Using GIS to Characterize and Predict Cetacean Habitat Use Kathy Vigness-Raposa NRS 509 November 26, 2003

Using GIS to Characterize and Predict Cetacean Habitat Use Kathy Vigness-Raposa NRS 509 November 26, 2003 Geographic information systems (GIS) are an innovative tool for organizing and manipulating geospatial data. GIS has been used extensively to characterize and predict the habitat use of land mammals. However, only in the past few years has this tool begun to be utilized by scientists interested in the distribution of marine mammals. Traditional methods for studying cetacean distribution have not considered external factors that may be influencing the observed patterns. Though the same geographic area may be surveyed multiple times, it is often not known whether the same oceanographic environment is being sampled. The oceanographic environment encompasses greater than 70% of the Earth s surface. In addition to its vast spatial extent, it is complicated by temporal patterns that vary on multiple scales. Given its spatiotemporal complexity, it is almost guaranteed that the results of cetacean sighting surveys conducted at discrete locations and times are being influenced by external factors. These factors can be categorized as environmental, biotic, and anthropogenic (Davis et al. 1998). Various studies have used GIS to explore the relationship between cetacean distribution and these factors in an attempt to characterize and predict habitat use. Environmental variables Environmental variables included physiography, climatology, hydrography and geomorphology, and may involve diel, seasonal, interannual, or decadal patterns of variability or periodicity. Physiographic variables commonly investigated included bottom depth and bottom depth slope (e.g., Davis et al. 1998, Gregr and Trites 2001, Hamazaki 2002), and distance of the cetacean sighting to shore (e.g., Ersts and Rosenbaum 2003, Torres et al. 2003). National Oceanic and Atmospheric Administration (NOAA) digital nautical charts were often utilized to obtain raster grids of bathymetry, from which bottom depth slope was derived using the SLOPE command in Environmental Systems Research Institute, Inc. (ESRI) software. These variables are relatively constant and easily obtained, making them effective metrics for long-term management decisions. Climatological variables included such features as sea surface temperature (SST), SST gradient, and SST front probabilities. Sea surface temperature data were typically derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite imagery (e.g., Davis et al. 1998, Waring et al. 2001, Hamazaki 2002), and/or were also collected in situ during the cetacean sighting surveys (e.g., Davis et al. 1998) or associated oceanographic surveys (e.g., Gregr and Trites 2001). Prior to importing the AVHRR satellite data into a GIS, there was some degree of processing to obtain SST values (Hamazaki 2002), and extensive, highly technical processing to derive frontal probabilities (Waring et al. 2001). The in situ data could be directly incorporated into a GIS. However, in order to associate point temperature data from oceanographic surveys with non-overlapping point cetacean sightings, the temperature data needed to be interpolated (Gregr and Trites 2001). Using GIS, temperature values were assigned to the cetacean sightings using triangular irregular networks (TINs). Hydrographic variables included surface salinity (e.g., Davis et al. 1998, Gregr and Trites 2001) and the presence of warm- or cold-core rings (identified by the depth of the 15 C isotherm in the Gulf of Mexico; Davis et al. 1998, Davis et al. 2002). Geomorphologic variables studied were sediment types and other seafloor structures. Naud et al. (2003) showed that minke whales, a species with a relatively coastal distribution, were observed more often near underwater sand dunes than other bottom types. Most authors included the caveat that although cetacean habitat can be defined in terms of environmental variables, there is an underlying assumption that the physical environment itself

is not significant. Cetacean distribution is often primarily influenced by prey distribution; prey distribution, in turn, is influenced by oceanographic features such as water temperature, salinity, and bathymetry (Katona and Whitehead 1988, Baumgartner 1997). Gregr and Trites (2001) suggested that the relationship between prey and oceanographic conditions is either that conditions are ideal for primary productivity, or conditions concentrate prey in specific areas. They demonstrated that by carefully examining the temporal scale of the relationship between cetacean distribution and oceanographic conditions in a GIS, the relationship between the oceanography and the prey can be inferred. They also stressed that the temporal and spatial scales over which data are pooled may affect the ability to detect possible relationships, and should be selected carefully. Biotic variables Biotic variables that may influence habitat use include such factors as prey distribution, competition among animals, reproduction, predation, and intraspecific preferences. None of the papers reviewed that included prey data in their analyses used a GIS. Jaquet et al. (1996) examined the relationship between satellite-derived measurements of ocean color (a first-order approximation for primary productivity) and historical sperm whaling catches using nonparametric statistics. Reilly and Fiedler (1994) and Davis et al. (2002) measured surface chlorophyll in situ with flow-through fluorometers during their sighting surveys and derived relationships between sighting distributions and multiple variables with canonical correspondence analysis (CCA) and linear regression, respectively. Different social groups of a species may utilize different habitats based on their biological needs. Ersts and Rosenbaum (2003) used a GIS to investigate the habitat use of different social groups of humpback whales on their wintering grounds. Humpback whales migrate to the wintering grounds to calve and mate, and groups engaged in either of those social activities have significantly different biological requirements, which were found to be reflected in their habitat use. Different populations of a species may also utilize different habitats, as was studied by Torres et al. (2003). Two morphologically indistinguishable populations of bottlenose dolphins, one of which is severely depleted, exist off the U.S. east coast. Using a GIS to correlate genetic data samples with bottom depth and distance from shore, Torres et al. (2003) were able to define habitats exclusively used by each of the populations. By more accurately defining the boundary between the two populations, future sighting surveys can be better designed, and management decisions regarding fishery by-catch can be improved. Anthropogenic factors Anthropogenic factors include such variables as historical hunting, pollution, ship activity, commercial and recreational fishing, oil and gas development and production, and seismic exploration. Gregr and Trites (2001) used historical whaling data and contemporary physiographic and climatological data in a GIS to predict critical habitat for five cetacean species. They then compared the predicted habitats to current anecdotal sighting results to suggest the impact historical whaling may have had on populations. Schick and Urban (2000) used a GIS to compare the distance migrating bowhead whales were seen from an oil drilling rig when it was operating and when it was not, and discovered that the operating rig resulted in a significant loss of habitat for the migrating whales. Analytical Procedures GIS has primarily been used to correlate external factors with the sighting locations of cetaceans. Creating the data layers has involved extensive use of GIS functionality. Limited data are directly available for the oceanographic environment. NOAA s digital nautical charts have been a valuable source for bathymetric data. In addition, NOAA s AVHRR satellite data have provided raster grids of sea surface temperature. However, because GIS is in its relative

infancy in the oceanographic environment, many of the other data layers needed to be derived. For example, the SLOPE command in ESRI s software was often used to derive the variable bottom slope, which provided a sense of how rapidly the depth of the seafloor was changing. Once a data layer was created, the data often had to be manipulated in order to correlate the layers to the cetacean sighting data. Point data were interpolated in GIS using triangular irregular networks (TINs) (Gregr and Trites 2001). In addition, data were binned spatially and temporally to investigate relationships at different scales (e.g., Hamazaki 2002). In all cases, the geospatially correlated data were exported from the GIS to a more substantial statistics package for detailed analyses. Future of GIS in the Study of Cetacean Habitat Use The use of GIS in the study of cetacean habitat use is still in its infancy. All of the studies that have been conducted have focused on the qualitative aspects of habitat use, investigating the presence or absence of observations under certain conditions. A significant step would be to use the defined habitats to interpret perceived changes or trends in abundance estimates. Fiedler and Reilly (1994) used relative abundance and environmental variables in a CCA to define a habitat quality index. This index was used to differentiate between decreases in abundance estimates due to loss of habitat because of El Niño and decreases in abundances that might be due to tuna-dolphin purse seine by-catch. A major step would be to include quantitative data on absolute abundance estimates in the analyses. By correlating absolute abundance with specific habitat conditions, the accuracy and precision of abundance estimates could be improved. Furthermore, since the entire distribution of a population cannot be sampled, abundance estimates of the entire population could be derived from the limited survey area based on habitat use. Another major area of opportunity for using GIS in this area of study is either in characterizing and predicting subsurface habitat, or in three-dimensional modeling of habitat. Because of the state of the science, most studies have focused their investigations on surface conditions. However, most cetacean species spend over 80% of their time at depth. Therefore, surface conditions are not really characterizing most of the habitat that the animals are selecting. Annotated Bibliography Davis, R.W., G.S. Fargion, N. May, T.D. Leming, M. Baumgartner, W.E. Evans, L.J. Hansen, and K. Mullin. 1998. Physical habitat of cetaceans along the continental slope in the north-central and western Gulf of Mexico. Marine Mammal Science 14(3): 490-507. Davis et al. (1998) describe how they used a GIS to integrate cetacean sightings with environmental data. The environmental variables included sea surface temperature (SST), SST gradient, depth of 15 C isotherm (proxy for determining location of warm- and cold-core rings), water temperature at 100 m, surface salinity, bottom depth, and bottom-depth gradient. Similar to most marine mammal studies involving geospatial data, GIS was used to extract the environmental data at each cetacean sighting, but statistical analysis of the data was conducted using other tools. In this study, they used Statistical Analysis System (SAS) and SYSTAT to conduct Kruskall-Wallis one way analysis of variance to attempt to differentiate the species with regards to the environmental variables. There was a strong differentiation of species with respect to bottom depth only, and the authors suggest that the distribution of cetaceans is probably better explained by the availability of prey. Interestingly, Davis et al. (2002) describe additional sighting surveys that included sampling zooplankton and micronekton biomass, but the authors did not use a GIS in this study to integrate or analyze the cetacean and environmental data.

Ersts, P.J. and H.C. Rosenbaum. 2003. Habitat preference reflects social organization of humpback whales (Megaptera novaeangliae) on a wintering ground. Journal of Zoology, London 260: 337-345. This paper discussed the habitat use of different types of humpback whale groups in Antongil Bay, Madagascar. Antongil Bay is a wintering ground in the southwestern Indian Ocean where humpback whales migrate to calve and breed. ESRI software (ArcInfo and ArcView) was used extensively to derive digital data and create a 3-D surface model of Antongil Bay. Bottom depth and distance from shore were spatially joined with each sighting of a humpback whale group, which was classified into five group types. These data were exported for analysis in an external statistics package. Ersts and Rosenbaum (2003) provide detailed methods on how to develop digital data for the marine environment, which is not always readily available. They also present a thought-provoking hypothesis that intraspecific differences in habitat use may be confounding species-specific habitat models. Gregr, E.J. and A.W. Trites. 2001. Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Canadian Journal of Fisheries and Aquatic Science 58: 1265-1285. The authors used GIS to spatially correlate historical (1948-1967) whaling catch data (location, species, date of capture, and sex for sperm whales) with contemporary (1980-1998) environmental variables (month, depth, slope, depth class, sea surface temperature and sea surface salinity). They also used GIS to display the results of generalized linear models that characterized and predicted critical habitats for the cetacean species. This paper provides a wealth of information on geospatial data analysis and modeling. The authors provide detailed methods on how the independent variables were interpolated in the GIS to assign values to each grid cell, how the selection of scale in both a spatial and temporal sense may affect the discernment of patterns, and how regression models are built and tested. They further the science of habitat characterization by predicting critical habitats for unsurveyed areas. Hamazaki, T. 2002. Spatiotemporal prediction models of cetacean habitats in the midwestern North Atlantic Ocean (from Cape Hatteras, North Carolina, U.S.A. to Nova Scotia, Canada). Marine Mammal Science 18(4): 920 939. Hamazaki (2002) used GIS to integrate cetacean sighting, oceanographic and topographic data in the northwest Atlantic. These spatially correlated data were then exported to classify the habitats of each species and construct habitat prediction models with additional statistical tools. The probability distributions of the habitat models were displayed in GIS, and models for June and August were overlaid to investigate spatiotemporal habitat shifts. The author furthers the science of characterizing and predicting habitat use by constructing models for a large number of species (13 cetaceans) over a vast geographic area (35-45 N, 60-80 E). Schick, R.S. and D.L. Urban. 2000. Spatial components of bowhead whale (Balaena mysticetus) distribution in the Alaskan Beaufort Sea. Canadian Journal of Fisheries and Aquatic Science 57(11): 2193-2200. In this paper, the authors characterize the habitat use of migrating bowhead whales and investigate the influence of an oil drilling rig on their spatial distribution. A GIS of water depth, distance to shore, and distance to the rig was constructed. Whale sighting locations were used to sample the GIS coverages, which were also randomly resampled to determine if the random locations differed significantly from the observed locations. The authors also analyzed the data using the Mantel test, a significant advance for marine geospatial analysis. This paper is one of few to consider the spatial autocorrelation of the environmental variables, specifically how the

spatial dependence of the independent variables may influence the statistical significance of the analysis results. Torres, L.G., P.E. Rosel, C. D'Agrosa, and A.J. Read. 2003. Improving management of overlapping bottlenose dolphin ecotypes through spatial analysis and genetics. Marine Mammal Science 19(3): 502-514. In the western North Atlantic, two ecotypes of bottlenose dolphins are recognized, with the coastal morphotype significantly impacted by an epizootic and fishery by-catch. The two ecotypes can be separated genetically, but cannot be distinguished visually. In an attempt to facilitate identification during surveys, the authors used GIS to correlate the location of genetic biopsies with bottom depth and distance from shore. A Classification and Regression Tree (CART) analysis was conducted to define a static boundary between the two ecotypes. This paper demonstrates the use of GIS to post-stratify observations from past surveys, structure future sighting surveys more appropriately, and increase the reliability of minimum abundance estimates for both ecotypes. Furthermore, the GIS has significant implications for managing the depleted coastal ecotype population and the fisheries being implicated in its by-catch. 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. The authors attempted to address the question of competitive interactions between sperm and beaked whales by characterizing their habitat use. GIS was used to correlate sighting data with sea surface temperature, frontal boundaries, bottom slope, and the presence of submarine canyons. Logistic regression models were constructed to identify beaked and sperm whale habitats and predict the probability of presence. In this paper, GIS is used to discriminate the habitats of two species groups, which has traditionally not been possible. The availability of data in finer spatial resolution and the integration of these data in a GIS allow these ecological nuances to be quantified. Additional References Baumgartner, M. F. 1997. The distribution of Risso's dolphin (Grampus griseus) with respect to the physiography of the northern Gulf of Mexico. Marine Mammal Science 13(4):614-638. Davis, R. W., J. G. Ortega-Ortiz, C. A. Ribic, W. E. Evans, D. C. Biggs, P. H. Ressler, R. B. Cady, R. R. Leben, K. D. Mullin, and B. Wursig. 2002. Cetacean habitat in the northern oceanic Gulf of Mexico. Deep-Sea Res. Part I, Oceanograph Res Pap 49(1):121-142. Fiedler, P. C., and S. B. Reilly. 1994. Interannual variability of dolphin habitats in the eastern tropical Pacific. II: Effects on abundances estimated from tuna vessel sightings, 1975-1990. Fishery Bulletin 92:451-463. Jaquet, N., H. Whitehead, and M. Lewis. 1996. Coherence between 19th century sperm whale distributions and satellite-derived pigments in the tropical Pacific. Marine Ecology Progress Series 145:1-10. Katona, S., and H. Whitehead. 1988. Are Cetacea ecologically important? Oceanogr. Mar. Biol. Annu. Rev. 26:553-568. Reilly, S. B., and P. C. Fiedler. 1994. Interannual variability of dolphin habitats in the eastern tropical Pacific. I: Research vessel surveys, 1986-1990. Fishery Bulletin 92:434-450.