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Chapter 7: Animal Movements through the Seascape: Integrating Movement Ecology with Seascape Ecology


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The relationship between environmental patterning in the marine environment and the movements of organisms is central to our understanding of ecological structure, function and dynamics. Ecologists and marine managers increasingly recognise that knowledge on movements is a critical yet little understood process affecting the distribution of populations, their composition, gene flow, response to disturbance and management strategies. Rapid technological advances in marine remote sensing and digital telemetry now provide an unprecedented opportunity for ecologists to visualise and analyse the movements of real animals through multi-dimensional seascapes. This new understanding of the behavioural interactions between animals and the marine environment is at the frontier of seascape ecology and is set to reveal important new insights into the relationship between marine animals and their environment. 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.
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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
7.1 Introduction
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
habitat types.
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,
L1, 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).
Box 7.1
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 L1components 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
and space.
100 km2
100 m2
100 cm2
Scale (L)
Figure 7.2 Relevance of a hierarchical framework to the definition of an environment for highly
mobile animals.
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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. 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
scaleeectsonspecies. 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). 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
High density
Low density
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:
range-kernel/ (accessed 25 May 2017).
home-range area, while the 50% surface is taken to represent the core area of use
(Figure 7.3).
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).
Convex hull
(minimum convex polygon)
(local convex hull)
Kernel density estimate
(location based)
Kernel density estimate
(movement based)
Figure 7.6 Differences in precision of utilisation distribution using four different home-range
estimation techniques. Source:
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
600 000
580 000
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Northing (m)
100 000 120 000 140 000
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120 000140 000
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Easting (m)
120 000 140 000
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60 000 80 000
40 000
20 000
Time (s)
Easting (m)
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)
0.5–1.0 m
1. 5
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
7. 5
1.0–1.5 m 1.5–2.0 m 2.0–2.5 m 2.5–3.0 m
1 km
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
food resources.
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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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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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) (
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)
Protected and
managed areas
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
the seascape.
Addicott JF, Aho JM, Antolin MF, Padilla DK, Richardson JS, Soluk DA (1987) Ecological
neighbourhoods: scaling environmental patterns. Oikos 1: 340–346.
Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, Matthysen E
(2003) e application of ‘least-cost’ modelling as a functional landscape model.
Landscape and Urban Planning 64(4): 233–247.
Allen TFH, Starr TB (eds) (1982) Hierarchy: Perspectives for Ecological Complexity.
University of Chicago Press, Chicago, IL.
Amélineau F, Péron C, Lescroël A, Authier M, Provost P, Grémillet D (2014) Windscape
and tortuosity shape the flight costs of northern gannets. Journal of Experimental
Biology 217(6): 876–885.
Anderson AB, Jenkins CN (2006) Applying nature’s design: Corridors as a strategy for
biological conservation. Columbia University Press, New York, NY.
Andrienko N, Andrienko G (2013) Visual analytics of movement: An overview of methods,
tools and procedures. Information Visualization 12(1): 3–24.
Bailey H, Mate BR, Palacios DM, Irvine L, Bograd SJ, Costa DP (2009) Behavioural
estimation of blue whale movements in the Northeast Pacific from state-space model
analysis of satellite tracks. Endangered Species Research 10: 93–106.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 218
k k
218 Seascape Ecology
Barrell J, Grant J (2013) Detecting hot and cold spots in a seagrass landscape using local
indicators of spatial association. Landscape Ecology 28(10): 2005–2018.
Barry JP, Dayton PK (1991) Physical heterogeneity and the organization of marine
communities. In Kolasa J. & Pickett ST (eds) Ecological heterogeneity. Springer, New
York, NY, pp. 270–320.
Benhamou S, Cornélis D (2010) Incorporating movement behaviour and barriers to
improve kernel home range space use estimates. e Journal of Wildlife Management 74:
Benson SR, Eguchi T, Foley DG, Forney KA, Bailey H, Hitipeuw C, Samber BP, Tapilatu RF,
Rei V, Ramohia P, Pita J (2011) Large-scale movements and high-use areas of western
Pacific leatherback turtles, Dermochelys coriacea. Ecosphere 2(7): 1–27.
Betts MG, Fahrig L, Hadley AS, Halstead KE, Bowman J, Robinson WD, Wiens JA,
Lindenmayer DB (2014) A species-centered approach for uncovering generalities in
organism responses to habitat loss and fragmentation. Ecography 37(6): 517–527.
Biesinger Z, Bolker BM, Marcinek D, Grothues TM, Dobarro JA, Lindberg WJ (2013)
Testing an autonomous acoustic telemetry positioning system for fine-scale space use in
marine animals. Journal of Experimental Marine Biology and Ecology 448: 46–56.
Block BA, Jonsen ID, Jorgensen SJ, Winship AJ, Shaffer SA, Bograd SJ, Hazen EL, Foley DG,
Breed GA, Harrison AL, Ganong JE (2011) Tracking apex marine predator movements in
a dynamic ocean. Nature 475(7354): 86–90.
Boström C, Pittman SJ, Simenstad C, Kneib RT (2011) Seascape ecology of coastal biogenic
habitats: advances, gaps, and challenges. Marine Ecology Progress Series 427: 191–217.
Brenner J, Voight C, Mehlman D (2016) Migratory Species in the Gulf of Mexico Large
Marine Ecosystem: Pathways, reats and Conservation. e Nature Conservancy,
Arlington, VA.
Brooker RM, Feeney WE, White JR, Manassa RP, Johansen JL, Dixson DL (2016) Using
insights from animal behaviour and behavioural ecology to inform marine conservation
initiatives. Animal Behaviour 10.1016/j.anbehav.2016.03.012 (accessed 25 May 2017).
Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP (2013) Observing the unwatchable
through acceleration logging of animal behaviour. Animal Biotelemetry 1: 20.
Brown JS, Kotlier BP (2004) Hazardous duty pay and the foraging cost of predation.
Ecology Letters 7(10): 999–1014.
Brownscombe JW, Gutowsky LF, Danylchuk AJ, Cooke SJ (2014) Foraging behaviour and
activity of a marine benthivorous fish estimated using tri-axial accelerometer biologgers.
Marine Ecology Progress Series 505: 241–251.
Burt de Perera TB, Holbrook RI, Davis V (2016) e representation of three-dimensional
space in fish. Frontiers in Behavioural Neuroscience 10: 40.
Calabrese JM, Fagan WF (2004) A comparison-shopper’s guide to connectivity metrics.
Frontiers in Ecology and the Environment 2(10): 529–536.
Connolly RM, Hindell JS (2006) Review of nekton patterns and ecological processes in
seagrass landscapes. Estuarine, Coastal and Shelf Science 68(3): 433–444.
Creel S, Christianson D (2008) Relationships between direct predation and risk effects.
Trends in Ecology and Evolution 23(4): 194–201.
os T, Finn DS, Gillanders BM, Hadwen WL,
Harrod C, Hermoso V, Jennings S, Kilada RW (2015) Human effects on ecological
connectivity in aquatic ecosystems: Integrating scientific approaches to support
management and mitigation. Science of the Total Environment 534: 52–64.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 219
k k
Animal Movements through the Seascape 219
Crooks KR, Sanjayan M (2006) Connectivity conservation: maintaining connections for
nature. In Crooks KR, Sanjayan M (eds) Connectivity Conservation. Cambridge
University Press, Cambridge, pp. 1–20.
Crowder L, Norse E (2008) Essential ecological insights for marine ecosystem-based
management and marine spatial planning. Marine Policy 32(5): 772–778.
Dale MR, Fortin MJ (2010) From graphs to spatial graphs. Annual Review of Ecology,
Evolution, and Systematics 41: 21–38.
Dance MA, Rooker JR (2015) Habitat-and bay-scale connectivity of sympatric fishes in an
estuarine nursery. Estuarine, Coastal and Shelf Science 167: 447–457.
Demšar U, Buchin K, Cagnacci F, Safi K, Speckmann B, Van de Weghe N, Weiskopf D,
Weibel R (2015a). Analysis and visualisation of movement: an interdisciplinary review.
Movement Ecology 3(1): 5.
Demšar U, Buchin K, van Loon EE, Shamoun-Baranes J (2015b) Stacked space-time
densities: a geovisualisation approach to explore dynamics of space use over time.
Geoinformatica 19: 85–115.
Demšar U, van Loon E (2013) Visualising movement: the seagull. Significance 10:
Demšar U, Virrantaus K (2010) Space-time density of trajectories: exploring
spatio-temporal patterns in movement data. International Journal of Geographical
Information Science 24(10): 1527–1542.
Dodson JJ (1988) e nature and role of learning in the orientation and migratory
behaviour of fishes. Environmental Biology of Fishes 23(3): 161–182.
Downs JA, Horner MW, Tucker AD (2011) Time-geographic density estimation for home
range analysis. Annals of GIS 17(3): 163–171.
Downs JA, Horner MW, Hyzer G, Lamb D, Loraamm R (2015) Voxel-based probabilistic
space-time prisms for analysing animal movements and habitat use. International
Journal of Geographical Information Science 28: 875–890.
Dunning JB, Danielson BJ, Pulliam HR (1992) Ecological processes that affect populations
in complex landscapes. Oikos 1: 169–175.
Eckert SA, Moore JE, Dunn DC, van Buiten RS, Eckert KL, Halpin PN (2008) Modelling
loggerhead turtle movement in the Mediterranean: importance of body size and
oceanography. Ecological Applications 18(2): 290–308.
Edelhoff H, Signer J, Balkenhol N (2016) Path segmentation for beginners: an overview of
current methods for detecting changes in animal movement patterns. Movement
Ecology 4(1): 21.
Eiler JH, Masuda MM, Spencer TR, Driscoll RJ, Schreck CB (2014) Transactions of the
American Fisheries Society 143(6): 1476–1507.
Espinoza M, Farrugia TJ, Webber DM, Smith F, Lowe CG (2011) Testing a new acoustic
telemetry technique to quantify long-term, fine-scale movements of aquatic animals.
Fisheries Research 108: 364–371.
Espinoza M, Lédée EJ, Simpfendorfer CA, Tobin AJ, Heupel MR (2015) Contrasting
movements and connectivity of reef-associated sharks using acoustic telemetry:
implications for management. Ecological Applications 25(8): 2101–2118.
Fauchald P (1999) Foraging in a hierarchical patch system. American Naturalist 153:
Fauchald P, Erikstad KE, Skarsord H (2000) Scale-dependent predator-prey interactions:
the hierarchical spatial distribution of seabirds and prey. Ecology 81: 773–783.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 220
k k
220 Seascape Ecology
Fauchald P, Tveraa T (2006) Hierarchical patch dynamics and animal movement pattern.
Oecologia 149(3): 383–395.
Finn JT, Brownscombe JW, Haak CR, Cooke SJ, Cormier R, Gagne T, Danylchuk AJ (2014)
Applying network methods to acoustic telemetry data: modelling the movements of
tropical marine fishes. Ecological Modelling 293: 139–149.
Fisher HI (1971) Experiments on homing in Laysan albatrosses, Diomedea immutabilis.
e Condor 73(4): 389–400.
Fodrie FJ, Yeager LA, Grabowski JH, Layman CA, Sherwood GD, Kenworthy MD (2015)
Measuring individuality in habitat use across complex landscapes: approaches,
constraints, and implications for assessing resource specialization. Oecologia 178(1):
Foley MM, Halpern BS, Micheli F, Armsby MH, Caldwell MR, Crain CM, Prahler E, Rohr
N, Sivas D, Beck MW, Carr MH (2010) Guiding ecological principles for marine spatial
planning. Marine Policy 34(5): 955–966.
Freedman RM, Espasandin C, Holcombe EF, Whitcraft CR, Allen BJ, Witting D, Lowe CG
(2016) Using movements and habitat utilization as a functional metric of restoration for
estuarine juvenile fish habitat. Marine and Coastal Fisheries 8(1): 361–373.
Furey NB, Dance MA, Rooker JR (2013) Fine-scale movements and habitat use of juvenile
southern flounder Paralichthys lethostigma in an estuarine seascape. Journal of Fish
Biology 82(5): 1469–1483.
Game ET, Grantham HS, Hobday AJ, Pressey RL, Lombard AT, Beckley LE, Gjerde K,
Bustamante R, Possingham HP, Richardson AJ (2009) Pelagic protected areas: the
missing dimension in ocean conservation. Trends in Ecology and Evolution 24: 360–369.
Getz WM, Saltz D (2008) A framework for generating and analyzing movement paths on
ecological landscapes. Proceedings of the National Academy of Sciences 105(49):
Green AL, Maypa AP, Almany GR, Rhodes KL, Weeks R, Abesamis RA, Gleason MG,
Mumby PJ, White AT (2015) Larval dispersal and movement patterns of coral reef fishes,
and implications for marine reserve network design. Biological Reviews 90(4):
Grober-Dunsmore R, Pittman SJ, Caldow C, Kendall MS & Frazer TK (2009) A landscape
ecology approach for the study of ecological connectivity across tropical marine
seascapes. In Nagelkerken I (ed.) Ecological Connectivity among Tropical Coastal
Ecosystems. Springer, Heidelberg, pp. 493–530.
Gurarie E, Bracis C, Delgado M, Meckley TD, Kojola I, Wagner CM (2016) What is the
animal doing? Tools for exploring behavioural structure in animal movements. Journal of
Animal Ecology 85(1): 69–84.
Hammerschlag N, Luo J, Irschick DJ, Ault JS (2012) A Comparison of spatial and
movement patterns between sympatric predators: Bull Sharks (Carcharhinus leucas) and
Atlantic Tarpon (Megalops atlanticus). PLoS ONE 7(9): e45958.
Hammerschlag-Peyer CM, Layman CA (2010) Intrapopulation variation in habitat use by
two abundant coastal fish species. Marine Ecology Progress Series 415: 211–220.
Hatcher BG, Imberger J, Smith SV (1987) Scaling analysis of coral reef systems: an
approach to problems of scale. Coral Reefs 5(4): 171–181.
Haury LR, McGowan JA, Wiebe PH (1978) Patterns and processes in the time-space scales
of plankton distributions. In Steele JH (ed.) Spatial Pattern in Plankton Communities.
Plenum Press, New York, NY.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 221
k k
Animal Movements through the Seascape 221
Hays GC, Scott R (2013) Global patterns for upper ceilings on migration distance in sea
turtles and comparisons with fish, birds and mammals. Functional Ecology 27(3):
Heithaus MR, Wirsing AJ, Burkholder D, omson J, Dill LM (2009) Towards a predictive
framework for predator risk effects: the interaction of landscape features and prey escape
tactics. Journal of Animal Ecology 78(3): 556–562.
Heupel M, Semmens J, Hobday A (2006) Automated acoustic tracking of aquatic animals:
scales, design and deployment of listening station arrays. Marine and Freshwater
Research 57: 1–13.
Heupel MR, Simpfendorfer CA, Hueter RE (2004) Estimation of shark home ranges using
passive monitoring techniques. Environmental Biology of Fishes 71: 135–142.
Hitt S, Pittman SJ, Brown KA (2011b). Tracking and mapping sun-synchronous migrations
anddielspaceusepatternsofHaemulon sciurus and Lutjanus apodus in the US Virgin
Islands. Environmental Biology of Fishes 92(4): 525–538.
Hitt S, Pittman SJ, Nemeth RS (2011a) Diel movements of fishes linked to benthic seascape
structure in a Caribbean coral reef ecosystem. Marine Ecology Progress Series 427:
Hoechstetter S, Walz U, Dang LH, inh NX (2008) Effects of topography and surface
roughness in analyses of landscape structure: a proposal to modify the existing set of
landscape metrics. Landscape Online 3: 1–4.
Holling CS (1992) Cross-scale morphology, geometry, and dynamics of ecosystems.
Ecological Monographs 62(4): 447–502.
Horodysky AZ, Cooke SJ, Brill RW (2015) Physiology in the service of fisheries science: why
thinking mechanistically matters. Reviews in Fish Biology and Fisheries 25(3): 425–447.
Hovel KA, Regan HM (2008) Using an individual-based model to examine the roles of
habitat fragmentation and behavior on predator–prey relationships in seagrass
landscapes. Landscape Ecology 23(1): 75–89.
KN, Iverson SJ, Kocik JF, Flemming JE (2015) Aquatic animal telemetry: a panoramic
window into the underwater world. Science 348(6240): 1255642.
Hyrenbach KD, Forney KA, Dayton PK (2000) Marine protected areas and ocean basin
management. Aquatic Conservation: Marine and Freshwater Ecosystems 10: 435–458.
Irlandi EA, Crawford MK (1997) Habitat linkages: the effect of intertidal saltmarshes and
adjacent subtidal habitats on abundance, movement, and growth of an estuarine fish.
Oecologia 110(2): 222–230.
Jackson AL, Inger R, Parnell AC, Bearhop S (2011) Comparing isotopic niche widths among
and within communities: SIBER–Stable Isotope Bayesian Ellipses in R. Journal of Animal
Ecology 80(3): 595–602.
Jacoby DM, Brooks EJ, Croft DP, Sims DW (2012) Developing a deeper understanding of
animal movements and spatial dynamics through novel application of network analyses.
Methods in Ecology and Evolution 3(3): 574–583.
Jelbart JE, Ross PM, Connolly RM (2006) Edge effects and patch size in seagrass landscapes:
an experimental test using fish. Marine Ecology Progress Series 319: 93–102.
Jelinski DE (2015) On a landscape ecology of a harlequin environment: the marine
landscape. Landscape Ecology 30: 1–6.
Jenerette GD, Shen W (2012) Experimental landscape ecology. Landscape Ecology 27(9):
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 222
k k
222 Seascape Ecology
Jonsen ID, Flemming JM, Myers RA (2005) Robust state–space modeling of animal
movement data. Ecology 86(11): 2874–2880.
Johnson AR, Wiens JA, Milne BT, Crist TO (1992) Animal movements and population
dynamics in heterogeneous landscapes. Landscape Ecology 7(1): 63–75.
Kranstauber B, Kays R, LaPoint SD, Wikelski M, Safi K (2012) A dynamic Brownian bridge
movement model to estimate utilization distributions for heterogeneous animal
movement. Journal of Animal Ecology 81(4): 738–746.
Kuhn CE, Johnson DS, Ream RR, Gelatt TS (2009) Advances in the tracking of marine
species: using GPS locations to evaluate satellite track data and a continuous-time
movement model. Marine Ecology Progress Series 393: 97–109.
Large SI, Fay G, Friedland KD, Link JS (2015) Critical points in ecosystem responses to
fishing and environmental pressures. Marine Ecology Progress Series 521: 1–7.
Lausch A, Schmidt A, Tischendorf L (2015) Data mining and linked open data: New
perspectives for data analysis in environmental research. Ecological Modelling 295: 5–17.
Layman CA, Quattrochi JP, Peyer CM, Allgeier JE (2007) Niche width collapse in a resilient
top predator following ecosystem fragmentation. Ecology Letters 10(10): 937–944.
Lea JS, Humphries NE, von Brandis RG, Clarke CR, Sims DW (2016) Acoustic telemetry
and network analysis reveal the space use of multiple reef predators and enhance marine
protected area design. Proceedings Royal Society B 283(1834) 20160717.
Lédée EJ, Heupel MR, Tobin AJ, Knip DM, Simpfendorfer CA (2015) A comparison
between traditional kernel-based methods and network analysis: an example from two
nearshore shark species. Animal Behaviour 103: 17–28.
Levin SA (1992) e problem of pattern and scale in ecology: e Robert H. MacArthur
award lecture. Ecology 73(6): 1943–1967.
Lima SL, Zollner PA (1996) Towards a behavioural ecology of ecological landscapes. Trends
in Ecology and Evolution 11(3): 131–135.
Luschi P (2013) Long-distance animal migrations in the oceanic environment: orientation
and navigation correlates. ISRN Zoology Article ID 631839.
Lyons AJ, Turner WC, Getz WM (2013) Home range plus: a space-time characterization of
movement over real landscapes. Movement Ecology 1: 1–14.
Marquet PA, Fortin MJ, Pineda J, Wallin DO, Clark J, Wu Y, Bollens S, Jacobi CM, Holt RD
(1993) Ecological and evolutionary consequences of patchiness: a marine-terrestrial
perspective. In Patch dynamics. Springer, Berlin, pp. 277–304.
Martin G, Makinen A, Andersson Å, Dinesen GE, Kotta J, Hansen J, Herkül K, Ockelmann
KW, Nilsson P, Korpinen S (2006) Literature review of the ‘Blue Corridors’ concept and
its applicability to the Baltic Sea. BALANCE project.
(accessed 25 May 2017).
McAlpine CA, Grigg GC, Mott JJ, Sharma P (1999) Influence of landscape structure on
kangaroo abundance in a disturbed semi-arid woodland of Queensland. e Rangeland
Journal 21(1): 104–134.
McGarigal K, Cushman SA (2002) Comparative evaluation of experimental approaches to
the study of habitat fragmentation effects. Ecological Applications 12(2): 335–345.
McGarigal K, Cushman SA (2005) e gradient concept of landscape structure. In Wiens J,
Moss M (eds) Issues and perspectives in landscape ecology. Cambridge University Press,
Cambridge, pp 112–119.
McGarigal K, Tagil S, Cushman SA (2009) Surface metrics: an alternative to patch metrics
for the quantification of landscape structure. Landscape Ecology 24(3): 433–450.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 223
k k
Animal Movements through the Seascape 223
McIntyre NE, Wiens JA (1999) Interactions between landscape structure and animal
behavior: the roles of heterogeneously distributed resources and food deprivation on
movement patterns. Landscape Ecology 14(5): 437–447.
McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model
connectivity in ecology, evolution, and conservation. Ecology 89(10): 2712–2724.
Meentemeyer V (1989) Geographical perspectives of space, time and scale. Landscape
Ecology 3: 163–173.
Moorcroft PR, Lewis MA, Crabtree RL (2006) Mechanistic home range models capture
spatial patterns and dynamics of coyote territories in Yellowstone. Proceedings of the
Royal Society of London B: Biological Sciences 273(1594): 1651–1659.
Morales JM, Ellner SP (2002) Scaling up animal movements in heterogeneous landscapes:
the importance of behaviour. Ecology 83(8): 2240–2247.
Morales JM, Fortin D, Frair JL, Merrill EH (2005) Adaptive models for large herbivore
movements in heterogeneous landscapes. Landscape Ecology 20(3): 301–316.
Morales JM, Haydon DT, Frair J, Holsinger KE, Fryxell JM (2004) Extracting more out of
relocation data: building movement models as mixtures of random walks. Ecology 85(9):
Mueller T, Fagan WF (2008) Search and navigation in dynamic environments: from
individual behaviours to population distributions. Oikos 117(5): 654–664.
Murchie KJ, Cooke SJ, Danylchuk AJ, Danylchuk SE, Goldberg TL, Suski CD, Philipp DP
(2011) ermal biology of bonefish (Albula vulpes) in Bahamian coastal waters and tidal
creeks: an integrated laboratory and field study. Journal of ermal Biology 36(1):
Nagelkerken I, Faunce CH (2008) What makes mangroves attractive to fish? Use of artificial
units to test the influence of water depth, cross-shelf location, and presence of root
structure. Estuarine, Coastal and Shelf Science 79(3): 559–565.
Nagelkerken I, Sheaves M, Baker R, Connolly RM (2015) e seascape nursery: a novel
spatial approach to identify and manage nurseries for coastal marine fauna. Fish and
Fisheries 16(2): 362–371.
Nams V (2005) Using animal movement paths to measure response to spatial scale.
Oecologia 143: 179–188.
Nams V (2006) Detecting oriented movement of animals. Animal Behaviour 72(5):
Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE (2008) A
movement ecology paradigm for unifying organismal movement research. Proceedings
of the National Academy of Sciences 105(49): 19052–19059.
Neil DT (2002) Cooperative fishing interactions between Aboriginal Australians and
dolphins in eastern Australia. Anthrozoos 15: 3–18.
Olds AD, Connolly RM, Pitt KA, Maxwell PS (2012) Habitat connectivity improves reserve
performance. Conservation Letters 5(1): 56–63.
Olds AD, Connolly RM, Pitt KA, Pittman SJ, Maxwell PS, Huijbers CM, Moore BR, Albert
S, Rissik D, Babcock RC, Schlacher TA (2016) Quantifying the conservation value of
seascape connectivity: a global synthesis. Global Ecology and Biogeography 25(1): 3–15.
Ovaskainen O, Luoto M, Ikonen I, Rekola H, Meyke E, Kuussaari M (2008) An empirical
test of a diffusion model: predicting clouded apollo movements in a novel environment.
e American Naturalist 171(5): 610–619.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 224
k k
224 Seascape Ecology
Palumbi SR (2004) Marine reserves and ocean neighbourhoods: the spatial scale of marine
populations and their management. Annual Review of Environment and Resources 29:
Papastamatiou YP, Lowe CG, Caselle JE, Friedlander AM (2009) Scale-dependent effects of
habitat on movements and path structure of reef sharks at a predator-dominated atoll.
Ecology 90: 996–1008.
Patton BW, Braithwaite VA (2015) Changing tides: ecological and historical perspectives
on fish cognition. Wiley Interdisciplinary Reviews: Cognitive Science 6(2): 159–176.
Pendoley KL, Schofield G, Whittock PA, Ierodiaconou D, Hays GC (2014) Protected
species use of a coastal marine migratory corridor connecting marine protected areas.
Marine Biology 161(6): 1455–1466.
Perry AL, Low PJ, Ellis JR, Reynolds JD (2005) Climate change and distribution shifts in
marine fishes Science 308(5730): 1912–1915.
Pinsky ML, Worm B, Fogarty MJ, Sarmiento JL, Levin SA (2013) Marine taxa track local
climate velocities. Science 341(6151): 1239–1242.
Pittman SJ, Brown KA (2011) Multi-scale approach for predicting fish species distributions
across coral reef seascapes. PloS One 6(5): e20583.
Pittman SJ, McAlpine CA (2003) Movements of marine fish and decapod crustaceans:
process, theory and application. Advances in Marine Biology 44(1): 205–294.
Pittman SJ, Monaco ME, Friedlander AM, Legare B, Nemeth RS, Kendall MS, Poti M, Clark
RD, Wedding LM, Caldow C (2014) Fish with chips: Tracking reef fish movements to
evaluate size and connectivity of Caribbean marine protected areas. PloS One 9: e96028.
Pittman SJ, Olds AD (2015) Seascape ecology of fishes on coral reefs. In Mora C (ed.)
Ecology of Fishes on Coral Reefs. Cambridge University Press, Cambridge, pp. 274–282.
Ries L, Fletcher Jr RJ, Battin J, Sisk TD (2004) Ecological responses to habitat edges:
mechanisms, models, and variability explained. Annual Review of Ecology, Evolution
and Systematics 35: 491–522.
Rolstad J, Løken B, Rolstad E (2000) Habitat selection as a hierarchical spatial process: the
green woodpecker at the northern edge of its distribution range. Oecologia 124(1):
Roughgarden J, Gaines S, Possingham H (1988) Recruitment dynamics in complex life
cycles. Proceedings of the National Academy of Sciences 85: 7418.
Ryall KL, Fahrig L (2006) Response of predators to loss and fragmentation of prey habitat: a
review of theory. Ecology 87(5): 1086–1093.
Schaefer JA, Messier F (1995) Habitat selection as a hierarchy: the spatial scales of winter
foraging by muskoxen. Ecography 18(4): 333–344.
Schick RS, Loarie SR, Colchero F, Best BD, Boustany A, Conde DA, Halpin PN, Joppa LN,
McClellan CM, Clark JS (2008) Understanding movement data and movement
processes: current and emerging directions. Ecology Letters 11(12): 1338–1350.
Schick RS, Roberts JJ, Eckert SA, Halpin PN, Bailey H, Chai F, Shi L, Clark JS (2013) Pelagic
movements of pacific leatherback turtles (Dermochelys coriacea) highlight the role of
prey and ocean currents. Movement Ecology 1: 11.
Schmidt DJ, Crook DA, Macdonald JI, Huey JA, Zampatti BP, Chilcott S, Raadik TA,
Hughes JM (2014) Migration history and stock structure of two putatively diadromous
teleost fishes, as determined by genetic and otolith chemistry analyses. Freshwater
Science 33: 193–206.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 225
k k
Animal Movements through the Seascape 225
Schooley RL, Wiens JA (2003) Finding habitat patches and directional connectivity. Oikos
102: 559–570.
Senft RL, Coughenour MB, Bailey DW, Rittenhouse LR, Sala OE, Swift DM (1987) Large
herbivore foraging and ecological hierarchies. BioScience 37(11): 789–799.
Seuront L (2015) On uses, misuses and potential abuses of fractal analysis in zooplankton
behavioural studies: A review, a critique and a few recommendations. Physica A:
Statistical Mechanics and its Applications 432: 410–434.
Shamoun-Baranes J, van Loon EE, Purves RS, Speckmann B, Weiskopf D, Camphuysen CJ
(2011) Analysis and visualization of animal movement. Biology Letters: rsbl20110764.
Silverman BW (1986) Density Estimation for Statistics and Data Analysis. CRC Press, Boca
Raton, FL.
Simberloff D, Cox J (1987) Consequences and costs of conservation corridors.
Conservation Biology 1(1): 63–71.
Simpfendorfer CA, Olsen EM, Heupel MR, Moland E (2012) ree-dimensional kernel
utilization distributions improve estimates of space use in aquatic animals. Canadian
Journal of Fisheries and Aquatic Sciences 69: 565–572.
Sims DW (2010) Tracking and analysis techniques for understanding free-ranging shark
movements and behaviour. In Carrier JC, Musick JA & Heithaus MR (eds) Sharks and
their relatives II: biodiversity, adaptive physiology, and conservation. CRC Press, Boca
Raton, FL, pp. 351–392.
Sims DW, Quayle VA (1998) Selective foraging behaviour of basking sharks on
zooplankton in a small-scale front. Nature 393: 460–464.
Sims DW, Southall EJ, Humphries NE, Hays GC, Bradshaw CJ, Pitchford JW, James A,
Ahmed MZ, Brierley AS, Hindell MA, Morritt D (2008) Scaling laws of marine predator
search behaviour. Nature 451(7182): 1098–1102.
Sims DW, Witt MJ, Richardson AJ, Southall EJ, Metcalfe JD (2006) Encounter success of
free-ranging marine predator movements across a dynamic prey landscape. Proceedings
of the Royal Society of London B: Biological Sciences 273(1591): 1195–1201.
Southwood TR (1977) Habitat, the templet for ecological strategies? e Journal of Animal
Ecology 46: 337–365.
Steele JH (1988). Scale selection for biodynamic theories. In Rothschild BJ (ed.) Toward a
eory on Biological-Physical Interactions in the World Ocean. Proceedings of the
NATO Advanced Research Workshop, Castéra-Verduzan, France, 1–5 June 1987.
Kluwer Academic Publishers, Dordrecht, pp. 513–526.
Steele JH (1989) e ocean ‘landscape’. Landscape Ecology 3(3): 185–192.
Stommel H (1963) Varieties of oceanographic experience. Science 139: 572–576.
Tang W, Bennett DA (2010) Agent-based modelling of animal movement: A review.
Geography Compass 4(7): 682–700.
Tarjan LM, Tinker MT (2016) Permissible Home Range Estimation (PHRE) in Restricted
Habitats: A new algorithm and an evaluation for sea otters. PLoS One 11(3): e0150547.
ayer GW, Bjorndal KA, Ogden JC, Williams SL, Zieman JC (1984) Role of larger
herbivores in seagrass communities. Estuaries 7(4): 351–376.
Tilley A, López-Angarita J, Turner JR (2013) Effects of scale and habitat distribution on the
movement of the southern stingray Dasyatis americana on a Caribbean atoll. Marine
Ecology Progress Series 482: 169–179.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 226
k k
226 Seascape Ecology
Townsend EC, Fonseca MS (1998) Bioturbation as a potential mechanism influencing
spatial heterogeneity of North Carolina seagrass beds. Marine Ecology Progress Series
169: 123–132.
Tracey JA, Sheppard JK, Lockwood GK, Chourasia A, Tatineni M, Fisher RN, Sinkovits RS
(2014b) Efficient 3D Movement-based Kernel Density Estimator and Application to
Wildlife Ecology. Proceedings of the 2014 Annual Conference on Extreme Science and
Engineering Discovery Environment. Article no. 14. Association for Computing
Machinery, New York, NY.
Tracey JA, Sheppard J, Zhu J, Wei F, Swaisgood RR, Fisher RN (2014a) Movement-based
estimation and visualization of space use in 3D for wildlife ecology and conservation.
PloS One 9: e101205.
Turchin P (1998) Quantitative Analysis of Movement: Measuring and Modelling
Population Redistribution in Animals and Plants. Sinauer Associates Publishers,
Sunderland, MA, p. 396.
Turgeon K, Robillard A, Grégoire J, Duclos V, Kramer DL (2010) Functional connectivity
from a reef fish perspective: behavioural tactics for moving in a fragmented landscape.
Ecology 91(11): 3332–3342.
Urban DL, O’Neill RV, Shugart HH (1987) Landscape ecology. BioScience 37: 119–127.
Urban DL, Minor ES, Treml EA, Schick RS (2009) Graph models of habitat mosaics.
Ecology Letters 12(3): 260–273.
Vergara PM, Saura S, Pérez-Hernández CG, Soto GE (2015) Hierarchical spatial decisions
in fragmented landscapes: Modelling the foraging movements of woodpeckers.
Ecological Modelling 300: 114–122.
Walsh WJ (1985) Reef fish community dynamics on small artificial reefs: the influence of
isolation, habitat structure, and biogeography. Bulletin of Marine Science 36(2): 357–376.
Wedding LM, Lepczyk CA, Pittman SJ, Friedlander AM, Jorgensen S (2011) Quantifying
seascape structure: extending terrestrial spatial pattern metrics to the marine realm.
Marine Ecology Progress Series 427: 219–232.
Weeks R, Green AL, Joseph E, Peterson N, Terk E (2016) Using reef fish movement to
inform marine reserve design. Journal of Applied Ecology 54: 145–152.
Wiens JA (1976) Population responses to patchy environments. Annual Review of Ecology
and Systematics 7(1): 81–120.
Wiens JA (1989) Spatial scaling in ecology. Functional Ecology 3(4): 385–397.
Wiens JA (1997) Metapopulation dynamics and landscape ecology. In Hanski I & Gaggiotti
O. Metapopulation Biology: Ecology, Genetics, and Evolution. Academic Press, San
Diego, CA, pp. 43–62.
Wiens JA, Milne BT (1989) Scaling of ‘landscapes’ in landscape ecology, or, landscape
ecology from a beetle’s perspective. Landscape Ecology 3(2): 87–96.
Wiens JA, Stenseth NC, Van Horne B, Ims RA (1993) Ecological mechanisms and
landscape ecology. Oikos 1: 369–380.
Wilson AD, Brownscombe JW, Krause J, Krause S, Gutowsky LF, Brooks EJ, Cooke SJ
(2015) Integrating network analysis, sensor tags, and observation to understand shark
ecology and behaviour. Behavioural Ecology 26(6): 1577–1586.
Winter S, Yin Z-C (2011) e elements of probabilistic time geography. Geoinformatica 15:
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 227
k k
Animal Movements through the Seascape 227
Wirsing AJ, Heithaus MR, Frid A, Dill LM (2008) Seascapes of fear: evaluating sublethal
predator effects experienced and generated by marine mammals. Marine Mammal
Science 24(1): 1–5.
With KA (1994) Using fractal analysis to assess how species perceive landscape structure.
Landscape Ecology 9: 25–36.
Wright S (1943) Isolation by distance. Genetics 28(2): 114–138.
Wu J, Loucks OL (1995) From balance of nature to hierarchical patch dynamics: a paradigm
shift in ecology. Quarterly Review of Biology 1: 439–466.
Yeager LA, Acevedo CL Layman CA (2012) Effects of seascape context on condition,
abundance, and secondary production of a coral reef fish, Haemulon plumierii. Marine
Ecology Progress Series 462: 231–240.
Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to
movement: a review. Landscape Ecology 27(6): 777–797.
Trim Size: 170mm x 244mm Single Column Pittman c07.tex V2 - 08/28/2017 6:54pm Page 228
k k
... Research on juvenile habitat use of Western Atlantic reef fishes is mainly from field work conducted in the Caribbean (e.g., Adams and Ebersole 2002;Nagelkerken 2009;Huijbers et al. 2013;Pittman et al. 2017). Much of this work has emphasized ontogenetic movements by reef fishes that characteristically settle in habitats such as seagrass meadows, mangrove shorelines, and hardbottom patches. ...
... 5.7). A broader, seascape approach encompassing the full cross-shelf corridor from nearshore settlement habitat to offshore reefs (e.g., Nagelkerken et al. 2015;Pittman et al. 2017) could be further developed for select groups of east Florida fishes. We discuss these topics further in Chaps. ...
Full-text available
Based on new and prior information, 266 species of fishes from 72 families are now recorded from nearshore reef habitats of east Florida. The most species rich families include wrasses and parrotfishes (26), jacks (17), grunts (14), gobies (12), scaly blennies (11), sea basses (10), damselfishes (9), snappers (7) and combtooth blennies (7). We estimate over 30 fish species spawn on or adjacent to nearshore hardbottom reefs. Several different families, including the site-attached blennies and damselfishes, build nests on hardbottom microhabitats. Many species are primarily represented by early life stages. The most prevalent trophic category among species is generalized invertivory followed by planktivory and piscivory. Composition of ichthyofaunal assemblages can differ between the southern and northern areas of mainland east Florida. Potential explanations for differences in prominent species (sailors choice, black margate, hairy blenny) along the north-south gradient of east Florida’s mainland may include species-specific habitat preferences, temporal variation in recruitment, differences in reef structure, differences in sampling designs, species reaching biogeographic distributional limits, and other factors. Relative frequency of fishes in visual censuses reveals approximately 40 species that typify the assemblage; top ranked species are hairy blenny, silver porgy, black margate, slippery dick, sergeant major, porkfish, cocoa damselfish, seaweed blenny, doctorfish, and sailors choice.
Technical Report
Full-text available
This report have been published as a result from project ECOnnect. The aim of the project ECOnnect was to study what the sea in the central Gulf of Bothnia will look like in 2120. This was done by analysing present and future environmental conditions and species distribution, ecosystem services, and connectivity in the central Gulf of Bothnia. This report focuses on the evaluation of existing and future networks of protected areas from a connectivity perspective. The project was financed through the Interreg Botnia- Atlantica cross-border cooperation programme. It started in June 2018 and ended in May 2022.
Technical Report
Full-text available
PS 6 seeks to ensure that project studies take account of all of these factors in an approach that is driven by the understanding of ecological patterns and processes and not project impacts. Once biodiversity value has been defined at an ecologically appropriate scale then AoI can be used to define the potential direct, indirect and cumulative impacts on the biodiversity values that have been determined and quantified. However, no comprehensive guidance on how to define different spatial scales of analyses are provided. Therefore, this document seeks to provide additional guidance to support the determination of ecologically appropriates scales of analysis in order to meet the requirements of PS 6 (and aligned IFI environmental and social risk frameworks). The focus of the guidance is on defining ‘seascape’ and EAAAs, including the relationship of these areas with a project’s AoI.
Full-text available
Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or ‘hidden states’. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis. Electronic supplementary material The online version of this article (doi:10.1186/s40462-016-0086-5) contains supplementary material, which is available to authorized users.
Full-text available
Resource managers use restoration to offset estuarine habitat loss; however, there is limited information about how functionally successful restorations have been, particularly with respect to use by mobile marine predators. Restoration monitoring efforts typically use point-of-capture metrics to assess fish community recovery and habitat use, but this provides little insight on how fish habitat use changes through time. Using translocation experiments, we integrated the movements of California Halibut (Paralichthys californicus), a conservation target species, into a point-of-capture monitoring program in a restored tidal creek estuary. Larger Halibut (>25 cm) were captured more frequently in the main stream Channel, while small Halibut (<25 cm) were typically caught in the innermost marsh creeks. We actively acoustically tracked Halibut (n=20; size range = 26.6-60.5 cm TL) to identify their preferred habitats and challenged these habitat associations with translocations to a different habitats. Large Halibut showed a trend of small LoCoH activity spaces and remained in areas with high water flow and sandy substratum near eelgrass beds. Individuals translocated to Marshes returned to the Channel and exhibited large movement distances from their initial location to their last tracked position; however, Halibut translocated from Marshes to the Channel remained in Channel habitat and had comparatively smaller distances between their first and last tracked points. Large Halibut likely selected the Channel because higher water flow could lead to higher prey concentrations. Small Halibut used Marshes more frequently, likely because Marshes have temperatures thought to maximize growth rates. Our study can serve as a proof of concept that linking point capture and tracking data can provide valuable information to restoration including that Halibut utilize estuaries in a size-segregated manner based on environmental conditions. This suggests that tidal creek estuaries with a variety of channel types and morphologies, like our study site, may be well-suited to support Halibut.
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Marine protected areas (MPAs) are commonly employed to protect ecosystems from threats like overfishing. Ideally, MPA design should incorporate movement data from multiple target species to ensure sufficient habitat is protected. We used long-term acoustic telemetry and network analysis to determine the fine-scale space use of five shark and one turtle species at a remote atoll in the Seychelles, Indian Ocean, and evaluate the efficacy of a proposed MPA. Results revealed strong, species-specific habitat use in both sharks and turtles, with corresponding variation in MPA use. Defining the MPA's boundary from the edge of the reef flat at low tide instead of the beach at high tide (the current best in Seychelles) significantly increased the MPA's coverage of predator movements by an average of 34%. Informed by these results, the larger MPA was adopted by the Seychelles government, demonstrating how telemetry data can improve shark spatial conservation by affecting policy directly.
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The impacts of human activities on the natural world are becoming increasingly apparent, with rapid development and exploitation occurring at the expense of habitat quality and biodiversity. Declines are especially concerning in the oceans, which hold intrinsic value due to their biological uniqueness as well as their substantial sociological and economic importance. Here, we review the literature and investigate whether incorporation of knowledge from the fields of animal behaviour and behavioural ecology may improve the effectiveness of conservation initiatives in marine systems. In particular, we consider (1) how knowledge of larval behaviour and ecology may be used to inform the design of marine protected areas, (2) how protecting species that hold specific ecological niches may be of particular importance for maximizing the preservation of biodiversity, (3) how current harvesting techniques may be inadvertently skewing the behavioural phenotypes of stock populations and whether changes to current practices may lessen this skew and reinforce population persistence, and (4) how understanding the behavioural and physiological responses of species to a changing environment may provide essential insights into areas of particular vulnerability for prioritized conservation attention. The complex nature of conservation programmes inherently results in interdisciplinary responses, and the incorporation of knowledge from the fields of animal behaviour and behavioural ecology may increase our ability to stem the loss of biodiversity in marine environments.
One of the biggest threats to the survival of many plant and animal species is the destruction or fragmentation of their natural habitats. The conservation of landscape connections, where animals, plants, and ecological processes can move freely from one habitat to another, is therefore an essential part of any new conservation or environmental protection plan. In practice, however, maintaining, creating, and protecting connectivity in our increasingly dissected world is a daunting challenge. This fascinating volume provides a synthesis on the current status and literature of connectivity conservation research and implementation. It shows the challenges involved in applying existing knowledge to real-world examples and highlights areas in need of further study. Containing contributions from leading scientists and practitioners, this topical and thought-provoking volume will be essential reading for graduate students, researchers, and practitioners working in conservation biology and natural resource management.
In the last two decades it has become increasingly clear that the spatial dimension is a critically important aspect of ecological dynamics. Ecologists are currently investing an enormous amount of effort in quantifying movement patterns of organisms. Connecting these data to general issues in metapopulation biology and landscape ecology, as well as to applied questions in conservation and natural resource management, however, has proved to be a non-trivial task. This book presents a systematic exposition of quantitative methods for analyzing and modeling movements of organisms in the field. Quantitative Analysis of Movement is intended for graduate students and researchers interested in spatial ecology, including applications to conservation, pest control, and fisheries. Models are a key ingredient in the analytical approaches developed in the book; however, the primary focus is not on mathematical methods, but on connections between models and data. The methodological approaches discussed in the book will be useful to ecologists working with all taxonomic groups. Case studies have been selected from a wide variety of organisms, including plants (seed dispersal, spatial spread of clonal plants), insects, and vertebrates (primarily, fish, birds, and mammals).