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Record W2303280660 · doi:10.14288/1.0075054

Landscape spatial patterns and forest fragmentation in managed forests in southeast British Columbia : perceptions, measurements, and scale

2009· article· en· W2303280660 on OpenAlexaboutno aff
Robert G. D’Eon

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyFragmentation (computing)Scale (ratio)Forest fragmentationSpatial ecologyForestryEnvironmental resource managementEcologyPhysical geographyCartographyRemote sensingEnvironmental scienceBiodiversity

Abstract

fetched live from OpenAlex

Forest spatial patterns are a central topic in contemporary landscape ecology, largely because of concerns about forest fragmentation. Forest fragmentation is thought to be a major threat to biodiversity because remnant forest patches, left from human disturbances such as logging, would support fewer species and be more prone to local extinctions because they are small and isolated from each other, as predicted from an extension of island biogeography theory. These and other theoretical predictions stemming from the forest fragmentation paradigm remain virtually unchallenged by empirical data. I investigated landscape spatial patterns in managed forests of the Slocan Valley in southeast British Columbia, and focused my investigations on theoretical predictions concerning forest fragmentation and the distinction between habitat amount and spatial configuration effects. I first investigated human perception of fragmentation to assess the usefulness of current methods in quantifying landscape spatial pattern and to investigate definitions and confusion about fragmentation. I then used traditional landscape indices to test predictions about fragmentation trends in the Slocan Valley by focusing on the effect of forest harvesting on old growth forest fragmentation. I then created a unique method of assessing landscape connectivity, the inverse of fragmentation, using a scale-dependent, organism-centered technique based on an organism's ability to move between habitat patches. Finally, I tested mule deer scale-dependant selection of forest edges, patch size, and logging roads relative to amount of forest, since these landscape elements are implicated in the fragmentation issue and are either untested or unresolved for mule deer. I found people associate fragmentation with high patch density, which was highly correlated with amount of harvesting, illustrating the confusion between habitat amount and spatial configuration. Landscape indices were of very limited use in deriving absolute values of fragmentation, and are likely best used to compare landscapes and pattern trends. I found little evidence of an old growth forest fragmentation trend in the Slocan Valley. Most predictions concerning a fragmentation trend were falsified. Using an organism-centered method to assess connectivity among old growth patches, I found the landscape to be accessible to all old growth associates at maximum dispersal distances, with the exception of the northern flying squirrel (Glaucomys sabrinus). At median dispersal distances however, only larger more vagile carnivorous birds could access all old growth patches in the landscape. Of particular concern are flying squirrels which had access to only 10% of the landscape at median dispersal distances. Mule deer displayed selection of landscape elements at the landscape scale only. The best predictors of mule deer winter use were mature forest patch size and amount of mature forest. Because of high correlation between these two variables, distinction between them was difficult and illustrates this persistent problem in empirical work. Empirical field studies are direly needed to test the existing fragmentation theoretical framework. Future work must distinguish between habitat loss effects and independent fragmentation effects.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.131
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.173
Teacher spread0.167 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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