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Record W4292881468

Biodiversity response to forest management depends on taxonomic group : a meta-analysis in Europe

2008· preprint· en· W4292881468 on OpenAlexaboutno aff
Yoan Paillet, Laurent Bergès

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2008
Typepreprint
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityGroup (periodic table)GeographyTaxonomic rankComputer scienceEnvironmental resource managementEcologyBiologyEnvironmental scienceChemistry
DOInot available

Abstract

fetched live from OpenAlex

Among global changes, intensification of forest management is a major issue at the beginning of the 21st century e.g. for bio-fuel production. Past and present pressures on forest resources have led to a drastic decrease in the surface area of unmanaged forests in Europe: they currently represent less than 1% of the total forest area (vs. 13% on the west coast of the United States and 40-52% in Canada). Modifications in forest structure, composition and dynamics inevitably lead to changes in the biodiversity of forest dwelling species. However, the possible gains and losses in biodiversity due to forest management have never been assessed at a pan-European scale.\nIn the current study, carried out within the framework of ALTER-Net, we hypothesized that biodiversity was higher in unmanaged forests although we expected contrasted responses for different taxa and ecological groups. We also identified the gaps in the knowledge concerning the response of biodiversity to forest management in Europe. We used a meta-analytical approach to do this. Meta-analysis is a quantitative review of the literature that combines the results of several independent studies examining the same question. Meta-analysis output accounts for the fact that all the studies are not equally reliable. In this study, we used species richness as a simple, though imperfect, index of biodiversity. The term "forest management" encompassed any anthropogenic pressures related to direct forest resource use (clearfelling, selective felling, any form of tree retention, grazing, planting or drainage). We analysed 51 published papers containing 122 individual comparisons of species richness between unmanaged and managed forests throughout Europe.\nSpecies richness was slightly higher in unmanaged than in managed forests but the difference was only marginally significant. However, when we divided the studies according to taxonomic and/or ecological groups, we found both contrasted and more significant responses: (i) bryophytes, lichens, fungi and saproxylic beetles, which mainly depend on deadwood and/or large trees, were penalised by forest management; (ii) on the contrary, vascular plants were favoured by forest management; (iii) the response for carabids or birds was unclear and probably depends on other factors not included in our analysis (e.g. landscape patterns). This study also highlighted the need for more research, especially in temperate and Mediterranean regions and/or for some groups such as mammals or soil invertebrates.\nTo our knowledge, this study has been the first to compare biodiversity in managed and unmanaged European forests using meta-analysis. The conclusions of this study support an active conservation policy, creating new unmanaged forest reserves and encouraging management methods that mimic natural forest dynamics and structures. These actions would contribute to achieving the 2010 biodiversity target. Moreover, we suggest the creation of a coordinated European research network to study and monitor biodiversity of different taxa in managed and unmanaged forests.

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)0.016
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.042
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.227
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations0
Published2008
Admission routes1
Has abstractyes

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