In this paper, the authors examined the relationships between movement, diving, and strategy success in Gray seals (Halichoerus grypus) off the Scotian Shelf and adjacent areas of eastern Canada. Establishing spatial and temporal information to where these predators forage is essential to understanding higher trophic interactions, and yet there is little information on foraging behavior in large marine carnivores. Adult gray seals of known age were captured and fitted with satellite-relay data loggers (SRDLs), with a subset also having stomach-temperature radio transmitters. Bathymetry data was tied into the grey seal satellite locations, providing additional information. It was found that about 80% of the locations where foraging took place were in waters less than 100 m deep, suggesting that these animals tended to forage along shorelines. These data also suggested that the single most important predictor of feeding was bottom time, but estimates for total distance traveled, angular variance, and bathymetry factors contributed significantly. Additionally, these features were found to be scale dependent - at the3 hour scale, average bottom time and distance traveled were the most important predictors for forging frequency, whereas at the 6 hour and 24 hour time scales distance traveled alone was the most important factor. At the 12 hour interval, bathymetry was the most important predictor.

This article was useful because it tied an understanding of predator foraging behavior to ecosystem structure and functioning. The use of habitat by predators in an area is tied (to some degree) to the quality and availability of resources in an area. In order to survive, a predator must consistently track spatial and temporal distributions of prey as they can have a strong effect on the energetic costs associated with foraging and overall predator survival.

The Galapagos sea lion (Zalophus wollebaecki) is widely distributed throughout the Galapagos Archipelago, and occurs primarily on gently sloping rocky shores and sandy beaches along nearly all the central, west, and south islands. The authors predicted that marine-derived nutrients transported by the sea lines would have a significant effect on the local terrestrial plants, both on their chemical composition and physiology. They wished to analyze the spatial extent and magnitude of marine nutrient transport to terrestrial ecosystems provided by these marine mammals, within the framework of nutrient exchange between marine and terrestrial ecosystems. Because sea lions are generally restricted to the shoreline, their study demonstrated that Z. wollebaecki is an effective vector, with the transported nutrients occurring in high concentrations in the soils used by shoreline plants. This nutrient transport by the sea lions occured primarily through defecation and secondarily by onshore mortality, and was restricted by the topography of the island.

For this study, the geographical position of each sea lion colony was registered through the use of GPS devices. Colony positions (obtained from boat-based operations) were downloaded from the GPS units into ArcView and geocoded onto a topographical geo-referenced map of the Archipelago. Using both direct and indirect methods of observation, the spatial extent of the sea lions reproductive colonies were calculated from these maps by establishing a buffer zone around each colony using the Spatial Analyst extension. The buffer criterion was a circle with a radius of 10 m surrounding the position of each colony on the map (which is a conservative approximation of the extent of the sea lion’s movements on land). Finally, the spatial extent of the colonies on each island was calculated by the integration of the polygon buffer defined by the overlapping circles. These tools are invaluable for examining area and habitat use, and provide a means to estimate density distributions.

For this paper, the authors set about to examine how different physical characteristics can lead to differentiation within a species, and eventually to speciation. They used the polar bear (Ursus maritimus) to examine this question. Polar bears evolved from brown bears after formation of the Arctic sea ice landscape approximately 2.5 million years ago. The authors considered two sets of attributes: one based on information from satellite-collared polar bears, and the other from the spatial pattern of sea ice. From the telemetry data, they transformed the decimal latitude/longitude coordinate system using GIS. For the sea ice pattern component, they randomly selected 28, 300 x 300 km blocks (90,000 km2) across the study area, with each of these blocks representing the estimated average annual home range of an adult female polar bear. For each block, the percentage of the area covered by sea ice was assessed using sea ice maps (digitized using SPANST GIS). Only areas > 20% sea ice cover were included in this analysis as polar bears rarely use thinly distributed pack ice. A calculation was performed to determine the fractal dimension of ice cover for each block. They found a strong correlation between the seasonal range of polar bears to the amount of sea ice available, indicating that spatial characteristics of the sea ice landscape are linked to the hierarchical pattern of polar bear groupings. In conclusion, the authors postulated that once a new sea ice environment formed, directional selection resulted in allopatric speciation while current stabilizing selection forces maintain present groupings due to breeding exchanges among the population.

Movement patterns and pathways are strongly influenced by environmental structure. Biogeographical patterns of individuals within a species can reflect the present and the past biology of an organism, and provide information about the history of the physical environment of an organism (and/or population). One such process of species evolution, allopatric speciation, begins as a result of geological changes in the physical structure the Earth. The study of the temporal dynamics of a species geographical range and spatial distribution may help to determine how the size and geometry of habitats can affect community structure and population dynamics, which in turn may be critical for the successful management and conservation of a species.

The Florida manatee is an endangered marine mammal that inhabits freshwater and estuarine ecosystems in Florida. Unfortunately, these near shore areas are also heavily used by people, and collisions with watercraft are of concern due to the resultant morbidity and mortality to the local manatee population. In an effort to characterize the manatee’s distribution and abundance, 41 manatees (19 males and 22 females), were captured and fitted with VHF radio transmitters in the Tampa Bay region of Florida, and had their movement paths mapped for period of at least 30 consecutive days. In this paper, telemetry data were used to develop a model to predict movement paths and corridors of manatees within Tampa Bay. Points were collected that were entered into the GIS ArcInfo, and a point coverage file was developed and then joined with the visual observation data to form a single map of point locations. The raster maps of the movement paths for each tagged manatee were converted into maps of number of visits per cell, and mean time per visit per cell. Based on qualitative evaluations by field biologists, the model that was produced performed reasonably well in estimating manatee locations. Movement corridor locations were less certain, but reasonable with the understanding that manatee spatial cognition would permit directed movement between places.

An important component of protection planning and permitting involves knowing (to some degree) where the protected species tends to aggregate. Telemetry data is especially useful in this regard as it provides multiple locations over time for individual animals rather than a single location of a group of animals at a single instant in time. This makes it useful for quantifying survivorship, home ranges, behavior, foraging habits, seasonal movement patterns, habitat use, and other more arcane uses such as the differences in movement and distribution of animals by sex. The end results of this analysis are similar to those of home range mapping, a large part of my project.

The Atlantic bottlenose dolphin (Tursiops truncatus) population along the eastern coastal United States is thought to consist of two distinct ecotypes; coastal and offshore. Each of these types is thought to exhibit distinct geographic ranges and morphometrics. Presently, why these differences exist in this species are poorly understood. It has been noted that the ranging patterns of populations vary from (1) permanent local ranges to (2) seasonal migration to (3) short-term seasonal site fidelity. In an attempt to understand the bottlenose dolphins ecology and social system, the author surveyed approximately 100 km of inshore and coastal estuarine habitat (including tidal creeks and marshes) near Hilton Head Island in South Carolina (USA) from October 1994 through December 1998. A standard photo identification methodology was employed using distinctive fin characteristics to identify individuals. These prints were digitized into an ArcInfo GIS database, and a map was created with this positional information. An adaptive kernel (ADK) method to estimate home range from a probability distribution was used, because it was less sensitive to sample size and provided a more accurate distribution. The 95% ADK ranges for each pair of dolphins were overlaid in ArcView and compared to the relationships among individual range locations. Polygons were created that represented the area of overlap between the dolphin’s ranges. From this analysis the home range boundaries and core use areas were defined for 20 resident dolphins. These dolphins tended to occur in inshore waters, were moderately mobile, displayed strong site fidelity, and exhibited distinct patterns of core use within their home ranges. These ranging patterns revealed a geographic partitioning along social and environmental parameters within the resident population.

Generally, animals within habitats of high productivity have smaller home ranges than animals in habitats of lower productivity. Also, the size of an animal's home range is usually correlated with its body mass, the distribution of mates, competitive interactions, and the amount and distribution of available resources. In addition to these variables (which must be taken into consideration), the calculation of home range is sensitive to the number of sightings of an individual, and the home range size tends to increase with sample size (although there are statistical methods available to compensate for this effect). While there are admitted limitations, knowledge of home range size and use patterns is important because it can reveal other aspects of population ecology such as social structure, foraging strategies, and the progression of disease.

The Arc Marine data model is presented as a generalized template designed to guide the operation of GIS projects pertaining to the marine environment. For this particular study, Arc Marine was extended to fit the research goals of the whale satellite telemetry tagging program at Oregon State University’s Marine Mammal Institute (MMI). This study sought to customize the basic Arc Marine data model to take advantage of satellite telemetry data for mapping the distribution and movement of endangered marine animal species. The article presents the Arc Marine data model as a way to standardize data management and analysis, which in turn would provide rapid dissemination of data and a simplified approach to carrying out complex analyses. The entities within the Arc Marine data model considered most critical to this study were the InstantaneousPoint feature class, the Vehicle object class, and the MarineEvent object class. Ultimately, the Arc Marine data model defines a structure for storing in a GIS a variety of deep ocean and coastal features. It also unifies satellite telemetry data gathered from multiple surveying platforms into a standard schema which allows for the community-wide advantage of data interoperability, while reducing analytic complexity.

I found this article to be of interest because it promises to provide a data model that is flexible in handling a wide array of data types and tag types. The empty geodatabase resulting from the Arc Marine schema can then be filled with data which can be automatically organized into appropriate feature classes and relationships for managing, analyzing, and potentially publishing data. This synthesis simplifies a variety of questions as they pertain to marine animal tracking, species interactions across their ranges, and the relationships between physical and biological processes.

Biogeography, which is the study of the geographical distributions of organisms, has the potential to play a crucial role in systematic marine conservation planning. Systematic planning involves an overview of an entire system - this involves identifying conservation targets, collecting information, establishing goals, creating a collection of areas for consideration, and identifying areas of particular importance for conservation action. Current approaches to marine conservation are based on terrestrial approaches which often seek to earmark a percentage of each identifiable habitat type, while trying to minimize overall costs; that is, reducing the perimeter of the protected area to a minimum to avoid impacting economic concerns. Additionally, “hotspots,” or areas of high biodiversity, are often given additional priority. This paper was a review of the current status of marine biogeography, and sought to assess ways in which current marine conservation projects could incorporate biogeographic information into their planning for conservation management. The authors acknowledged that scientific knowledge of marine systems is increasing rapidly thanks to recent advances in genetics, GIS, and remote sensing. They felt that such knowledge and tools will play an important role for marine planning in the future.

Marine systems differ from terrestrial systems in numerous ways, which could have broad implications for conservation planning. Unlike terrestrial systems, marine environments are physically connected by enormous volumes of seawater which provide a continuity of habitats. These differences lead to biological connections which are unique to the marine environment, creating a truly three dimensional system whose boundaries may be blurred over space and time. Despite these challenges, an understanding of spatial context and connections is needed to set conservation priorities that will ensure the continued existence of multiple species (and their habitats) within a healthy, functioning ecosystem. GIS may play a major role in the future for marine data due to its increasing ability to handle large, dynamic, 3-dimensional data sets.

Harbor seals (Phoca uitulina richardsi) are one the most widely distributed and common pinnipeds in the coastal waters off southern Alaska. Between the late 1970s and 1980s, declines that ranged between 60%-80% were documented for harbor seals in some areas in the northern Gulf of Alaska. To further exacerbate this problem, in 1989 the Exxon Valdez ran aground on Bligh Reef in northeastern Prince William Sound (PWS), spelling approximately 40 million liters of crude oil. In this paper the authors sought to describe the distribution and movement patterns of harbor seals that were satellite tagged and tracked in PWS between the years 1992-1997. They were interested in determining if the movement patterns of PWS seals were similar to those from other areas, and whether there were differences in movements among age and sex classes. Sensor data were merged with location records to produce a data file that averaged the daily locations of each seal, which was then plotted using ArcInfo and ArcView. Another data file was created that included the average position of each haul-out bout, and this second data file was used to calculate distances from haul-out sites to at-sea locations. By following the sequential locations of individual seals using the GIS, they were able to distinguish three general types of movement patterns: (1) movement restricted to near the tagging location, (2) movement to glacial fjords, and (3) movement out of PWS into the Gulf of Alaska. Overall, they found that juvenile seals tended to move more than adults and had larger home ranges. The mean distances between successively used haul-outs were <10 km for adults and <20 km for juveniles. The average haul-out to at-sea distance was 5-10 km for adults and 10-25 km for juveniles. There also appeared to be movement interactions between sex, age, and the month, indicating seasonal and gender variation.

This paper illustrates the effectiveness of combining positional data from multiple data files to create maps in a GIS that examine distances and variation between foraging trips and time spent at haul-out sites. This allows one to make inferences concerning foraging and potential dispersal patterns. Also, by having a temporal element to the data, inferences can be made as to when foraging was taking place. This information is valuable when examining behavioral strategies of these animals. It must be noted that the authors admitted that poor satellite coverage at night resulted in limited information on nighttime movements. This may have caused an underestimation of both home range size and distance traveled.

Matthiopoulos et al. wished to provide estimates for the number of grey seals (Halichoerus grypus) off the coastal waters of the United Kingdom. They approached this task on two fronts - through the use of aerial surveys and satellite telemetry from 110 tagged individuals that were predominantly older than two years of age. Data were obtained using Argos Satellite Relay Data Loggers (SRDLs) primarily off the northern coasts of the British Isles between May to September (outside of the breeding and moulting season). The primary aim of the study was to present best estimates for grey seal spatial usage, and provide a template for future efforts at establishing a methodology for estimating the spatial distribution of animals. They admitted that there were gaps in their data that represented a high priority for future data collection.

Although an analysis by GIS software is not formally mentioned in this article, it uses the data collected to create a series of color-coded choropleth maps (pp. 479, 483-486) to illustrate the geographic locations of individual grey seals and groups during the population’s offshore time, and gives an idea of the offshore sampling effort dedicated to the study. This is an effective use of GIS to convey density information in a visual manner that is both clear and provides a great deal of information quickly. This work is also important because the knowledge gained about the grey seals use of space will be directly applied to resolving conflicts between the conservation of grey seals, the management of fish stocks, and the licensing of marine exploitation by the oil industry. Their findings that grey seal usage is characterized by a small number of hotspots indicate that both parties could be particularly well served by localized conservation efforts.

This study was conducted from January 1999 to October 2002 in the coastal waters of Cleveland Bay Dugong Protected Area, located in northeast Queensland, Australia. Snubfin (Orcaella heinsohni), and Indo-Pacific humpback dolphins (Sousa chinensis), co-exist in sympatry within this area, and throughout most of their range in Australian waters. The author conducted boat-based surveys within Cleveland Bay, and collected data on the space and habitat use of both species. He found that core areas of use (50% of kernel range) for both species were located close to river mouths and human modified habitat near the port of Townsville. Within their core areas, traveling activities and foraging were the dominant behavioral traits. These 2 species of dolphin’s representative ranges (95% of kernel range) overlap substantially, with many areas displaying a strong similarity in the space use habits of both species. There were subtle differences, however, with snubfin dolphins preferring slightly shallower (1 to 2 m) water and humpback dolphins preferring slightly deeper water (2 to 5 m). There were also differences in habitat makeup between the two species – shallow areas with seagrass ranked high in habitat preference for snubfin dolphins, whereas the humpback dolphins preferred dredged channels. The author concluded that these slight differences in habitat preferences (i.e. resource partitioning) may be the principal factor responsible for maintaining the peaceful coexistence between the snubfin and humpback dolphins.

This article made great use of currently available GIS software capabilities. To estimate a fixed kernel Utilization Distribution (UD) for each species, all school sightings were converted into an ArcView point coverage and then used with the ArcView Animal Movement Analyst extension. All school positions were recorded using handheld 12-channel global positioning system (GPS) units. Kernel ranges of core area and representative range probabilities of occurrence were calculated using smoothing parameters calculated via the least squares cross validation procedure (also available within ArcGIS). These types of analyses will be especially useful for determining the core home ranges of juvenile Steller sea lions in Alaska.

The objectives of this study were to describe Steller sea lion (SSL) pup dispersal patterns, determine the source of variability in interhaul-out movements and haul-out use, and determine if round trip distance and duration during travel varied significantly with age, sex, month, or membership in either the Western Population Segment (WPS) or the Eastern Population Segment
(EPS) of SSLs. This was accomplished by the deployment of 103 satellite data recorders (SDRs), 74 of which were on EPS juveniles and 29 of which were on WPS juveniles between the months of March 1998 to November 2001. Validation of location errors was performed by obtaining simultaneous GPS locations. In general, the authors found that Steller sea lion females and pups dispersed from the natal rookery at an early age, and that their foraging strategies were different among individuals; some conformed to a central place foraging strategy, while others chose a multiple central place foraging strategy that included multiple haul-out sites close to seasonal aggregations of prey. Overall, they found that round-trip distance and duration increased with juvenile SSL age, that round-trip distance tended to be greater for the WPS juveniles than the EPS juveniles, and that the round trip duration was greater for females than males. 90% of the round trips were <15 km from haul-outs, and 84% were < 20 hours, providing strong evidence that nearshore areas close to haul-out sites are critical for developing juveniles.

Optimal foraging theory proposes that foraging animals should not travel further than necessary from a central place, or should choose a central place that minimizes travel distance. For a lactating female, this may explain the expansion of their foraging range in the spring as a female nursing a pup is estimated to consume twice as much energy as a female of the same age without a pup. Significant reductions in prey availability due to events such as the El Nino Southern Oscillation can have dramatic effects on pinniped survival. The selection and use of these sites may depend upon multiple factors such as seasonal resource availability, conspecific competition, fisheries competition, and predation. Ultimately, these areas may be absolutely critical to the survival of the developing juvenile SSL, and may warrant protection as critical habitat to preserve the species.

The main objective of this study was to examine the potential for linkages between the spatial distributions of primary productivity across the region of the northwestern Hawaiian Island chain, and how this productivity aligned with trends in subpopulations of Hawaiian monk seals. Primary productivity was measured by sea surface temperature (SST), chlorophyll content in the water column, and the vertical structure of the water column itself. This data was obtained through measurements and by using geographically referenced information which was then input into a GIS to create raster mesoscale maps that reflected these variations. A great example of this can be found on page 907 for sea surface temperature data. The transformed data set was imported into a GIS using a pixel size of 17 km. Distributions of Hawaiian monk seals, taken from aerial surveys and other related population counts, were examined in relation to the maps created for the measured primary production variables. The author found that there was a great deal of spatial heterogeneity in primary productivity within the study area, and that the local patterns in Hawaiian monk seal abundance have been shaped to a high degree by the quality of the surrounding marine biotic environment.

I found this paper to be of value because there have been prominent changes in abundance between the Western and Eastern subpopulations of Hawaiian monk seals, with a precipitous decline in the Western population. This is a close analog to the Steller sea lion populations that I am studying. These patterns indicate a striking spatial component, which suggested to this author that the variation may be related to biotic forcing from the marine environment. This examination may be useful to my own analysis, and may provide an argument for the conservation of critical core habitats for management purposes.

The objective of this study was to identify the role of physical environmental characteristics in determining patterns of activity and fine scale space use of dugongs which had been fitted with a radio collar. The focus was mainly on intertidal and subtidal seagrass habitats, using a combination of GPS telemetry, GIS, and spatial modeling. The authors used fixed kernel estimators with a least -squares cross-validation smoothing function to measure dugong home ranges, which was accomplished through the Animal Movement Analyst extension in ArcView v3.3. They interpolated a high-resolution (200 m) bathymetric surface using the kriging function in ArcGIS Geostatistical Analyst 9.0 with tide corrected depth values, and imported geo-referenced bathymetry maps into the GIS for the other habitats under study. They found that both tidal and diel cycles influenced dugong movement. The tracked dugongs tended to be closer to shore at high tide and at night, and they postulated that this behavior may be related to the avoidance of predators or watercraft.

Recent advances in telemetry techniques and capabilities are currently being used to provide unprecedented fine scale detail on movement and spatial behavior of many animal species. These advances in telemetric technologies have occurred along with advances in spatial theory, computing power, and the increasing use of GIS software. Taken together, these spatial analytical techniques are providing powerful conservation management tools, linking together the behavioral ecology of a species and the environmental variables within its habitat. Models produced from these data can be overlain on maps to provide an idea of movement patterns and habitat use at scales that are suitable for informing policymakers and managers.

Womble et al. set out to assess changes in the temporal and spatial distribution patterns of Steller sea lions (Eumetopias jubatus) at a number of terrestrial sites. Aerial surveys (n=39) were conducted in northern Southeast Alaska covering a total of 28 terrestrial sites from March 2001 until May 2004. Digital photographs were stored and later downloaded to a computer, with the clearest digital image of each group imported into a GIS software (ESRI’s ArcView 3.2a), where each image was counted twice. The goal was to classify seasonal distribution patterns of Steller sea lions and determine to what extent the seasonal distribution could be explained by seasonal fluctuations of prey concentration. They found that during December, 55% of the sea lions in the study area were located near over-wintering herring aggregations. During May, 56% were found near aggregations of spring spawning forage fish. In July, 78% were found near summer migratory corridors of salmon. And in the fall (September), 44% of the sea lions were found near the autumn migratory corridors of salmon. This range of 44% to 78% suggests that there may be variations within the population concerning foraging strategies. In addition to bottom-up factors, other factors that may influence the distribution of the sea lions at these terrestrial sites may include predation risk, localized depletion of prey near haul-outs, the physical attributes of the haul-out sites themselves, and the availability of potential mating opportunities.

In order to understand the distribution, movement, and migration patterns of large, higher trophic predators, an underlying understanding of the reasons why they move is required. These reasons are complex, with factors that oscillate seasonally and seasonal patterns that vary geographically. There are a variety of strategies for prey exploitation that Steller sea lions may use to exploit localized prey resources that are predictable in space and time. Because the sea lions must move tens to hundreds of kilometers to take advantage of seasonally adjusted prey, these strategies will only be successful if the prey aggregations are consistent from year to year. While energy might be conserved if this is the case, unexpected fluctuations in food supply may be particularly damaging to an animal that has come to rely upon seasonal availability, which may ultimately influence fitness and survival.

Steller sea lions (SSLs) energy demands are high during the spring, when males are preparing for extended fasts on breeding territories and females are pregnant and lactating. For this study, aerial surveys were conducted every 7 to 10 days from March to May in 2002 and 2003. This study sought to provide insight into the seasonal foraging ecology of sea lions by linking the seasonal distribution of sea lions at haul-out sites in the spring to the distribution of herring and eulachon aggregations. The spring season was chosen because the costs of lactation for females are considered the most expensive aspect of mammalian reproduction, and the energetic costs associated with territorial maintenance for males are critical factors regarding the reproductive biology of pinnipeds. To synthesize the geographic ecology of the SSLs in relation to herring and eulachon aggregations, all known sea lion haul-outs were compiled into a database from the National Marine Fisheries Service, and the location and timing of herring and eulachon aggregations were obtained from the Alaska Department of Fish and Game. This database was incorporated into a GIS software package, and a map was created with coverages of SSL haul-outs, herring spawning locations, and eulachon spawning locations. This map was then used to determine the distance between Steller sea lion haul-out sites and Pacific herring and eulachon spring spawning sites. Buffers were put in place for swimming distances of 20, 40, 60, 80, and 100 km. The authors concluded that the number of sea lions was greater at herring spawning sites in 2003, which corresponded to a higher herring biomass. They felt that seasonally aggregated, high-energy prey species influence the seasonal distribution of sea lions and may be paramount to their reproductive success.

The close proximity of a high-energy prey species may be critical to the costly otariid breeding strategy. This fact may dictate that breeding colonies and haul-outs be close to these prey resources, and may supersede other concerns such as predator avoidance and interspecies competition. They are so important, as a matter fact, that they may dictate the timing of breeding cycles, reproductive rates, individual and group body size, and the distribution of several other predator species. Understanding the role of seasonal variation in prey species may provide insight into Steller sea lion movement patterns over multiple scales.

Estimation of the utilization distribution (UD), which is the distribution of an animal’s position within a plane, is central to studies that focus on the home range of a species. Both parametric and nonparametric methods are used to estimate the UD. This paper goes about to explain how one might proceed with home-range data analysis if simple parametric models are inappropriate for determining the UD of a species. The author focuses on probability density estimation approaches - that is, describing the home range of an animal in terms of a probabilistic model. He points out that the Fourier transform method to estimate the UD has the disadvantage of density estimates occasionally taking on negative values, and that the estimation can only be made on a specified finite region of the plane. Kernel methods, on the other hand, are free of these problems and provide alternative approaches (and, they are conceptually easier to understand and explain). A scaled-down probability density function (a.k.a. the “kernel”) is placed over each data point and the estimator is constructed by adding the n components. Therefore, where there is a high concentration of points the kernel estimate has a higher density than where there are few points. Because a kernel is a density, the resulting estimate is a true probability density function itself. The smoothing parameter controls the amount of variation in each component of the estimate. Depending on the type of analyses required, either a fixed kernel method or an adaptive kernel method can be applied, each having different strengths and weaknesses.

Much of the work that I'm going to be involved in requires the ability to establish critical habitat, which is tied to the definition of what constitutes a core home-range. ArcGIS has the ability to estimate a fixed kernel utilization distribution through the use of the Animal Movement Analyst extension. This tool, in addition to a loose coupling with a statistical package, will be central when examining satellite telemetry data as a point coverage file.