97
tence (McMahon et al. 2012). Nevertheless, there are various
valid points in the light of animal welfare that are of concern
and must be addressed appropriately. In addition to the
increase of drag caused by the attachment of an external
device, the extra weight load still represents a major issue for
many marine predators.
We believe that, as a further improvement, the scientific
community should foster the development of new technologies and the on-going miniaturization of tags. To reduce
hydrodynamic drag, Computational Fluid Dynamics (CFD)
has proven to be a crucial tool to assess and improve the
design of bio-telemetry devices (Pavlov et al. 2007; Balmer
et al. 2014). In addition, CFD minimizes the risk of injury
and other potentially adverse effects. Furthermore, potential
capture and attachment methods for different animal groups
should be reviewed and discussed among researchers. It is
evident that tag effects must be more thoroughly investigated, and in particular the focus should be directed towards
metabolic costs and fitness consequences (McIntyre 2015). It
must not be forgotten that we, as scientists, have the responsibility to ensure the wellbeing of the animal. This is not only
relevant from an animal welfare viewpoint, but also from a
scientific position, as we aim to record and understand the
natural behavior of animals in their environment.
Conservation
The marine environment is predicted to face marked changes
by the year 2040 (Brierley and Kingsford 2009), so there is
no time to lose. The current challenges require multidisciplinary collaboration to develop effective strategies that rely
on an improved understanding of the threat that climate
change imposes on species, and the way that it interacts with
their natural coping mechanisms (Dawson et al. 2011).
Telemetry-derived data have a tremendous potential to
inform resource management and conservation, but unfortunately, relatively few examples of their application exist
(Wilson et al. 2015a). The financial cost of collecting telemetry data is relatively high, which makes it essential to critically evaluate the conservation benefit of the currently used
strategies (McGowan et al. 2017).
One of these strategies, is to combine high resolution biotelemetry data with environmental data and data on anthropogenic activities to build individual-based models, with the
aim of predicting habitat use in the (near) future (Grimm
et al. 1999; Stillman et al. 2003; Bestley et al. 2013; Stillman
et al. 2015; van der Vaart et al. 2016). In these models, different scenarios of anticipated anthropogenically-driven environmental change can be simulated. Once predictive models
have been implemented, the outcome can be used for conservation purposes. One extensive study found that modelled
physical changes under the Intergovernmental Panel on
Climate Change A2 scenario predict increased species overlap and a potential for niche compression across the North
Pacific (Hazen et al. 2013). For species that are already under
threat, such changes could exacerbate population declines or
inhibit recovery. These models can undoubtedly be used,
among other applications, to design marine protected areas
(MPAs), which can be a powerful tool for attenuating anthropogenic threats. Several studies have already conducted
extensive tracking programs on seabirds to identify foraging
hotspots, and used those spatial patterns to inform MPA
design (Le Corre et al. 2012; Thaxter et al. 2012; Lascelles
et al. 2016).
However, to ensure that these MPAs are highly efficient,
adaptive and dynamic management is necessary (Agardy
et al. 2003; Maxwell et al. 2015). At present, the majority of
marine management approaches (e.g., quota setting, total
allowable catches, and MPAs) are relatively static in contrast
to the ocean itself and the majority of ocean uses (Agardy
1994; Hyrenbach et al. 2000; Crowder and Norse 2008).
Some marine predators are highly mobile and travel great
distances over different temporal scales, as is the case, for
example, for Arctic terns Sterna paradisaea (Egevang et al.
2010), and gray whales Eschrichtius robustus (Mate et al.
2015). Others, like Atlantic bluefin tuna Thunnus thynnus
show complex spatial dynamics, (e.g., homing) and population structure (e.g., several subpopulations of different sizes)
(Fromentin and Lopuszanski 2014). To effectively manage
this highly dynamic marine system, conservation measures
must become more flexible in space and time in the same
way as both the environment and the resource users have
(Hyrenbach et al. 2000).
A robust understanding of the spatiotemporal distribution
and ecology of migratory species is necessary for successful
conservation. Conditions experienced during the nonbreeding period may have carry-over effects on breeding
performance, which in turn may affect population dynamics
(Harrison et al. 2011). Consequently, the anthropogenic
impact outside the breeding season also needs to be taken
into consideration when making management decisions.
Conservation efforts can be targeted at areas where the
majority of the population congregate, as is the case for little
auks Alle alle at two key areas located in the Greenland Sea
and off Newfoundland (Fort et al. 2013).
Fast-acquisition satellite telemetry can provide evidencebased information on individual animal movements to delineate interspecific relationships, and can be used to increase
the efficacy of conservation planning (Gredzens et al. 2014).
Complimentary co-management, customized for each location, would be advisable when different species use similar
habitats (Gredzens et al. 2014). By including data from different species, we obtain more information about commensal
foraging associations or multispecies feeding flocks and
intra- and interspecific interactions (Barlow et al. 2002; Elliott
Bio-telemetry as an Essential Tool in Movement Ecology and Marine Conservation
tence (McMahon et al. 2012). Nevertheless, there are various
valid points in the light of animal welfare that are of concern
and must be addressed appropriately. In addition to the
increase of drag caused by the attachment of an external
device, the extra weight load still represents a major issue for
many marine predators.
We believe that, as a further improvement, the scientific
community should foster the development of new technologies and the on-going miniaturization of tags. To reduce
hydrodynamic drag, Computational Fluid Dynamics (CFD)
has proven to be a crucial tool to assess and improve the
design of bio-telemetry devices (Pavlov et al. 2007; Balmer
et al. 2014). In addition, CFD minimizes the risk of injury
and other potentially adverse effects. Furthermore, potential
capture and attachment methods for different animal groups
should be reviewed and discussed among researchers. It is
evident that tag effects must be more thoroughly investigated, and in particular the focus should be directed towards
metabolic costs and fitness consequences (McIntyre 2015). It
must not be forgotten that we, as scientists, have the responsibility to ensure the wellbeing of the animal. This is not only
relevant from an animal welfare viewpoint, but also from a
scientific position, as we aim to record and understand the
natural behavior of animals in their environment.
Conservation
The marine environment is predicted to face marked changes
by the year 2040 (Brierley and Kingsford 2009), so there is
no time to lose. The current challenges require multidisciplinary collaboration to develop effective strategies that rely
on an improved understanding of the threat that climate
change imposes on species, and the way that it interacts with
their natural coping mechanisms (Dawson et al. 2011).
Telemetry-derived data have a tremendous potential to
inform resource management and conservation, but unfortunately, relatively few examples of their application exist
(Wilson et al. 2015a). The financial cost of collecting telemetry data is relatively high, which makes it essential to critically evaluate the conservation benefit of the currently used
strategies (McGowan et al. 2017).
One of these strategies, is to combine high resolution biotelemetry data with environmental data and data on anthropogenic activities to build individual-based models, with the
aim of predicting habitat use in the (near) future (Grimm
et al. 1999; Stillman et al. 2003; Bestley et al. 2013; Stillman
et al. 2015; van der Vaart et al. 2016). In these models, different scenarios of anticipated anthropogenically-driven environmental change can be simulated. Once predictive models
have been implemented, the outcome can be used for conservation purposes. One extensive study found that modelled
physical changes under the Intergovernmental Panel on
Climate Change A2 scenario predict increased species overlap and a potential for niche compression across the North
Pacific (Hazen et al. 2013). For species that are already under
threat, such changes could exacerbate population declines or
inhibit recovery. These models can undoubtedly be used,
among other applications, to design marine protected areas
(MPAs), which can be a powerful tool for attenuating anthropogenic threats. Several studies have already conducted
extensive tracking programs on seabirds to identify foraging
hotspots, and used those spatial patterns to inform MPA
design (Le Corre et al. 2012; Thaxter et al. 2012; Lascelles
et al. 2016).
However, to ensure that these MPAs are highly efficient,
adaptive and dynamic management is necessary (Agardy
et al. 2003; Maxwell et al. 2015). At present, the majority of
marine management approaches (e.g., quota setting, total
allowable catches, and MPAs) are relatively static in contrast
to the ocean itself and the majority of ocean uses (Agardy
1994; Hyrenbach et al. 2000; Crowder and Norse 2008).
Some marine predators are highly mobile and travel great
distances over different temporal scales, as is the case, for
example, for Arctic terns Sterna paradisaea (Egevang et al.
2010), and gray whales Eschrichtius robustus (Mate et al.
2015). Others, like Atlantic bluefin tuna Thunnus thynnus
show complex spatial dynamics, (e.g., homing) and population structure (e.g., several subpopulations of different sizes)
(Fromentin and Lopuszanski 2014). To effectively manage
this highly dynamic marine system, conservation measures
must become more flexible in space and time in the same
way as both the environment and the resource users have
(Hyrenbach et al. 2000).
A robust understanding of the spatiotemporal distribution
and ecology of migratory species is necessary for successful
conservation. Conditions experienced during the nonbreeding period may have carry-over effects on breeding
performance, which in turn may affect population dynamics
(Harrison et al. 2011). Consequently, the anthropogenic
impact outside the breeding season also needs to be taken
into consideration when making management decisions.
Conservation efforts can be targeted at areas where the
majority of the population congregate, as is the case for little
auks Alle alle at two key areas located in the Greenland Sea
and off Newfoundland (Fort et al. 2013).
Fast-acquisition satellite telemetry can provide evidencebased information on individual animal movements to delineate interspecific relationships, and can be used to increase
the efficacy of conservation planning (Gredzens et al. 2014).
Complimentary co-management, customized for each location, would be advisable when different species use similar
habitats (Gredzens et al. 2014). By including data from different species, we obtain more information about commensal
foraging associations or multispecies feeding flocks and
intra- and interspecific interactions (Barlow et al. 2002; Elliott
Bio-telemetry as an Essential Tool in Movement Ecology and Marine Conservation