Is Diversity the Missing Link in Coastal Fisheries Management?

Fisheries management has historically focused on the population elasticity of target fish based primarily on demographic modeling, with the key assumptions of stability in environmental conditions and static trophic relationships. The predictive capacity of this fisheries framework is poor, especially in closed systems where the benthic diversity and boundary effects are important and the stock levels are low. Here, we present a probabilistic model that couples key fish populations with a complex suite of trophic, environmental, and geomorphological factors. Using 41 years of observations we model the changes in eastern Baltic cod (Gadus morhua), herring (Clupea harengus), and Baltic sprat (Sprattus sprattus balticus) for the Baltic Sea within a Bayesian network. The model predictions are spatially explicit and show the changes of the central Baltic Sea from cod-to sprat-dominated ecology over the 41 years. This also highlights how the years 2004 to 2014 deviate in terms of the typical cod–environment relationship, with environmental factors such as salinity being less influential on cod population abundance than in previous periods. The role of macrozoobenthos abundance, biotopic rugosity, and flatfish biomass showed an increased influence in predicting cod biomass in the last decade of the study. Fisheries management that is able to accommodate shifting ecological and environmental conditions relevant to biotopic information will be more effective and realistic. Non-stationary modelling for all of the homogeneous biotope regions, while acknowledging that each has a specific ecology relevant to understanding the fish population dynamics, is essential for fisheries science and sustainable management of fish stocks

3-(2H-1,3-Benzodioxol-5-ylmeth­yl)-2-(2-meth­oxy­phen­yl)-1,3-thia­zolidin-4-one

The title mol­ecule, C18H17NO4S, features a 1,3-thia­zolidine ring that is twisted about the S—C(methyl­ene) bond. With reference to this ring, the 1,3-benzodioxole and benzene rings lie to either side and form dihedral angles of 69.72 (16) and 83.60 (14)°, respectively, with the central ring. Significant twisting in the mol­ecule is confirmed by the dihedral angle of 79.91 (13)° formed between the outer rings. Linear supra­molecular chains along the a-axis direction mediated by C—H⋯O inter­actions feature in the crystal packing

4-(Pyrimidin-2-yl)-1-thia-4-aza­spiro­[4.5]decan-3-one

The title compound, C12H15N3OS, features an envelope conformation for the 1,3-thia­zolidin-4-one ring with the S atom as the flap atom. The pyrimidine ring is almost orthogonal to the 1,3-thia­zolidin-4-one ring as indicated by the N—C—C—N torsion angle of −111.96 (18)°. Supra­molecular dimers are formed in the crystal structure through the agency of C—H⋯O contacts occurring between centrosymmetrically related mol­ecules. These are linked into supra­molecular tapes along [100] via C—H⋯S contacts

(E)-1-(2,4-Dinitro­phen­yl)-2-pentyl­idenehydrazine

The title compound, C11H14N4O4, is essentially planar with an r.m.s. deviation for the 19 non-H atoms of 0.152 Å. The conformation about the C=N bond is E, and the mol­ecule has a U-shape as the butyl group folds over towards the aromatic system. An intra­molecular C—H⋯N inter­action occurs. The crystal packing is dominated by N—H⋯O hydrogen bonding and C—H⋯O contacts, leading to twisted zigzag supra­molecular chains along the c direction. The crystal packing brings two nitro O atoms into an unusually close proximity of 2.686 (4) Å. While the nature of this inter­action is not obvious, there are several precendents for such short nitro–nitro O⋯O contacts of less than 2.70 Å in the crystallographic literature

Fish stock development in the central Baltic Sea (1976-2000) in relation to variability in the environment

Fish stock development in the Central Baltic Sea (1976-2000) in relation to variability in the environment - DTU Orbit (15/04/14) Fish stock development in the Central Baltic Sea (1976-2000) in relation to variability in the environment - DTU Orbit (15/04/14) Köster F, Möllmann C, Neuenfeldt S, Vinther M, St. John M, Tomkiewicz J et al. Fish stock development in the Central Baltic Sea (1976-2000) in relation to variability in the environment. I C E S Marine Science Symposia. 2003;219:294-30

A statistical model for estimation of fish density including correlation in size, space, time and between species from research survey data

Trawl survey data with high spatial and seasonal coverage were analysed using a variant of the Log Gaussian Cox Process (LGCP) statistical model to estimate unbiased relative fish densities. The model estimates correlations between observations according to time, space, and fish size and includes zero observations and over-dispersion. The model utilises the fact the correlation between numbers of fish caught increases when the distance in space and time between the fish decreases, and the correlation between size groups in a haul increases when the difference in size decreases. Here the model is extended in two ways. Instead of assuming a natural scale size correlation, the model is further developed to allow for a transformed length scale. Furthermore, in the present application, the spatial- and size-dependent correlation between species was included. For cod (Gadus morhua) and whiting (Merlangius merlangus), a common structured size correlation was fitted, and a separable structure between the time and space-size correlation was found for each species, whereas more complex structures were required to describe the correlation between species (and space-size). The within-species time correlation is strong, whereas the correlations between the species are weaker over time but strong within the year