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Effects of Forest Harvesting on Nest Predation in Cavity-nesting Waterfowl

2001· article· en· W2173281238 on OpenAlexaffabout
Johanna P. Pierre, Heather Bears, Cynthia A. Paszkowski

Bibliographic record

VenueThe Auk · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWoodpeckerNest (protein structural motif)PredationGeographyForestryEcologyHabitatWaterfowlSouth carolinaBiologyPolitical science

Abstract

fetched live from OpenAlex

Waterfowl populations inNorth America are threatened by habitat loss (Owen and Black 1990), but effects of habitat destruction and fragmentation on waterfowl nesting in forested landscapes are poorly known.Increased nest predation is often attributed to habitat fragmentation and may be particularly evident in smaller habitat patches and at habitat edges (Paton 1994, Andrn 1995).However, relatively few studies conducted in forest-dominated landscapes show edge effects at either natural or anthropogenic edges (Paton 1994, Andrn 1995, P6ysa et al. 1997).Lack of edge effects in forest-dominated landscapes may be due to relatively low predator species richness and abundance, and lack of predator attraction to edges (Andrn 1995).However, predator abundance and nest predation may increase with increased deforestation of the landscape (Andrn 1995, Hartley and Hunter 1998).Effects of habitat destruction and fragmentation on nest predation of cavity-nesting waterfowl are unknown.We know of only one study of nest predation in cavity-nesting waterfowl in forest-dominated landscapes (P6ysa et al. 1997).This study found no edge effects at natural (lake) edges in a forested landscape, but did not investigate effects of forest harvesting.Thus, we experimentally investigated effects of forest harvesting on cavity-nesting waterfowl in the boreal mixedwood forest of western Canada, an important breeding and summering area for waterfowl.Although deforestation and fragmentation have proceeded relatively slowly in that region, large areas of forest have recently become available for harvesting.We used artificial waterfowl cavity nests E-mail: jpierre@gpu'srv'ualberta'ca to test the following hypotheses: (1) nest-predation levels in cutblocks (clearcuts with ->8% of trees remaining) differ from predation levels in uncut forest, (2) nest-predation levels in riparian forest buffer strips differ from predation levels in uncut forest, (3) nest-predation levels in uncut forest vary with distance from the riparian forest edge, and (4) nest predation is higher around lakes in harvested versus unharvested landscapes.Methods.--Weconducted research from May through July in 1997 and 1998, in the boreal mixedwood forest surrounding 10 lakes in north-central Alberta, Canada.Six of the 10 study lakes were part of the TROLS (Terrestrial and Riparian Organisms, Lakes and Streams) project, a large-scale multidisciplinary study using experimental forest harvesting protocols at 12 lakes to determine effects of different buffer strip widths on aquatic and terrestrial boreal systems.Study lakes were in three clusters and ranged in size from 8.6 to 103.6 ha.Forests surrounding study lakes were dominated by trembling aspen (Populus tremuloides), balsam poplar (P.balsamifera), white spruce (Picea glauca), black spruce (P.mariana), and jack pine (Pinus banksiana).Extensive commercial forest harvesting began in this region in 1993.Forest harvesting is carried out in two to three passes 10 years apart, creating a mosaic landscape of harvested patches of various ages and unharvested stands.Average cutblock size is approximately 30 ha and cutblocks contain ->8% residual trees.When forest surrounding lakes is harvested, a forest buffer strip 100 m wide separates riparian vegetation and the adjacent lakeshore from harvesting activity.The purpose of buffer strips is to protect lake water quality.(Although riparian vegetation separated the forest from the lake edge around

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 score0.016
Threshold uncertainty score0.187

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)0.0000.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations9
Published2001
Admission routes2
Has abstractyes

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