Achieving Reliable Estimates of the Spatial Distribution of Kelp Biomass

Abstract

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.

Variation in Population Structure and Standing Stocks of Kelp Along Multiple Environmental Gradients and Implications for Ecosystem Services

“…All the measured size-related kelp canopy plant characteristics, as well as kelp canopy density, are reduced with water depth. This was in accordance with previous studies, as the amount of light that reaches the seafloor, and thus available for kelp photosynthesis, is reduced with depth (Kain, 1971a;Vigander, 2007;Bekkby et al, 2009;van Son et al, 2020). Also, the abundance of epiphytes on the stipe decreased with depth, which is most likely directly caused by light limitation for photosynthetic species.…”

“…However, in the present study, less than 2% of the area suitable for kelp forest growth had a higher exposure than the highest wave exposure level sampled (1.8 km 2 /sec), thus the lack of data from such areas will not have biased the overall conclusions. Other Norwegian studies found up to a doubling in biomass in moderately wave exposed compared to more sheltered areas, with a levelling off toward more exposed sites (Andersen, 2007;Pedersen et al, 2012;Norderhaug et al, 2012;van Son et al, 2020). A similar, but somewhat weaker pattern was found for L. hyperborea biomass in north-western France by Gorman et al (2013) and for canopy density in the United Kingdom (Smale et al, 2016).…”

Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

“…Further investigation on the best way to incorporate such categorical variables is advised, along with possible refinement of the AOA threshold. Possible solutions include using ordered variables, or conversion of the sediment grain size classes to generalised sediment fractions [73]. We also note that the survey design (video stations) was made long before these oceanographic datasets were available.…”

Kelp in the Eastern Canadian Arctic: Current and Future Predictions of Habitat Suitability and Cover

“…Changes in environmental conditions can affect not only the suitability of kelp habitat, but also the abundance of the species that are present. Macroalgal abundance can be estimated in terms of density, biomass and percent cover (e.g., Gorman et al, 2013;Bajjouk et al, 2015;Young et al, 2015;Krumhansl et al, 2016;van Son et al, 2020). However, quantitatively calculating these various estimates of abundance for currently unstudied areas and future scenarios cannot be done using most SDM tools, which rely on presence/absence input data.…”