Species richness in North Atlantic fish: Process concealed by pattern.

Aim: Previous analyses of marine fish species richness based on presence-absence data have shown changes with latitude and average species size, but little is known about the underlying processes. To elucidate these processes we use metabolic, neutral and descriptive statistical models to analyse how richness responds to maximum species length, fish abundance, temperature, primary production, depth, latitude and longitude, while accounting for differences in species catchability, sampling effort and mesh size. Data: Results from 53,382 bottom trawl hauls representing 50 fish assemblages. Location: The northern Atlantic from Nova Scotia to Guinea. Time period: 1977–2013. Methods: A descriptive generalized additive model was used to identify functional relationships between species richness and potential drivers, after which nonlinear estimation techniques were used to parameterize: (a) a ‘best’ fitting model of species richness built on the functional relationships, (b) an environmental model based on latitude, longitude and depth, and mechanistic models based on (c) metabolic and (d) neutral theory. Results: In the ‘best’ model the number of species observed is a lognormal function of maximum species length. It increases significantly with temperature, primary production, sampling effort, and abundance, and declines with depth and, for small species, with the mesh size in the trawl. The ‘best’ model explains close to 90% of the deviance and the neutral, metabolic and environmental models 89%. In all four models, maximum species length and either temperature or latitude account for more than half of the deviance explained. Main conclusions: The two mechanistic models explain the patterns in demersal fish species richness in the northern Atlantic almost equally well. A better understanding of the underlying drivers is likely to require development of dynamic mechanistic models of richness and size evolution, fit not only to extant distributions, but also to historical environmental conditions and to past speciation and extinction ratesS

Data underlying the publication: Offshore wind farms contribute to epibenthic biodiversity in the North Sea.

Video footage of epibenthic organisms at and around the scour protection in offshore wind farms was collected using a Remotely Operated Vehicle.  The epibenthic community structure was assessed for species abundance and species diversity (species richness (S), species evenness (E) and Shannon diversity index (H)). Species density for individual species were calculated as the number of individuals per m2 in a video frame; species density of clustering species was calculated in percentage as covered area per video frame. These different types of densities were combined by transforming them to the ordinal Marine Nature Conservation Review (MNCR) SACFOR scale.  Statistical analyses were performed using the software package R version 3.6.3 with several functions from the ‘vegan package’. Before statistical analyses, species with only 1 observation in the dataset were removed to minimize the influence of rare species in multivariate analyses. To obtain a balanced dataset, the Monte Carlo resampling strategy was applied (by 100 randomized repetitions).  For further information see manuscript.  </p

Data underlying the publication: The potential impact of human interventions at different scales in offshore wind farms to promote flat oyster (Ostrea edulis) reef development in the southern North Sea.

Stepwise procedure to quantify the effect of interventions that stimulate oyster reef development in offshore windfarms. An assessment was made in the southern North Sea, of all offshore wind farms and the infrastructure therein,  present up to the date 31 December 2020, and of areas designated for future wind farms. Wind farm data was obtained from wind farm owners, wind farm websites, and from web-based sources www.4coffshore.com and www.emodnet.ec.europe.eu.  Data on physical conditions (i.e. shear stress from Kamermans et al. (2018) and suspended particle matter from Gayer (2020)) were determined for the wind farm locations using using GoogleEarth. The effects of various interventions on oyster reef development were estimated quantitatively from assumptions based upon various previous studies. For further information see manuscript: https:/doi.org/10.1051/alr/2023001</p

Data underlying the publication: Offshore wind farms contribute to epibenthic biodiversity in the North Sea.

Video footage of epibenthic organisms at and around the scour protection in offshore wind farms was collected using a Remotely Operated Vehicle.  The epibenthic community structure was assessed for species abundance and species diversity (species richness (S), species evenness (E) and Shannon diversity index (H)). Species density for individual species were calculated as the number of individuals per m2 in a video frame; species density of clustering species was calculated in percentage as covered area per video frame. These different types of densities were combined by transforming them to the ordinal Marine Nature Conservation Review (MNCR) SACFOR scale.  Statistical analyses were performed using the software package R version 3.6.3 with several functions from the ‘vegan package’. Before statistical analyses, species with only 1 observation in the dataset were removed to minimize the influence of rare species in multivariate analyses. To obtain a balanced dataset, the Monte Carlo resampling strategy was applied (by 100 randomized repetitions).  For further information see manuscript.  </p

Data underlying the publication: The potential impact of human interventions at different scales in offshore wind farms to promote flat oyster (Ostrea edulis) reef development in the southern North Sea.

Stepwise procedure to quantify the effect of interventions that stimulate oyster reef development in offshore windfarms. An assessment was made in the southern North Sea, of all offshore wind farms and the infrastructure therein,  present up to the date 31 December 2020, and of areas designated for future wind farms. Wind farm data was obtained from wind farm owners, wind farm websites, and from web-based sources www.4coffshore.com and www.emodnet.ec.europe.eu.  Data on physical conditions (i.e. shear stress from Kamermans et al. (2018) and suspended particle matter from Gayer (2020)) were determined for the wind farm locations using using GoogleEarth. The effects of various interventions on oyster reef development were estimated quantitatively from assumptions based upon various previous studies. For further information see manuscript: https:/doi.org/10.1051/alr/2023001</p