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Phil. Trans. R. Soc. B (2010) 365, 2157–2162
doi:10.1098/rstb.2010.0107
Introduction
Animal ecology meets GPS-based
radiotelemetry: a perfect storm of
opportunities and challenges
Francesca Cagnacci1,*, Luigi Boitani2, Roger A. Powell3
and Mark S. Boyce4
1
Research and Innovation Centre, Environment and Natural Resources Area, Edmund Mach Foundation,
via Mach 1, 38010 San Michele all’Adige, Trento, Italy
2
Deparment of Animal and Human Biology, University of Rome ‘La Sapienza’,
viale dell’Università 32, 00185 Rome, Italy
3
Department of Biology, North Carolina State University, Raleigh, NC 27695, USA
4
Department of Biological Sciences, University of Alberta, Edmonton, Canada T6G 2E9
Global positioning system (GPS) telemetry technology allows us to monitor and to map the details
of animal movement, securing vast quantities of such data even for highly cryptic organisms. We
envision an exciting synergy between animal ecology and GPS-based radiotelemetry, as for other
examples of new technologies stimulating rapid conceptual advances, where research opportunities
have been paralleled by technical and analytical challenges. Animal positions provide the elemental
unit of movement paths and show where individuals interact with the ecosystems around them. We
discuss how knowing where animals go can help scientists in their search for a mechanistic understanding of key concepts of animal ecology, including resource use, home range and dispersal, and
population dynamics. It is probable that in the not-so-distant future, intense sampling of movements coupled with detailed information on habitat features at a variety of scales will allow us to
represent an animal’s cognitive map of its environment, and the intimate relationship between
behaviour and fitness. An extended use of these data over long periods of time and over large spatial
scales can provide robust inferences for complex, multi-factorial phenomena, such as meta-analyses
of the effects of climate change on animal behaviour and distribution.
Keywords: global positioning system technology; biotelemetry; animal movement; autocorrelation;
mechanistic models; fitness
In the history of science, rapid conceptual advances
have often been stimulated by technological innovations. Such technological milestones included Galileo
and Kepler designing and looking into telescopes,
finding evidence for Copernicanism (and falsifying
the Tolemaic system); Hooke and van Leeuwenhoek
peering into microscopes and describing cells, thus
laying the basis for modern microbiology; Sanger
and Maxam and Gilbert developing DNA sequencing,
which spawned molecular biology; and high-speed
computers fostering the emergence of nonlinear
dynamics. In this Theme Issue we herald an exciting
synergy between animal ecology and global positioning
system (GPS)-based radiotelemetry. New telemetry
technology allows us to monitor and to map the details
of animal movement, securing vast quantities of such
data even for highly cryptic organisms. Conceptual
advances have been made with roots in Lagrangian
* Author for correspondence (francesca.cagnacci@iasma.it).
One contribution of 15 to a Theme Issue ‘Challenges and
opportunities of using GPS-based location data in animal ecology’.
characterization of the details of movement by
individual animals, which ultimately leads to Eulerian
characterization of population process. Recent theory
uses stochastic movement models to challenge our
mechanistic understanding of home range and dispersal. We are beginning to tackle the complexities of
memory and perception, and how these behavioural
processes yield the individual variation that is so pervasive in nature. It is probable that in the not-so-distant
future, intense sampling of movements coupled with
detailed information on habitat features at a variety
of scales will allow us to represent an animal’s cognitive map of its environment. Models of resource
selection by animals give us fundamental insights
into the mechanisms behind the distribution and
abundance of organisms. Indeed, this powerful
synergy between science and technology is rapidly
shaping the very structure of the discipline of ecology.
In animal ecology, reality is observed at approximately the same space – time scale as an observer’s
experience. Direct human observations cannot provide
thorough and standard data that will allow falsification
of ecological hypotheses for all animals and all kinds of
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Introduction. Animal ecology meets GPS radiotelemetry
research questions. The most logical step is moving the
point of observation from the observer to the observed
(i.e. the studied animal). For example, to quantify
accurately where animals go, using technology to let
animals show us where they are has many advantages.
Interestingly, marine biology, where boundaries to
human senses are apparent, was the first field of
animal ecology to use telemetry (Boyd et al. 2004).
Biotelemetry (the remote measurement of state
variables of individual, free-living animals) has been
the technological revolution that has allowed the
expansion of the mechanistic approach to the ecology
of large animals (Cooke et al. 2004). From the
first time-depth recorders attached to Weddell seals
(Kooyman 1965) to the multi-sensor ‘daily diary’
(Wilson et al. 2008), biotelemetry devices have
evolved, spawning increasing numbers of studies and
peer-reviewed publications (Ropert-Coudert et al.
2010). Continued technological developments have
broadened the potential (and definition) of biotelemetry devices to allow sampling of environmental
variables around tagged animals (Fedak 2004;
Hooker et al. 2007).
Among the variables that can be measured for a
free-living animal, its position in space allows intuitive
and immediate ways to relate the animal to its environment. One could ask what phenotypes (adaptations)
are required to be in a certain place at a certain
time. What are the consequences for being there?
Why is the animal there? Animal positions provide
the elemental unit of movement paths and show
where individuals interact with the ecosystems around
them. Those interactions ultimately determine animal
fitness (Nathan et al. 2008). In other words, animal
locations provide the ‘live’ point of contact between
ecology and evolution. Thus, knowing where animals
go is of utmost importance as human activities affect
climate and habitat, and thereby challenge the very
persistence of species (Thomas et al. 2004).
Animal tracking (the monitoring and recording of
animals’ sequential positions) initially, and for a long
time, relied on VHF (very high-frequency) technology;
that is, animals have been equipped with transmitters
emitting at radio frequencies that can be received by
radio receivers (Craighead 1982). A few automated
radiotelemetry systems have been developed (e.g.
Cedar Creek, University of Minnesota; Rongstad &
Tester 1969; ARTS, http://www.princeton.edu/
~wikelski/research/index.htm; Croofoot et al. 2008),
but, even for automated systems, VHF technology
requires receivers to be close enough to the animals
to triangulate animal positions. Therefore, animal
tracking has traditionally relied on researchers to be
in the field, with the potential to affect animal behaviour (Cooke et al. 2004; but see, for marine biology,
Hill 1994 on geolocation, and Wilson & Wilson
1988 and Wilson et al. 2007 on dead-reckoning).
The advent of satellite telemetry allows remote
tracking of animal positions and movements. Telemetry using GPS, in particular, has several technical
advantages, including the ability to determine position
on the surface of the Earth (or in the air) with high
precision and accuracy 24 h a day, with position
updates available in rapid succession. Argos satellites
Phil. Trans. R. Soc. B (2010)
using Doppler-based positioning (Tomkiewicz et al.
2010) are less precise but provide the only automated
reporting of data available in North America. We now
can obtain nearly continuous, systematically scheduled
datasets of animal positions, though the technology is
subject to some serious technical limitations
(Tomkiewicz et al. 2010).
Cooke et al. (2004) listed several limitations for
monitoring animals remotely with biotelemetry. The
most apparent is the cost of units to be fitted to individual animals. Unfortunately, funding often dictates
that few individuals in a study can be equipped with
the very advanced, but very costly, units (Rodgers
2001; Hebblewhite & Haydon 2010). We expect prices
to drop as more researchers use GPS technology,
because off-the-shelf devices are subject to freemarket forces. Standardization of device functions
and data types will help prices to fall as well, and can
also be an advantage for comparing among studies
and for data sharing in large-scale studies (Tomkiewicz
et al. 2010; Urbano et al. 2010). In addition, GPS
wildlife telemetry is based on a widespread technology
and can take advantage of components developed for
other sectors of business, with a huge market. This
should assure constant technological improvements
and decreased costs, and stimulate attention to a
second large limitation: at present, GPS telemetry is
confined to large animals or those capable of carrying
solar chargers for batteries (Tomkiewicz et al. 2010).
GPS technology was pioneered on large vertebrates,
such as elephants (Douglas-Hamilton 1998), moose
(Rodgers et al. 1996; Edenius 1997) and bears
(Schwartz & Arthur 1999). The size of the devices is
decreasing, however, and each decrease in size
increases the range of animal species for which they
are available (e.g. foxes, tortoises, pigeons). Recent
technological advances have also allowed GPS applications in marine environments (Schofield et al.
2007; Tomkiewicz et al. 2010).
Clearly, the choice of a technical tool for a specific
study requires critical evaluation in light of the goals
and scope of the study. Hebblewhite & Haydon
(2010) compare advantages and disadvantages of
GPS telemetry in ecology, and offer examples of
areas of study that have benefited from this technology.
Ecologists and managers must evaluate, during the
design of a study, the potential advantages and challenges, from data handling to analyses, of using
GPS-based technology. Providing a guide to the
accomplishment of this task is the reason for this
Theme Issue.
To make good use of GPS-based location data one
must have a measure of device error. The most difficult aspect in testing GPS devices and building a
valid theory of error is the change in performance
of devices from the laboratory to free-ranging animals
(Tomkiewicz et al. 2010). System function is
influenced by environmental conditions (e.g. climatic
factors, habitat types, terrain roughness) and animal
behaviour (e.g. movement, orientation of the collar).
The effect of the latter is still largely unexplored
because of the difficulties of setting up appropriate
field tests (Heard et al. 2008). The two types of error
that plague GPS telemetry are spatial inaccuracy of
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Introduction. Animal ecology meets GPS radiotelemetry F. Cagnacci et al.
the locations acquired and missing data in the form of
failed location attempts (Frair et al. 2004). Their combined effect can lead to mistaken inferences on animal
spatial behaviour, especially those involving movement
paths and habitat selection (Frair et al. 2010). When
planning a field study with GPS technology, users
should evaluate the potential effect of errors on their
analyses within the context of their ecological questions. Pragmatically, they should assess the potential
sources of error in the field, test the sensitivity of
GPS units accordingly and consider potential solutions, if necessary. Frair et al. (2010) provide a
comprehensive synthesis of predominant causes of
errors, their implications for ecological analyses and
tools for correcting analyses.
Large datasets, made available by advanced technology, present serious problems of data management, as
underlined for microarrays from genomic techniques
(Hess et al. 2001), physiological and behavioural data
from sensors (Cooke et al. 2004; Rutz & Hays 2009),
animal-borne video imagery (Moll et al. 2007), and
high-resolution spatio-temporal movement data
(Nathan et al. 2008). Challenges include preservation
of data integrity and consistency, avoidance of data
redundancy, automation of data download, filtering
and storage, management of specific data types, and
definition of standards for objects and formats
(Urbano et al. 2010). For GPS-based locations (and
other animal-borne data from biotelemetry), flexibility
in integrating data from different devices, the ability to
manage spatial time series and the possibility to integrate tools for data analysis and visualization in a
single software environment are also desirable characteristics (Urbano et al. 2010). Tools to meet these
requirements already exist (Shekhar & Chawla 2003;
Hall & Leahy 2008) but have rarely been used to
develop ad hoc solutions for management of biotelemetry data (but see Cagnacci & Urbano 2008; Hartog
et al. 2009). Better systems to support these data
ensure better quality and efficient allocation of
resources (i.e. funds are used for developing persistent
data management systems instead of for manual data
handling), while better systems also feed back to
ways of conducting research. The choice to tag individual animals, the potential consequences on the
welfare and behaviour of those animals (Tomkiewicz
et al. 2010), and large costs argue that the data produced should persist in science beyond the initial
study. After being used for its original research purposes, location data should be used to answer other
ecological questions. Those questions can extend
over long periods of time and over large spatial scales
beyond the limits of the original research (and funding). Such extended use can provide robust
inferences for complex, multi-factorial phenomena,
such as meta-analyses of movement (Hebblewhite &
Haydon 2010) or of the effects of climate change on
animal behaviour and distribution. Despite several
initiatives currently being developed (e.g. http://www.
movebank.org and http://www.topp.org, accessed
28 February 2010; see Urbano et al. 2010 for other
examples), persistence of animal ecological data is
lower than that for other fields of biology. In the
future, GPS-based location data and other biotelemetry
Phil. Trans. R. Soc. B (2010)
2159
datasets will hopefully converge towards initiatives similar to GenBank for genetic sequences (http://www.ncbi.
nlm.nih.gov/GenBank, accessed 28 February 2010).
Datasets of GPS-based locations are, essentially,
time series of spatial data, usually collected with
high-frequency, systematic schedules. This fact has
two important consequences. First, position time
series represent movement paths (Nathan et al.
2008), and the higher the frequency of positions, the
more trustworthy the movement paths. Trustworthiness also depends on the physiological and
behavioural characteristics of the animals that affect
step length (Calenge et al. 2009; Owen-Smith et al.
2010). Second, position time series are highly correlated datasets, both in time and space (Boyce et al.
2010; Fieberg et al. 2010); the higher the frequency
of positions, the stronger the correlation (or autocorrelation, i.e. correlation that changes as a function of
temporal or spatial distance between observations;
sensu Fieberg et al. 2010). These two consequences
provide opportunities for GPS-based research. Datasets of high-frequency positions present opportunities
to study how animals interact with their environments,
while also offering opportunities and analytical challenges for extracting behavioural information and
dealing with correlated data in model. On one side,
these opportunities and challenges should promote
the adoption of analytical methods that: (i) model correlation explicitly as an intrinsic and relevant property
of data (Wittemyer et al. 2008; Boyce et al. 2010;
Fieberg et al. 2010), with particular emphasis on
individual-level variability and processes (Gillies et al.
2006; Forester et al. 2007); (ii) disentangle patterns
of trajectories that correspond to behavioural phases
or to specific behaviours (Morales et al. 2004; Fryxell
et al. 2008; Boyce et al. 2010; Merrill et al. 2010); and
(iii) investigate the mechanisms underlying autocorrelation, such as internal state and memory (Dalziel et al.
2008; Van Moorter et al. 2009). On the other side, the
abundance of data from few individuals decreases the
quality of inferences (due to small sample sizes;
Rodgers 2001; Fieberg et al. 2010), including population-level inferences (Morales et al. 2010).
When a new technology with the potential to
improve empirical research emerges, enthusiastic
adoption may lead to reassessment of classic questions.
While returning to old questions is insightful, it should
not restrict the scope of research. Science should be
enhanced by, not limited to, technology. In other
words, more robust raw data should underpin more
comprehensive theoretical frameworks.
Understanding how and why animals move are the
goals of mechanistic approaches to animal ecology,
including how and why animals use specific resources,
how and why animals interact with conspecifics, and
how and why they compete and reproduce. Ultimately,
we gain an understanding of fitness. Therefore, modelling animal movement is basic for all questions in
animal ecology. Ecology is fundamentally spatial,
with ecological processes occurring on heterogeneous
landscapes. Movement is the glue that ties ecological
processes together. This is a dynamic and growing
area of research that includes mechanistic approaches
to understanding home range formation (Mitchell &
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Introduction. Animal ecology meets GPS radiotelemetry
Powell 2004; Moorcroft & Lewis 2006; Van Moorter
et al. 2009; Kie et al. 2010; Smouse et al. 2010), assessing the effect of memory on orienting or limiting
movement (Gautestad & Mysterud 2006; Dalziel
et al. 2008; Van Moorter et al. 2009; Wolf et al.
2009), estimating movement and habitat preference
simultaneously (Rhodes et al. 2005; Johnson et al.
2008; Fieberg et al. 2010), incorporating individual
stochasticity in dynamic movement models ( Jonsen
et al. 2003; Morales et al. 2004; Patterson et al.
2008) and upscaling from individual movement to
population dynamics (Morales et al. 2010). Critical
evaluation of the predictions of these models requires
dense location data and, thus, thrives on large, highfrequency GPS-based datasets (Smouse et al. 2010).
Perhaps the most crucial limit to understanding why
animals move is understanding what resources they
use at specific places and times, and at different
scales (Fryxell et al. 2008; Wittemyer et al. 2008;
Beyer et al. 2010; Gaillard et al. 2010; Morales et al.
2010; Owen-Smith et al. 2010; Smouse et al. 2010).
Despite the availability of robust theoretical frameworks, such as optimality (Owen-Smith et al. 2010)
and functional responses (Merrill et al. 2010), and
despite the development of well-tested and innovative
analytical approaches (Dalziel et al. 2008; Wiens
et al. 2008), comparing animal choices with availability
of resources remains difficult (Beyer et al. 2010).
GPS-based locations provide robust spatio-temporal
datasets with the potential for fine-scale associations
between animals and habitat features. Nonetheless,
GPS-based locations do not provide information on
the actual resources that animals use or information
on activities of animals. Obtaining data on resources
and behaviours will be possible only with integration
of data from other bio-sensors (e.g. accelerometers,
magnetic switches; Cooke et al. 2004; Rutz & Hays
2009) and high-precision remote sensing of climatic
and environmental variables (Hebblewhite & Haydon
2010; Urbano et al. 2010). If both animal-borne information and environmental information reach the same
level of detail and reliability, the assessment of the
relationship between habitat use and performance of
animals will be a much more affordable task (Gaillard
et al. 2010).
We should note at this point that researchers who
observed their animals directly 50 years ago, before
the advent of telemetry (and those who still observe
their animals today), could see what resources their
animals used and could watch and note their behaviours. Regular VHF telemetry, used to find the
animals and observe them more regularly or to
obtain data on key parameters such as mortality, is
still the technique of choice for several research questions. New GPS-based telemetry is a formidable
technological advance but it is not the best technology
for all purposes. Perhaps more researchers should
observe their research animals directly today. Moreover, GPS-based location datasets are not yet what
they can potentially be. The technology is limited by
battery life and by archival memory (Hebblewhite &
Haydon 2010; Tomkiewicz et al. 2010). Analogously,
several advanced analytical techniques suggested in
this Theme Issue are not yet developed fully, or still
Phil. Trans. R. Soc. B (2010)
require exceptional computational power and highly
specific skills. Conscious, pragmatic revisits to welltested methods could be the solution of choice for an
appropriate use of GPS-based datasets (Fieberg et al.
2010; Frair et al. 2010; Kie et al. 2010).
In this Theme Issue, we confront both pragmatic
and theoretical issues of using GPS-based location
data and derive examples from the existing deployments. We thus lay a platform to orient scientists in a
critical choice of the right tool for their studies, offering an overview of the technology currently available
and discussing its advantages and limitations (Hebblewhite & Haydon 2010; Tomkiewicz et al. 2010). We
advise on appropriate handling and use of GPS
location data (Frair et al. 2010; Urbano et al. 2010)
and analysis. We focus on peculiarities of these datasets, such as correlation and recognition of spatiotemporal patterns that are common to other ecological
and biological data (Boyce et al. 2010; Fieberg et al.
2010; Merrill et al. 2010). We promote the need to
lay strong theoretical foundations before taking up a
study, and offer syntheses and perspectives of the
approaches used in several theoretical frameworks,
such as movement (Smouse et al. 2010), resource
and space use (Beyer et al. 2010; Kie et al. 2010;
Smouse et al. 2010), optimality (Owen-Smith et al.
2010), functional responses (Merrill et al. 2010),
population dynamics (Morales et al. 2010) and habitat – performance relationships (Gaillard et al. 2010).
Scientists are compelled to make the best use of
their data, to account for research efforts and economical investments, and to enhance integrated and
comprehensive theoretical frameworks. We have discussed how better datasets can help ecologists in
their search for a mechanistic understanding of key
traditional concepts and new frameworks of animal
ecology. Only integration of analytical and technical
advancements within solid theoretical frameworks
will allow us to tackle the intimate complexity of ecosystems, and their sensitivity to a changing planet.
The idea of this Theme Issue was stimulated by discussions
at the GPS-Telemetry Data: Challenges and Opportunities
for Behavioural Ecology Studies workshop organized by the
Edmund Mach Foundation (FEM) in September 2008 and
held in Viote del Monte Bondone, Trento, Italy. Funding
of the workshop by the Autonomous Province of Trento to
F.C. is gratefully acknowledged.
M.S.B. was supported by the Alberta Conservation
Association and the Natural Sciences and Engineering
Research Council of Canada. F.C. was partly supported by
grant N. 3479 BECOCERWI, Autonomous Province of
Trento, Italy. F.C. greatly thanks all those who strongly
supported the idea of a discussion meeting on GPStelemetry data from its very beginning—particularly
C. Chemini, A. Rizzoli, R. Viola—and those who
supported her interest in GPS-telemetry, including
A. Brugnoli, C. Furlanello, R. Giovannini, S. Nicoloso,
U. Zamboni, M. Zanin and many others. Finally, she
warmly thanks A. Parasporo, A. Cipriano, S. Colzada,
G. Cagnacci and T. Cipriano for the personal support.
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