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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.