions de micro-habitats dans ce travail peuvent être utilisées pour planifier de futures opérations de coupe forestière, mais d'autres travaux sont nécessaires pour déterminer s'il est possible d'aménager une forêt boréale de façon écologique.Key Words: AnuransBoreal forestEcosystem managementFireForestryMicrohabitat useStepwise logistic regressionMots-clés: AmphibiensForêt borêaleAménagement des écosystèmesFeuForesterieUtilisation du micro-habitatRégression logistique

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.233

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 communication0.0000.001
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 head0.006
GPT teacher head0.203
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 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

Citations23
Published2001
Admission routes4
Has abstractyes

Explore more

Same venueEcoscienceSame topicWildlife Ecology and ConservationFrench-language works237,207