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Record W2217187888 · doi:10.1186/s13750-015-0050-7

What is the impact of active management on biodiversity in boreal and temperate forests set aside for conservation or restoration? A systematic map

2015· article· en· W2217187888 on OpenAlexaff
Claes Bernes, Bengt Gunnar Jonsson, Kaisa Junninen, Asko Lõhmus, S. Ellen Macdonald, Jörg Müller, Jennie Sandström

Bibliographic record

VenueEnvironmental Evidence · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Alberta
FundersRoyal Swedish Academy of SciencesStiftelsen för Miljöstrategisk Forskning
KeywordsSet-asideBiodiversityForest managementFellingEnvironmental resource managementGeographyTaigaTemperate rainforestAgroforestryEcologyEcosystemForestryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract Background The biodiversity of forests set aside from forestry is often considered best preserved by non-intervention. In many protected forests, however, remaining biodiversity values are legacies of past disturbances, e.g. recurring fires, grazing or small-scale felling. These forests may need active management to keep the characteristics that were the reason for setting them aside. Such management can be particularly relevant where lost ecological values need to be restored. In this review, we identified studies on a variety of interventions that could be useful for conserving or restoring any aspect of forest biodiversity in boreal and temperate regions. Since the review is based on Swedish initiatives, we have focused on forest types that are represented in Sweden, but such forests exist in many parts of the world. The wide scope of the review means that the set of studies is quite heterogeneous. As a first step towards a more complete synthesis, therefore, we have compiled a systematic map. Such a map gives an overview of the evidence base by providing a database with descriptions of relevant studies, but it does not synthesise reported results. Methods Searches for literature were made using online publication databases, search engines, specialist websites and literature reviews. Search terms were developed in English, Finnish, French, German, Russian and Swedish. We searched not only for studies of interventions in actual forest set-asides, but also for appropriate evidence from commercially managed forests, since some practices applied there may be useful for conservation or restoration purposes too. Identified articles were screened for relevance using criteria set out in an a priori protocol. Descriptions of included studies are available in an Excel file, and also in an interactive GIS application that can be accessed at an external website. Results Our searches identified nearly 17,000 articles. The 798 articles that remained after screening for relevance described 812 individual studies. Almost two-thirds of the included studies were conducted in North America, whereas most of the rest were performed in Europe. Of the European studies, 58 % were conducted in Finland or Sweden. The interventions most commonly studied were partial harvesting, prescribed burning, thinning, and grazing or exclusion from grazing. The outcomes most frequently reported were effects of interventions on trees, other vascular plants, dead wood, vertical stand structure and birds. Outcome metrics included e.g. abundance, richness of species (or genera), diversity indices, and community composition based on ordinations. Conclusions This systematic map identifies a wealth of evidence on the impact of active management practices that could be utilised to conserve or restore biodiversity in forest set-asides. As such it should be of value to e.g. conservation managers, researchers and policymakers. Moreover, since the map also highlights important knowledge gaps, it could inspire new primary research on topics that have so far not been well covered. Finally, it provides a foundation for systematic reviews on specific subtopics. Based on our map of the evidence, we identified four subtopics that are sufficiently covered by existing studies to allow full systematic reviewing, potentially including meta-analysis.

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)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.067
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0440.034
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.065
GPT teacher head0.268
Teacher spread0.203 · 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 designSystematic review
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

Citations71
Published2015
Admission routes1
Has abstractyes

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