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Edge Influence on Forest Structure and Composition in Fragmented Landscapes

2005· article· en· W2099591330 on OpenAlexafffund
Karen A. Harper, S. Ellen Macdonald, Philip J. Burton, Jiquan Chen, Kimberley D. Brosofske, Sari C. Saunders, E. S. Euskirchen, Dar A. Roberts, Malanding Jaiteh, Per‐Anders Esseen

Bibliographic record

VenueConservation Biology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsCanadian Forest ServiceUniversity of AlbertaUniversité du Québec à Montréal
FundersJoint Fire Science ProgramNatural Sciences and Engineering Research Council of CanadaU.S. Forest ServiceHarvard UniversitySvenska Forskningsrådet FormasU.S. Department of Agriculture
KeywordsAbiotic componentEcologyForest fragmentationHabitat fragmentationTemperate rainforestContext (archaeology)HabitatBiotic componentFragmentation (computing)GeographyTemperate forestEnhanced Data Rates for GSM EvolutionEcosystemTemperate climateEnvironmental scienceBiologyComputer science

Abstract

fetched live from OpenAlex

Abstract: Although forest edges have been studied extensively as an important consequence of fragmentation, a unifying theory of edge influence has yet to be developed. Our objective was to take steps toward the development of such a theory by (1) synthesizing the current knowledge of patterns of forest structure and composition at anthropogenically created forest edges, (2) developing hypotheses about the magnitude and distance of edge influence that consider the ecological processes influencing these patterns, and (3) identifying needs for future research. We compiled data from 44 published studies on edge influence on forest structure and composition in boreal, temperate, and tropical forests. Abiotic and biotic gradients near created forest edges generate a set of primary responses to edge creation. Indirect effects from these primary responses and the original edge gradient perpetuate edge influence, leading to secondary responses. Further changes in vegetation affect the edge environment, resulting in ongoing edge dynamics. We suggest that the magnitude and distance of edge influence are a direct function of the contrast in structure and composition between adjacent communities on either side of the edge. Local factors such as climate, edge characteristics, stand attributes, and biotic factors affect patch contrast. Regional factors define the context within which to assess the ecological significance of edge influence (the degree to which the edge habitat differs from interior forest habitat). Our hypotheses will help predict edge influence on structure and composition in forested ecosystems, an important consideration for conservation. For future research on forest edges in fragmented landscapes, we encourage the testing of our hypotheses, the use of standardized methodology, complete descriptions of study sites, studies on other types of edges, synthesis of edge influence on different components of the ecosystem, and investigations of edges in a landscape context.

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)0.003
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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

Citations1,375
Published2005
Admission routes2
Has abstractyes

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