Fossils document the existence of trees and wood-associated organisms from almost 400 million years ago, and today there are between 400,000 and 1 million wood-inhabiting species in the world. This is the first book to synthesise the natural history and conservation needs of wood-inhabiting organisms. Presenting a thorough introduction to biodiversity in decaying wood, the book studies the rich diversity of fungi, insects and vertebrates that depend upon dead wood. It describes the functional diversity of these organisms and their specific habitat requirements in terms of host trees, decay phases, tree dimensions, microhabitats and the surrounding environment. Recognising the threats posed by timber extraction and forest management, the authors also present management options for protecting and maintaining the diversity of these species in forests as well as in agricultural landscapes and urban parks.

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Abstract. Signals of species interactions can be inferred from survey data by asking if some species occur more or less often together than what would be expected by random, or more generally, if any structural aspect of the community deviates from that expected from a set of independent species. However, a positive (or negative) association between two species does not necessarily signify a direct or indirect interaction, as it can result simply from the species having similar (or dissimilar) habitat requirements. We show how these two factors can be separated by multivariate logistic regression, with the regression part accounting for species-specific habitat requirements, and a correlation matrix for the positive or negative residual associations. We parameterize the model using Bayesian inference with data on 22 species of wood-decaying fungi acquired in 14 dissimilar forest sites. Our analyses reveal that some of the species commonly found to occur together in the same logs are likely to do so merely by similar habitat requirements, whereas other species combinations are systematically either over-or underrepresented also or only after accounting for the habitat requirements. We use our results to derive hypotheses on species interactions that can be tested in future experimental work.

Abstract. Signals of species interactions can be inferred from survey data by asking if some species occur more or less often together than what would be expected by random, or more generally, if any structural aspect of the community deviates from that expected from a set of independent species. However, a positive (or negative) association between two species does not necessarily signify a direct or indirect interaction, as it can result simply from the species having similar (or dissimilar) habitat requirements. We show how these two factors can be separated by multivariate logistic regression, with the regression part accounting for species-specific habitat requirements, and a correlation matrix for the positive or negative residual associations. We parameterize the model using Bayesian inference with data on 22 species of wood-decaying fungi acquired in 14 dissimilar forest sites. Our analyses reveal that some of the species commonly found to occur together in the same logs are likely to do so merely by similar habitat requirements, whereas other species combinations are systematically either over-or underrepresented also or only after accounting for the habitat requirements. We use our results to derive hypotheses on species interactions that can be tested in future experimental work.