No abstract

A series of candidate statistical indices is used in an attempt to capture spatial patterns of fish populations from research survey data. To handle diffuse population limits, indices are designed not to depend on arbitrary delineation of the domain. They characterize the location (centre of gravity and spatial patches), the occupation of space (inertia, isotropy, positive area, spreading area, and equivalent area), statistical dispersion (Gini index and coefficient of variation of strictly positive densities), and microstructure. Collocation between different ages and years is summarized by a global index of collocation. Indices are estimated for hake from a bottom-trawl data series in the Bay of Biscay in autumn of 1987-2004. The study provides a detailed description of the spatial patterns of different hake age groups, age 3 appearing to be a turning point in these dynamics. Capturing spatial patterns through indices allows the comparison of surveyed populations and identification of trends and outliers in the time-series. Spatial indices are used in a multivariate approach to obtain an overview of the relationships between the different spatial indices characterizing the spatial behaviour of six age groups of hake, and to assess their persistence through time.

M. 2006. Waves of agitation inside anchovy schools observed with multibeam sonar: a way to transmit information in response to predation. e ICES Journal of Marine Science, 63: 1405e1417.Most pelagic fish live in schools. To allow fast reactions, for instance to predator attacks, these collective structures require behavioural mechanisms authorizing fast, coordinated movements. Considering the large number of individuals constituting a school of small pelagic fish, a crucial premise to coordinated movements and school reorganization is an ability to transfer quickly and efficiently information across the whole collective structure. We observed anchovy school movements and reactions to sea-lion attacks while the ship was drifting in Peruvian waters. The main process of information transfer we could observe was that of waves of agitation crossing large anchovy schools. The average speed of these waves (7.45 m s À1 ) was much greater than the average 0.3 m s À1 school speeds measured during this experiment. The internal organization of each school modified dramatically after the waves of agitation had crossed them. Changes in school external morphology and internal structure were described and measured using geostatistics. Our results show that information transfer is a crucial process for the cohesion and plasticity of schools. As such, it allows efficient reactions of schools of pelagic fish to variations in their immediate environment in general, and to predation in particular.

Shin, Y-J., Shannon, L. J., Bundy, A., Coll, M., Aydin, K., Bez, N., Blanchard, J. L., Borges, M. F., Diallo, I., Diaz, E., Heymans, J. J., Hill, L., Johannesen, E., Jouffre, D., Kifani, S., Labrosse, P., Link, J. S., Mackinson, S., Masski, H., Möllmann, C., Neira, S., Ojaveer, H., ould Mohammed Abdallahi, K., Perry, I., Thiao, D., Yemane, D., and Cury, P. M. 2010. Using indicators for evaluating, comparing, and communicating the ecological status of exploited marine ecosystems. 2. Setting the scene. – ICES Journal of Marine Science, 67: 692–716. Background is provided to the selection of ecological indicators by the IndiSeas Working Group, and the methodology adopted for analysis and comparison of indicators across exploited marine ecosystems is documented. The selected indicators are presented, how they are calculated is explained, and the philosophy behind the comparative approach is given. The combination of selected indicators is intended to reflect different dynamics, tracking processes that display differential responses to fishing, and is meant to provide a complementary means of assessing marine ecosystem trends and states. IndiSeas relied on inputs and insights provided by the local experts from participating ecosystems, helping to understand state and trend indicators and to disentangle the effect of other potential ecosystem drivers, such as climate variability. This project showed that the use of simple and available indicators under an ecosystem approach can achieve a real, wide-reaching evaluation of marine ecosystem status caused by fishing. This is important because the socio-economics of areas where fishing activities develop differs significantly around the globe, and in many countries, insufficient data are available for complex and exhaustive analyses.

Understanding the ecological and anthropogenic drivers of population dynamics requires detailed studies on habitat selection and spatial distribution. Although small pelagic fish aggregate in large shoals and usually exhibit important spatial structure, their dynamics in time and space remain unpredictable and challenging. In the Gulf of Lions (north-western Mediterranean), sardine and anchovy biomasses have declined over the past 5 years causing an important fishery crisis while sprat abundance rose. Applying geostatistical tools on scientific acoustic surveys conducted in the Gulf of Lions, we investigated anchovy, sardine and sprat spatial distributions and structures over 10 years. Our results show that sardines and sprats were more coastal than anchovies. The spatial structure of the three species was fairly stable over time according to variogram outputs, while year-to-year variations in kriged maps highlighted substantial changes in their location. Support for the McCall's basin hypothesis (covariation of both population density and presence area with biomass) was found only in sprats, the most variable of the three species. An innovative method to investigate species collocation at different scales revealed that globally the three species strongly overlap. Although species often co-occurred in terms of presence/absence, their biomass density differed at local scale, suggesting potential interspecific avoidance or different sensitivity to local environmental characteristics. Persistent favourable areas were finally detected, but their environmental characteristics remain to be determined.

Since the mid-1990s, drifting Fish Aggregating Devices (dFADs), artificial floating objects designed to aggregate fish, have become an important mean by which purse seine fleets catch tropical tunas. Mass deployment of dFADs, as well as the massive use of GPS buoys to track dFADs and natural floating objects, has raised serious concerns for the state of tropical tuna stocks and ecosystem functioning. Here, we combine tracks from a large proportion of the French GPS buoys from the Indian and Atlantic oceans with data from observers aboard French and Spanish purse seiners and French logbook data to estimate the total number of dFADs and GPS buoys used within the main fishing grounds of these two oceans over the period 2007–2013. In the Atlantic Ocean, the total number of dFADs increased from 1175 dFADs active in January 2007 to 8575 dFADs in August 2013. In the Indian Ocean, this number increased from 2250 dFADs in October 2007 to 10 300 dFADs in September 2013. In both oceans, at least a fourfold increase in the number of dFADs was observed over the 7-year study period. Though the relative proportion of natural to artificial floating objects varied over space, with some areas such as the Mozambique Channel and areas adjacent to the mouths of the Niger and Congo rivers being characterized by a relatively high percentage of natural objects, in no region do dFADs represent <50% of the floating objects and the proportion of natural objects has dropped over time as dFAD deployments have increased. Globally, this increased dFAD use represents a major change to the pelagic ecosystem that needs to be closely followed in order to assess its impacts and avoid negative ecosystem consequences.

The distribution of egg and larvae of mackerel, horse mackerel, sardine, hake, megrim, blue whiting and anchovy along the European Atlantic waters (south Portugal to Scotland) during 1998 is described. Time of the year, sea surface temperature and bottom depth are used to define the spawning habitat of the different species. Mackerel, horse mackerel, and sardine eggs and larvae presented the widest distribution, whereas megrim and anchovy showed a limited distribution, restricted to the Celtic Sea and the Bay of Biscay respectively. Correspondingly mackerel, horse mackerel and sardine showed the highest aggregation indices. Blue whiting larvae were found at the lowest temperatures, whereas anchovy eggs and larvae were found in the warmest waters. The analysis is a basis for evaluation of ongoing changes in the pelagic ecosystem of the north-east Atlantic.

Amandè, M. J., Chassot, E., Chavance, P., Murua, H., Delgado de Molina, A., and Bez, N. 2012. Precision in bycatch estimates: the case of tuna purse-seine fisheries in the Indian Ocean. – ICES Journal of Marine Science, 69: . Estimating bycatch, i.e. the incidental catch of non-target marine animals and undersized individuals of target species, by raising observer data to the whole fishery is routine practice. The annual bycatch of the European tropical tuna purse-seine fishery over the period 2003–2009 was estimated at 11 590 t [95% confidence interval: (8165–15 818 t)], corresponding to 4.7% of the tuna landings. An analysis of the variability in the precision of this estimate, based on generalized linear models and Monte Carlo simulations, showed that the current sampling coverage of the tropical tuna fishery observer programme, which is 4.6% of the fishing trips, resulted in large uncertainties in bycatch estimates by species, i.e. none of the estimates have a relative root mean square error smaller than 50%. Although the overall magnitude of bycatch of the fishery appeared to be small, the current sampling coverage was insufficient to give any reliable estimate for low-occurring species, such as marine turtles, some oceanic pelagic sharks, and some billfishes. Increasing the sampling coverage would likely improve bycatch estimates. Simulation outputs were produced to help define (i) trade-offs between the priority species to be monitored, (ii) the estimation precision, (iii) expected accuracy, and (iv) the associated sampling costs.