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Landscape effects of disturbance, habitat heterogeneity and spatial autocorrelation for a ground beetle (Carabidae) assemblage in mature boreal forest

2012· article· en· W2081456209 on OpenAlexaff
F. Guillaume Blanchet, J. A. Colin Bergeron, John R. Spence, Fangliang He

Bibliographic record

VenueEcography · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEcologySpatial heterogeneityVegetation (pathology)BorealGround beetleHabitatSpatial variabilityEnvironmental scienceBiological dispersalSpatial distributionDisturbance (geology)Spatial analysisLandscape ecologyTaigaGeographyGeologyBiologyRemote sensingPopulationGeomorphology

Abstract

fetched live from OpenAlex

Ground beetle (Carabidae) assemblages are speciose and frequently employed as indicators of ecosystem function. It is thus important to understand the factors that affect their spatial distributions so as to better interpret how ecosystem variation influences the structure of their assemblages. We evaluated how anthropogenic disturbances, habitat heterogeneity, and spatial autocorrelation (unmeasured spatially structured habitat variables and/or dispersal limitation) influence the distribution of carabids on a mature boreal forest landscape. We worked with a large sample of carabids from 194 sites from a near‐regular grid covering 70 km 2 . Data about forest floor cover, vegetation structure, soil drainage, and topography were associated with each site. We modelled the structure of carabid assemblages using these variables together with Moran’s eigenvector maps (MEM) as spatial constraints. Overall, our model explained about half of the variation in ground beetle assemblages. Forest floor cover, soil drainage, and vegetation structure were the principal factors useful for explaining carabid assemblages. The spatial patterns described by the MEMs for the ground beetle assemblages seem to result from spatial autocorrelation in soil drainage, floor cover, topography, and vegetation structure; however, this spatial autocorrelation independently and uniquely explained only 1.4% of the variation in assemblages. This was much less important than the explanatory power of the environmental variables considered; e.g. ground cover descriptors and vegetation structure by themselves accounted, respectively, for 12.1 and 4.5% of the variation in the carabid data. The spatial distribution of ground beetles in undisturbed forest was only mildly affected by surrounding anthropogenic disturbances, but clearly small patches were invaded by species characteristic of open habitat. We conclude that to help conserve ground beetle diversity at the landscape scale in boreal forests, mature forest patches should be large and connected to other patches and the surrounding forest.

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)0.002
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 score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.005
GPT teacher head0.219
Teacher spread0.214 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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