Most species-abundance matrices subjected to ordination are analysed by applying one ordination method to one single weighting (transformation) of the raw abundance values. We argue that such an approach is sub-optimal for identification of species-environment relationships. In order to capture the full range of qualitative and quantitative components of variation unique to each species-abundance matrix, data sets covering the full range of abundance weights from presence/absence to raw abundance data should be subjected to ordination. Furthermore, two or more ordination methods should be used in parallel to enhance detection of artifacts in the results. We describe and exemplify a multiple parallel ordination (MPO) procedure, which ensures that both qualitative and quantitative properties of the data set are revealed. This procedure implies that different ordination methods are applied in parallel, each with different weightings of elements in the species-abundance matrix. Two species-abundance matrices are used to ex-sommerfeltia

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Kelp forests are highly productive systems that are important ecologically and commercially as well as in a blue carbon perspective. Given their importance, there is an urgent need to achieve reliable estimates of the spatial distribution of their biomass. Species distribution modelling is a powerful tool for producing such estimates, but it requires a solid framework, including important environmental covariates that have a direct effect on their biomass, a proper sampling strategy, and an independent evaluation dataset. Using Laminaria hyperborea as a model species, we developed a modelling framework considering these requirements and necessary steps to produce reliable predictions. Our modelling framework included proportion of hard substrate and bottom wave exposure, both crucial covariates that have a direct effect on the biomass of L. hyperborea, but rarely included in modelling studies. Furthermore, we devised a sampling strategy with field observations covering the whole environmental covariate space present in the study area. Subsequently, we fitted GAMs relating the field observations of the biomass of L. hyperborea to relevant environmental covariates. The best model containing the predictors bottom wave exposure, depth, and proportion hard substrate explained most of the variance in the dataset (83.1% deviance explained). This model was then used to predict the spatial distribution of biomass across the whole study area. To assess the reliability of the biomass predictions, we used an independent dataset of L. hyperborea biomass observations from the same area. This independent dataset correlated very well with spatial predictions of biomass based on our best model (R = 0.85). In total, we predicted a biomass of 457,000 tonnes in a 1,150 km 2 study area on the West coast of Norway. Our modelling framework provides the means for developing a biomass model on a broader geographical scale. Such a model will be invaluable in improving kelp management regimes as well as for estimating the contribution of kelp forests to ecosystem services such as carbon sequestration and climate budgets.

Abstract:The biological reason for a species' presence under given environmental conditions is that the species possesses traits that make establishment and survival, usually also reproduction, possible under these conditions. Biological traits analysis (BTA), when coupled with environmental variables, can provide information regarding which traits are to be expected for a given environmental state. As such, BTA provides complementary information to multivariate analysis of community data based on species composition. In this study, BTA was conducted on a data set of sediment macrofauna collected from a temperate fjord system and related to a wide range of environmental variables. The biological traits were analysed in a multiple parallel ordination framework, which can enhance the reliability of the extracted gradient structure and evaluate the importance of weight given to abundance. Two traitclines, gradients in functional attributes of the species, were found in the study area. The first traitcline was related to bottom currents and sediment constituents while the second traitcline was related to current strength and particle deposition on the bottom. Together with a companion study of gradients in species composition (coenoclines), this study of functional features (traitclines) illustrates that the species composition may consist of taxonomically different, but functionally similar species, giving rise to strong gradients in species composition but weak gradients in trait category composition when subjected to ordination analyses.