Comparison of methods for determining key marine areas from tracking data

There is an urgent need to identify key marine areas for conservation, particularly in the high seas. A range of techniques have been applied to tracking data from higher predators, particularly seabirds and pinnipeds, to determine the areas of greatest use. This study compared three commonly used methods—kernel, first-passage time and state-space modelling—and a new approach, minimum displacement rate, for the analysis of data from the wandering albatross Diomedea exulans of Bird Island, South Georgia, tracked during the chick-rearing period. Applied to a single track, these four models identified similar marine areas as important. The greatest similarity in areas identified occurred when model assumptions were shared (such as slow speed indicating spatial preference) even when methods modelled these assumptions differently (e.g. Bayesian inference versus cumulative density surface). A gridded overlap approach applied to all tracks revealed core areas not apparent from results of any single analysis. The gridded approach also revealed spatial overlap between methods based on different assumptions (e.g. minimum displacement rate and kernel analysis) and between individuals. Although areas identified as important by kernel and first-passage time analysis of a single track were biased towards resting locations during darkness, this does not negate the requirement for their protection. Using the gridded overlap approach, two distinct core regions were identified for the wandering albatross; one close to the breeding colony and another 800 km to the North–West in the high seas. This convenient and pragmatic approach could be applied to large data sets and across species for the identification of a network of candidate marine protected areas in coastal and pelagic waters.

[1]  O. Ovaskainen,et al.  State-space models of individual animal movement. , 2008, Trends in ecology & evolution.

[2]  J. Parrish,et al.  Understanding and addressing seabird bycatch in Alaska demersal longline fisheries , 2009 .

[3]  R. Phillips,et al.  Foraging strategies of grey-headed albatrosses Thalassarche chrysostoma: integration of movements, activity and feeding events , 2004 .

[4]  Mark Bolton,et al.  Seabird foraging ranges as a preliminary tool for identifying candidate Marine Protected Areas , 2012 .

[5]  David W. Macdonald,et al.  Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home- range analyses with least-squares cross-validation , 2005 .

[6]  E. Woehler,et al.  A new approach to selecting Marine Protected Areas (MPAs) in the Southern Ocean , 2007, Antarctic Science.

[7]  David Grémillet,et al.  Spatial ecology and conservation of seabirds facing global climate change: a review , 2009 .

[8]  Ian D. Jonsen,et al.  ROBUST STATE-SPACE MODELING OF ANIMAL MOVEMENT DATA , 2005 .

[9]  M. Witt,et al.  Assessing accuracy and utility of satellite-tracking data using Argos-linked Fastloc-GPS , 2010, Animal Behaviour.

[10]  R. Phillips,et al.  Foraging ecology of albatrosses and petrels from South Georgia: two decades of insights from tracking technologies , 2007 .

[11]  R. Furness,et al.  Free as a bird? Activity patterns of albatrosses during the nonbreeding period , 2010 .

[12]  K. Eckert,et al.  Modeling loggerhead turtle movement in the Mediterranean: importance of body size and oceanography. , 2008, Ecological applications : a publication of the Ecological Society of America.

[13]  Robert M. Suryan,et al.  Foraging destinations and marine habitat use of short-tailed albatrosses: A multi-scale approach using first-passage time analysis , 2006 .

[14]  William A. Link,et al.  On thinning of chains in MCMC , 2012 .

[15]  Scott A. Shaffer,et al.  A comparison of indirect measures of feeding behaviour based on ARGOS tracking data , 2007 .

[16]  Lucas N Joppa,et al.  Understanding movement data and movement processes: current and emerging directions. , 2008, Ecology letters.

[17]  T. Hart,et al.  Time series analysis of biologging data: autocorrelation reveals periodicity of diving behaviour in macaroni penguins , 2010, Animal Behaviour.

[18]  Bruce T. Milne,et al.  Animal movements and population dynamics in heterogeneous landscapes , 1992, Landscape Ecology.

[19]  J. Croxall,et al.  Feeding strategies and diets of breeding grey-headed and wandering albatrosses at South Georgia , 2003 .

[20]  S. O'Brien,et al.  Use of kernel density estimation and maximum curvature to set Marine Protected Area boundaries: Identifying a Special Protection Area for wintering red-throated divers in the UK , 2012 .

[21]  David Pinaud,et al.  Quantifying search effort of moving animals at several spatial scales using first‐passage time analysis: effect of the structure of environment and tracking systems , 2007 .

[22]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[23]  Jason Matthiopoulos,et al.  Habitat preference, accessibility, and competition limit the global distribution of breeding Black-browed Albatrosses , 2011 .

[24]  G. Odell,et al.  Swarms of Predators Exhibit "Preytaxis" if Individual Predators Use Area-Restricted Search , 1987, The American Naturalist.

[25]  D. Oro,et al.  Oceanographic habitat of an endangered Mediterranean procellariiform: implications for marine protected areas. , 2006, Ecological applications : a publication of the Ecological Society of America.

[26]  G. Marshall,et al.  Wind field and sex constrain the flight speeds of central‐place foraging albatrosses , 2009 .

[27]  R. Powell,et al.  An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis , 1996 .

[28]  L. M. Tranquilla,et al.  Variation in Marine Distributions of Cassin's auklets (Ptychoramphus aleuticus) Breeding at Triangle Island, British Columbia , 2008 .

[29]  M. Rosenblatt.  Remarks on Some Nonparametric Estimates of a Density Function , 1956 .

[30]  L. Einoder,et al.  A review of the use of seabirds as indicators in fisheries and ecosystem management , 2009 .

[31]  Daniel P. Costa,et al.  Fractal landscape method: an alternative approach to measuring area-restricted searching behavior , 2007, Journal of Experimental Biology.

[32]  L. Brotóns,et al.  Assessing the location and stability of foraging hotspots for pelagic seabirds: An approach to identify marine Important Bird Areas (IBAs) in Spain , 2012 .

[33]  William J. Sutherland,et al.  The optimal search path in a patchy environment , 1990 .

[34]  Rory P. Wilson,et al.  When do wandering albatrosses Diomedea exulans forage , 1992 .

[35]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[36]  John P. Croxall,et al.  Foraging ecology and interactions with fisheries of wandering albatrosses (Diomedea exulans) breeding at South Georgia , 2004 .

[37]  H. Weimerskirch,et al.  Environmental heterogeneity and the evolution of foraging behaviour in long ranging greater albatrosses , 2003 .

[38]  K. Beazley,et al.  Selecting focal species for marine protected area network planning in the Scotia-Fundy region of Atlantic Canada , 2005 .

[39]  Henri Weimerskirch,et al.  PREY DISTRIBUTION AND PATCHINESS: FACTORS IN FORAGING SUCCESS AND EFFICIENCY OF WANDERING ALBATROSSES , 2005 .

[40]  S. Garthe,et al.  Fine-scale foraging behaviour of a medium-ranging marine predator. , 2009, The Journal of animal ecology.

[41]  Miles L. Logsdon,et al.  Fractal analysis of narwhal space use patterns. , 2004, Zoology.

[42]  Ian D. Jonsen,et al.  Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model , 2007 .

[43]  H. Weimerskirch,et al.  Conserving pelagic habitats: seascape modelling of an oceanic top predator , 2011 .

[44]  Pierre Jouventin,et al.  Foraging Strategy of Wandering Albatrosses Through The Breeding Season: A Study Using Satellite Telemetry , 1993 .

[45]  Torkild Tveraa,et al.  USING FIRST‐PASSAGE TIME IN THE ANALYSIS OF AREA‐RESTRICTED SEARCH AND HABITAT SELECTION , 2003 .

[46]  B. Worton.  Kernel methods for estimating the utilization distribution in home-range studies , 1989 .

[47]  A. Webb,et al.  Radio-telemetry as a tool to define protected areas for seabirds in the marine environment , 2009 .

[48]  P. I. Miller,et al.  Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds , 2012 .

[49]  Scott A. Shaffer,et al.  Analytical approaches to investigating seabird-environment interactions: a review , 2009 .

[50]  V. Afanasyev,et al.  Foraging behaviour of four albatross species by night and day , 2007 .

[51]  H. Weimerskirch,et al.  ALTERNATIVE FORAGING STRATEGIES AND RESOURCE ALLOCATION BY MALE AND FEMALE WANDERING ALBATROSSES , 1997 .

[52]  Toby A Patterson,et al.  Classifying movement behaviour in relation to environmental conditions using hidden Markov models. , 2009, The Journal of animal ecology.

[53]  John P. Croxall,et al.  EFFECTS OF SATELLITE TRANSMITTERS ON ALBATROSSES AND PETRELS , 2003 .

[54]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[55]  Y. Ropert‐Coudert,et al.  Is abdominal implantation of devices a good alternative to external attachment? A comparative study in Adélie penguins , 2010, Journal of Ornithology.

[56]  P. A. Prince,et al.  Quantifying habitat use in satellite-tracked pelagic seabirds : application of kernel estimation to albatross locations , 2000 .

[57]  Anders Nielsen,et al.  Improving light-based geolocation by including sea surface temperature , 2006 .

[58]  Rory P. Wilson,et al.  When three per cent may not be three per cent; device-equipped seabirds experience variable flight constraints , 2012 .

[59]  Franklin B. Schwing,et al.  Untangling the relationships among climate, prey and top predators in an ocean ecosystem , 2008 .

[60]  D. Preatoni,et al.  Radio-tracking squirrels: Performance of home range density and linkage estimators with small range and sample size , 2007 .

[61]  Jason Matthiopoulos,et al.  The use of space by animals as a function of accessibility and preference , 2003 .

[62]  Sidney Redner,et al.  A guide to first-passage processes , 2001 .

[63]  Charles M. Bishop,et al.  Linear tracks and restricted temperature ranges characterise penguin foraging pathways. , 2008 .

[64]  S. Garthe,et al.  Protected areas for seabirds in German offshore waters: Designation, retrospective consideration and current perspectives , 2012 .

[65]  R. Phillips,et al.  Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review , 2009 .

[66]  Simon Benhamou,et al.  Animal movements in heterogeneous landscapes: identifying profitable places and homogeneous movement bouts. , 2008, Ecology.

[67]  Henri Weimerskirch,et al.  Does Prey Capture Induce Area‐Restricted Search? A Fine‐Scale Study Using GPS in a Marine Predator, the Wandering Albatross , 2007, The American Naturalist.

[68]  Bernie J. McConnell,et al.  Estimating space‐use and habitat preference from wildlife telemetry data , 2008 .

[69]  G. R. Martin.  Eye structure and amphibious foraging in albatrosses , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.