**4. Ocean Colour and Fisheries**

Cara Wilson

NOAA/NMFS/SWFSC ERD, 1352 Lighthouse Ave., Pacific Grove, CA 93950

# 4.1 Introduction 

In the last half century the world fish harvest has increased more than
four-fold from 20 million tons in 1950 to over 80 million tons in 2000
*\[FAO Fisheries Departme*nt, 2004\]. At the same time the number of
overexploited and depleted stocks have increased, and an expanding
population and problems with food supply have increased the pressure on
fisheries resources. Better management and understanding of fisheries
are needed to both maximize the utility of the current resources, and to
ensure their sustainability into the future. However, these issues are
complicated by the significant interannual variations that occur in fish
populations, and sorting out fluctuations caused by anthropogenic
effects (overexploitation, habitat alteration, pollution, etc.) from
those caused by natural environmental variability is not trivial. The
fundamental question of what drives the interannual variability of fish
stocks was first posed over 100 years ago with the formation of the
International Council for the Exploration of the Seas (ICES), and still
has not been adequately resolved *\[Bakun and Bro*ad, 2003*; Kendall and
Duk*er, 1998\].

Satellite data provide an environmental context within which to examine
these issues, by measuring parameters of the habitat and ecosystems that
influence marine resources at high temporal and spatial scales. In the
broadest sense fisheries encompasses not just commercial fish stocks,
but all living marine resources, which for threatened and endangered
species involves efforts to help the populations recover. There are two
primary ways that ocean colour data are used within fisheries. One is to
find populations, usually a commercial fish stock to increase fishing
efficiency, but also in some cases for conservation, for example trying
to identify locations of endangered right whales in order to minimize
the number of lethal interactions with ships. A second application is
characterizing and monitoring the environment of living marine
resources. For example, satellite chlorophyll can be used to observe
changes in the timing of the spring bloom that can effect recruitment
*\[Platt et a*l., 2003\], to classify the productivity of the oceans
*\[Sherman et a*l., 2005\], to detect interannual differences in the
frontal structures that are important to fisheries *\[Bograd et a*l.,
2004*; Polovina et a*l., 2001\] and to map the spatial extent of the
ocean experiencing lower productivity during an El Niño event *\[Wilson
and Adam*ec, 2001\]. Additionally, ocean colour data is used to monitor
a number of issues that impact fisheries, such as harmful algal blooms,
coastal pollution, and derelict fishing gear (if moved out of this
chapter) that are discussed in further detail elsewhere in this volume.

# 4.2 Ocean colour and the oceanic food web 

Satellite chlorophyll provides an index of phytoplankton biomass, which
is the base of the oceanic food chain, or food web, as depicted in
simplified form in Fig 4.1. The relationship between satellite
chlorophyll data and a specific fish stock depends upon the number of
linkages between phytoplankton and the higher trophic level. For some
species, such as anchovies and sardines, which eat phytoplankton at some
points in their life cycle, the linkage can be direct *\[Ware and
Thoms*on, 2005\], whereas for other species there are many trophic
levels in between and the relationship can be quite non-linear. There
can also be spatial disconnects between satellite measurements of the
ocean surface and demersal and deep-water species. Nonetheless,
chlorophyll is the only biological component of the marine ecosystem
accessible to remote sensing, and as such it provides a key metric to
measuring ecosystems on a global scale. As discussed in chapter 2,
satellite chlorophyll measurements are the primary component in
algorithms to calculate the primary productivity (PP) of the ocean.
Global PP measurements, in conjunction with fish catch statistics and
food web models, such as shown in Fig 4.1, can be used to estimate the
carrying capacity of the world's fisheries. In the open ocean 2% of the
PP is needed to support the fishery catch, but in coastal regions the
requirement ranges from 24-35%, suggesting that these systems are at or
beyond their carrying capacity *\[Pauly and Christens*en, 1995\], which
is cause for concern as the bulk of the world's fish catch comes from
coastal areas. In a similar manner, discrepancies between the values of
satellite derived PP and reported fish catches have been used to
demonstrate spurious trends in global fish catches as reported by the
Food and Agriculture Organization (FAO) of the United Nations *\[Watson
and Pau*ly, 2001\]. In this instance satellite ocean colour data provide
an important objective baseline against which to gauge data that can
have socio-economic biases.

# 4.3 Harvesting 

Locating and catching fish is becoming more challenging as fish stocks
dwindle and move further offshore, thus increasing the search time, cost
and effort. Satellite data can help to increase the efficiency of
fishing efforts by identifying oceanographic features that are often the
sites of fish stock congregation and migration such as temperature
fronts, meanders, eddies, rings and upwelling areas *\[Chen et a*l.,
2005*; Fiedler and Berna*rd, 1987*; Laurs et a*l., 1984\]. Both
satellite ocean colour and sea surface temperature (SST) data have been
used for this purpose. SST and ocean colour often have similar patterns
as generally warm, nutrient-depleted water has low chlorophyll
concentrations and cold, nutrient-rich water has high chlorophyll. SST
can also be an important factor for determining potential fishing
grounds since different fish species have different optimal temperature
ranges. Generally SST data have been used more often than ocean colour
data in fisheries applications. There are two main reasons for this.
One, satellite SST are a more established data source, with data going
back to 1985, whereas SeaWiFS, the first satellite to consistently
provide satellite chlorophyll data on a global basis, was not launched
till 1997. Secondly, SeaWiFS is a privately owned satellite, and while
its data have always been freely available, with a time delay, to the
research community, the availability of real-time data needed by fishers
for identifying potential fishing areas has only been available on a
commercial basis. For economic reasons this has resulted in SeaWiFS data
being unavailable to many fishers in the world, as the commercial costs
of the real-time data can be prohibitive, particularly for those in
under-developed countries, where 70% of the fish for human consumption
comes from *\[FAO Fisheries Departme*nt, 2004\].

However, satellite ocean colour data offer additional information that
SST does not provide. As discussed above chlorophyll, unlike SST,
directly measures the component of the ecosystem that supports the
oceanic food web, and it has an important role to play in fisheries
applications and fisheries science. For example, there can be fronts and
features evident in chlorophyll data that are not seen in SST data
*\[Laurs et a*l., 1984*; Solanki et a*l., 2001\]. The Transitional Zone
Chlorophyll Front (TZCF) in the North Pacific is an important feature
easily visible by ocean colour, separating low productivity waters of
the central gyre from more productive waters in the North Pacific, but
it is not as easily detectable by SST. The TZCF and the eddies
associated with it are an important foraging ground for many commercial
fish such as swordfish and tuna *\[Laurs and Ly*nn, 1991\].

Obviously to effectively help fishers more easily locate fish schools,
satellite ocean colour data must be available in a near-real time basis.
There are international differences in how this satellite data is
disseminated to fishers. For example the SeaWiFS satellite is privately
owned, and its chlorophyll data is only available on a real-time basis
to fishers on a commercial basis from the SeaStar company. Customers of
this service can receive custom-tailored maps of ocean colour, as well
as other oceanic parameters derived from satellite data, directly
onboard their fishing vessel. In contrast, data from the Indian IRS-P4
ocean-colour (in conjunction with satellite SST from AVHRR) is used by
the Indian National Center for Ocean Information Services (INCOIS) to
make maps of potential fishing zones (PFZ, see Fig 4.2), which are
freely disseminated several times a week throughout coastal India by
fax, phone, internet, electronic display boards, newspaper and radio
broadcasts. Studies on the effectiveness of the PFZ advisories have
suggested that they have helped reduce search time by up to 70%, and
have significantly increased the catch per unit effort (CPUE) *\[Solanki
et a*l., 2003*; Zainuddin et a*l., 2004\].

National agencies also serve different constituencies. The mandate of
NOAA Fisheries in the USA for example, is to manage and conserve marine
resources, and they are not allowed to provide services such as
distributing 'fish finding maps' that would compete with commercial
interests. However in other countries, notably Japan and India, the
national fisheries agencies are actively involved with helping increase
the efficiency of their fishing fleets.

# 4.4 Ecosystem-Based Management

There is growing awareness that the traditional approach of focusing on
a specific species is inadequate to successfully manage them *\[Browman
and Stergi*ou, 2005*; Sherman et a*l., 2005\]. Interactions with other
species, complex predator-prey dynamics, and temporal and spatial
variability in physical aspects of the ecosystem all need to be
incorporated into an ecosystem-based approach to management. While
obviously not all of these aspects can be addressed by satellite data,
the high spatial and temporal resolution of satellite ocean colour data
makes it an efficient tool to characterize and monitor marine ecosystems
in order to better manage them. For example, satellite derived primary
productivity is a one of the indicators used in the assessment of Large
Marine Ecosystems (LME) *\[Sherman et a*l., 2005\].

## 4.4.1 Habitat Assessment 

## Recruitment 

A fundamental issue in fisheries oceanography is understanding how
environmental variability affects annual recruitment, the number of new
individuals entering a stock. Recruitment is an important parameter
because the bulk of mortality occurs in the development of larvae from
eggs. Most fish have planktonic larval stages that are strongly
influenced by ocean circulation and can have narrow ranges of optimal
thermal conditions. Availability of a good food source is important for
successful recruitment and hence many fish reproduce near the seasonal
peak in phytoplankton abundance. A long-standing hypothesis in fisheries
has been that recruitment success is tied to the degree of timing
between spawning and the seasonal phytoplankton bloom, the Cushing-Hjort
or match-mismatch hypothesis. This hypothesis has been difficult to
address with traditional shipboard measurements that have limited
spatial and temporal resolution, but with satellite ocean colour data,
interannual fluctuations in the timing and extent of the seasonal bloom
can be clearly seen. In an application on the Nova Scotia Shelf, the
timing of the spring bloom determined from satellite ocean colour was
compared with available in situ data on larval survival of haddock, an
important commercial fish species. Comparison of these two independent
data sets indicated that highly successful year classes of haddock are
associated with exceptionally early spring blooms of phytoplankton (Fig
4.3), confirming the match-mismatch hypothesis *\[Platt et a*l., 2003\].
A comparable study has also documented a relationship between the timing
of the spring bloom and the growth rate of shrimp *\[Fuentes-Yaco et
a*l., in press\]. These studies demonstrate that it can be possible to
separate ecosystem-associated variability in fish stocks from other
components such as human exploitation or predation effects. The
satellite time series permits the extraction of value-added products, in
this case the timing of the seasonal biological cycle.

## Animal habitat: the TZCF

Satellite ocean colour data, in conjunction with telemetry data from
tagged sea turtles have shown that in the North Pacific the Transitional
Zone Chlorophyll Front (TZCF) is an important foraging ground and
migration pathway for endangered loggerhead turtles *\[Polovina et a*l.,
2004\]. As seen in Fig 4.4 these turtles follow the TZCF, and spend time
foraging in the high chlorophyll eddies that are associated with
meandering of the front. Other apex predators such as albacore tuna also
use the front as a migratory corridor *\[Laurs and Ly*nn, 1991*;
Polovina et a*l., 2001\]. The degree of meandering of the TZCF seems to
impact trophic transfers and the level of productivity associated with
the front. Periods with more meandering of the front have had
significantly higher CPUE of albacore, suggesting that the enhanced
convergence creates more productive foraging grounds *\[Polovina et
a*l., 2001\].

Extracting information about animal habitat by using satellite telemetry
location information in conjunction with environmental satellite data,
such as tracking sea turtles along the TZCF, has been used on an
extensive array of animals. For example the Census of Marine Life
program Tagging of Pacific Pelagics (TOPP) is involved with tagging a
suite of more than 20 marine predators and integrating the tagging
information with satellite data to better understand the animals habitat
usage *\[Block et a*l., 2003\]. Relating the movements and activities of
these animals to satellite-derived features is an important advancement
toward understanding environment-population linkages in marine
ecosystems.

## Survey support

Fishery independent surveys are a crucial part of stock assessment. Just
as ocean colour data can be used to increase fishing CPUE by identifying
front locations, and other features where fish tend to congregate, these
data are also routinely used by fisheries cruises doing survey
assessments for management and stock assessment. The near real-time data
are valuable for locating fronts and other relevant features to sample
across, as well as placing the results in a larger spatial context.

## 4.4.2 Conservation & Management 

### Right Whales 

One of the most endangered marine species is the Northern right whale
(*Eubalaena glacialis*), with an estimated population of 300 individuals
*\[International Whaling Commissi*on, 1998*; Kraus et a*l., 2005\].
While historically, right whale populations were severely depleted by
commercial whaling, at present the primary cause of mortality in the
North Atlantic is from ship strikes *\[National Marine Fisheries
Servi*ce, 2005\]. The primary habitat of the North Atlantic right whale
is in coastal or shelf waters, which also have heavy amounts of ship
traffic. The recovery plan of NOAA (USA) for this species has focused on
developing methods to identify the locations of right whale populations,
in order to reduce ship traffic in these regions and lessen the number
of whale-vessel collisions. This plan involves both limiting fishing in
certain areas when whales are typically abundant, a procedure known as
\"seasonal area management\" (SAM), and another strategy known as
dynamic area management (DAM), with a synoptic level of control. Under
DAM, if a group of whales is identified, NOAA Fisheries will limit
activities in the area. Currently research is underway to improve both
management strategies by predicting the location of right whale
congregations using satellite measurements of sea surface temperature
and ocean colour *\[Mong*er, 2006\]. Because the distribution of right
whales is strongly correlated with the distribution of their prey, which
appears to be primarily calanoid copepods *\[Kenney et a*l., 2001\],
satellite chlorophyll data is a reasonable proxy for their food source.
SST data is used in this method to estimate the development time of the
copepods and to determine fronts where whale aggregation is likely.
Currently the project is in the proof of concept stage, with the aim of
being incorporated into management decisions.

## Ghost nets

Marine debris and abandoned fishing nets, "ghost nets", pose a serious
hazard to many marine mammals and sea birds. Endangered sea turtles,
seals, and whales are among the species that can get entangled in these
nets, with often-lethal results. The nets also get ensnared on coral
reefs, physically damaging the reef structure and destroying the flora
and fauna dependent upon a healthy reef ecosystem *\[Donohue et a*l.,
2001\]. Subsequently, management efforts are being lead to develop
methods to identify and prioritize the likely locations of marine debris
to facilitate finding and removing marine debris and ghostnets.
Satellite ocean colour data have been used in the subtropical North
Pacific to identify likely locations of debris convergence, which were
used in directing a field observation program to ground-truth the
detection method *\[Pichel et a*l., 2003\]. Satellite measurements of
SST, chlorophyll, and chlorophyll gradient, analyzed in conjunction with
observer sightings per unit effort were combined to generate a map of
the likelihood of debris density, as seen in Fig. 4.5. Ocean colour data
were an important component to this identification method; initial
efforts using just satellite SST data were significantly less effective.

# 4.5 Fisheries and Climate

The SeaWiFS ocean colour sensor was launched in August of 1997, just
prior to the 1997/98 El Niño which was one of the strongest ENSO (El
Niño Southern Oscillation) events of the century. This satellite data,
in synergy with data from an extensive array of moorings across the
equatorial Pacific, has contributed enormously to our understanding of
ENSO dynamics and their ecosystem impacts. Deepening of the thermocline,
and cessation of upwelling along the equator and in the coastal
ecosystems lowers ocean productivity and causes significant drops in the
anchovy fisheries of Peru and Chile *\[Alamo and Bouch*on, 1987*;
Escribano et a*l., 2004\]. However, other species are positively
impacted by El Niño, for example increases are observed in the biomass
of sardine and mackerel *\[Bakun and Bro*ad, 2003*; Niquen and Bouch*on,
2004\]. Satellite ocean colour data has demonstrated that the effects of
El Niño are not constrained to just the equatoral and coastal upwelling
regions, but extend throughout most of the Pacific Ocean. For example
during the 1997/98 event the TZCF was shifted \~5° south of it regular
position *\[Bograd et a*l., 2004\], and lower chlorophyll values
occurred across most of the subtropical Pacific *\[Wilson and Adam*ec,
2001\].

The longest currently operating ocean colour sensor was launched in
1997, so it is presently impossible to detect decade-scale variability
with just this data. However it is possible to observe long-term changes
by comparing climatological SeaWiFS data against data from the Coastal
Zone Color Scanner (CZCS), which operated between 1979-1985. For example
the present wintertime position of the TZCF in the Pacific is about 5°
further north than it was during CZCS time period (Fig 4.6). This shift
has also been seen in SST data used as a proxy for the TZCF *\[Bograd et
a*l., 2004\]. Data from these two different satellites have also been
used to demonstrate regions of the ocean which have experienced
significant changes in the amount of chlorophyll and primary
productivity in the past twenty years *\[Gregg and Conkrig*ht, 2002*;
Gregg et a*l., 2003\].

There is significant long-term temporal variability in fish stocks, and
for over 150 years scientists have been trying to differentiate the
effects of interannual variability, overfishing and long-term changes
such as regime shifts, which are characterized by relatively rapid
changes in the baseline abundances of both exploited and unexploited
species *\[Polovi*na, 2005\]. Long-term variations in ecosystems often
follow trends or patterns also observed in ocean and atmosphere
parameters *\[Hare and Mant*ua, 2000*; Mantua et a*l., 1997*; Peterson
and Schwi*ng, 2003\]. For example, as seen in Fig 4.7, a shift in the
North Pacific in the 1970's between a shrimp dominated ecosystem to one
populated primarily by several species of bottom-dwelling groundfish
species coincided with a regional change from a cool to a warm climate
*\[Anderson and Pia*tt, 1999*; Botsford et a*l., 1997\]. While similar
phenomena have been seen for many different stocks, and in all ocean
basins, the mechanisms that link large-scale ocean and atmosphere
dynamics to changes in population abundances are not always clear
*\[Bauma*nn, 1998*; Botsford et a*l., 1997\] and the relationships are
not always constant over time *\[Sol*ow, 2002\]. Ecosystem changes
related to regime shifts are not in themselves harmful to the ecosystems
as a whole *\[Bakun and Bro*ad, 2003\], but in order to maintain
sustainability, management practices must be flexible enough to
recognize and accommodate them *\[Polovi*na, 2005\]. One of the current
limitations of satellite data is their relatively short time-spans. For
fisheries applications it is crucial that climate quality records of
ocean colour be maintained so that existing satellite records will be
able to serve as a benchmark against which to gauge future changes.

# 4.6 Summary and Conclusions

-   Satellite data measures oceanic parameters of habitat and ecosystems
    that influence marine resources at spatial and temporal resolutions
    that are impossible to achieve any other way.

-   The high spatial resolution provides an important geographical
    context for interpreting other data and results.

-   The daily to weekly temporal resolution allows for effective
    monitoring of many oceanic features and permits the extraction of
    value-added products such as the timing of seasonal events.

-   Near real time satellite data is crucial for optimizing sampling for
    fisheries survey cruises for management and stock assessment and
    also for increasing the efficiency of fishing effort.

-   Timeseries of science quality satellite data are needed to
    understand linkages between climate and ecosystems, and to
    characterize and monitor ecosystems as part of an ecosystem based
    approach to fisheries management.

# Acknowledgements

Ishio Asanuma, Gene Feldman, Jesus Morales and Shalesh Nayak made
contributions to an earlier version of this chapter.

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**Acronyms**

AVHRR Advanced Very High Resolution Radiometer

CPUE Catch per Unit Effort

CZCS Coastal Zone Color Scanner

DAM Dynamic Area Management

EFH Essential Fish Habitat

ENSO El Niño Southern Oscillation

FFM Fish Finding Maps

FAO Food and Agriculture Organization

ICES International Council for the Exploration of the Seas

IRS-P4 Indian Remote Sensing Satellite

LME Large Marine Ecosystem

OCM Ocean Colour Monitor (on IRS-P4)

NMFS National Marine Fisheries Service (USA)

NOAA National Ocean and Atmospheric Administration (USA)

PDO Pacific Decadal Oscillation

PFZ Potential Fishing Zone

SAM Seasonal Area Management

SST Sea Surface Temperature

TZCF Transitional Zone Chlorophyll Front

![](media/image1.jpeg){width="5.997916666666667in"
height="4.497916666666667in"}

Figure 4.1 Simplified oceanic food web, showing the varying complexities
in the linkages between phytoplankton, which is measured by satellite
ocean colour data, and higher trophic levels. Modified from Pauly and
Christensen \[1993\].

![](media/image2.png){width="4.861111111111111in"
height="2.9166666666666665in"}

Figure 4.2. Map showing potential fishing zones (PFZs) off of the Bay of
Bengal, derived from ocean colour data from India's Ocean Colour Monitor
(OCM) sensor. Figure reproduced from *Pavan Dayarek* \[2003\].

*Need better figure! They claim PFZs are distributed on the web, but
they don't appear to be.*

![](media/image3.jpeg){width="2.3661417322834644in"
height="3.150787401574803in"}

![](media/image5.pct){width="3.998031496062992in"
height="2.498031496062992in"}

Figure 4.3 The timing of the spring phytoplankton bloom peak in the
Northwest Atlantic is shown on the left derived from SeaWiFS ocean
colour data. Colours change from blue indicating early (March) to red
indicating late (July). On the right is the relationship between
survival index (normalized to recruitment) of haddock larvae and the
local anomalies in bloom timing. Data from the continental shelf east of
Southern Nova Scotia (Canada) for the periods 1979--1981 and 1997--2001.
Adapted from *Platt et al* \[2003\]*.*

![](media/image7.png){width="4.807638888888889in"
height="2.9618055555555554in"}

Figure 4.4. Track of a tagged Loggerhead turtle (black line) overlain on
SeaWiFS chlorophyll data along the Transitional Zone Chlorophyll Front
in the North Pacific Ocean. Figure reproduced from *Polovina et al.*
\[2004\].

Permission to use figure received from FO, though not needed, US gov
work.

![](media/image8.png){width="5.995833333333334in"
height="4.316666666666666in"}

Fig. 4.5. Likelihood of debris density (derelict fishing nets, etc.) in
the North Pacific subtropical convergence zone, based upon satellite
measurements of chlorophyll, SST and observer sightings. Figure courtesy
of Dave Foley (JIMAR).

![](media/image9.jpeg){width="5.9944444444444445in"
height="2.1555555555555554in"}

Fig 4.6 Interannual variation in the position of the TZCF indicated by
the mean January position from CZCS data (1979-1985) in purple, and from
SeaWiFS data (1998-2004) in red. The winter TZCF is presently \~5°
further north from its position during the CZCS era.

![](media/image10.png){width="5.99375in" height="4.63125in"}

Fig 4.7 Changes in the composition of small-mesh trawl catches in the
Gulf of Alaska between 1953 and 1997, in relation to climate indices
(left) and shown visually in photos of bottom trawl surveys on the
right. Figures reproduced from *\[Anderson and Pia*tt, 1999\] and
*\[Botsford et a*l., 1997\].

Permission to use bar graph figure received from MEPS, though not
needed, US gov work. Permission asked from Science for the photos
published in the Botsford paper.