While symbiotic associations between anemones and crustaceans appear to be common in tropical waters, few such associations are known from temperate waters, except for the symbiosis between hermit crabs and anemones. In this study, observations made with ROVs (remotely operated vehicles) suggested that certain shrimps (Pandalus borealis, P. propinquus, P. montagui and in particular Spirontocaris liljeborgii and Lebbeus polaris) associate with the anemone Bolocera tuediae and the cerianthid Pachycerianthus multiplicatus by aggregating beneath their tentacles. The lithodid crab Lithodes maja was also observed associating with B. tuediae. Laboratory experiments suggested that female crabs have a stronger association than males. The associations are presumably facultative commensalistic, as the species of crustaceans live as non-symbionts on the sea floor as well, and the 2 anthozoans do not seem to gain any benefits from the associations. In the field, S. liljeborgii had the closest association with both species of anthozoans, while B. tuediae was the preferred host of all associating species. The main benefit for the crustaceans to be associated with the anthozoans is protection against predators. In the case of the shrimps, access to a food source is probably also important.
Increased focus on predictive aspects of ecology has recently been urged by scientists and policy makers to provide solutions to pressing societal needs. Current challenges include the large knowledge gap on the spatial distribution of marine biodiversity, and its associated goods and services, and the dependence of model performance on spatial resolution. We evaluated the importance of resolution on the predictive power and precision of empirical models of distributions of marine sessile invertebrates and macroalgae along the Swedish west coast. This was done by simulating the limits to prediction, based on 2 independent simulated proportions of biological variables, and comparing these limits to observed models at different resolutions. Simulations showed the highest achievable predictive power (r 2) and precision (RMSE) of models at fine resolutions (~1 m). In contrast to the simulations, the performance of quantitative models was better at relatively coarse resolutions (~100 m). Increased model performance at coarse resolutions could not be explained by differences in sampling or spatial variability. Instead, the improvement is likely caused by the mechanistic coupling (direct or indirect) between predictor variables, depth and hard substratum cover and patterns at coarser scales, whereas complex processes, e.g. biological interactions, shape patterns at finer scales. This match between resolution and the scale at which environmental variables operate may differ among systems, which could explain the discrepancy in outcomes between our study and previous studies. Furthermore, we provide an approach for error analysis that identifies contributions of different model components to the total uncertainty, thus facilitating model optimization.
It is widely acknowledged that mapping of benthic diversity is needed to aid in the management and conservation of marine ecosystems, but the choice of scale is contingent upon the patterns of spatial structure inherent to the benthos, which are often unknown. In this paper, spatial autocorrelation analysis is used to detect and describe fine-scale patterns of spatial structure in assemblages of epibenthic megafauna of the seabed below 20 m depth at the Koster fjord/archipelago area (Sweden). Presence/absence of benthic organisms was obtained from video images, which had been collected by means of a remotely operated vehicle. For sample sizes (grain) of <1 m 2 , and maximum between-sample distance (lag) of 200 m, rank-correlograms revealed the presence of patches in all 12 sites surveyed, and faunal homogeneity (positive spatial autocorrelation) was always detected within distances <20 m, though there was variation across sites in the sizes of patches. These findings were further used to resample the data at coarser grain sizes, to enable exploring of the faunal composition of the patches. We conclude that spatial autocorrelation analysis can greatly improve the design of sampling schemes by ensuring parsimoniousness, and maximizing the chances of detecting patterns. The procedure shown here is especially well suited to carry out subsequent mapping because it can readily discriminate between types of biotopes.
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