90
missions are detected by a hydrophone in the receivers.
However, the detection efficiency, i.e., the attenuation of
sound, varies between different environments and water conditions, which represent a major limitation of acoustic telemetry (Donaldson et al. 2014). Often, multiple acoustic
receivers are organized in a comprehensive array or network
to cover the pre-defined study area, enabling the tracking of
each individual’s movements. The data are stored within the
receiver station and can either be downloaded by recovering
the device or via wireless technology (Dagorn et al. 2007;
Donaldson et al. 2014; Hussey et al. 2015). Such acoustic
receivers can also be installed on mobile platforms, such as
predators, to detect encounters with tagged individuals. This
means that interactions between a predator and its prey, as
well as spatiotemporal patterns of predator and prey distribution can be studied (Lidgard et al. 2014).
Movement Ecology
Movement is a fundamental characteristic of many species,
and as such, plays an important role in the survival and
reproduction of individuals. This, in turn, affects the structure and dynamics of populations and ecosystems. Therefore,
to manage marine ecosystems properly, it is imperative to
understand the causes, patterns, mechanisms, and consequences of individual movement. Data derived from biotelemetry can provide insight into animals’ movement
ecology and show interactions within the ecosystem they
inhabit (Cagnacci et al. 2010). This enables a mechanistic
understanding of movement ecology, including foraging
behavior and seasonal migration. However, interpreting biotelemetry data remains a challenge in certain applications,
(e.g., habitat modelling), due to the inherent features of
telemetry data. These features include spatial and temporal
auto-correlation, uneven sampling intervals, uneven sampling effort across individuals and uneven detectability
across different habitats (Aarts et al. 2008). Choosing an
appropriate analytical approach for the respective research
questions is, therefore, crucial. An overview of the currently
available methods is described in Carter et al. (2016).
Foraging Behavior
Optimal foraging theory predicts that every individual strives
to minimize its foraging effort, while maximizing its foraging success to assure its survival and reproduction—the drivers for natural selection (MacArthur and Pianka 1966;
Schoener 1971; Pyke 1984). For instance, Antarctic fur seals
have distinct foraging strategies, with associated trade-offs
related to habitat availability, travel costs, prey accessibility
and prey quality (Arthur et al. 2016). We can differentiate
between top predators that are central place foragers (CPFs),
which regularly return to a specific location between foraging trips to feed young, store food, or rest, and thus face spatial constraints on foraging (Orians and Pearson 1979), and
roaming foragers, a term we use for all non-CPFs. CPFs prefer foraging habitat near their central place, because, when
travel time between the central site and the prey resource
increases, their net energy gain decreases (Andersson 1978).
For both CPFs and roaming foragers, efficiency is determined by the trade-offs between energy expenditures and
gains (Shoji et al. 2016). Individuals may further optimize
their foraging efficiency by concentrating on a specific prey
type or the exploitation of a specific habitat. In stable environments, specialization in foraging strategies can be highly
advantageous as individuals decrease search and handling
costs, and reduce their niche overlap with other individuals,
thus minimizing competition (Bolnick et al. 2003). A potential cost of specialization is that individuals may lack the
flexibility to respond to environmental change (Bolnick et al.
2003; McIntyre et al. 2017). Within the light of rapid environmental change as a result of anthropogenic activities,
behavioral plasticity in foraging behavior becomes particularly important. Climate change may affect top predators’
geographic ranges and energy balance by altering the distribution and abundance of prey populations (MacLeod 2009;
Hazen et al. 2013). Additionally, food resources may change
at the local scale due to other anthropogenic influences.
Local depletion and competition with fisheries can be
regarded as general examples of the latter (Lidgard et al.
2014; Cronin et al. 2016). A topical issue is the future
European Union ban on fisheries discards, currently providing a major resource for a wide range of seabird species
(Garthe et al. 1996; Furness 2003; Votier et al. 2010, 2013;
Bicknell et al. 2013; Krüger et al. 2017).
Specialization may require specific foraging behaviors
(Patrick and Weimerskirch 2014). Single individuals may
apply multiple foraging strategies or only a single one, or they
may temporarily switch between strategies. When individuals
use different foraging strategies, their choice may depend on
a range of intrinsic and extrinsic factors (Patrick et al. 2013;
Camphuijsen et al. 2015). These factors are not mutually
exclusive, as the underlying processes influence each other
notably. As such, it is difficult to identify the causal effects of
each of these factors separately. Therefore, a multifactorial
approach is required to investigate their influence on foraging
ecology. Integrating multiple types of data at different scales
enhances our understanding of a predator’s foraging behavior
as well as the circumstances that lead to foraging success, as
these can differ not only between and within species, but even
within individuals depending on the conditions (Austin et al.
2006b; Watanabe and Takahashi 2013; McIntyre et al. 2017).
Intrinsic factors can strongly influence foraging efficiency.
One of these factors is age or experience. It is generally
B. C. Heylen and D. A. Nachtsheim
missions are detected by a hydrophone in the receivers.
However, the detection efficiency, i.e., the attenuation of
sound, varies between different environments and water conditions, which represent a major limitation of acoustic telemetry (Donaldson et al. 2014). Often, multiple acoustic
receivers are organized in a comprehensive array or network
to cover the pre-defined study area, enabling the tracking of
each individual’s movements. The data are stored within the
receiver station and can either be downloaded by recovering
the device or via wireless technology (Dagorn et al. 2007;
Donaldson et al. 2014; Hussey et al. 2015). Such acoustic
receivers can also be installed on mobile platforms, such as
predators, to detect encounters with tagged individuals. This
means that interactions between a predator and its prey, as
well as spatiotemporal patterns of predator and prey distribution can be studied (Lidgard et al. 2014).
Movement Ecology
Movement is a fundamental characteristic of many species,
and as such, plays an important role in the survival and
reproduction of individuals. This, in turn, affects the structure and dynamics of populations and ecosystems. Therefore,
to manage marine ecosystems properly, it is imperative to
understand the causes, patterns, mechanisms, and consequences of individual movement. Data derived from biotelemetry can provide insight into animals’ movement
ecology and show interactions within the ecosystem they
inhabit (Cagnacci et al. 2010). This enables a mechanistic
understanding of movement ecology, including foraging
behavior and seasonal migration. However, interpreting biotelemetry data remains a challenge in certain applications,
(e.g., habitat modelling), due to the inherent features of
telemetry data. These features include spatial and temporal
auto-correlation, uneven sampling intervals, uneven sampling effort across individuals and uneven detectability
across different habitats (Aarts et al. 2008). Choosing an
appropriate analytical approach for the respective research
questions is, therefore, crucial. An overview of the currently
available methods is described in Carter et al. (2016).
Foraging Behavior
Optimal foraging theory predicts that every individual strives
to minimize its foraging effort, while maximizing its foraging success to assure its survival and reproduction—the drivers for natural selection (MacArthur and Pianka 1966;
Schoener 1971; Pyke 1984). For instance, Antarctic fur seals
have distinct foraging strategies, with associated trade-offs
related to habitat availability, travel costs, prey accessibility
and prey quality (Arthur et al. 2016). We can differentiate
between top predators that are central place foragers (CPFs),
which regularly return to a specific location between foraging trips to feed young, store food, or rest, and thus face spatial constraints on foraging (Orians and Pearson 1979), and
roaming foragers, a term we use for all non-CPFs. CPFs prefer foraging habitat near their central place, because, when
travel time between the central site and the prey resource
increases, their net energy gain decreases (Andersson 1978).
For both CPFs and roaming foragers, efficiency is determined by the trade-offs between energy expenditures and
gains (Shoji et al. 2016). Individuals may further optimize
their foraging efficiency by concentrating on a specific prey
type or the exploitation of a specific habitat. In stable environments, specialization in foraging strategies can be highly
advantageous as individuals decrease search and handling
costs, and reduce their niche overlap with other individuals,
thus minimizing competition (Bolnick et al. 2003). A potential cost of specialization is that individuals may lack the
flexibility to respond to environmental change (Bolnick et al.
2003; McIntyre et al. 2017). Within the light of rapid environmental change as a result of anthropogenic activities,
behavioral plasticity in foraging behavior becomes particularly important. Climate change may affect top predators’
geographic ranges and energy balance by altering the distribution and abundance of prey populations (MacLeod 2009;
Hazen et al. 2013). Additionally, food resources may change
at the local scale due to other anthropogenic influences.
Local depletion and competition with fisheries can be
regarded as general examples of the latter (Lidgard et al.
2014; Cronin et al. 2016). A topical issue is the future
European Union ban on fisheries discards, currently providing a major resource for a wide range of seabird species
(Garthe et al. 1996; Furness 2003; Votier et al. 2010, 2013;
Bicknell et al. 2013; Krüger et al. 2017).
Specialization may require specific foraging behaviors
(Patrick and Weimerskirch 2014). Single individuals may
apply multiple foraging strategies or only a single one, or they
may temporarily switch between strategies. When individuals
use different foraging strategies, their choice may depend on
a range of intrinsic and extrinsic factors (Patrick et al. 2013;
Camphuijsen et al. 2015). These factors are not mutually
exclusive, as the underlying processes influence each other
notably. As such, it is difficult to identify the causal effects of
each of these factors separately. Therefore, a multifactorial
approach is required to investigate their influence on foraging
ecology. Integrating multiple types of data at different scales
enhances our understanding of a predator’s foraging behavior
as well as the circumstances that lead to foraging success, as
these can differ not only between and within species, but even
within individuals depending on the conditions (Austin et al.
2006b; Watanabe and Takahashi 2013; McIntyre et al. 2017).
Intrinsic factors can strongly influence foraging efficiency.
One of these factors is age or experience. It is generally
B. C. Heylen and D. A. Nachtsheim