One of the most critical ecosystem functions provided by shallow coastal habitats is as nurseries for the juveniles of fish. Many of the studies that have assessed the nursery function of structurally complex coastal habitats have compared seagrass with unstructured sand and mud and as such, seagrass has emerged as the most important coastal nursery habitat for juvenile fishes. Although considerably less work has focussed on the nursery provision of structurally complex macroalgae within coastal nursery seascapes, recent work has started to highlight that the nursery provision of canopy-forming macroalgae may in fact be comparable with that of seagrass. This review collates research published on the important nursery role of macroalgae within both tropical and temperate coastal seascapes and highlights the importance of smaller canopy-forming brown algae from the Fucalean genera (particularly Sargassum spp.) as core nursery areas for juvenile fishes, particularly emperors (Lethrinidae), rabbitfishes (Siganidae), wrasse and parrotfishes (Labridae), goatfishes (Mullidae), groupers (Serranidae), surgeonfish (Acanthuridae) and damselfish (Pomacentridae) within tropical back-reef systems. Similarly, in temperate nursery seascapes, fucoid (Cystoseira spp.) and macroalgae-dominated reefs were important nursery habitats for damselfish (Chromis chromis), groupers and numerous species of wrasse and sparids (Sparidae). Although the overall density of juvenile fish was not shown to be higher in kelp relative to other temperate nursery habitats, kelp was important in the recruitment of Notolabrus celiodotus (wrasse), Paralabrax clathrus (Serranidae), Brachyistius frenatus (Embiotocidae), Heterostichus rostratus (Clinidae) and Sebastes spp. (Scorpaenidae). Although not interchangeable (fish communities were often different), the nursery function of structurally complex macroalgae was found to be similar to that of seagrass in both temperate and tropical seascapes.

We report the crystal structures of four synthetic members of the variscite group (space group type Pbca) and of bonacinaite, the first naturally occurring scandium arsenate member of the metavariscite group. All structures were determined using single-crystal X-ray intensity data. The following members were all synthesised under either mild hydrothermal conditions or by wet-chemical methods (<90°C). CrAsO4⋅2H2O (deep green): a = 8.894(2), b = 9.946(2), c = 10.206(2) Å and V = 902.8(3) Å3; R1 = 2.14%. Tl3+PO4⋅2H2O (colourless): a = 10.2848(7), b = 8.8578(6), c = 10.3637(7) Å and V = 944.14(11) Å3 (data at –173°C); R1 = 2.56%. MnSeO4⋅2H2O (pale pink): a = 10.441(2), b = 9.2410(18), c = 10.552(2) Å and V = 1018.1(3) Å3; R1 = 2.19%. A different method of preparation of MnSeO4⋅2H2O yielded crystals with very similar unit-cell parameters, a = 10.4353(5), b = 9.2420(5) and c = 10.5349(6) Å; R1 = 2.25%. CdSeO4⋅2H2O (colourless) has a = 10.481(1), b = 9.416(1), c = 10.755(1) Å and V = 1061.4(2) Å3; R1 = 1.53%. The thermal behaviour of the two selenate members was studied by a combination of DSC and TG, supplemented by PXRD. Bonacinaite (IMA2018-056), metavariscite-type natural (Sc,Al)(As,P)O4⋅2H2O (ideally ScAsO4⋅2H2O), crystallises in the space group P21/n, with a = 5.533(1), b = 10.409(2), c = 9.036(2) Å, β = 91.94(3)° and V = 520.11(18) Å3; R1 = 3.66%. The structural formula, supported by chemical analysis, is (Sc0.807(1)Al0.193)(As0.767(7)P0.233)O4⋅2H2O. All structures are based on frameworks built by corner-sharing of TO4 tetrahedra (T = P5+, As5+ or Se6+) with MO4(H2O)2 (M = Mn2+, Cd2+, Cr3+, Sc3+ or Tl3+) octahedra. The flexible frameworks are reinforced by partly bifurcated, strong to weak hydrogen bonds.

The crystal chemistry of all known synthetic and natural members of the variscite and metavariscite groups is discussed and compared, and the relative stabilities are evaluated. With the aid of the COMPSTRU program (Bilbao Crystallographic Server), a quantitative comparison of the crystal structures in both groups is given. Calculations of the structural and topological complexity reveal that the metavariscite structure type is structurally and topologically simpler than that of variscite. It is suggested that metavariscite and phosphosiderite are metastable kinetically stabilised phases, in contrast to thermodynamically stable variscite and strengite, respectively. The 3D frameworks of the members of both groups have been shown to be potential electrode materials for rechargeable Li ion batteries.

Increasing complexity of products and design processes leads to intensive collaboration of different stakeholders in technical product development. This causes a demand for suitable methods of collaboration across department interfaces, as between design and simulation. The paper investigates typical barriers of collaboration at this interface and measures to overcome them. Methods of complexity management form links based on literature and empirical data from online surveys and interview studies. The framework uses a set of structural metrics to analyse collaboration networks systematically.

Evdokimovite, ideally Tl4(VO)3(SO4)5(H2O)5, was found in a fumarole of the 1st cinder cone of the North Breach of the Great Fissure Tolbachik volcano eruption of 1975–1976, Kamchatka Peninsula, Russia. Evdokimovite occurs as thin, colourless needles up to 0.09 mm long associated with shcherbinaite, pauflerite, bobjonesite, markhininite, karpovite and microcrystalline Mg, Al, Fe and Na sulfates. Evdokimovite is monoclinic, P21/n, a = 6.2958(14), b = 10.110(2), c = 39.426(11) Å , β = 90.347(6)º, V = 2509.4(10) Å3 and Z = 4 (from single-crystal diffraction data). The eight strongest lines of the powder X-ray diffraction pattern are (I/d/hkl): 57/9.793/011, 100/8.014/013, 26/6.580/006, 19/ 4.011/026, 29/3.621/118, 44/3.522/125, 19/3.010/036, 21/2.974/212. Chemical composition determined by the electron microprobe analysis is (wt.%): Tl2O 55.40, VO2 14.92, SO3 25.83, H2O 5.75, total 101.90. The empirical formula for evdokimovite calculated on the basis of (Tl + V + S) = 12 a.p.f.u. is Tl4.10V2.83S5.07H10.00O27.94. The simplified formula is Tl4(VO)3(SO4)5(H2O)5. The crystal structure was solved by direct methods and refined to R1 = 0.11 on the basis of 3660 independent observed reflections. V4+O6 octahedra and SO4 tetrahedra share common corners to form two types of vanadyl-sulfate chains, [(VO)(H2O)2(SO4)2]2– and [(VO)2(H2O)3(SO4)3]2–. Thallium atoms are located in between the chains. The structure can be described as a stacking of layers of two types, A and B. The A layer contains [(VO)2(H2O)3(SO4)3]2– chains and the Tl2 and Tl3 atoms, whereas the B layer contains [(VO)(H2O)2(SO4)2]2– chains and the Tl1 atoms. Stacking of the layers can be described as ...A’*BAA’B*A*..., where A and A’ denote A layers with opposite orientations of the [(VO)2(H2O)3(SO4)3]2– chains, and the A* and B* layers are rotated by 180º relative to the A and B layers, respectively. [(VO)2(H2O)3(SO4)3]2– chains are modulated and are arranged to form elliptical tunnels hosting disordered Tl(4), Tl(4A) and Tl(4B) sites. The new mineral is named in honour of Professor Mikhail Dmitrievich Evdokimov (1940–2010), formerly of the Department of Mineralogy, St Petersburg State University, for his contributions to mineralogy and petrology, and especially for teaching mineralogy to several generations of students at the University. Evdokimovite is the most complex V4+ sulfate known to date with structural information amounting to 1130 bits per unit cell, which places evdokimovite among minerals with the complexity of the vesuvianite group.

Structural complexity of minerals is characterized using information contents of their crystal structures calculated according to the modified Shannon formula. The crystal structure is considered as a message consisting of atoms classified into equivalence classes according to their distribution over crystallographic orbits (Wyckoff sites). The proposed complexity measures combine both size- and symmetry-sensitive aspects of crystal structures. Information-based complexity parameters have been calculated for 3949 structure reports on minerals extracted from the Inorganic Crystal Structure Database. According to the total structural information content, IG, total, mineral structures can be classified into very simple (0–20 bits), simple (20–100 bits), intermediate (100–500 bits), complex (500–1000 bits), and very complex (> 1000 bits). The average information content for mineral structures is calculated as 228(6) bits per structure and 3.23(2) bits per atom. Twenty most complex mineral structures are (IG, total in bits): paulingite (6766.998), fantappieite (5948.330), sacrofanite (5317.353), mendeleevite-(Ce) (3398.878), bouazzerite (3035.201), megacyclite (2950.928), vandendriesscheite (2835.307), giuseppetite (2723.097), stilpnomelane (2483.819), stavelotite-(La) (2411.498), rogermitchellite (2320.653), parsettensite (2309.820), apjohnite (2305.361), antigorite (m = 17 polysome) (2250.397), tounkite (2187.799), tschoertnerite (2132.228), farneseite (2094.012), kircherite (2052.539), bannisterite (2031.017), and mutinaite (2025.067). The following complexity-generating mechanisms have been recognized: modularity, misfit relationships between structure elements, and presence of nanoscale units (clusters or tubules). Structural complexity should be distiguished from topological complexity. Structural complexity increases with decreasing temperature and increasing pressure, though at ultra-high pressures, the situation may be different. Quantitative complexity measures can be used to investigate evolution of information in the course of global and local geological processes involving formation and transformation of crystalline phases. The information-based complexity measures can also be used to estimate the 'ease of crystallization' from the viewpoint of simplexity principle proposed by J.R. Goldsmith (1953) for understanding of formation of simple and complex mineral phases under both natural and laboratory conditions. According to the proposed quantitative approach, the crystal structure can be viewed as a reservoir of information encoded in its complexity. Complex structures store more information than simple ones. As erasure of information is always associated with dissipation of energy, information stored in crystal structures of minerals must have an important influence upon natural processes. As every process can be viewed as a communication channel, the mineralogical history of our planet on any scale is a story of accumulation, storage, transmission and processing of structural information.

Coastal ecosystems have been increasingly subjected to poor water quality. Remote sensing has been used to monitor water quality, but few studies have integrated remotely sensed data with compositional and/or abundance data of coral reef taxa. In the present study, fish biomass was assessed along the Jakarta Bay Thousand Island reef system and variation in the biomass of selected fish families related to substrate cover and remotely sensed data. Overall, fish biomass and the biomass of each of the families Acanthuridae, Apogonidae, Caesionidae, Chaetodontidae, Ephippidae, Pomacentridae, Labridae and the subfamily Scaridae were much higher mid- and offshore than inshore. Substrate cover and chlorophyll-a concentrations proved to be significant predictors of spatial variation in fish biomass, suggesting an important impact of reef degradation and eutrophication on reef fish abundance.

Beds of Modiolus modiolus, in areas of moderate to strong tidal currents, develop into reefs with a relief of wave like undulations 0.09–0.45 m in amplitude and length scales of 6–18 m. Cores taken by diver operated suction sampler were targeted at positions on the ridges and troughs of a reef, in the Irish Sea off north-west Wales, allowing the fauna to be compared between adjoining ridges and troughs. Sessile epifauna was mostly attached to the larger mussels clumped together on the ridges. The crevice fauna and infauna were also nearly three times more abundant on the ridges, but the lists of species were similar from the two sub-habitats. Species richness was higher on the ridges, however, diversity and evenness measures were similar for ridges and trough samples. The Modiolus sub-habitats were found to be distinct from other macrofaunal assemblages in the wider southern Irish Sea. Deposition of faecal pellets in the spatially complex habitat amongst the mussels provided conditions suitable for an infauna more typical of inshore muddy sands enriched by organic matter. The scale of the ridge and trough morphology may increase variability between replicates when grabs are used remotely to sample this type of biogenic feature.