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CIFOR-ICRAF s’attaque aux défis et aux opportunités locales tout en apportant des solutions aux problèmes mondiaux concernant les forêts, les paysages, les populations et la planète.

Nous fournissons des preuves et des solutions concrètes pour transformer l’utilisation des terres et la production alimentaire : conserver et restaurer les écosystèmes, répondre aux crises mondiales du climat, de la malnutrition, de la biodiversité et de la désertification. En bref, nous améliorons la vie des populations.

CIFOR–ICRAF publishes over 750 publications every year on agroforestry, forests and climate change, landscape restoration, rights, forest policy and much more – in multiple languages.

CIFOR–ICRAF addresses local challenges and opportunities while providing solutions to global problems for forests, landscapes, people and the planet.

We deliver actionable evidence and solutions to transform how land is used and how food is produced: conserving and restoring ecosystems, responding to the global climate, malnutrition, biodiversity and desertification crises. In short, improving people’s lives.

Pseudoreplication in tropical forests and the resulting effects on biodiversity conservation

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Tropical forest ecosystems are threatened by habitat conversion and other anthropogenic actions. Timber production forests can augment the conservation value of primary forest reserves, but studies of logging effects often yield contradictory findings and thus inhibit efforts to develop clear conservation strategies. We hypothesized that much of this variability reflects a common methodological flaw, simple pseudoreplication, that confounds logging effects with preexisting spatial variation. We reviewed recent studies of the effects of logging on biodiversity in tropical forests (n = 77) and found that 68% were definitively pseudoreplicated while only 7% were definitively free of pseudoreplication. The remaining proportion could not be clearly categorized. In addition, we collected compositional data on 7 taxa in 24 primary forest research plots and systematically analyzed subsets of these plots to calculate the probability that a pseudoreplicated comparison would incorrectly identify a treatment effect. Rates of false inference (i.e., the spurious detection of a treatment effect) were >0.5 for 2 taxa, 0.3-0.5 for 2 taxa, and

DOI:
https://doi.org/10.1111/cobi.12004
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