Reconstruction of original body size and estimation of allometric relationships for the longfin inshore squid (Loligo pealeii) and northern shortfin squid (Illex illecebrosus)

Quantification of predator-prey body size relationships is essential to understanding trophic dynamics in marine ecosystems. Prey lengths recovered from predator stomachs help determine the sizes of prey most influential in supporting predator growth and to ascertain size-specific effects of natural mortality on prey populations (Bax, 1998; Claessen et al., 2002). Estimating prey size from stomach content analyses is often hindered because of the degradation of tissue and bone by digestion. Furthermore, reconstruction of original prey size from digested remains requires species-specific reference materials and techniques. A number of diagnostic guides for freshwater (Hansel et al., 1988) and marine (Watt et al., 1997; Granadeiro and Silva, 2000) prey species exist; however they are limited to specific geographic regions (Smale et al., 1995; Gosztonyi et al., 2007). Predictive equations for reconstructing original prey size from diagnostic bones in marine fishes have been developed in several studies of piscivorous fishes of the Northwest Atlantic Ocean (Scharf et al., 1998; Wood, 2005). Conversely, morphometric relationships for cephalopods in this region are scarce despite their importance to a wide range of predators, such as finfish (Bowman et al., 2000 ; Staudinger, 2006), elasmobranchs (Kohler, 1987), and marine mammals (Gannon et al., 1997; Williams, 1999).

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed