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Animal Movements through the Seascape: Integrating
Movement Ecology with Seascape Ecology
Simon J. Pittman, Benjamin Davis and Rolando O. Santos-Corujo
All around us animals are on the move. Even those that appear sedentary for much of
their life will at some period in time exhibit movement that is often critical to their
survival, be it during the early life stage through dispersal via wind, water, or even being
carried by another organism. Some of the most spectacular and fascinating natural phe-
nomena on earth are the mass movements of organisms such as salmon migrations from
the ocean to distant upland rivers to spawn (Eiler et al. 2014); and whales, seabirds,
seals, turtles and sharks ,which travel thousands of kilometres across oceans to com-
plete their lifecycle; each individual exhibiting astonishing endurance and navigational
precision (Hays & Scott 2013; Luschi 2013). For instance, several species of albatross and
many other far-ranging seabirds, perform the impressive feat of returning to the same
nest site year after year (Fisher 1971). Leatherback turtles have been recorded travel-
ing distances of more than 11 000 kilometres between nesting and foraging sites across
the Paciﬁc Ocean (Benson et al. 2011), returning to nest at the very same beach where
they were born. Despite their small size, some zooplankton swim a vertical distance
of several thousand feet during regular day and night migrations to avoid predators in
the deep during daytime and to search for food in shallower waters during nocturnal
periods. ese and many other marine animals schedule movements to coincide with
speciﬁc patchiness in environmental conditions and biological patterning (e.g., tempera-
ture,salinity,prey,predators,mates,habitatedges,etc.). Often dynamic spatial processes
such as tides, winds and currents are used to enable safe and energy eﬃcient movements
between ocean spaces, but these same processes also impede movements. From an eco-
logical perspective, the movement of animals is a fundamental ecological process that
is central to understanding how organisms respond to seascape structure and dynamics
(Wiens et al. 1993; Pittman & McAlpine 2003; Nathan et al. 2008). Movement patterns
throughout the life cycle inﬂuence gene ﬂow, social organisation, community structure,
evolutionary processes and patterns of biodiversity (Moorcroft et al. 2006; Nathan et al.
2008). e way that animals use space and the timing of space use, together with their
locomotion and navigational abilities, have been shaped by natural selection to max-
imise their ﬁtness and survival (Nathan et al. 2008).
People too have adapted their space-use patterns to coincide with movements
of marine animals to maximise ﬁtness and survival. Ancient indigenous coastal
Seascape Ecology, First Edition. Edited by Simon J. Pittman.
© 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.
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190 Seascape Ecology
communities in Australia, Polynesia and elsewhere have long used their intimate
ecological knowledge of marine animal movements to optimise success in ﬁshing. In
Queensland, Australia, migrating mullet were such an important seasonal phenomenon
for the Quandamooka people that they timed their own migration to the coast from
the mountains, to coincide with the spawning run. Fishermen of the Quandamooka
people of Moreton Bay prepared for the migrating mullet (Mugil cephalus)enteringthe
bay by using observations on a sequence of animal movements as spatial and temporal
cues. e ﬁrst cue for the beginning of the mullet migration was the migration of a
terrestrial animal, the ‘hairy grub’, a species of moth caterpillar, which travel, linked
in single ﬁle, to form a long chain. Next, the presence of sea eagles helped ﬁshers to
locate and track the schools of mullet as they entered the bay. en ﬁshers in the water
would ‘call’ the dolphins by slapping the water with their spears and digging the spears
in the sand. e dolphins, would then herd the ﬁsh into the nets and were rewarded
with hand-fed ﬁsh (Neil 2002). is special way of ﬁshing was celebrated in a social
gathering, or corroboree, called Bulka Booangun. In this way, the legend of ‘calling
the dolphins’ integrates spirituality with traditional ecological knowledge of animal
movement and plays out as an interconnected sequence of phenomena in time and
space linking intimately the movement of people with the movements of other species
across landscape and seascape.
Knowledge of movement patterns is now becoming a crucial information require-
ment for modern marine management, particularly where there is a need to maintain,
enhance or exploit seascape connectivity, anticipate biological invasion pathways, assess
the ecological eﬀects caused by disturbance events, protect important ‘blue corridors’,
or to identify and map critical habitat for conservation planning (Crowder & Norse
2008; Foley et al. 2010; see also Chapter 9 in this book). Widespread modiﬁcations to
the marine environment by human activities have resulted in fragmented seascapes,
degraded and newly created habitat, geographical shifts in the spatial arrangement of
habitat mosaics and disruptions or improvements to connectivity (Crook et al. 2015).
ese changes now provide impetus to gain a more reliable understanding of the spatial
ecology of animal movements and the interaction with the surrounding seascape at a
range of spatial scales. For instance, with warming oceans, marine animals are shifting
geographically to maintain access to favourable conditions, which can involve extended
movements to track food and relocate to cooler waters (Perry et al. 2005; Pinsky et al.
2013). Studies of the inﬂuence of seascape patterning on species movement rates in
response to broad-scale environmental change will help to address questions related
to species extinction risk and spatial adaptation to the dynamic distributions of ﬁshes
and their associated ﬁsheries.
Furthermore, understanding the movement response to seascape structure at the level
of the individual animal will allow us to better understand the individualistic responses
that occur within a species, or within a speciﬁc life stage or gender. Sometimes the
high variability and ﬂexibility of behavioural traits can confound our attempts to gener-
alise results from investigations of behavioural relationships in seascape ecology. Such
knowledge is critical for understanding the ecology and evolution of species and their
response to ecological change (Brooker et al. 2016). It was not until relatively recently
that we started to understand how ﬁsh perceive the world around them and how animals’
movements through space could aﬀect the way they learn and remember spatial infor-
mation (Patton & Braithwaite 2015). For example, ﬁeld observations and experimental
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Animal Movements through the Seascape 191
research in spatial cognition using mesocosms or microlandscapes has shown that ﬁsh
create multidimensional mental representations (or mental maps) of the environment
to aid in navigation (Burt de Perera et al. 2016). Although spatial learning in ﬁsh has
been known for some time (Dodson 1988), such studies have been relatively ﬁne scale
and have rarely beneﬁted from the integration of detailed maps of the seascape, thus
limiting their application to marine environments.
Increasing scientiﬁc attention is being given to the how, why, where and when of
animal movements, with important implications for our knowledge, exploitation and
management of impacts to marine life from anthropogenic activities. From a seascape
ecology perspective, however, we frame our questions on the ecological relevance of
seascape structure. For example: How do animals respond to the shape of patches, edges
and ecotones, gaps between focal habitat patches, topographic features on the seaﬂoor,
wave exposure gradients, plankton patch size and patch density? What are the mecha-
nisms and biological and ecological implications of movement responses to the spatial
structuring of seascapes? is chapter reviews some of the many tools and techniques
for quantifying the spatial and temporal patterning of marine animal movements, with
the purpose of encouraging a greater integration of movement ecology within seascape
ecology. We recognise a shift in complexity of these tools to account for complex mul-
tidimensional movement patterns (e.g., account for three- and four-dimensional space
use) and also the inclusion of a wider range of sensors capturing physiological data on
organisms as they navigate through seascapes, which greatly increases the realism in
modelling animal-seascape interactions. roughout we also identify some key knowl-
edge gaps and present research ideas and challenges to guide future studies.
Understanding of the behavioural interactions between animals and spatial structure
reveal important ecological insights that were not previously accessible with traditional
conceptual models and techniques. A deepening of our spatially explicit understanding
of marine animal movements and of the functional signiﬁcance of these movements has
major implications for marine ecology and marine stewardship.
7.1.1 Why Animal Movement is Central to Seascape Ecology
Organisms move through the seascape in response to the spatial structure they per-
ceive (e.g., thermoclines, prey patchiness, refuge spaces, etc.) and at the same time can
function as creators of dynamic spatial structure through their physical presence and
foraging activities. For example, feeding by stingrays, dugongs and green turtles inﬂu-
ences the spatial heterogeneity of seagrass seascapes (ayer et al. 1984; Townsend &
Fonseca 1998). It is becoming clear through studies of both structural and functional
connectivity that some spatial structures will facilitate movements to varying degree
and some will inhibit or constrain movements (Grober-Dunsmore et al. 2009; Turgeon
et al. 2010; Pittman & Olds 2015; Crooks et al. 2015; see also Chapters 5 and 9 in this
book). What do diﬀerent patterns of connectivity mean for the spatial distribution of
biodiversity, ﬁsheries potential, or the growth and survival of individuals?
In terrestrial systems, it has long been recognised that understanding the key ﬂows
and movements of animals, plants, material and energy across landscapes will help iden-
tify the optimal spatial arrangement of habitat patches to guide the prioritisation of
conservation actions (Crooks & Sanjayan 2006). For example, corridors of favourable
habitat structure are typically considered to facilitate connectivity between patches and
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192 Seascape Ecology
this concept has been widely integrated into conservation planning, albeit with mixed
success (Simberloﬀ & Cox 1987; Anderson & Jenkins 2006). In marine systems, however,
few attempts have been made to investigate how spatial arrangement of seascape patches
inﬂuence animal movement and this limits our understanding of the function of habitat
corridors or ‘blue corridors’ (but see also Chapters 9 and 10 in this book).
e theoretical framework of landscape ecology has incorporated animal movement
dynamics as one of the main underlying mechanisms rooted in the interaction between
spatially explicit patterns of habitat mosaics and ecological processes inﬂuencing repro-
duction, mortality, ﬁtness and access to resources (Wiens 1976, 1989; Ryall & Fahrig
2006). Movement behaviour, speciﬁcally the scale of space use, is at the core of land-
scape ecology. e point is best highlighted through the concept of ecological neigh-
bourhoods (Wright 1943; Southwood 1977; Addicott et al. 1987; Pittman & McAlpine
2003; Palumbi 2004); an extension of the concept of habitat, whereby habitat is scaled
by the movements of individuals and movements are inﬂuenced by the patterning of
the landscape (or seascape). Addicott et al. (1987) deﬁned ecological neighbourhoods
using three properties: an ecological process, a time scale appropriate to that process
and an organism’s activity or inﬂuence during that time period. As such they state that
the ecological neighbourhood of an organism, for a given ecological process (such as
movement), is the region within which that organism is active or has some inﬂuence
during the appropriate period of time. e ecological neighbourhood space can be used
as a sample unit area for quantifying the environment, or perhaps just as a technique for
anchoring the study at an ecologically meaningful place and period of time (e.g., daily
home range, spawning migration, territoriality, etc.).
Seascape ecology, the marine equivalent of landscape ecology, with a focus on
studying the geometric patterning in the marine environment and its ecological con-
sequences, provides an appropriate conceptual and analytical framework to examine
movement patterns (Pittman & McAlpine 2003). All seascapes are patchy at some scale
or another and some patches are more accessible than others, or will oﬀer greater qual-
ity for foraging or refuge from predators, depending on their location and biophysical
characteristics. With this is mind, the landscape ecologist John Wiens encouraged us
to visualise the heterogeneous landscape mosaic as a cost-beneﬁt surface, with ‘peaks’
where beneﬁts exceed costs and ‘valleys’ where costs exceed beneﬁts, corresponding
to high-quality and poor-quality patches (Wiens 1997). In fact, this perspective of
organism-landscape interactions is analogous to the use of the terms ‘prey landscapes’
(e.g.,Simset al. 2006) when referring to patchiness in food resources, or ‘seascapes
of fear’ (Wirsing et al. 2008) to communicate the response of prey to the spatial dis-
tribution of predators across the seascape. In addition to predator-prey distributions,
competitors and breeding partners will also be distributed heterogeneously in time
and space. us, when animals navigate across a seascape, behavioural decisions are
made that will usually result in a tradeoﬀ to maximise growth and survival. As such,
movement patterns are often highly predictable with regular high use areas being easily
identiﬁable (hence patchiness), although exploratory excursions to relocate are also
common, particularly when environmental changes make a seascape less optimal for
growth and survival (Pittman & McAlpine 2003).
In terrestrial ecology, the integration of spatial pattern with animal movement has long
been recognised (Wiens et al. 1993; Lima & Zollner 1996), with many examples in the
Journal of Ecology, Animal Ecology, Movement Ecology and Landscape Ecology. Although
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Animal Movements through the Seascape 193
interest in applying landscape ecology approaches for examining species-seascape
pattern relationships in marine ecosystems has increased in recent years, investigation
of the consequences of seascape structure for marine animal movements are among
the most overlooked applications of landscape ecology (Grober-Dunsmore et al. 2009;
Boström et al. 2011; see also Chapter 9 in this book). An exceptional example of
applying the landscape ecology perspective to coastal seascapes is that of Irlandi &
Crawford (1997) whom recognised that the juxtaposition of intertidal salt marshes and
subtidal seagrasses can inﬂuence the movements and growth of coastal ﬁshes through
trophic transfer during tidal migrations. Using enclosure experiments, Irlandi &
Crawford (1997) found that estuarine pinﬁsh (Lagodon rhomboides) movements were
greater where intertidal marsh edge existed in close proximity to subtidal seagrass
compared with marsh edge adjacent to unvegetated subtidal. is seascape conﬁg-
uration resulted in greater abundance and growth rates in ﬁshes moving across the
continuously vegetated tidal corridor. In landscape ecology, this ecological boosting
eﬀect of spatial conﬁguration is known as landscape complementation where resources
in both patch types are not substitutable (organisms cannot forage elsewhere), or
landscape supplementation where resources are substitutable (organism can forage
elsewhere) (Dunning et al. 1992). Such studies linking spatial patterning to the key
ecological process of movement and its consequences for organism biology are still rare
in marine ecology (Olds et al. 2016). However, even though progress in this direction
has been slow and largely constrained by data availability, we are now on the verge of
some breakthroughs fuelled by advances in the design and application of technologies
such as telemetry, biologging, spatial mapping and modelling.
7.1.2 Advances in Movement Ecology and its Application in Marine Systems
Technological advances in biotelemetry and the development of spatial statistical and
visualisation approaches have increased our capacity to understand the dynamics of
spatial activities of marine species such as foraging, homing navigation to nest or
shelter sites and relocation for mating (Pittman & McAlpine 2003; Grober-Dunsmore
et al. 2009; Block et al. 2011). e technological revolution in animal tracking and
mapping combined with a greater spatial awareness in society has also fuelled the
emergence of a new subdiscipline of ecology called movement ecology, which focuses
on understanding the movement of organisms in the context of their internal states,
traits, constraints and interactions among themselves and with the environment (i.e.,
‘external factors’) (Nathan et al. 2008). For many animals, particularly those that are
highly mobile moving through spatially heterogeneous seascapes, the spatial structure
of the surrounding environment will comprise some of these key external factors.
For example, the complex process of movement will depend on feedback interactions
between an individual and the surrounding environment such as the ability of an
individual to sense its environment, remember landmarks, construct mental maps
and process information, as well as biological, physical and chemical attributes of
the seascape that can hinder or facilitate movement and inﬂuence the pattern of the
movement pathway (Getz & Saltz 2008). Figure 7.1 expands the conceptual model
of movement ecology by placing organism movement behaviour within its broader
ecological context, with a focus on the relationship between movement patterns and
the spatial patterning of surrounding seascape, as well as recognizing the consequences
of these interactions for the condition of individuals.
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194 Seascape Ecology
Figure 7.1 Conceptual model of movement ecology incorporating focal patterning from landscape
ecology. Seascape structure and conditions can act as drivers for the internal state of individual
animals (physiological and psychological states), can inﬂuence their navigation (mental maps,
orientation), motion capacity (locomotory morphology/traits) and ultimately the movement process,
which results in a speciﬁc pathway in time and space. Source: Adapted from Nathan et al. (2008).
7.1.3 Tracking and Mapping Capabilities
In the ﬁeld of movement informatics, movement observations are spatiotemporal
signals carrying information that can reveal underlying mechanisms driving the
movement patterns (Nathan et al. 2008). Tracking of marine animals using a wide
range of devices is now providing vast amounts of data allowing us to reconstruct
movement pathways across a variety of spatiotemporal scales. With the most detailed
locational information, it is possible to map a representation of the actual movement
trajectory that can reveal behavioural decisions resulting from: foraging, responses to
seaﬂoor structure or water column properties, evasion of predators, or attraction to
other organisms. Pathways can be quantiﬁed to measure characteristics such as path
complexity using measures of the tortuosity, sinuosity and application of fractal indices
(Turchin 1998). Emerging digital and communications technologies have reﬁned our
ability to measure movement at the resolution of fractions of seconds with concomitant
spatial precision, while kinematical (e.g., acceleration), physiological (e.g.,heartbeat
and temperature) and behavioural (e.g., vocalisations) information are simultaneously
recorded (Getz & Saltz 2008). is has signiﬁcantly broadened the scope of questions
that we can address in seascape-movement ecology and boosted the accuracy of
evidence that we can collect to help answer these questions.
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Animal Movements through the Seascape 195
Two main forms of digital positioning technology are used to track animals in the wild,
each with diﬀerent advantages and disadvantages and suitability for diﬀerent applica-
tions. GPS-based satellite tags provide virtually unlimited global coverage and are ideal
for studying movements of some marine megafauna and avian species at a range of scales
in time and space (Kuhn et al. 2009; Hussey et al. 2015). However, since tags need to
be exposed to air to transmit signals, in the marine environment their application is
restricted to studying far-ranging movements of animals that regularly surface (such
as cetaceans and turtles). Acoustic telemetry systems on the other hand are capable
of near-continuous tracking of animals underwater, operating via ultrasonic commu-
nications between animal-borne transmitter tags and receivers (Hussey et al. 2015).
Animals can be acoustically tracked either manually, with tag transmissions detected by
a boat-based hand-held hydrophone (Hitt et al. 2011a), or passively through a network
of ﬁxed location underwater receivers that continuously listen for tag transmissions
(Jacoby et al. 2012). While the former is capable of high resolution positioning (on a
scale of a few metres), it is only feasible to track one individual at a time and the duration
of tracking is limited by constraints of a high labour demand (Papastamatiou et al. 2009;
Hitt et al. 2011a). Conversely, passive acoustic tracking enables automated monitoring
of multiple individuals over extended periods and, while it has historically been lim-
ited to coarser spatial resolutions (>∼15 m) (Hussey et al. 2015), recent developments
mean tag positions can triangulated between three or more receivers with high levels of
precision (∼2–5 m) (Espinoza et al. 2011; Biesinger et al. 2013; Dance & Rooker 2015).
It is largely because of the increasing precision and relatively low labour demands
that passive acoustic telemetry is proving to be a powerful tool for quantifying detailed
movement patterns in marine environments across a range of scales. Receivers can be
strategically arranged, oﬀering ﬂexibility to address speciﬁc questions, and often reﬂect
a tradeoﬀ between areal coverage and precision (Heupel et al. 2006). Close clustering of
receivers results in high overlap in listening range, and thus high-resolution positioning,
enabling reconstructed movements to be more tightly associated with environmental
structure on maps (Espinoza et al. 2011). Sparser distribution of receivers enables
broader range movements to be detected, but with less accuracy. In some circum-
stances integration of high and low resolution receiver arrays is desirable, for instance,
to look at detailed home-range movements and connectivity between patches when
animals relocate their home ranges (e.g., ontogenetic shifts) (Pittman et al. 2014). e
suite of capabilities now possible with passive acoustic telemetry suitably mirrors the
multiscale, multispecies framework that underpins the ﬁeld of seascape ecology.
To better understand an animal’s perception of its environment, digital tracking
tags can be used in conjunction with integrated data loggers that record information
on physiology, movement characteristics, or the environment it is moving within.
Geolocated measures of physiological factors (e.g., heart rate, body temperature)
and movement characteristics (e.g., acceleration, turning angle) can help to discern
behavioural mechanisms underpinning movements and space use, such as foraging,
prey avoidance, mating behaviours and movements between patches (Murchie et al.
2011; Hussey et al. 2015). Meanwhile, by getting the animal to act as a sensor of its own
environment, more accurate information on details of temperature, salinity, depth,
and light conditions can be used to explain patterns of movement (Hussey et al. 2015;
Horodysky et al. 2015). Crucially, regular depth measurements (obtained through
pressure loggers) can also be synchronised with geographical ﬁxes, enabling animal
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196 Seascape Ecology
positions to be located in three spatial dimensions, with massive implications for our
potential to understand how marine animals interact with their environment.
7.2 Using Animal Movements to Scale Ecological Studies
It appears that the more we track and map marine animal movements the more we are
discovering that many marine animals move across far greater spatial scales than was
previously assumed while others show high site attachment. From the perspective of
studying marine animal ecology, this new knowledge on marine animal movements
is central to deﬁning and delineating seascape patterning at functionally meaningful
spatial and temporal scales. e absence of information, or the lack of continuous
observation on the way animals use space through time, can all too often result in
insuﬃcient consideration of seascape context potentially resulting in misleading
conclusions on the primary drivers of ecological patterns and processes (Addicott
et al. 1987; Meetenmeyer 1989; Wiens 1989; Pittman & McAlpine 2003). Quantitative
and spatially explicit information on animal movements can address several critical
assumptions that exist in many ecological ﬁeld studies. Movement data will help to
select ecologically meaningful scales with which to deﬁne an ecological space, or area
of interest, to guide the design of ﬁeld data collection or data acquisition. With this in
mind, movement data will also challenge the assumption of a single patch focus (e.g.,
seagrasses or coral reefs), particularly for highly mobile organisms that use multiple
patch types in daily home range excursions, for example, between foraging and resting
areas, ontogenetic habitat shifts across mosaics of patch types and seasonal migrations
for breeding (Pittman & McAlpine 2003).
Although it has been widely acknowledged in conceptual models that marine
organisms are inﬂuenced by patterns and processes occurring at a range of scales
of space, time and organisational complexity (Haury et al. 1978; Hatcher et al. 1987;
Steele 1988, 1989; Barry & Dayton 1991; Holling 1992; Levin 1992; Marquet et al.
1993), few marine ecologists have designed studies that consider responses to habitat
with an assumption that marine animals are likely to respond to and be constrained
by the composition and spatial arrangement of resources in a hierarchical way, as
has been revealed for many terrestrial animals (Senft et al. 1987; Schaefer & Messier
1995; McAlpine et al. 1999; Rolstad et al. 2000). Even when a focal scale has been
determined, we propose that measurements carried out at only a single scale cannot, by
deﬁnition, incorporate important patterns and processes at scales above and below the
focal scale and a single scale approach is therefore limited in ecology (Pittman & Olds
2015). Working at one scale is particularly inappropriate for studies of multispecies
assemblages since species vary in their response to the environment due to functional
diﬀerences related to dietary requirements, habitat specialisation, foraging tactics and
body size (Betts et al. 2014). Similarly, ecological studies constrained to a single patch
type when questions are addressing habitat use for species that use multiple patch types
can also only oﬀer an incomplete picture of animal ecology. As argued by Roughgarden
et al. (1988), ‘studies at only one of the habitats tell no more than half the story’.
erefore, if information on animal movement is not available, the assumption of single
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Animal Movements through the Seascape 197
habitat use should be considered carefully or rejected entirely. If an assumption is to be
made, then a multihabitat type assumption may be more suitable, thus allowing for the
consideration of broader scale movement and potential linkages between component
theory to facilitate scale awareness and operational measures of scale (Wu & Loucks
1995). e hierarchical frameworks are by deﬁnition a simpliﬁed composite of complex-
ity, which in reality varies across a space-time continuum, but would be less pragmatic
for analytical purposes. Nevertheless, these spatial hierarchical constructs, allow one to
focus on an event at a particular scale, while recognizing that there are other scales rele-
vant to that event (Urban et al. 1987). For example, Pittman & McAlpine (2003) present
a three-level spatial hierarchy (adapted from Allen & Starr 1982) whereby lower levels,
L−1, occupy less space and are characterised by processes operating at faster rates and
ﬁner time scales (Box 7.1). In contrast, higher levels, L+1, are of broader temporal and
spatial scales. us, there is no single correct scale or level for observations and ulti-
mately, the appropriate scales will depend on a consideration of the questions asked, the
organisms studied and the time period considered (Wiens 1989).
Fauchald & Tveraa (2006) tracked the movements of individual Antarctic petrel
(alassoica antarctica) using satellite transmitters to quantify the spatial dynamics
and spatial scales of their foraging behaviour. e authors understood that marine
pelagic ﬁsh and krill are organised in a nested hierarchical structure (Stommel 1963;
Haury et al. 1978; Fauchald 1999; Fauchald et al. 2000) and so analysed the bird’s
movements to see if they exhibited nested search strategies. e study of 36 birds used
ﬁrst-passage time (FPT), a scale-dependent measure of the animal’s search eﬀort at
each point on its movement trajectory and showed that the birds did indeed exhibit a
spatially nested search strategy to ﬁnd food whereby they travelled faster over longer
distances (>100 km scale) to locate large clusters of patches and then within those
areas they concentrated their search at ﬁner scales to locate smaller patches within
which to feed. Over time, the broadest scale patches lasted weeks, while at ﬁner scale
patches of plankton disappeared or moved within days. Similarly, for basking sharks
(Cetorhinus maximus) locating prey across the continental shelf of northwest Europe,
movement tracks reconstructed from satellite-linked archival transmitters and linked
to maps of zooplankton biomass suggested that sharks exhibited directed movement
through a cross-shelf gradient of prey in order to locate the preferred high-density prey
patches where they then exhibited a reduction in speed and high turning rates as they
fed (Sims & Quayle 1998; Sims et al. 2006).
For an example of how a hierarchical framework might be relevant to deﬁning the
environment for highly mobile animals, consider an assemblage of juvenile ﬁsh using
a tidally dominated inshore area. In order to evade predators in deeper water and to
forage amongst intertidal seagrasses and mangroves, the animals move back and forth
with the ﬂooding and ebbing tide through a mosaic of patches from the subtidal at low
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198 Seascape Ecology
Box 7.1 (Continued)
tide to the intertidal at high tide. A conceptual framework (Figure 7.2) can be constructed
with the home range or ecological neighbourhood as the focal level (L) and intermediate
in the hierarchy. The focal level is the level at which the phenomenon or process under
study characteristically operates and is a functional part of a higher and lower level.
At any single level, the consequences and signiﬁcance can only be understood at a
higher level(s) and the mechanistic explanation must be investigated at a lower level(s).
The ﬁner scale L−1components of the mosaic (i.e., within-patch level) may consist of
structure such as seagrass leaf length, epiphyte biomass and patch size and these may
explain some of the ﬁsh distribution patterns found at high tide. For instance, some
animals may have a preference for relatively large patches of long leaf seagrasses. This
relationship, however, may not adequately explain the patterns at the broader scale
of the home range, which may also include unvegetated subtidal areas where animals
spend considerable time at low tide. At the extent of the home range, distributions may
also be inﬂuenced by the spatial arrangement of patches of seagrasses and the relative
proximity of seagrasses to complementary resources in adjacent mangroves and coral
reefs (L+1). Lower level explanations may be further lost at the L+1level, which would
include the environment surrounding the home range (i.e., where animal distributions
and abundance respond to a suite of physico-chemical constraints such as a gradient in
wave action, salinity, temperature, turbidity etc.). In this way, the intermediate level of
the hierarchy is deﬁned by the spatial activity patterns of the animal(s) within relevant
time periods, thus anchoring the hierarchy to an ecologically meaningful scale in time
Figure 7.2 Relevance of a hierarchical framework to the deﬁnition of an environment for highly
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Animal Movements through the Seascape 199
7.2.1 Building Movement Scales into Conceptual and Operational
Conceptual and operational frameworks for the study of movement responses to
seascape structure have not yet emerged in the new ﬁeld of behavioural seascape
ecology and are rarely found in the terrestrial landscape ecology literature. An example
of an operational framework for scaling the seascape using information on animal
movements is shown in Figure 7.3. is framework was developed to guide decision
making through a logical sequence of procedures when employing a landscape ecology
approach to the study of connectivity in tropical marine seascapes. e framework
consists of three components: (i) developing a conceptual framework; (ii) determining
appropriate scales for analysis and (iii) conducting geospatial analyses scaled to the
organism or process of interest. e operational framework also identiﬁes critical
feedback loops that emphasise the importance of iteratively adapting the framework as
new information becomes available.
188.8.131.52 Component 1: Build a Conceptual Model
e conceptual framework will determine the data needs and feed into decision mak-
ing with regard to data collection protocols. Ultimately, the type of ecological ques-
tions will determine appropriate scales. For instance, studies focused on understanding
seascape connectivity throughout the daily home range (i.e., routine foraging or terri-
torial movements) may be conducted at diﬀerent scales than studies focused at connec-
tivity throughout the lifecycle (Pittman & McAlpine 2003). Where appropriate data are
unobtainable, computer simulations can be used to explore connectivity and examine
184.108.40.206 Component 2: Selecting Scale
e second step of the framework is the selection of an appropriate scale at which to
conduct the study. Selecting appropriate scales (i.e., refer to Schneider Chapter 4 for
spatial scale deﬁnition and examples) for connectivity studies is important because
ecological phenomena (spatial patterns processes, animal populations and materials)
exhibit considerable variability in time and space. Likewise, environmental factors that
inﬂuence ecological phenomena also exhibit signiﬁcant spatiotemporal variability (e.g.,
nutrient ﬂows, current patterns, salinity gradients etc.). As a consequence, animals
respond to environmental heterogeneity at diﬀerent scales and in diﬀerent ways
(Johnson et al. 1992), which is linked to the way animals move throughout the seascape
(i.e., home ranges of highly mobile species compared with more sedentary species, or
diﬀerences between life stages of the same species).
erefore, to be ecologically meaningful, the extent of the seascape used for connec-
tivity studies should be scaled to reﬂect the natural history and ecology of the organism
or process being studied (Wiens & Milne 1989; Pittman & McAlpine 2003). is means
that the size (spatial extent) and grain (resolution) of the seascape would diﬀer among
organisms. For reef ﬁshes, organism-scaled home range size may require pilot studies
that use tracking or ﬁxed station sampling to determine temporal and spatial aspects
of connected habitats (patch uses). However, the problem is even more complex for
seascape studies of multispecies ﬁsh assemblages typical of coral reefs. e spatial extent
of the seascape may need to be large enough to include all critical resource patches
used by the organisms being studied including those used during diﬀerent life phases.
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200 Seascape Ecology
Figure 7.3 An operational framework that applies a landscape ecology perspective to the study of
connectivity in tropical marine seascapes. The framework consists of three components: developing a
conceptual framework that guides the study, determining appropriate scales for analysis and
conducting geospatial analyses scaled to the organism of interest. Solid arrows indicate directional
ﬂow among the subcomponents. Broken arrows represent directional ﬂow and important feedback
loops among and within the three components. Source: Developed through personal communication
with C. Jeﬀrey, NOAA Biogeography Branch.
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Animal Movements through the Seascape 201
For example, a review of movement patterns of 210 species of ﬁshes associated with
tropical coral reefs revealed that the scale of movements is inﬂuenced by a range of
factors including body size, gender, behaviour, population density, habitat characteris-
tics, season, tide and time of day, with some ﬁsh moving less than 0.1 kilometres and
others tens to hundreds and even thousands of kilometres (Green et al. 2015).
220.127.116.11 Component 3: Tools Identiﬁcation
e third component of the operational framework is the selection of the appropriate
analytical tool for conducting the geospatial analysis. Tool selection is driven primarily
by the questions asked and the type of data available (Calabrese & Fagan 2004). Spatially
explicit visual interpretation or extracted data on species distributions are correlated
with spatial pattern metrics to determine structural connectivity within seascapes, or
potential connectivity if information on species movement is known. Alternatively,
graph-theory analysis can be used to combine metrics such as interpatch distance
with telemetry data to determine actual or functional connectivity. Information
gained from spatial pattern analysis and graph theory could be further incorporated
in computer simulation models to identify relationships among seascape structure
and fauna, or to predict the inﬂuence of future changes in seascape structure on
species distribution and abundance. However, as pointed out by Shamoun-Baranes
et al. (2011), the steps needed to go from data collection to gain new ecological
insight, such as data organisation, exploration, visualisation, quantiﬁcation, inference
and generalisation of movement data, can be extremely demanding. At every step
of the process, great care must be taken to consider scale eﬀects, errors associated
with spatial data and the potential for propagation of errors through spatial analyses
(Wedding et al. 2011).
7.3 Advances in the Visualisation and Quantiﬁcation
of Space-use Patterns
With the increasing spatiotemporal precision and size of animal tracking data sets, we
now have the opportunity to capture and analyse data on how animals use space in ways
that better approximate reality and oﬀer novel insights into the spatial ecology of ani-
mals. However, challenges remain in ﬁnding eﬀective ways to make sense of these vast
and complex data sets. Here we describe several techniques and recent advances for the
spatial analysis and mapping of organism space-use patterns.
7.3.1 Estimating and Mapping Utilisation Distributions
Typically, animal space-use is visualised as a utilisation distribution using kernel density
estimation (KDE) techniques. is is a way of representing the probability of ﬁnding
an animal in a given place at a given time through aggregating positional data, and is
a commonly accepted means of estimating home-range dynamics. It works by placing
probability decay kernels around individual location points and summing them into
continuous density surfaces, essentially providing a heat map representing intensity
of space use (Silverman 1986) (Figure 7.4). e 95% probability surface (the smallest
area in which 95% of geographical ﬁxes occur) is often taken to estimate the broader
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202 Seascape Ecology
Figure 7.4 Kernel density estimation with diﬀerent colours representing iso-surfaces of diﬀerent
kernel densities. The black line represents the home range as deﬁned by minimum convex polygon
(MCP) techniques. Red contour lines are 95% kernel home range and the blue contour line is 50% core
range. Black dots are GPS locations of an animal. Source: http://gis4geomorphology.com/home-
range-kernel/ (accessed 25 May 2017).
home-range area, while the 50% surface is taken to represent the core area of use
When overlaid onto benthic seascape maps, these surfaces can be used to infer
preferences for diﬀerent patch types and diﬀerent seascape compositions and to
assess variations in space use among individuals, species, or across time periods. Such
techniques have previously been used to deﬁne and examine the home ranges of marine
ﬁshes (grunts and snapper) from manual tracking data on coral reefs (Hitt et al. 2011a,
Hitt et al. 2011b) and black-tip reef sharks from passive acoustic networks in coastal
waters (Heupel et al. 2004). Hitt et al. (2011a, b) quantiﬁed the size and shape of day
and night space use patterns for several reef ﬁsh and then used that space to sample
the patch-mosaic structure of the underlying benthic seascape providing for the ﬁrst
time a seascape context to help explain the movement ecology of two reef ﬁsh species.
e stepwise spatial analysis process of quantifying individual space use and seascape
structure is summarised in Figure 7.5.
KDE represents a signiﬁcant advance on its predecessor, minimum convex polygons
(MCPs), which involve drawing a simple boundary around the outer extent of animal
movements, thus placing profound restrictions on geographic interpretation of space
use. Although KDE techniques oﬀer a means of visualizing relatively detailed structure
of home ranges, they have historically neglected information contained in the serial
correlation of points, by assuming position ﬁxes are independent rather than connected
by movements. By placing kernels over consecutive points to form segments or paths,
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Animal Movements through the Seascape 203
Analyse organism-seascape relationships
Apply spatial pattern metrics
Clip out seascape sample units
Map activity space
Map benthic seascape
Interpret aerial imagery
Track fish movements
Figure 7.5 Stepwise spatial analysis to map and quantify benthic seascape patterning at a spatial
extent deﬁned using the diel activity space calculated from tracking an individual ﬁsh following the
methods of Hitt et al. (2011a).
(minimum convex polygon)
(local convex hull)
Kernel density estimate
Kernel density estimate
Figure 7.6 Diﬀerences in precision of utilisation distribution using four diﬀerent home-range
estimation techniques. Source: https://www.werc.usgs.gov/ProjectSubWebPage.aspx?
SubWebPageID=1&ProjectID=258 (accessed 25 May 2017).
recent adaptations have allowed utilisation distributions to be represented more
accurately and more precisely (Figure 7.6), enabling more detailed interpretation
of space-use and animal-landscape interactions (Benhamou & Cornélis 2010). is
produces what is known as a movement-based kernel density estimate (MKDE).
Movement-based kernel density estimates can be further enhanced by modelling the
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204 Seascape Ecology
uncertainty in location of the animal between paired ﬁxes. is is often done with a
Brownian decay function, which factors in a random walk model, constrained by the
distance between points and animal velocity (Kranstauber et al. 2012; Demšar et al.
2015a). e eﬀect is that the kernel becomes ‘pinched’ between consecutive data points
(resembling a dumbbell), as the movement path between points is more likely to be
direct than convoluted.
One criticism of KDE and MKDE techniques is that kernel surfaces often extend into
areas that are not part of the animal’s home range, as an artefact of smoothing the
probability decay kernels placed around data points or segments. Even though obvious
boundaries (such as lakes for terrestrial animals, land for marine animals) can be easily
‘cut out’ of surfaces (Benhamou & Cornélis 2010), KDE can ultimately provide mislead-
ing pictures of home ranges. To address this issue a nonparametric utilisation distribu-
tion visualisation tool, Local Convex Hulls (LoCoHs), was developed (Lyons et al. 2013).
LoCoH involve drawing polygons around points closely clustered in space and time, with
each polygon containing an equal number of points. ese hulls are then sorted by the
density of points within them and subsequently merged into density isopleths represent-
ing probability surfaces (e.g., 50% isopleth, containing 50% of location ﬁxes) (Figure 7.6).
Since hulls hug the edges of position ﬁxes, animal trajectories can be more reliably and
tightly associated with landscape structure and sharp boundaries, which allows the user
to examine the inﬂuence of habitat edges, connection corridors and other structural
features on movement. In addition, adaptations to LoCoH and KDE algorithms have
been developed to constrain home ranges using environmental boundaries and gradi-
ents based on known geographical range and suitable habitat. For example, Tarjan &
Tinker (2016) developed the Permissible Home Range Estimation (PHRE) algorithm to
restrict sea otter (Enhydra lutris) space-use estimates from overlapping terrestrial land
and from extending too far oﬀshore.
7.3.2 Analysing Spatiotemporal Utilisation Patterns
What if we want to observe spatiotemporal interactions in movement patterns?
Utilisation distributions can account for temporal correlation of points, but they are
still limited by being static; that is, they do not allow analysis of space use through time,
obscuring potentially important space-time interactions that typify daily routines, such
as movement between foraging and refuge areas. To address this issue, data has typically
been split into discrete time windows, e.g. to compare day versus night utilisation distri-
butions. For example, Hitt et al. (2011a) split movement data into predeﬁned diel periods
to investigate diﬀerences in daytime versus night-time habitat utilisation. is approach,
however, can subsume more detailed movement behaviours such as ecologically mean-
ingful changes in movement path complexity within each period (Hitt et al. 2011b).
Recently, enterprising collaborations between ecologists and information scientists
have helped to create new techniques to visualise continuous space use through time,
shedding light on patterns lost through static techniques (Demšar et al. 2015a). Here,
data are represented in a volumetric space-time cube (STC), where the xand yaxes rep-
resent two spatial dimensions and the z(vertical) axis time. In essence, this technique is
analogous to multiple 2D kernel density maps stacked on top of one another on a contin-
uous time axis. Generally, the bottom of the zaxis represents midnight and the top the
following midnight, such that the time-space trajectory for a single day is plotted in the
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Animal Movements through the Seascape 205
60 000 80 000
100 000 120 000 140 000
80 000 100 000
120 000140 000
60 000 80 000
80 000 100 000
120 000 140 000
60 000 80 000
60 000 80 000 100 000
120 000 140 000
60 000 80 000
Figure 7.7 (a) Daily tracks of the movement of one lesser black-backed gull (Larus fuscus) collected
over one month in the Netherlands; (b) Space-time cube of the same tracks with the z-axis showing
time of day in seconds and ranging from midnight at the bottom to midnight at the top; (c) space-time
kernel density estimate of the seagull’s movements; (d) iso-surface with high intensity value to show
two core areas of use separated by space and time. These areas are spatiotemporal hotspots and
indicate previously unknown particularities in this gull’s movement: consistently spending nights at a
mainland location outside the nest and days in and around the nest. Source: Adapted from Demšar &
van Loon (2013).
STC, before the trajectory for the following day is overprinted on the same space. To deal
with visual clutter of multiple overlapping trajectories, space-time paths are smoothed
into continuous density surfaces in similar ways to utilisation distributions, but with 3D
kernels placed over whole trajectories, rather than segments or points (Demšar et al.
2015b). Space-time densities have previously been used to analyse daily movement pat-
terns of a seagull (Figure 7.7), revealing two spatiotemporal hotspots separated by diﬀuse
movements: a night-time nesting site based on an island and a mainland feeding site that
the animal visited several times during the day (Demšar & van Loon 2013).
Rather than placing probability decay functions around whole trajectories, an alter-
native is to smooth the data by constructing probabilistic space-time prisms between
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206 Seascape Ecology
consecutive position ﬁxes. In a similar way to Brownian functions with MKDE,
space-time prisms incorporate information on maximum velocity of the animal, to
deﬁne the area of space potentially used between position ﬁxes (Winter & Yin 2011;
Downs et al. 2015), with probability weighted towards the estimated space-time
path (i.e., the trajectory connecting the sequence of points). is results in elliptical
kernels that are greatest halfway between points and represent actual uncertainty
about movement paths that is scaled by the animal’s movement capabilities rather
than an arbitrary decay function (as opposed to a parametric approach such as KDE).
However, since space-time prisms are based on a single individual’s movements, data
from multiple days or individuals are not easily aggregated and are usually displayed
separately (Downs et al. 2015). Furthermore, where depth or altitude is an important
element of an animal’s habitat, the time axis uses up a valuable third spatial dimension.
7.3.3 Visualizing Movement Patterns across Three Spatial Dimensions
Computer visualisations have emerged as powerful tools for exploring movements and
space use, enabling analysts to identify generalised patterns from complex multidi-
mensional datasets and draw insight through their tacit sense of space and knowledge
of ecological phenomena (Andrienko & Andrienko 2013). e ability to represent
complex space-use patterns through computer-based visualisations is continually pro-
gressing and while such tools have been primarily developed for GPS data on terrestrial
animal or vehicle movements, they are equally applicable to marine organisms. By
representing movements in two spatial dimensions (xand y), we are mapping only a
simple planar portion of an animal’s spatial use. In reality, an animal moves within a
3D geographic domain (x,yand z), thus compressing movements into two dimensions
reduces our ability to link spatial behaviours with environmental heterogeneity and the
movements of other animals (Tracey et al. 2014a). is is problematic where animals
exhibit vertical movements through the water column and where the use of structures
extending vertically into the water column (e.g., coral, rocky reefs and seamounts) can
form critical components of animal activity spaces. Accounting for depth (or altitude
for birds) becomes particularly relevant when comparing space use patterns between
species, whereby two animals may be located in the same general space (x, y) on the
horizontal plane, but occupy very diﬀerent water depths (z on the vertical dimen-
sion) and may therefore never interact with one another. Simpfendorfer et al. (2012)
demonstrated this problem by comparing two-dimensional and three-dimensional
kernel techniques to represent volumetric space use of two individual European eels
(incorporating data from pressure sensors). e analysis revealed that 2D methods had
signiﬁcantly overestimated overlap in space use. More recently, 3D movement-based
MKDEs have been implemented to visually analyse volumetric movement of animals in
a 3D environment (including terrestrial, avian and marine species) (Tracey et al. 2014a,
Tracey et al. 2014b). By representing movements in the same number of dimensions
as they occur, these state-of-the-art techniques allow more biologically accurate
interpretations of space use and promise novel ecological insights from telemetry data.
Figure7.8showsthe3DMKDEforadugong(Dugong dugon), which was tagged with
an Argos satellite GPS tag and depth recorder and tracked for 41 days over seagrass
beds in Hervey Bay, Australia (Tracey et al. 2014a). 3D models deﬁned the extent of the
home range movements and the proportion of the home range at diﬀerent depths and
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Animal Movements through the Seascape 207
Dugong depth (m)
0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.40.0 0.1 0.2 0.3 0.40.0 0.1 0.2 0.3 0.40.0 0.1 0.2 0.3 0.4
1.0–1.5 m 1.5–2.0 m 2.0–2.5 m 2.5–3.0 m
Figure 7.8 Three-dimensional movement-based kernel density estimates (3D MKDE) for an individual
satellite tracked dugong overlain on 10 m resolution bathymetry. The probabilities representing 3D
space use were mapped to 10 (x)×10 (y)×0.5 (z- depth) metre cubes (voxels). (a). The 99% contour
volumes for 3D MKDEs based on locations when tidal heights ranged from 0.5–1.0 (red), 1.0–1.5
(orange), 1.5–2.0 (yellow), 2.0–2.5 (light green) and 2.5–3.0 (green) metres are shown. Based on the 3D
MKDEs for each tidal height category, the probability that the dugong could have been at diﬀerent
water depths was computed into 0.5 m bins (b). The value on the y-axis is the upper depth value for
each 0.5 m bin (i.e., 0 indicates 0.0–0.5 m depth). Source: Reproduced from PloS One (Tracey et al.
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208 Seascape Ecology
In another study, 3D analysis of Paciﬁc leatherback turtle (Dermochelys coriacea)
trajectories revealed that subsurface variables (e.g., currents and prey) play an important
role in shaping movement patterns such as changes in depth to locate patches of food,
therefore explaining signiﬁcant variance in geographic space use that would have been
missed by typical 2D analyses (Schick et al. 2013).
We have discussed two diﬀerent types of 3D model, one incorporating time as
the third dimension and one incorporating a third spatial dimension. Now we face
the challenge of ﬁnding ways to visually represent three spatial dimensions, as well
as a continuous temporal dimension (x,y,z&t), in what would eﬀectively be a
four-dimensional model. e answer is likely to lie in animation of kernels through
time and interactive manipulation of data, drawing upon tools that are used currently
to monitor boat and aircraft traﬃc (Andrienko & Andrienko 2013).
7.4 Linking Animal Movement Patterns to Seascape Patterns
For preliminary exploration of seascape-movement patterns, a simple visual interpre-
tation of the spatial and temporal association between an animal movement path and
the environmental conditions in the surrounding seascape can be achieved by simply
overlaying the movement path, or utilisation space (e.g.,Hittet al. 2011 a and b), on
maps of various environmental variables (e.g., benthic habitat maps, bathymetry, sea
surface temperature, chlorophyll concentration, etc.) in a geographical information
system. In this way, the response to structure can be explored by identifying any
changes to pathways at boundaries, concentrations of activity in certain environmental
conditions, avoidance behaviour, or responses to varying levels of spatial and temporal
heterogeneity. For quantitative analyses, environmental conditions along pathways, or
within a utilisation space, can be extracted and modelled statistically to examine eco-
logical relationships. Such data can be extracted at a range of spatial scales from a wide
range of variables and modelled to allow multiple interactions between environmental
predictors (Pittman & Brown 2011). Ultimately, the operational scale(s) of the study will
inﬂuence the analytical technique for linking animal movements to the surrounding
Although the study of the ecological causes and consequences of 2D spatial pattern-
ing in landscape ecology has contributed greatly to our ecological knowledge, it also
represents a considerable oversimpliﬁcation of the true variability in structural com-
plexity that exists in nature (McGarigal et al. 2009; Lausch et al. 2015). In contrast to
the conventional 2D planar surfaces composed of discrete and internally homogenous
patches, real seascapes exhibit multidimensional structural complexity at a range of
spatial scales that is also functionally meaningful to organisms and ecological processes.
Many terrestrial and marine studies are now quantifying continuous heterogeneity by
applying topographical metrics and surface analysis to 3D terrains (Hoechstetter et al.
2008; McGarigal & Cushman 2005; McGarigal et al. 2009; Wedding et al. 2011). In
turn, metrics designed to quantify surface features and topographic complexity have
performed well as predictors of ﬁsh diversity and species distributions across coral reef
seascapes (Wedding et al. 2011; Barrell & Grant 2013).
Spatially continuous representation of seascape patterns (spatial gradients) can be
combined with novel surface characterisation of animal movement such as KUD, 3D
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Animal Movements through the Seascape 209
and space-time density approaches to identify critical thresholds in animal-seascape
relationships. Critical thresholds (i.e., where a small change in the seascape results in
abrupt changes in the movement state) can be identiﬁed using statistical nonlinear
models and multivariate techniques that regress continuous seascape surfaces with
movement surfaces (Large et al. 2015). For instance, the 3D movement-based kernel
density estimator developed by Tracey et al. 2014 could be useful to understand the
niche partitioning of species in the water column in relation to remotely sensed, con-
tinuously mapped seaﬂoor structure and ﬁner scale within-habitat structure (e.g.,reef
rugosity, seagrass canopy high). Analytical tools that incorporate temporal variability
such as time-geographic density estimation (Downs et al. 2011) and space-time density
of trajectories (Demšar & Virrantaus 2010), could also be combined with surface
metrics that quantify spatial patterning of the seascape to provide a dynamic context in
the study of movement-seascape relationships.
To help guide the selection of analytical procedure we oﬀer Figure 7.9, which
recommends modelling techniques, data requirements and considerations and recom-
mended reading when addressing a speciﬁc set of questions on animal-seascape spatial
relationships. ese techniques are also explained in more detail within the chapter.
7.4.1 Linking Individual Movement Trajectories to Seascape Structure
In addition to mapping and quantifying home range data, high resolution movement
tracks typically provide suﬃcient information to classify movement phases into dis-
tinct ecological activities such as foraging, escaping or chasing, resting or migrating
(i.e., movement or behaviour states) (Turchin 1998; Nathan et al. 2008; Demšar et al.
2015a). e twist and turns of a movement path (path topology) and the spatial scales
at which animals change between directed to random walks provide signals of drivers
of movement and dispersal (Nams 2005, 2006). e ability to quantify speed, direction,
turning angle and shape of track forms the basis of several models used in diﬀerent
landscape ecology studies (e.g., fractal analysis, state-space models, random walks) to
test hypotheses on the interaction between speciﬁc ecological activities and speciﬁc
seascape characteristics (Pittman & McAlpine 2003; Schick et al. 2008). Analysis of path
topology, the geometric properties of a trajectory, has received renewed interest with
a growing number of metrics available for segmenting and characterizing movement
paths (reviewed by Edelhoﬀ et al. 2016; Gurarie et al. 2016).
From a foraging theory perspective, this analytical approach can be applied to assess
how foraging activity is inﬂuenced by seascape patterning such as fragmentation that
can increase interpatch distances, abundance of food and the energetic requirements
to locate food (McIntyre & Wiens 1999). For example, Tilley et al. (2013) compared
the orientation behaviour of acoustically tracked stingrays (Dasyatis americana)across
a spatially heterogeneous lagoon with a computer-generated correlated random walk
(CRW) model to measure the spatial scale at which directional bias occurred. e study
determined that individual stingrays orientate at spatial scales up to 100 m as a result
of experiential learning of depth and key topographical features such as patch reefs.
e authors conclude that rays may use the distribution of patch reefs as a network of
refuges, connected by pathways of potential foraging areas. e movement response to
diﬀerent oceanic ‘windscapes’ has also been examined. For northern gannets (Morus
bassanus), Amélineau et al. (2014) monitored energy expenditure, ﬂight patterns and
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210 Seascape Ecology
Figure 7.9 Summary and guidance for analytical tool selection for speciﬁc types of research questions
when quantifying space use patterns and linking animal movement to seascape structure.
wind force and direction resulting in the identiﬁcation of three behavioural states based
on movement trajectories: a high tortuosity path at medium speed while foraging; a
straight path at high speed while commuting; and a straight path at low speed while
resting. Although wind force strongly shaped ﬂight energy expenditure, gannets did not
optimise their ﬂight paths to avoid strong wind.
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Animal Movements through the Seascape 211
Spatial models such as hierarchical state-space models and individual-based models
(IBMs) can also be used to quantify the spatial interactions between organisms and
seascapes (Morales et al. 2004; Jonsen et al. 2005; Eckert et al. 2008; see also Chapter 8
in this book). By considering diﬀerent types of random walk models this approach cal-
culates the probabilities of an individual changing to a diﬀerent movement state when
environmental conditions change (Morales et al. 2004). Eckert et al. (2008) used hier-
archical Bayesian state-space models with satellite telemetry data to predict movement
pathways for juvenile loggerhead turtles (Caretta caretta) to quantitatively determine
how oceanographic covariates inﬂuence movement states. e models indicated that
some of the turtles were more likely to switch to intensive search behaviour (slower
travel rate with higher turn angles) when encountering deeper waters where they are
thought to be feeding. In contrast, larger individuals used ocean currents to perform
directed movements (faster travel rate, lower turn angles) towards patchy ephemeral
e need to incorporate increased behavioural complexity in models of space-use
was highlighted by Lima & Zollner (1996). In general, ecologists that have attempted to
build realistic behaviour into models have obtained better ﬁts when predicting move-
ments and when linking movement behaviour to landscape patterns, including response
to patch edges, gaps between patches and switches between behavioural states (Schick
et al. 2008).
7.4.2 Individual Movement and Seascape Connectivity
In landscape ecology, functional connectivity is generally deﬁned by the movement
response of organisms to various structural features of the landscape (Schooley & Wiens
2003). In the sea too, measurements of movement can be used to test the permeability
and connectivity of seascapes perceived by diﬀerent marine species (Grober-Dunsmore
et al. 2009; Dale & Fortin 2010). For instance, movement may be more diﬃcult through
some patch types than others and habitat edges may diﬀer in their permeability or
attraction (Ries et al. 2004). In addition to ﬁeld observations and telemetry, landscape
ecologists apply graph-theoretical approaches such as network analysis to examine
connectivity. Network analysis can integrate seascape features (e.g., patch type, size,
isolation) with movement data to determine the paths that oﬀer the least to highest
resistance to movement, or to identify least-cost pathways (Grober-Dunsmore et al.
2009; Urban et al. 2009; Dale & Fortin 2010; see also Chapter 10 in this book). In
addition, graph models provide the opportunity to test the hypothesis of nonlinear
seascape connectivity as a function of increasing dispersal capacity (Urban et al. 2009).
Analysis of telemetry data with network analysis methods is increasing in ecology
and has been applied eﬀectively to several marine species (Jacoby et al. 2012; Dale &
Fortin 2010; Finn et al. 2014). Finn et al. (2014) used network analysis derived from
graph theory to visualise the direction of movement and number of movements
between acoustic receivers (nodes) for individual ﬁsh (boneﬁsh, barracuda and permit)
across coral reef ecosystems oﬀ the island of Culebra, eastern Puerto Rico. Spatial
movement graphs reﬂected individual behavioural diﬀerences with some ﬁsh exhibiting
the pattern of a central place forager (boneﬁsh), while others cruised along a territory
(great barracuda and permit). e study was able to detect home ranges and site
ﬁdelity of diﬀerent marine ﬁsh species in relation to distance from shore and habitat
types. Such an approach could contribute to the quantiﬁcation of interhabitat energetic
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212 Seascape Ecology
ﬂows and support marine management and restoration activities by incorporating
individual movement variability into the design of marine protected area networks
and ecological corridors (Grober-Dunsmore et al. 2009; Jacoby et al. 2012). Similarly,
Jacoby et al. (2012) overlaid movement data of sharks (detected with an array of
acoustic receivers) with spatially explicit information on seascape characteristics to
demonstrate how network analysis could be used to predict how animal movements
and their home range might be impacted by disturbances such as habitat loss. Lédée
et al. (2015) compared kernel-based estimators of home range with a network analysis
for acoustically tracked sharks in Queensland showing that although the two analyses
provided similar results for estimates of core use areas, the network analysis was able
to identify movement pathways that can be used to identify which corridors are most
important for maintaining connectivity across the study region. Patch-based graphs,
graphs that model the relationships among patches of habitat, show great promise in
the operationalisation of graph-theoretic approaches in seascape ecology by measuring
connectivity for focal species between patches of habitat across real seascapes. e
use of network analysis in acoustic monitoring studies, especially within a seascape
context, is still in its infancy and its utility will be determined through a broader range
of marine applications addressing questions such as: Which areas of the seascape are
connected? Which patches are most important for connectivity? What are the critical
thresholds in species response to changing spatial structure? How does connectivity
inﬂuence the spread of invasive species?
7.4.3 Linking Species Interactions and Physiology with Movements across
Integrating species interaction and physiological measurements with movement data
will help to disentangle the full spectrum of ecological consequences resulting from
interaction with seascape patterning. For instance, species interactions are known to
signiﬁcantly aﬀect space-use patterns across heterogeneous seascapes (Connolly &
Hindell 2006; Boström et al. 2011), but are rarely considered in explanations of
space-use patterns. As additional data across multiple years and seascape types are
collected, it is expected that diﬀerent movement analyses (e.g., network analysis and
time-geographic density estimation, 3D KUD) will be eﬀective in assessing social and
predator-prey interactions within and among species (Finn et al. 2014; Tracey et al.
2014a and b). For example, by combining a time-series of satellite tagged individual bull
shark (Carcharhinus leucas)andtarpon(Megalops atlanticus), Hammerschlag et al.
(2012) was able to determine how tarpon movement changed relative to the bull sharks’
core area of habitat utilisation (quantiﬁed with KUD) and with respect to the spatial
structural properties of their habitat. e majority of edge eﬀect and fragmentation
studies on predator-prey dynamics have concentrated mostly on direct predation eﬀects
(Creel & Christianson 2008). However, empirical studies have shown that risk eﬀects
can be signiﬁcant and sometimes substantially larger than direct eﬀects (Brown &
Kotlier 2004; Heithaus et al. 2009). Since seascape properties can inﬂuence both
predator and prey behaviour, it is essential to adapt movement ecology studies to test
how changes in allocation time, vigilance, foraging, aggregation and movement patterns
(i.e., evidence predation risk) are inﬂuenced by spatial attributes of the seascape.
It is important to recognise that animal-seascape relationships will be mediated
by nonspatial factors too, such as the health and condition of the individual or
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Animal Movements through the Seascape 213
individualistic behaviour including social dynamics (Wilson et al. 2015). Currently,
we know very little about how movement and animal-seascape interactions translate
to individual ﬁtness or the persistence of populations within metapopulations. One
way forward is to integrate data from multiple techniques such as tracking telemetry,
accelerometers and other biologgers, together with chemical tissue sampling and maps
of seascape structure. In this way, movement ecology and seascape ecology studies
can integrate knowledge to understand the ecological processes that inﬂuence an
organism’ ﬁtness in relationship to the spatial attributes of seascapes. is suggests the
augmentation of sequential positions with complementary data on the physiological
(e.g., hormonal, temperature) and behavioural (e.g., feeding rates) state of the focal
individual to explain how inﬂuential external factors (e.g., seascape characteristics,
species interaction) inﬂuence the overall ﬁtness of individuals (Nathan et al. 2008;
Brownscombe et al. 2014; Demšar et al. 2015a).
Triaxial accelerometers measure changes in velocity over time in three dimensions,
which have helped to obtain estimates of ﬁne-scale behaviour, such as foraging
behaviour and energy expenditure in a wide range of species (Murchie et al. 2011;
Brown et al. 2013; Demšar et al. 2015a). Using this approach, two studies on boneﬁsh
(Albula spp.) demonstrated how swimming and foraging behaviours and their energetic
scope varied among individuals, over diel cycles and habitat types (Murchie et al.
2011; Brownscombe et al. 2014). ese examples illustrate the potential of using
accelerometers to develop bioenergetics models within a seascape ecology context
and create seascape-ﬁtness maps to identify optimal energetic seascape conditions.
In addition, bio-loggers incorporating both location and environmental sensors that
collect oceanographic parameters such as conductivity, temperature, depth and salinity
could be used to assess the spatial structure of pelagic environments. Since pelagic
seascapes are characterised by spatial and temporal discontinuities in energy and matter
(Jelinski 2015), the inﬂuence of patterning in open waters can also be analysed using a
landscape ecology approach and bio-loggers could help us to understand how pelagic
species respond to oceanographic features, such as fronts, eddies, etc. In addition,
video observations can be collected simultaneously with locational and accelerometer
data that could serve as ground truthing for behaviour types and state-space dynamics
(Demšar et al. 2015a). One example of this is the REMUS SharkCam developed by
Woods Hole Oceanographic Institute (2015), an autonomous underwater vehicle
equipped with video cameras and navigational and environmental loggers that enable it
to locate and track the movement behaviour of large marine species such as the North
Atlantic white shark (Carcharodon carcharias) (https://www.whoi.edu/main/remus-
sharkcam, accessed 25 May 2017).
Furthermore, novel combinations of diﬀerent measurements have been used to
explain variation in movement behaviour across spatially heterogeneous seascapes.
One example is the integrated analyses of animal movement pathways and stable
isotope analysis (Layman et al. 2007a; Hammerschlag & Layman 2010). e relative
positioning of organisms in a 𝛿13C-𝛿15 N2Dspaceor𝛿13C-𝛿15N-𝛿34 S3Dspacecan
reveal important aspects of trophic structure and niche space (Layman et al. 2007;
Jackson et al. 2011), since diﬀerent isotopes can provide proxies for the trophic position
and ultimate sources of dietary carbon of organisms (Fry 2007). Novel quantitative
metrics based on these representations of the niche (i.e.,2Dor3Disotopicspace)
may be powerful tools to test ecological theory and study ecological responses to
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214 Seascape Ecology
anthropogenic impacts such as habitat fragmentation (Layman et al. 2007; Schmidt
et al. 2014). For example, diﬀerent isotopic metrics such as isotopic range, niche width
(e.g., convex hull or ellipses), central tendency and niche overlap (for details Layman
et al. 2007; Jackson et al. 2011) could be implemented in movement-seascape ecology
studies to assess intraspeciﬁc tradeoﬀs in movement, foraging strategies and individual
specialisation (i.e., variation in resource use within the same species sex and age/stage
class) in relation to seascape characteristics (Fodrie et al. 2015).
7.4.4 Experimental Seascapes to Investigate Animal Response to Seascape
Experimental approaches in seascape ecology can provide direct evidence of causation
and have played an important role in linking patterns to processes in seascape ecology
(Boström et al. 2011 and Chapter 5 in this book). Experiments can also provide vali-
dation data for models and ﬁeld observations and generate hypotheses and parameter
values to guide model development. Where controlled manipulative experiments are
required to address questions about speciﬁc spatial conﬁgurations, then the options are
likely to include either physical alteration of the seascape, or the use of artiﬁcial patches,
such as artiﬁcial seagrass units, mangrove and patch reefs. Artiﬁcial patches have been
applied successfully to examine the ecological consequences of seascape context, struc-
tural complexity, patch size, edge eﬀects and patch proximity (e.g.,Jelbartet al. 2006;
Walsh 1985; Nagelkerken & Faunce 2008; Yeager et al. 2012), but are typically limited in
spatial scale to just a few metres or tens of metres; often considerably ﬁner scales than
the home ranges of the species under investigation. Although broad-scale manipulation
of seascape structure may be unfeasible, or unethical for the purpose of experimental
studies, we can make use of seascapes that have already been altered, or are about to be
altered (i.e., opportunistic experiments), to conduct studies on movement behaviour,
both before and after the spatial conﬁguration has been modiﬁed. ese experiments
could still reveal strong inference on causes driving the movement response of marine
animals if we can also eﬀectively account for variability in ecological characteristics
among diﬀerent seascapes. Replication, however, is often not possible due to the
scale of the experiments and where broad scale structural changes have occurred,
the opportunity is often a one-time event. A review of the challenges and progress
in landscape experiments along with guidance on the ideal features of a manipulative
experiment has been provided in the landscape ecology literature (e.g., McGarigal &
Cushman 2002; Jenerette & Shen 2012).
Wiens & Milne (1989) advocate using experimental microlandscapes to exam-
ine pattern-process linkages with the potential that such studies serve as a model of
broader scale landscapes. ere are several advantages of this microlandscape approach:
(i) measurements may be taken with a level of detail that is diﬃcult to attain at a broader
scale; (ii) sample sizes may be greater, or sampling at a given intensity may provide
a more accurate representation of the phenomenon being investigated; (iii) experi-
mental manipulations may be conducted with relative ease and (iv) experiments or
observations may be replicated over many plots or treatments with relative ease.
Johnson et al. (1992) proposed that general ecological principles will emerge from
experimental studies using microlandscapes (with appropriately scaled animals) that
may be translated to other organisms and broader spatial scales. e biggest limitation
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Animal Movements through the Seascape 215
is that scaling up can lead to erroneous conclusions and conducting microlandscape
experiments at organism-relevant scales is challenging for many mobile marine species.
However, this approach may be insightful for small-bodied individuals and those with
a relatively small home range, or to understand how highly mobile species respond
to structure in a speciﬁc part of their home range (edge response, crossing unsuitable
patch types). Perhaps, the biggest challenge in scaling up movement patterns are the
complexities of individual behaviour (Morales & Ellner 2002). However, the use of
microseascapes to examine behavioural responses to seascape patterning in the way
that landscape ecologists have done is extremely rare, yet this approach, combined with
spatial modelling such as individual-based modelling (see Chapter 8 in this book) could
provide strong inference on pattern-process relationships in seascapes.
7.4.5 Mechanistic Models
Even though the probabilistic models and empirical studies mentioned above do
not necessarily allow one to reveal and disentangle the mechanistic underpinnings
of movement directly, the statistics they provide could serve as assessment criteria
for simulation models that do implement and combine diﬀerent movement mecha-
nisms (Mueller & Fagan 2008). Empirical studies can help in the parameterisation of
individual-based simulations (i.e., individual-based models IBMs) and increase the
ability to identify multiple characteristic movement statistics as emergent responses to
the complex spatial heterogeneity of seascapes and biological distributions (Mueller &
Fagan 2008; Nathan et al. 2008; Schick et al. 2008). For example, an IBM has been
applied to test if change in seascape composition and conﬁguration inﬂuenced
predator-prey interactions and cohort size for a group of settling juvenile blue crabs
(Callinectes sapidus) (Hovel & Regan 2008; see also Chapter 8 in this book). IBM
models incorporating diﬀerent movement simulations and states can reveal which
combination of spatial patterns, organism internal state, dispersal abilities facilitate or
impede movement across seascapes (Morales et al. 2005; Vergara et al. 2015). Other
examples in terrestrial studies have combined artiﬁcial neural networks with IBMs to
model complex animal movement (Schick et al. 2008). Individual-based neural network
algorithms are generally advantageous because the technique integrates qualitatively
diﬀerent input information can be used to explore how diﬀerent alternative movement
mechanism (e.g., nonoriented, oriented, spatial memory) could induce a variation of
emergent patterns under diﬀerent seascape scenarios (Mueller & Fagan 2008; Nathan
et al. 2008; Schick et al. 2008). Hence, the parameterisation of this type of model with
identiﬁed and quantiﬁed behavioural responses to seascape structure could help deter-
mine how movement mechanisms interplay to inﬂuence the functional connectivity,
dispersion and persistence of marine populations within spatially dynamic seascapes.
7.5 Implications of Animal-Seascape Understanding
for Marine Stewardship
Advances in our understanding of animal-seascape relationships will have many bene-
ﬁts to ecosystem-based management including informing the design of both static and
dynamic marine protected areas (Hyrenbach et al. 2000; Game et al. 2009; Pittman et al.
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216 Seascape Ecology
2014; Espinoza et al. 2015; Lea et al. 2016), building coherence into network design
(Weeks et al. 2016), as well as incorporating key animal movement pathways, ‘blue cor-
ridors’, or ‘blueways’, into marine spatial planning (Martin et al. 2006; Pendoley et al.
2014; Brenner et al. 2016). It is widely acknowledged that connectivity among protected
areas is an essential part of ecological coherence. In the Gulf of Mexico and in the
Baltic region, migratory pathways have been identiﬁed to enhance spatial planning. For
example, to understand migratory pathways across the Gulf of Mexico, identify priority
spaces and relevant scales for management, e Nature Conservancy synthesised and
mapped large volumes of satellite telemetry data for highly mobile species (marine ﬁsh,
sea turtles, marine mammals and birds) (Brenner et al. 2016). e study identiﬁed partial
migratory corridors and movement density within the corridors, occurrence hotspots,
locations of aggregations and multispecies aggregations and areas of potential threat to
migratory movements from human activities (Figure 7.10).
Furthermore, using animal movements to improve our scaling of habitat will have
practical implications for the identiﬁcation and mapping of habitat concepts important
for legislated protection, such as essential ﬁsh habitat and critical habitat. Understand-
ing functional connectivity between multiple patch types across the seascape can guide
spatial prioritisation activities in conservation planning and ensure that decision makers
are aware of the consequences of the potential disruptions to connectivity of environ-
mental change. Fortunately, connectivity can also be facilitated by management actions
such as prioritizing protection for connected seascapes (Nagelkerken et al. 2015; see
also Chapter 9 in this book). Likewise, patterns of ﬁsh movements have application to
Number of species
(n = 10)
Figure 7.10 Overlay of marine species corridors for 10 marine species (representing ﬁshes, sea turtles
and marine mammals) and protected and management areas in the Gulf of Mexico. Source:Provided
with permission by Jorge Brenner of The Nature Conservancy (Brenner et al. 2016).
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Animal Movements through the Seascape 217
the design and evaluation of habitat restoration and habitat creation projects for marine
species. For example, knowledge of ﬁsh movement patterns can be used as a functional
metric for assessing the performance of restoration projects for estuarine ﬁsh habitat
(Freedman et al. 2016).
For ecology, we expect that many new insights into the relationships between
marine animals and the seascape will emerge from the application of spatial technolo-
gies for both mapping individual animal movements and mapping the surrounding
seascape structure. New developments in home range estimators, particularly those
that incorporate time, network analyses using graph theoretic approaches, dynamic
individual-based models combined with advances in animal tracking technologies
and habitat mapping have greatly facilitated the quantiﬁcation of spatial patterns in
movement ecology. Many interesting and useful questions can be addressed through
integration of movement ecology within seascape ecology, particularly studies that
link seascape patterning to movement behaviour and the consequences for organism
biology such as growth, energetics, condition and survival. Studies are needed to
understand the behavioural movement responses to structural features, such as:
corridors of favourable patch composition and gaps of unfavourable patch compo-
sition; movement responses to boundary structure; the use of seascape features as
navigational aids; the importance of cognitive mapping of the seascape in marine
animal movements; and spatial patterns that facilitate or restrict connectivity, which
is required to complete the life cycle. Finally, it is worth recognizing that researchers
studying moving objects, such as ships, cars or people, face similar challenges in data
analysis, interpretation and visualisation suggesting that we may beneﬁt from the
review and application of approaches and tools from these other disciplines to advance
our knowledge of the how, why, where and when of animal movements through
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