Dr John Haas
SUBJETIVISMO, INDIVIDUALISMO, RELATIVISMO INFLUENCIAS ADVERSAS
Antarctic krill (Euphausia superba) is a key stone species in the short Southern Ocean food chain which, at its most simple, can be described as comprising of three trophic levels; primary production; krill; and krill predators (Reid et al., 1999a; Atkinson et al., 2001). Krill is the primary prey item for many species of marine birds and mammals, notably the large populations of central place foragers located at South Georgia and the South Shetland Islands (Boyd and Murphy, 2001; Reid et al., 2004). The reproductive success of these species has been linked to krill availability (Croxall et al., 1988b; Murphy et al., 1998; Croxall et al., 1999; Boyd, 2002). Because of the vital ecosystem role krill plays, for the last 20 years it has been the subject of international long term monitoring programmes (see Hewitt and Demer 1994; Agnew 1997). Currently, several nations conduct annual surveys of krill, the results of which contribute to krill fisheries management models (Trathan et al., 2001; Hewitt et al., 2004).
Antarctic krill form aggregations and exhibit a spatially patchy distribution, both hor- izontally and vertically through the water column (Watkins, 2000; Watkins and Brierley,
2002). Aggregations vary in size from 20 m to larger than 20 km, and density, with diffuse aggregations, known as layers, having an internal density of 1 or 2 g/m3 and higher density
aggregations, or swarms, having internal densities of 100 g/m3 to 1 kg/m3 (Brierley et al.,
1998; Watkins and Murray, 1998; Woodd-Walker et al., 2003). During research cruises conducted by British Antarctic Survey (BAS) at South Georgia (54oS 35oW) up to 20% of krill biomass has been observed in a single swarm (Brierley et al., 1997b). The large variation in the spatial structure and density of krill make spatial modelling problematic which is confounded by measurement error (Hewitt and Demer, 2000; Demer, 2004).
Recently, it has been suggested that management models be extended to limit the spatial overlap between krill predators and krill fisheries, since it is believed that many species of krill predator return to the same area to forage (Reid et al., 2004). Because these predators show fidelity to specific foraging sites and when rearing offspring are constrained in their foraging range this creates the potential for krill fisheries to have a dispropor- tionately detrimental impact on krill predator populations: an identical biomass of krill caught by a fishery within or outside of the foraging range of a krill predators would have a different effect on the krill predator population (Murphy et al., 1997; Boyd et al., 2002). The need to assess the potential for competition between krill fisheries and predators has given rise to krill populations being assessed at the level of individual aggregations (Reid et al., 2004). Krill density, either at the scale of an individual aggregations or at the larger transect scale is often calculated from acoustic data that are generally observed from research ships, steaming line transect surveys, equipped with scientific echosounders (see Brierley and Watkins 1996 for Antarctic krill survey techniques, and more generally Simmonds and MacLennan 2005).
The use of scientific echosounders provides a non-evasive remote sensing technique that allows the observation of krill through the water column over a large area, often to a depth of 250 m. Typically, scientific echosounder transducers are mounted facing vertically downwards on the hull of a research vessel that carries out a line transect survey through the study site collecting acoustic observations of krill (e.g. Brierley et al. 1999b). These observations are then partitioned to identify those returns coming from krill which are scaled to determine krill density (Simmonds and MacLennan, 2005).
Acoustic analysis of krill can be broadly divided into two groups: (1) those that consider krill in arbitrary along transect intervals, known as elementary distance sample units (EDSU, Reid et al. 2000a), and are typically from 100 m to 1 nautical mile long and (2) those that assess krill at the individual krill aggregation, or swarm scale (Barange et al., 1993; Brierley et al., 1999b; Woodd-Walker et al., 2003). Historically, krill have been assessed in EDSUs because until relatively recently it has not been possible to objectively identify aggregations nor has it been required for all surveys (Reid et al.,
2000a). For example, large scale acoustic surveys, which are often used to estimate mean area biomass ( ˆρ), use EDSU. However, alternative ecosystem approaches use the shape of aggregations to predict the species composition and provide indicators of environmental variability (Paramoa et al., 2007).
A single data observation cycle from an echosounder is known as a ping and comprises of transmit and receive components. A pulse of sound, with known characteristics, is transmitted into the water column and returned, (or backscattered) sound is received at discrete intervals by the same transducer. This backscattered sound is proportional to the density of krill in the echosounder sampling volume. Thus, one ping is a vector containing backscattered acoustic energy from discrete intervals, standardised for sampling volume from a narrow cone in the water column directly under the research vessel. Vectors arising from sequential pings are combined in a matrix that is used for further analysis, that includes the identification of acoustic returns arising from krill and the scaling of these returns to determine krill density (Reid and Simmonds, 1993).
Krill are generally identified using a multi-frequency acoustic approach (for Antarctic krill see Madureira et al. 1993; Brierley et al. 1998; Watkins and Brierley 2002), in which two or more frequencies of sound are used to sample the water column. Since the amount of energy backscatttered by a krill aggregation is proportional to the acoustic frequency of the echosounder observing it and the length of the krill, these are used as arguments in theoretical models to predict the acoustic energy backscattered by krill (target strength models, e.g. Demer and Conti 2003, 2005). The results of these models are used to set the upper and lower bounds of the acoustic energy that can be expected to be returned, for each acoustic frequency by aggregations of krill within the study site. The difference in acoustic backscatter between frequencies is then calculated and used to separate the acoustic observations into those arising from krill and those from other scatterers. Con- ventionally, two frequencies 38 and 120 kHz have been used to observe the water column during krill surveys and the krill length frequency distribution within the survey site is estimated by net sampling (Brierley et al., 1998; Reiss et al., 2008).
Where the multi-frequency technique identifies an acoustic sample as being krill the sample from a single frequency, typically 120 kHz at that sample location, is used to calculate krill density. Target strength models are used to estimate the number of krill in the observation and length-wet mass models (eg Morris et al. 1988) are used to calculate the mass of krill in the samples. Krill density estimation can take place at a variety of spatial scales which is dependent on the purpose of the survey i.e from small-scale predator prey interactions to large-scale biomass estimates (Hewitt and Demer, 2000).
Acoustic surveys of krill are generally performed from research vessels and are often conducted in rough weather since there is considerable time and financial pressure on
ship operations. The quality of acoustic observations is influenced by weather conditions. As weather conditions deteriorate vessel motion increases, this in turn decreases the de- tectability of krill aggregations. Beyond a range of vessel motion, that is unique to the scientific echosounder, the detectability of krill will drop to zero. Zero detectability occurs when either the pulse of acoustic energy transmitted by an echosounder fails to propagate through the water column, or when reception of the acoustic energy backscattered by wa- ter column targets fails. The transmitted acoustic pulse may fail to propagate correctly when the water surrounding an echosounder transducer becomes aerated. Excessive vessel motion causes pings to be dropped and entire columns in the acoustic observation matrix will record the acoustic backscatter of krill as zero and are unrepresentative of krill in the the echosounder sampling volume.
In many cases, the issue of missing pings is ignored and missing pings are therefore assumed to contribute zero intensity (e.g. Brierley et al. 1997a) and thus transect krill density can be drastically underestimated when the number of missing pings is large. A more realistic approach estimates krill density for the missing pings using the mean of the non-missing pings. This approach is not ideal. For example, a threshold intensity is used to determine if surface intensities are krill and a block of missing pings located in a low intensity area may falsely indicate a krill swarm is present. Conversely, blocks of missing pings located within krill swarms may falsely ignore one or more krill swarms, or will split a single swarm into multiple swarms.
Krill intensity values are spatially correlated - values from adjacent pings tend to be more similar than values from distant pings. For this reason, krill intensities from nearby pings should be used to estimate the intensity of missing pings. A smoother-based method can use neighbouring pings to estimate those which are missing. However the extent of the smoothing should be determined locally. For instance, areas of water absent of krill only require a rigid flat surface, while areas with swarms will exhibit rapid changes in surface intensity and require a relatively flexible surface.
To make use of information on krill distribution surrounding a missing ping this re- search uses a smoother-based method that employs thin-plate regression splines (TPRS) with locally determined flexibility. This method permits surface flexibility to be targeted so that the smoothness permitted in each area of the surface is appropriate. This smoother based method uses the spatial structure of krill distribution to estimate the intensities of missing pings and the predictions from the fitted surface are used to reconstruct krill density within missing pings to allow more accurate quantification of krill density.