Using scrutinized (i.e., identified to species) acoustic survey data collected by the Peruvian marine research institute from 1983 to 2003 (42 surveys), we examine changes in the spatial aggregation of the four most commercially important pelagic fish speciesanchoveta, sardine, jack mackerel and mackereland how they relate to changes in fish abundance, regime shifts (long-lasting changes in temperature anomalies), and season.
We examine three measures of aggregation or concentration. The first, which is due to Petitgas (1998), models the distribution of acoustic backscatter within ESDU and compares it to a case of uniform concentration of biomass in all ESDU. We compute the integrated area between the empirical biomass concentration distribution and the uniform case to obtain a measure proportional to the amount of concentration of biomass in a small number of standard acoustic sampling units (1 or 2 n.mi., ESDU). The second measure is based on the variogram used in kriging. We are only interested in whether there is significant spatial autocorrelation in fish abundance (for each species) and over what distance (range) this autocorrelation operates. After computing the empirical variogram, we perform linear regressions of the variogram against distance for different (cumulative) distance bins, choosing the one giving the best fit as the range. All cases where the slope of the best fit is negative or not significantly different from 0 have range set to 0. The third measure is based on Ripleys K to test for randomness or clustering. Here we wish to examine whether the high abundance ESDU are clustered. We choose the top 25% quantile biomass ESDU and compute a modification of Ripleys K for marked point processes, the empirical K (Swartzman et al. 2002). We then use Monte Carlo randomization of the marks (biomasses) among ESDUs to provide a range for randomness to compare to the empirical K and test for randomness, clustering or inhibition. The output of this measure is a factor (C, R or I) depending on whether the species is clustered, inhibited, or random for that survey.
Finally, Classification and Regression Trees (CART) is used to model each of the measures (for each of the four species) as a function of average biomass per ESDU, season, standard error in ESDU biomass, percentage of ESDU having non-zero biomass, survey year, and sea surface temperature anomaly at Chimbote Peru (as a measure of regime changes as well as ENSO events). We provide results and some interpretation.
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