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Open AccessJournal ArticleDOI

Impacts of salvage logging on biodiversity: A meta‐analysis

TLDR
The results suggest that salvage logging is not consistent with the management objectives of protected areas, and substantial changes, such as the retention of dead wood in naturally disturbed forests, are needed to support biodiversity.
Abstract
Logging to "salvage" economic returns from forests affected by natural disturbances has become increasingly prevalent globally Despite potential negative effects on biodiversity, salvage logging is often conducted, even in areas otherwise excluded from logging and reserved for nature conservation, inter alia because strategic priorities for post-disturbance management are widely lackingA review of the existing literature revealed that most studies investigating the effects of salvage logging on biodiversity have been conducted less than 5 years following natural disturbances, and focused on non-saproxylic organismsA meta-analysis across 24 species groups revealed that salvage logging significantly decreases numbers of species of eight taxonomic groups Richness of dead wood dependent taxa (ie saproxylic organisms) decreased more strongly than richness of non-saproxylic taxa In contrast, taxonomic groups typically associated with open habitats increased in the number of species after salvage loggingBy analysing 134 original species abundance matrices, we demonstrate that salvage logging significantly alters community composition in 7 of 17 species groups, particularly affecting saproxylic assemblagesSynthesis and applications Our results suggest that salvage logging is not consistent with the management objectives of protected areas Substantial changes, such as the retention of dead wood in naturally disturbed forests, are needed to support biodiversity Future research should investigate the amount and spatio-temporal distribution of retained dead wood needed to maintain all components of biodiversity

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Citations
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Where are Europe's last primary forests?

TL;DR: In this article, Sabatini et al. discuss the importance of gender diversity in soccer and discuss the role of gender in the sport of soccer in terms of sportswriting.
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Mapping the forest disturbance regimes of Europe

TL;DR: In this paper, the authors used satellite data to map three decades of forest disturbances across continental Europe, and analyzed the patterns and trends in disturbance size, frequency and severity, finding that 17% of Europe's forest area was disturbed by anthropogenic and/or natural causes.
Journal ArticleDOI

Young and old forest in the boreal: critical stages of ecosystem dynamics and management under global change

TL;DR: In this paper, the authors studied the change of boreal forest age class distributions and structures due to the use of clearcut short-rotation harvesting, more frequent and severe natural disturbances due to climate warming in certain regions.
References
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Journal ArticleDOI

Conducting Meta-Analyses in R with the metafor Package

TL;DR: The metafor package provides functions for conducting meta-analyses in R and includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models.
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Simultaneous inference in general parametric models.

TL;DR: This paper describes simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters, and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalizedlinear models, linear mixed effects models, the Cox model, robust linear models, etc.
Journal ArticleDOI

Generalized linear mixed models: a practical guide for ecology and evolution

TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
Journal ArticleDOI

Statistical Methods for Meta-Analysis.

TL;DR: In this paper, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
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