Mesopelagic fish belong to the mid-trophic level functional group in

the marine ecosystem, and they are a key component of oceanic

food web, linking primary and tertiary consumers. Despite their

ecological relevance, little is known about the distribution patterns

of mesopelagic fishes (Young et al. 2001), and there is a strong

demand for more research for ocean-biogeochemical-population

(Lehodey 2004) and ecosystem (Kloser et al. 2009, Handegard et al.

2012) models. Fisheries acoustics provides a suitable tool for

monitoring mesopelagic fish abundance and distribution (Simmonds

& MacLennan 2005). Using acoustics from vessels of opportunity

may provide the best chance of achieving the ocean basin scale

spatial coverage required to inform ecosystem models.

Introduction

1. Identify large-scale patterns of pelagic fish abundance using area

backscattering coefficient (sa in m2 m-2) at 38 kHz as a measure

of biological abundance across the South Pacific Ocean.

2. Determine the vertical distribution of sa.

3. Explore the relationship between distribution of sa and

environmental and geographic variables to determine the major

factors driving pelagic fish distribution.

Objectives

Two return trips between New Zealand and Chile in Oct-Dec 2007

and 2010 from RV Kaharoa deploying ARGO buoys.

Data collected using a Simrad ES60 echosounder with hull-

mounted 38 kHz transducer (2000 W power, 1.024 ms pulse).

Analysis software Sonardata Echoview 4.90 and 5.0 (Myriax).

Acoustic records integrated in 10 n miles elementary distance

sampling units (EDSU) along the vessel track, and in 50 m depth

bins. Maximum data range up to 600 m depth.

Boosted regression trees (BRT) were fitted in R (version 2.13.2),

using GMB package version 1.6–3.1 plus custom code available

online (Elith et al. 2008).

Methods

Results

Conclusions The key finding was that there was a strong correlation between chl a and distribution of acoustic backscatter

as shown by BRT and spatial overlap (Fig. 2). The process underlying this correlation is likely food availability.

Major vertical patterns of pelagic fish distribution were diel migration and layering.

Major horizontal patterns were coastal intensification and oceanic patchiness, with abundance decreasing

from the coasts towards the core of the south tropical gyre.

Different mesopelagic species may have different acoustic properties, so correlation between acoustic backscatter

and fish abundance may not be linear. Nevertheless, acoustic information based on total backscatter still provided

further valuable knowledge about patterns of distribution and productivity.

There is a growing interest for developing programs focused on global ecosystem models for studying large-scale

patterns of distribution and abundance of biota and their changes over long time scales. This study provides data to

validate model predictions of mid-trophic functional groups.

Reference Escobar-Flores, P., O’Driscoll, R., & Montgomery, J. (2013).

Acoustic characterization of pelagic fish distribution across the

South Pacific Ocean. Marine Ecology Progress Series, 490, 169–

183. doi:10.3354/meps10435

* Email: pesc003@aucklanduni.ac.nz

Acknowledgments and contact Funding for this study was provided by the Advanced Human Capital Programme, National Commission of Scientific and

Technological Research CONICYT CHILE, through a Bicentennial Becas-Chile Master scholarship.

1School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand

2National Institute of Water and Atmospheric Research, Private Bag 14-901, Kilbirnie, Wellington 6241, New Zealand

Pablo Escobar-Flores1*, Richard L. O’Driscoll2, John C. Montgomery1

Acoustic characterization of pelagic fish

distribution across the South Pacific Ocean

-chlorophyll (chl a in mg m-3)

-sea surface temperature

(SST in degrees Celsius)

-sea surface height (SSH in m)

-minimum distance

from main land

masses (km)

-depth (m)

GEOGRAPHIC

PARAMETERS

- time since

sunrise (hours)

TEMPORAL

VARIABLE

1 EDSU = 10 n miles

10 n miles 20 n miles

ENVIRONMENTAL

VARIABLES

Fig. 2. Vertically summed acoustic backscatter coefficient (sa in m2 km−2) per 10 n miles elementary distance sampling

unit along each transect overlying maps of chl a (mg m−3) for the area and period of study: (a) T1-2007, (b) T2-2007,

(c) T1-2010, and (d) T2-2010. Maximum bar size = 140 m2 km−2. Grey bars: daytime; black bars: nighttime.

Table 1: Table 1. Comparison of total acoustic area backscattering coefficient (sa) along the 4 transects. EDSU:

elementary distance sampling unit size (10 n miles).

Fig. 3. Proportion of total acoustic backscatter coefficient (sa in m2 km−2) summarised in 50 m depth bins for each

individual transect: (a) T1-2007, (b) T2-2007, (c) T1-2010, and (d) T2-2010.

Fig. 4. Longitudinal section along transect T1-2010. (a) Vertically summed acoustic backscatter coefficient (sa in m2 km−2) in 10 n

miles elementary distance sampling units along the transect. (b) Daytime and nighttime. (c) Mean volume backscattering strength

(Sv in decibels) in each cell (red-brown= strong, blue-grey= weak). Each cell in the colour central panel is 10 n miles long by 50 m

deep. (d) Bottom depth.

Table 2. Summary of boosted regression trees model parameters (lr : learning rate; tc: tree complexity; nt: number of trees) and

performance. Model parameters were optimised by k-fold cross-validation (CV); SE: standard error.

Table 3. Summary of relative contributions (%) of the predictor variables for boosted regression trees Models 2 and 3, with cross-

validation data on 2462 locations. See Table 2 for model descriptions and parameters.

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Fig. 1. Different species of mesopelagic fish caught with mid-water trawls over

the Chatam Rise, western South Pacific Ocean.