Anna Norberg

TL;DR: HMSC is operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and is implemented as R- and Matlab-packages which enable computationally efficient analyses of large data sets.

TL;DR: This work compared the predictive performance of 33 variants of 15 widely applied and recently emerged species distribution model approaches in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDM that model each species individually combining the predictions afterward.

TL;DR: The R package “traitor” is provided to facilitate assessments of missing trait data and it is shown that where transformation improved the normality of the trait data, FD values from incomplete datasets were more accurate than before transformation.

TL;DR: The results show that functional traits are linked to the responses of wood-inhabiting fungi to variation in their environment, and thus environmental changes alter ecosystem functions via promoting or reducing species with different fruit-body types.

TL;DR: Joint species distribution modelling can incorporate both within-host dynamics of several taxa and host characteristics to model potential interactions in intestinal community, and provides new hypothesis for interactions between and among parasites and bacterial microbiota to be tested further with experimental studies.