Species richness in North Atlantic fish: process concealed by pattern
DOI10.5281/zenodo.4437450Zenodo4437450MaRDI QIDQ6696797
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 13 January 2021
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, the Mediterranean, the Arctic Sea. Time period 1977-2013 Methods A descriptive Generalised Additive Model was used to identify functional relationships between species richness and potential drivers, after which non-linear estimation techniques were used to parameterize: 1) a 'best' fitting model of species richness built on the functional relationships, 2) an environmental model based on latitude, longitude and depth, and mechanistic models based on 3) metabolic and 4) 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 conclusion 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 rates.
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