We present a new method for identifying areas of hunting activity within low-resolution
dive data, which can be used at the scale of individual dives. Our results show that
(1) of five potential indices, the hunting
lowres
time was the most correlated of the indices to the hunting
highres
time, (2) times allocated to foraging at the dive or trip scale were quite similar
when estimated by hunting
highres
and hunting
lowres
time, (3) 77Â % of total PrCA occurred in hunting
highres
mode segments of high-resolution dives and despite dive information being much more
degraded in low-resolution dives, 68Â % of total PrCA occurred in the hunting
lowres
segments which were also associated with four times more PrCA than transit
lowres
segments. Importantly, the concurrent prey capture attempts (PrCA) estimated from
high-resolution acceleration data for SES supported the low-resolution foraging effort
index identified with our method.
Unlike studies that only consider foraging behaviour within the bottom phase of a
dive 14], 35], 36], the “hunting time†method 21] considers potential foraging activity within the whole dive. We show that the same
method can be adapted to low-resolution dive data from SRDLs and still detects foraging
within a dive and most of the associated PrCA, despite being highly degraded information.
Foraging effort in low-resolution dives
Hunting
lowres
time
Of all the low-resolution foraging effort indices tested, the hunting
lowres
time was the best correlated to the hunting
highres
time. The good correlation between hunting
highres
and hunting
lowres
time and the similarities in average hunting times at the dive and trip scales indicate
that in most cases low-resolution dive segments of decreased vertical velocity (i.e.
“hunting
lowres
modeâ€) are also associated with increased vertical sinuosity (i.e. wiggles).
However, the correlation between hunting
highres
and hunting
lowres
time was overall lower for SES than the Weddell seal, and for both species some dives
were not well correlated. This may be due to several reasons. Firstly, high-resolution
dives are summarised by more numerous (on average, 12 segments; see 21], Additional file 4), shorter broken sticks segments which allow for the detection of behavioural changes
at a finer scale and more accurately than in low-resolution dives that are summarised
by five segments only. This is highlighted, for example, by the higher PrCA rate and
percentage of total PrCA associated with hunting
highres
segments compared to hunting
lowres
segments (Table 3). Moreover, SESs perform, on average, longer and deeper dives than Weddell seals,
which means that more information is lost when going from high- to low-resolution
data. Secondly, the estimation of foraging effort in high-resolution dives relies
on the calculation of vertical sinuosity which is not accessible in low-resolution
dives. Therefore, in the case where sinuous parts of high-resolution dives are also
associated with a decrease in vertical rates, the concordance between hunting
lowres
and hunting
highres
parts would be optimal. On the other hand, in some dives a seal could increase its
vertical sinuosity without increasing its time spent in the same part of the water
column (i.e. vertical velocity). This would be the case if the seal pursues a prey
moving faster vertically than horizontally in the water column or if “wiggling†while
transiting at a faster rate than the defined threshold. Another mismatch between hunting
highres
and hunting
lowres
parts would exist when a seal decreases its vertical transiting rate without performing
wiggles. This would be observed if a seal meanders at depth exploring the water column
horizontally to find a prey patch, orientate or glide (e.g. drift dives for elephant
seals, 42]). Simultaneously recorded information on prey encounters and 3D diving movements
would provide a better understanding of these different scenarios (e.g. 43]).
Nonetheless, our results are in agreement with our assumption that diving predators
adjust their diving behaviour to maximise the time spent in a prey patch by displaying
vertical ARS (i.e. increased vertical sinuosity and decreased vertical speed). Vertical
sinuosity is often used as an index of foraging effort and/or feeding success 12]–14], 44]. Moreover, even though acceleration data cannot discriminate between successful prey
capture attempts and unsuccessful ones and may not represent actual feeding success,
it is a powerful proxy for quantifying predator–prey encounters 33], 45]–47] providing valuable information on the distribution and abundance of prey in the water
column 29], 30], 48]. Similarly to the hunting
highres
time, the hunting
lowres
time has the advantage of incorporating the entire dive profile to detect intensified
foraging effort according to behavioural changes (see 21]) rather than a putative bottom phase. This method relies on a defined vertical velocity
threshold. Therefore, preliminary analyses are advised (as before using any index
or method on a new dataset) and would require the user to check the distribution of
vertical velocities to adapt the method to his/her dataset. Then, hunting
lowres
mode segments and hunting
lowres
time could be used as a tool to: (1) isolate areas of foraging behaviour within a
dive and (2) quantify the overall dive foraging effort using only low-resolution dive
datasets.
Bottom time indices
Although not the best indices, bottom times (bt80 and bt60) were also correlated with
hunting
highres
time for both species. It is a commonly accepted idea that foraging activity mainly
occurs during the bottom phase of a dive 15], 16], 18], 49] and so some measure of bottom time is often used as an index of foraging effort to
investigate habitat use and dive behaviour 31], 35]–38]. However, using only bt80 as an index of foraging effort in low-resolution dives
could be misleading in over- (SES) or under-estimating (the Weddell seal) the actual
time spent in intensive foraging mode (see 21]). Further, hunting occurred several times within a dive and not just during the bottom
phase. This is perhaps why, for both species, incorporating a greater proportion of
the dive profile in the bottom phase to calculate the bt (from bt80 to bt60) strengthened
the correlations with hunting
highres
time. Overall, we see two main limitations of using bottom time indices: these methods
(1) only consider a proportion of the dive profile and it is often difficult to accurately
define the actual bottom phase; and (2) assume that foraging is occurring only in
one part of the dive instead of considering behavioural variations within the dive.
Alternatively, the “hunting
lowres
time†method is a more appropriate measure of foraging effort, because it incorporates
the entire dive profile and detects within-dive behavioural changes.
TAD and transiting rate indices
For both species, there were weak correlations between the TAD index, the descent/ascent
rates and hunting
highres
time. Dive classification studies often assume that square-shaped dives are foraging
dives 12], 49], 50]. However, our results suggest that attributing an overall function to the dive based
only on its shape might oversimplify the complexity of the within-dive activity of
diving predators. Indeed, 21] demonstrated the dive complexity of seals (both Weddell seal and SES) that alternated
between transit and hunting behaviour several times within each dive. We tested descent
and ascent rates as possible candidates of foraging effort indices, as both are known
to influence foraging activity of marine predators in different ways: (1) reflect
favourable areas that a seal would want to reach and return to faster 15], 20], 50], (2) be used to prospect the water column and find a patch of prey 51], 52], (3) impact the time allocated to foraging activity due to its energetic costs 16], 39]. Without any information on changes in body condition or metabolic rate, it is difficult
to draw conclusions based on these assumptions. However, our results suggest that
only using transit rates poorly reflects the time spent in intensive foraging.
Ecological applications
During the last decade, SRDLs have been widely deployed on several species. These
tags were primarily designed to monitor animal behaviour, but the integration of other
sensors (temperature, conductivity, ambient light, etc.) provides insight into the
direct responses of individuals to their environment 38], 53], 54]. Since 2004, more than 270 000 CTD profiles were collected using CTD-SRDLs from SMRU
(Sea Mammal Research Unit, St Andrews, Scotland) in the frame of SEaOS (Southern Elephant
Seals as Oceanographic Samplers) and MEOP (Marine Mammal Exploration of the Oceans
Pole to Pole; hooded, crabeater, Weddell and southern elephant seals) programs 55], 56]. On average, two CTD profiles per day are transmitted and depending on the species
the number of low-resolution dive profiles associated per day can be up to 15 more
times (SES, Labrousse et al. unpublished data). Other projects like the Tagging of
Pacific Pelagics (TOPP) has also deployed thousands of similar tags including SRDLs
8]. These numbers are impressive and show that millions of low-resolution dive profiles
were or are to be analysed.
Our study showed that despite degraded information, insights on foraging activity
(i.e. detection of within-dive intensive foraging occurrences and quantification of
foraging effort) could be obtained when using low-resolution dive datasets as long
as using a metric that is based on the detection of changes in predator’s diving behaviour.
Our results were supported by independent PrCA, but the integration of complementary
sensors (e.g. video recorders, stomach/oesophageal temperature sensors) from which
feeding success could be inferred 43], 47], 57] would allow to further validate the method. This method was developed on a small
amount of individuals, but on numerous dives and on two species displaying a broad
range of different dive types 12], 13]. Moreover, the consistency of foraging strategies across different species 49] and the simplicity of the index suggest that this method could be applied to a broad
range of diving species. For example, the hunting
highres
and/or hunting
lowres
time could be included in the metrics calculated on board the tags and transmitted
by SRDLs.
The behavioural adjustments of top predators when diving are expected to primarily
reflect changes in their prey distribution in the three dimensions of the environment
3], 4]. Several methods have been developed to quantify how individuals concentrate their
search effort along a given path (e.g. Hidden Markov model 58], 59], first passage time 60], state space model 61], 62]) and used to relate the defined horizontal ARS to particular structures of the environment
(e.g. oceanographic features 31], 53], 63], sea ice 64], 65], topography 66], 67]). Bailleul et al. 36] underlined the importance of integrating a vertical index of foraging effort to better
identify foraging areas when studying deep-diving marine predators. Indeed, for many
marine predators, feeding occurs at depth and several studies demonstrated the association
between oceanographic features of the water column and predator’s diving behaviour
9], 29], 38], 52], 68]. The inclusion of hunting
lowres
time when predicting switching between movement states (see 69]) would allow integrating a quantification of foraging effort at depth (where they
encounter the prey) based on the detection of changes in diving behaviour and to relate
the actual predator’s behaviour in the three dimensions to the heterogeneous environment
they respond to. This method could therefore be a useful tool in both behavioural
and ecological studies to characterise and/or predict at broad and fine scale which
environmental features are likely to impact marine predators and their prey.