Download Characterization of Marine Habitats

The Use of GIS in the Characterization of Marine Habitats,
with a focus on Marine Mammals
Melissa Patrician
NRS 509
“Geographic Information Systems (GIS) were ‘born’ on land; they are around 35
to 40 years old, but only about 15 years ago did they ‘migrate’ to the sea”
(Valavanis 2002). GIS has become a standard technology in a number of
terrestrial analyses, but is only still being introduced into marine research. The
field of marine mammal science is a complex one, with habitat use continuing to
be a perplexing question for marine mammal researchers. GIS has the ability to
provide a simpler, more efficient way to study habitat use in marine mammals.
And although GIS still has its disadvantages in a marine environment, new
advances made in this technology daily will continue to make GIS an essential
tool in the characterization of marine habitats.
GIS is "an organized collection of computer hardware, software, geographic data,
and personnel designed to efficiently capture, store, update, manipulate, analyze,
and display all forms of geographically referenced information" (ESRI 1990). It is
this new technology that pushes the realm in which we are able to study marine
habitats. The mystery of how marine mammals utilize their habitats and where
these animals are when they are not visible has been a perplexing question for
researchers for a number of years (Kenney et al. 1994; Winn et al. 1986). This
information is extremely important for the conservation of marine mammal
species, particularly so for the North Atlantic right whale (Eubalaena glacialis).
This species is the most critically endangered large whale in the world (Kenney
and Wishner 1995). Knowing where these whales are and keeping accurate
records of their locations might give some insight into the reasons for their lack of
recovery. While GIS has not yet become a standard technology for the
monitoring of this species, it has been shown that with some adjustments, GIS
may be able to greatly increase the efficiency with which we study this population
(Moses 1997; Schick 2002).
GIS has been used to monitor a number of other marine mammal species
(Bjorge et al. 2002; Engleby 2003; Hamazaki 2002; Waring et al. 2001) as well
as sea turtle populations (Kinzel 2003). In these studies, GIS has played an
important role in a number of ways. Through satellite tagging, researchers are
able to track an animal’s movement and location and monitor its diving and
foraging behaviors. There are a number of advantages to using satellite tagging
over the traditional sighting surveys. The first is that tagging provides an
unbiased, non-invasive way of tracking the animals without disturbing their
environment. Tagging also allows researchers to track an animal while they are
underwater; an enormous advantage when the animal being tracked spends the
majority of its life underwater, as is the case with a number of marine mammal
species (Schick 2002).
One problem with sighting surveys is that researchers tend to look in areas in
which they expect to see whales, and therefore miss the portions of the
population that are not where researchers expect them to be (Schick 2002).
Also, since many cetaceans, baleen whales in particular, have very long
migration routes lasting several months and are mostly far offshore, visually
tracking these animals is impossible. Satellite tagging provides researchers
information about these animals’ locations and migratory routes in a much more
time and energy efficient way.
Once information about the animal’s location and habitat use is retrieved, it can
be entered into a GIS system to allow for complex analyses. A GIS model can
allows researchers to determine the type of habitat the population is found in,
how the animals are utilizing that habitat, the prey the population depends on,
what oceanographic conditions are optimal for the population, and how the
population responds to changes in oceanographic conditions or changes in water
quality. This information is crucial to conservationists. By knowing this
information, it can be determined which areas need to be protected, which prey
species are significant, and what elements affecting water quality are most
crucial to the survival of this population.
The use of GIS in other marine mammal and sea turtle studies shows that this
technology can also be used in the study of the North Atlantic right whale
(Eubalaena glacialis). When asked why GIS has not been used more in right
whale research, Dr. Robert Kenney, a right whale ecologist, replied “because
until recently, GIS software was very expensive and impossible or difficult to use
on many personal computers, and was capable only of drawing nice maps but
without much real analytical capability."
As Moses (1997) points out, this
technology is much more cost effective than sighting surveys, and much more
energy efficient as well. Based on the North Atlantic Right Whale Catalogue, the
population size of the North Atlantic Right Whale is ~350 individuals (Knowlton et
al. 1994). Using GIS to observe seasonal migrations, Moses (1997) found that
there were a number of whales not utilizing the normal summering grounds. She
then was able to estimate other potential summering ground alternatives using a
GIS model. This observation is consistent with genetic research showing that a
number of right whales which exist in the population are not recorded in the
catalogue (Frasier et al. 2003), therefore the population of right whales may be
much larger than researchers expect. And GIS is no longer just for “drawing nice
maps.” It allows for the merging of data to make a number of complex analyses
possible.
The main disadvantage of using GIS in marine research is that currently, GIS can
only perform two dimensional spatial analyses and the ocean is a three
dimensional space (Schick 2002). In order for a GIS system to be fully efficient
for the marine environment, it would need to contain the following components:
1) a vertical dimension, 2) the dynamics of marine processes (gyres, fronts,
upwelling, etc.) and 3) the dynamics of marine objects (species populations)
(Valavanis 2002). In July 2001, an ESRI marine special interest group was
formed with the goal to create a ArcGIS Marine Data Model.
The purpose of the ArcGIS marine data model is to represent a better integration
of many important features of the marine environment and to provide a more
accurate representation of location and spatial extent. It will allow for more
complex spatial analyses of marine data and will integrate a third and fourth
dimension, so that important vertical space and time components can be
incorporated into these analyses. In summary, the goal of the ArcGIS marine
data model is to more accurately represent the dynamic nature of water
processes (Breman 2002).
The adoption of GIS into marine mammal science will make a number of
analyses simpler and more time, energy, and cost efficient. Not only does this
technology increase the efficiency in which we can track animals to determine
their location and migratory patterns, it allows for the merging of this data with
oceanographic data (such as sea surface temperature, salinity, bathymetry) and
prey abundance data, so that complex analyses of these animals’ habitats can
be performed. GIS has already proven to be a useful tool in marine mammal
research and with the advancement of GIS technology to incorporate the three
dimensions of the ocean realm, there is no limit to the advancements in research
that will be made.
Annotated Bibliography:
Bjorge A., T. Bekkby, V. Bakkestuen and E. Framstad (2002)
Interactions between harbour seals, Phoca vitulina, and fisheries
in complex coastal waters explored by combined Geographic
Information System (GIS) and energetics modelling.
ICES
Journal of Marine Science 59: 29-42.
This study looked at the co-existence between harbor seals and many fisheries
in Norway. GIS was used to determine and display habitat use of harbor seals
and to integrate into the analysis the results from energetics modeling. It
became evident through this analysis that the bottom-set gillnet fishery was a
threat to the harbor seal population because the two inhabited the same niche
(100-200 m deep, just off the slope between the shelf and the archipelago),
causing not only a reduction of the seals’ prey species, but also entanglement of
the harbor seals. This is an example of how GIS has aided in the conservation of
a marine species.
Engleby L.K. (2002) Monitoring dolphin behavior and the
effects of restoration. In: Marine Geography: GIS for the
Oceans and Seas. Breman, J. (ed). ESRI Press: Redlands, CA.
p. 55-59.
The South Florida area has undergone severe environmental degradation over
the past fifty years, affecting water flow, nutrient influx and productivity. The
purpose of the Dolphin Ecology Project is to monitor the bottlenose dolphin
populations in the Florida Keys during this degradation. The project uses GIS to
determine the distribution and density of the dolphin population as well as
determine the prey the dolphins are feeding on and what types of habitat the
dolphins use to find their prey. In addition to this, GIS is also used to determine
how the dolphins respond to changes in their environment due to this
degradation and how the distribution of both the dolphins and their prey changes
with changes in water quality. The information obtained through these GIS
analyses is critical to the conservation of the bottlenose dolphin populations in
the Florida Keys.
Hamazaki T. (2002) Spatiotemporal prediction models of
cetacean habitats in the mid-western North Atlantic Ocean (from
Cape Hattera, North Carolina, U.S.A. to Nova Scotia, Canada).
Marine Mammal Science. 18(4): 920-939.
GIS was used in this study to create habitat prediction models for thirteen
cetacean species of the mid-western North Atlantic Ocean. These habitat
prediction models were based on oceanographic data (such as sea surface
temperature and monthly front probability) and topographic data (such as oceanbottom depth and slope).
These models proved to be very accurate when
compared to the current and historical habitats of these cetaceans, based on
shipboard surveys conducted from 1990-1997. What sets this study apart from
other habitat prediction model studies is that the author was able to show that
these models are able to not only predict distribution, but also to predict shifts in
habitat use and distribution based on oceanographic changes! This link between
changes in oceanographic processes and changes in habitat were made
possible exclusively through the use of GIS and could prove useful for marine
management policies.
Kinzel M.R. (2002) Green Sea Turtles Migration in the Gulf of
Mexico. In: Marine Geography: GIS for the Oceans and Seas.
Breman, J. (ed). ESRI Press: Redlands, CA. p. 25-33.
Satellite tagging is used to map sea turtle habitats and patterns of usage to allow
for a spatial analysis of the potential threats to these populations. This
information can also used to propose effective conservation measures which
would reduce or eliminated these threats. In this study, the home ranges and
movements (available from the satellite tagging information) of two mature
female sea turtles were mapped and analyzed using GIS technology. When this
data was overlaid with benthic substrate information using GIS software, it
became evident that the sea turtles inhabited areas of high sea-grass
concentration. Knowing this, sea-grass bed habitats have now become an
important focus for conservationists as areas of monitoring and potential
protection.
Moses E. and J.T. Finn (1997) Using geographic information
systems to predict North Atlantic Right Whale (Eubalaena
glacialis) habitat.
Journal of Northwest Atlantic Fisheries
Science. 22: 37-46.
This was the first study ever conducted in which GIS was used to predict the
habitat of the endangered North Atlantic right whale. Right whale sighting data,
bathymetry data, and sea-surface temperature data was used to create
coverages that were overlaid to predict the right whale habitats. These
parameters were chosen because they have been shown to influence copepod
distribution, which directly influences right whale distribution (Kenney and
Wishner 1995). GIS proved to be a very useful tool for predicting the habitat of
this species. The authors also discovered that a portion of the population is
currently not found in the known summering grounds. Using their GIS model,
they were able to predict the locations of other potential summering grounds may
be. Due to this species’ endangered status and lack of recovery, management of
the North Atlantic right whale is an extreme priority, and GIS may prove to be an
efficient way of monitoring this species.
Shick R. (2002) Using GIS to track right whales and bluefin tuna
in the Atlantic Ocean. In: Undersea with GIS. Wright, D.J. (ed).
ESRI Press: Redlands, CA. p. 65-84.
This author examines both the ways in which GIS can be usefully incorporated
into marine studies, as well as its limitations. The New England Aquarium uses
GIS to record right whale and bluefin tuna migratory patterns, to incorporate and
analyze data from a number of different sources, and to define speciesenvironment relationships using GIS spatial analysis.
Satellite tagging has proved to be immensely useful in the study of these large
whales. Through tagging, an animal’s migration patterns and movements can be
accurately studied. A large problem with the traditional ship and aerial surveys is
that researchers tend to look at where whales SHOULD be found and therefore,
a large portion of the population may not be surveyed simply because these
whales are not where the researchers expect them to be. There is a great deal
of genetic evidence to prove that there are more right whales than we currently
have records for (Frasier 2003). Since this whale species spends so much of
their time underwater, where they are not visible for cataloguing, satellite tagging
provides a unique opportunity to map areas where the whales are present and
moving through, compared to where sighting surveys are taking place.
Right whales are the most endangered of the large whale species and are of
critical importance because of their lack of recovery, despite their protection for
over 65 years. It is of extreme importance to be able to map their distribution
with a number of threatening factors, such as shipping lanes, to reduce the risk of
further detriment to the population. GIS is a crucial tool in this mapping and
overlaying of data. The major disadvantage to GIS currently is its inability to map
in three-dimensions, a necessity for a number of marine spatial analyses.
Stanbury K.B. and R.M. Starr (1999) Applications of geographic
information systems (GIS) to habitat assessment and marine
resource management. Oceanologica Acta. 22(6): 699-703.
The purpose of this article is to point out the advantages of using GIS in marine
resource management and in assessing marine habitats. As the article states, it
is often difficult in marine environments to assimilate data due to the temporal
changes and spatial nature of marine habitats. By using GIS, managers are able
to incorporate otherwise unrelated data, such as political boundaries and marine
habitat use, to aid in management policy decisions. With the many changes that
are occurring in coastal marine habitats due to anthropogenic factors relating to
economics and resource development, GIS proves to be an important tool in
observing how these changes are affecting current marine habitats and
predicting how they will affect these habitats in the future. These observations
can help answer questions related to marine resource management and guide
managers in marine and coastal policy decisions.
Waring G.T., T. Hamazaki, D. Sheehan, G. Wood and S. Baker
(2001) Characterization of beaked whale (Ziphiidae) and sperm
whale (Physeter macrocephalus) summer habitat in shelf-edge
and deeper waters off the northeast U.S. Marine Mammal
Science. 17(4): 703-717.
Sperm whales and beaked whales are both deep-diving cetaceans found mainly
in the shelf-edge waters of the Northeastern U.S. This paper used GIS
technology to analyze the sighting data from shipboard surveys (collected over
seven summers) in order to determine habitat use based on oceanographic and
bathymetric features. Survey track lines were created to compare the mean
number of sperm and beaked whale sightings. Whale distribution was overlaid
with oceanographic and bathymetric data. Because this was possible, it became
evident that habitat use and distribution of these whales could be predicted
based on bathymetric formations. These formations influence a number of
oceanographic processes, which often also influence prey distribution. By
mapping bathymetric formations, such as shelf edges, submarine canyons, sea
mounts, etc., it is possible to predict the habitat use of these cetaceans and
perhaps other marine mammal species as well?!
Additional Resources:
Breman J. (2002) Marine Geography: GIS for the Oceans and Seas. ESRI
Press: Redlands, CA. 204 p.
Breman J., D. Wright and P.N. Halpin (2002) The inception of the ArcGIS
Marine Data Model. In: Marine Geography: GIS for the Oceans and Seas.
ESRI Press: Redlands, CA. p. 3-9.
Environmental Systems Research Institute (ESRI) (1990) Understanding GIS,
the ARC/INFO method. Environmental Systems Research Institute, Inc.
Redlands, CA. 423 p.
Frasier T.R., R. Bower, M.W. Brown, S.D. Kraus and B.N. White (2003) Genetic
profiling of North Atlantic Right Whales: Application to Paternity Analysis. North
Atlantic Right Whale Consortium Annual Meeting. New Bedford, MA.
Kenney R.D. and K.F. Wishner (1995). The South Channel Ocean Productivity
EXperiment. Continental Shelf Research 15: 373-384.
Kenney R.D., H.E. Winn and M.C. Macaulay (1995) Cetaceans in the Great
South Channel, 1979-1989: right whale. Continental Shelf Research. 15(4/5):
385-414.
Knowlton A.R., S.D. Kraus and R.D. Kenney (1994) Reproduction in North
Atlantic right whales (Eubalaena glacialis). Canadian Journal of Zoology 72:
1297-1305.
Valavanis, V.D. (2002) Geographic Information Systems in Oceanography and
Fisheries. Taylor and Francis: New York, NY. 209 p.
Winn H.E., C.A. Price and P.W. Sorensen (1986) The distributional biology of
the right whale (Eubalaena glacialis) in the Western North Atlantic. Reports of
the International Whaling Commission, Special Issue 10: 129-138.