Table of Contents:
1. Annotated Bibliography
2. Additional Articles
3. Web Resources
R. Matteson
In partial completion of the requirements of
Geo 565
Geographic Information Systems and Science
at Oregon State University
, Winter 2007
Interests: Spatial behavior of ocean animals. Pelagic ecology. Habitat Use. Conservation.
Austin D, Bowen WD, McMillan JI (2004) Intraspecific variation in movement patterns: modeling individual behaviour in a large marine predator. Oikos 105:15-30
This paper examines grey seal foraging ecology by comparing real movements (as estimated using satellite tags) to randomly generated movements using the correlated random walk (CRW). Deviations from randomness are thought to reveal search tactics, and the CRW is one common way of categorizing and quantitatively describing variation in movement patterns. For each successive location in a given individual, they calculated move length, turning angle and net squared displacement. The authors categorized observed movements relative to how well the correlated random walk model predicted each behavior in three ways: 1) directed movement 2) residents 3) random walkers. They found sex and deployment season were significant predictors of movement type. They also compared movement lengths to something called the Le'vy distribution which is thought to describe a "flight search tactic", and found that less than 1/3 of the seals they observed (52 in all) fit this distribution, leading to the conclusion that their food patches were not randomly distributed.
Benoit-Bird KJ, Au WL (2003) Prey dynamics affect foraging by a pelagic predator (Stenella longirostris) over a range of spatial and temporal scales. Behavioral Ecology and Sociobiology 53:364-373
This paper examines spinner dolphin foraging in relation to its prey at several spatial and temporal scales. Synoptic acoustic data of both prey and predator allow new conclusions about predator-prey interactions. Active acoustic surveys were done off the leeward coasts of three Hawaiin islands to measure prey depth, patch geometry and density, as well as depth and abundance of spinner dolphins. Arcview's geographic information system with 3d analyst was used to determine the horizontal and vertical distribution of mesopelagic animals and spinner dolphins. The Webster method was used to identify patch boundaries in the mesopelagic commmunity. MANOVA was used to explore the relationships between dolphin presence, time of day, distance from the shore, cruise and island on prey patch characteristics. While past studies have shown that pelagic predators do not overlap with their prey at small scales (defined as less than 10 times an individual's body length), these simultaneous acoustic measurements support the conclusion that they do. This study suggests that synoptic data could change many fundamental ideas about predator-prey interactions in the pelagic.
Boyd IL (1996) Temporal scales of foraging in a marine predator. Ecology 77:2:426-434
Often it is difficult to measure key variables such as food distribution in the marine pelagic environment. This author argues the use of predator movements to estimate prey distribution. Boyd's study system is the Antarctic fur seal, which feeds primarily on krill. The frequency at which individuals encounter prey, they time they spend within patches, and the time they spend traveling within patches should theoretically reflect the dispersion of prey. If foraging behavior is rate-maximizing, then animals would be expected to remain in patches longer when patches are far apart or of low quality. Foraging bouts were defined within tag data based on a specific quality of diving behavior. They assumed that this diving behavior reflected time spent in a prey patch, and calculated the rate at which fur seals encountered prey patches per hour of time spent searching. Their main findings were that there was a bimodality in this patch encounter rate, and that clustering of prey patches (as estimated using predator dive data) varied from year to year.
Fauchald P, Erikstad KE, Skarsfjord H (2000) Scale-dependent predator-prey interactions: The hierarchical spatial distribution of seabirds and prey. Ecology 81:3:773-783
Fauchald et al examine the spatial distribution of murres (Uria spp) and their prey, capelin (Mallotus villosus) in the Barents sea. They explore the concept of hierarchical patch mosaic structure within this system at several scales, hypothesizing that the coherence between murre and capelin patches might decrease for decreasing scale, and that small-scale structures will change faster than large-scale structures. Visual shipboard bird surveys were done while prey densities were estimated acoustically and through trawling. Following hierarchical analysis at several scales, they concluded that murres actively track the spatial distribution of their prey at variable scales, and that the murres' forage strategy reflects the properties of the prey system. Their analysis also illustrates concepts in sampling design; it is important to obtain data at a scale that is appropriate to the pattern of interest; in order to avoid the situation where large-scale patterns mask smaller ones variance should be assigned carefully within nested analyses and it may be desirable to simultaneously sample a given pattern over a range of scales to avoid missing crucial pieces of information.
Fiksen O, Eliassen S, Titelman J (2005) Multiple predators in the pelagic: modelling behavioural cascades. Journal of Animal Ecology 74:423-429
This paper uses a modelling approach to explore optimal diel vertical migration in zooplankton prey facing predation by two functionally different predators: larger zooplankton and fish. They explain that vulnerability to each type of predator changes as the zooplankton grow, and that prey species attempt to manage their exposure to predatory risk by careful habitat selection (such as diel vertical migration). They were especially interested in how relative abundance of both zooplankton predators and fish would affect optimal distribution patterns, mortality and growth. One of their conclusions following experimentation was that there is an interaction between prey susceptibility to each predator type; in other words susceptibility to one functional predator type depends on the abundance of the other predator. This interaction is not symmetrical; if there are more predatory zooplankton, risk of being eaten by a fish increases, but in contrast increasing abundance of fish does not equal higher risk of being eaten by a larger zooplankton -- it does reduce growth rates though! There are a few limitations to the model: (1) it doesn't "incorporate the possibility of a behavioral response of the predator to the defensive strategy of its prey" (if zooplankton shift in the water column, fish are likely to follow) and (2) there are no environmental feedback mechanisms. However, it is still a useful illustrative tool.
Gregr EJ, Trites AW (2001) Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Canadian Journal of Fisheries and Aquatic Sciences 58:1265-1285
This article uses historical whaling records along with oceanographic measurements to predict critical habitat for five whale species: sperm, sei, fin, humpback and blue. Their working definition of "critical habitat" was "the measure of an area's ability to provide the resources necessary for the persistence of a population." General linear models were used to relate positions of historic (1948-1967) whale catches to several oceanographic predictor variables (modern data): depth, slope, depth class, temperature, salinity, and month. In constructing their predictive models, Gregr and Trites first postulated an a priori biological model based on exploratory results and their knowledge of each species. Then they generated stepwise regression models, adding and removing predictors until arrival at the "best" option. Because the oceanographic measurements they used in model construction were not observed synoptically with whale abundance, they did some exploratory testing of the sensitiviey of the predictions to the predictor variables by looking at cold years versus warm years. The equation for the linear component of the model is given on page 1270. Their interpretation is that whale presence depends on month, slope and depth class, and that optimal salinity and temperature exist for each depth class and month. The authors acknowledge bias in the models, in the form of lack of independence, whaling effort bias, interpolation of TINs, and the large time gap between the oceanographic and whale position data sets. They suggest refinement through field studies and analyses of predictor effects at multiple spatial and temporal scales.
Johnston DW, Westgate AJ, Read AJ (2005) Effects of fine-scale oceanographic features on the distribution and movements of harbour porpoises Phocoena phocoena in the Bay of Fundy. Marine Ecology Progress Series 295:279-293
Johnston et al used multiple techniques to complete a picture of regional distribution of the harbour porpoise in relation to fine-scale oceanographic features. Porpois locations were recorded by satellite telemetry as well as line transect visual surveys. Prey aggregations were identified through hydro-acoustic survey as well as trawling. RADARSAT synthetic aperture radar, a type of remote sensing, and LANDSAT7 CanImage, a derived resampled satellite product, were used to visualize fine scale oceanographic features. They focused these efforts on an area off the north tip of Grand Manan Island in the Bay of Fundy, where a large anticyclonic eddy and frontal system aggregate prey. This area is called the "long eddy" by locals and was noted in the written records of porpoise hunters as early as 1880. Kernel density functions were used to estimate utility distributions for individuals; no post hoc modification of data were done prior to kernel density calculation as it was felt that this would add bias and reduce the biological relevence of the analysis. Major findings included: (1) porpoise density was highest within the study area during flood tides. (2) Prey (Atlantic herring and euphausiids) are concentrated along the edge of the physical feature. (3) Porpoises target specific areas such as island wakes, and individuals may specialize within specific habitats. The authors suggest this integrated data approach for designating critical habitat for porpoises.
McMahon CR, Hays GC (2006) Thermal niche, large-scale movements and implications of climate change for a critically endangered marine vertebrate. Global Change Biology 12:1330-1338
McMahon and Hays argued that although long-term empirical data sets on leatherback distribution are lacking, predicting the implications of climate change on their habitat use is still possible. They had SMRU tag data from 9 total female leatherback taggings between 2002 and 2004, with a total cumulative tracking length of 3143 days. Using stepwise regression analysis, they concluded that temperature was the best a priori predictor of turtle density. The tagged turtles spent less than 2% of time in areas where the surface waters were below 15 C, suggesting that a 15 degree isotherm might represent a thermal constraint on movements of leatherbacks. Other publications confirm this general pattern; Brongersma (1972) reported only 6% of 188 observed turtles in latitudes to the north of the United Kingdom (where the 15 C isotherm would have been at that time). There has been a northerly movement of this isotherm since 1983: a change in 3 degrees latitude over 15 years. The authors suggest that for marine species such as this that show strong thermal dependence in their distribution, corresponding northerly shifts will also have occurred over the last couple of decades, although we do not have records to show it. This ocean warming trend is likely to continue and will have implications for the geographic range of leatherback turtles.
Shelden KEW, Moore SE, Waite JM, Wade PR, Rugh DJ (2005) Historic and current habitat use by North Pacific right whales Eubalaena japonica in the Bering Sea and Gulf of Alaska. Mammal Review 35:2:129-155
This paper is a meta-analysis of past and present North Pacific Right Whale distribution. The goal was to define areas and ecological parameters that are most critical to recovery of this species. Data were aggregated from a wide array of sources, academic, commercial and otherwise. Sightings per unit effort were calculated and visualized within ArcView. Where possible, these distributions were compared with bathymetry. Distribution maps starting around the early 1800s and ending in recent times (2002) illustrate the drastic decline in abundance and spatial distribution over time. They think spatial distribution of the small north Pacific population that remains is highly influenced by the necessity for efficient forage and may require prey patch aggregations with densities higher than 3000 copepods per cubic meter. The paper reviews the oceanography and productivity within various places where right whales are found, and suggests that a better understanding of zooplankton ecology is critical to understanding right whale occurence.
Swihart RK, Slade NA (1985) Testing for Independence of Observations in Animal Movements Ecology 66:4:1176-1184
One problem with many spatial statistical analyses of animal movement behavior is the violation of the assumption of independence, specifically, independence of successive positions. The authors recognized a need for a simple statistical test to use in the case of bivariate data (e.g. latitude and longitude) for the purpose of testing for independence. Through simulation, Schoener's ratio, (t^2/r^2), is explored as a means of describing independence in bivariate data; it's utility in univariate data had already been described. They suggest using this same test to establish a criterion for picking a time interval where autocorrelation is negligible, suggesting its use in chosing an appropriate sampling interval, or additionally as a numeric parameter that could be used as another descriptor of seasonal or interspecific timescaling of movements. A radio-tagged female cotton rat is used to illustrate the test, but the same procedures could be applied to marine animal movement data.
Additional Articles (not annotated here):
(Note: some of these are terrestrial examples that I found relevant.)
Anderson DJ (1982) The home range: a new nonparametric estimation technique. Ecology 63:103-112
Anderson DE, Rongstad OJ (1989) Home-range estimates of red-tailed hawks based on random and systematic relocations. Journal of Wildlife Management 53:802-807
Atkinson RPD, Rhodes CJ, Macdonald DW 2002 Scale-free dynamics in the movement patterns of jackals. Oikos 98:134-140
Austin D, McMillan JI, Bowen WD (2003) A three-stage algorithm for filtering erroneous Argos satellite locations. Marine Mammal Science 19:123-135
Ball JP, Nordengren C, Wallin K (2001) Partial migration by large ungulates: characteristics of seasonal moose ranges in North Sweden. Wildlife Biology 7:39-47
Batschelet E (1981) Circular statistics in Biology. Academic Press
Baumgartner MF, Mate BR (2003) Summertime foraging ecology of North Atlantic right whales. Marine Ecology Progress Series 264:123-135
Baumgartner MF, Cole TVN, Campbell RG, Teegarden GJ, Durbin EG (2003) Associations between North Atlantic right whales and their prey, Calanus finmarchicus, over diel and tidal time scales. Marine Ecology Progress Series 264:155-166
Baumgartner MF, Cole TVN, Clapham PJ, Mate BR (2003) North Atlantic right whale habitat in the lower Bay of Fundy and on the SW Scotian Shelf during 1999-2001. Marine Ecology Progress Series 264:137-154
Bergman CM, Schaefer JA and Luttich SN (2000) Caribou movement as a correlated random walk. Oecologia 123:364-374
Biggs DC, Leben RR, Ortega OJG (2000) Ship and satellite studies of mesoscale circulation and sperm whale habitats in the Northeast Gulf of Mexico during GulfCet II. Gulf of Mexico Science 18:15-22
Bovet P, Benhamou S (1988) Spatial analysis of animals' movements using a correlated random walk model. Journal of Theoretical Biology 131:419-433
Bovet P, Benhamou S (1991) Optimal sinuosity in central place foraging movements. Animal Behavior 42:57-62
Bowen WD (1997) Role of marine mammals in aquatic ecosystems. Marine Ecology Progress Series 158:267-274
Boyd IL, Arnould JPY, Barton T (1994) Foraging behaviour of Antarctic fur seals during periods of contrasting prey abundance. Journal of Animal Ecology 63:703-713
Boyd II (1996) Temporal scales of foraging in a marine predator. Ecology 77:426-434
Brown CW, Winn HE (1989) Relationship between the distribution pattern of right whales, Eubalaena glacialis, and satellite-derived sea surface thermal structure in the Great South Channel. Continental Shelf Research 9:247-260
Burt WH (1943) Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24:346-352
Clapham P, Good C, Quinn S, Reeves RR, Scarff JE, Brownell RL Jr (2004) Distribution of North Pacific right whales Eubalaena glacialis as shown by 19th and 20th century whaling catch and sighting records. Journal of Cetacean Research and Management 6:1-6
Croll DA, Tershy BR, Hewitt RP, Demer DA, Fiedler PC, Smith SE, Armstrong W, Popp JM, Kiekhefer T, Lopez VR (1998) An integrated approach to the foraging ecology of marine birds and mammals. Deep-Sea Research II 45:1353-1371
Davis LS, Boersma PD, Court GS(1996) Satellite telemetry of the winter migration of Adelie penguins. Polar Biology 16:221-225
DeSolla SR, Bonduriansky R, Brooks RJ (1999) Eliminating autocorrelation reduces biological relevance of home range estimates. Journal of Animal Ecology 68:221-234
Dixon KR, Chapman JA (1980) Harmonic mean measure of animal activity areas. Ecology 61:1040-1044
Dunn JE, Heithaus ER, Sawyer WB (1977) Analysis of radio telemetry data in studies of home range. Biometrics 33:85-101
Fauchald P (1999) Foraging in a Hierarchical Patch System. The American Naturalist 153:6:603-613
Fauchald P, Tveraa T (2003) Using first-passage time in the analysis of area-restricted search and habitat selection. Ecology 84:282-288
Fauchald P, Mauritzen M, Gjosaeter H (2006) Density-dependent migratory waves in the marine pelagic ecosystem. Ecology 87:11:2915-2924
Ferguson SH, Taylor MK, Messier F (1997) Space use by polar bears in and around Auyuittuq National Park, Northwest Territories during the ice-free period. Canadian Journal of Zoology 75:1585-1594
Ginnett TF, Demment MW (1997) Sex differences in giraffe foraging behaviour at two spatial scales. Oecologia 110:291-300
Giske J, Huse G, Fiksen O (1998) Modelling spatial dynamics of fish. Reviews in Fish Biology and Fisheries 8:57-91
Griffin RB (1999) Sperm whale distributions and community ecology associated with a warm-core ring off Georges Bank. Marine Mammal Science 15:33-52
Hastie G, Wilson B, Wilson LJ, Parsons KM, Thompson PM (2004) Functional mechanisms underlying cetacean distribution patterns: hotspots for bottlenose dolphins are linked to foraging. Canadian Journal of Zoology 75:1585-1594
Hooker SK, Whitehead H, Gowans S, Baird RW (2002) Fluctuations in distribution and patterns of individual range use of northern bottlenose whales. Marine Ecology Progress Series 225:287-297
Hull CL, Hindell MA, Michael K (1997) Foraging zones of royal penguins during the breeding season, and their association with oceanographic features. Marine Ecology Progress Series 153:217-228
Hunt GL, Schneider DC (1987) Scale-dependent processes in the marine environment. In: Croxall JP (ed) Seabirds feeding ecology and role in marine ecosystems. Cambridge University Press, Cambridge, p 7-43.
Hyrenbach KD, Dotson RC (2001) Post-breeding movements of a male Black-footed Albatross Phoebastria nigripes. Marine Ornithology 29:7-10
Hyrenbach KD, Fernandez P, Anderson DJ (2002) Oceanographic habitats of two sympatric North Pacific albatrosses during the breeding season. Marine Ecology Progress Series 233:283-301
Ishikawa K, Watanuki Y (2002) Sex and individual differences in foraging behaviour of Japanese cormorants in years of different prey availability. Journal of Ethology 20:49-54
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Jovallanos CL, Gaskin DE (1983) Predicting the movements of juvenile herring (Clupea harengus herengus) in the SW Bay of Fundy using computer simulation techniques. Canadian Journal of Fisheries and Aquatic Sciences 40:139-146
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- http://marinecoastalgis.net/. Treasure chest of marine GIS links from the Davey Jones' Locker lab, OSU Geosciences.
- http://coml.org/. The Census of Marine Life home page. Information about one collaborative research effort into the diversity, history and abundance of marine life.
- http://www.absc.usgs.gov/glba/gistools/animal_mvmt.htm. Hooge and Eichenlaub ArcView animal movement extension. A collection of more than 40 functions which can be imported into the ArcView environment to work with animal positional data. Most of the functions work on point shape files; a few work on polylines. They have a tool within the set that will convert between the two. The time dependent functions (speed or autocorrelation for example) require your shapefile to be in sequential order, timewise. Users must have the spatial analyst extension already to use this set of tools. Many of the functions included in the package, which is freely available online, had already been developed by other authors for specific research applications, but Hooge and Eichenlaub have made these tools that are easy for anyone in GIS practice to use without writing their own code, and shared it with the community.
- http://www.globec.org/. GLOBEC ("global ocean ecosystem dynamics") is a project attempting to increase understanding of how global change will affect the abundance, diversity and productivity of marine populations.
- http://seamap.env.duke.edu. The OBIS-SEAMAP project, led by Pat Halpin and Andy Read at Duke, organizes marine vertebrate positional data into a spatially referenced database. An online mapping tool allows visual exploration and limited analysis of these data which are freely available for download and further study (within acceptable terms of use as described on the website). An excellent example of the power of collaborative effort in science. (the obis-seamap acronym stands for Ocean Biogeographic Information System - Spatial Ecological Analysis of Megavertebrate Populations)