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# Convergence of marine megafauna movement patterns in coastal and open oceans

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Significance Understanding the key drivers of animal movement is crucial to assist in mitigating adverse impacts of anthropogenic activities on marine megafauna. We found that movement patterns of marine megafauna are mostly independent of their evolutionary histories, differing significantly from patterns for terrestrial animals. We detected a remarkable convergence in the distribution of speed and turning angles across organisms ranging from whales to turtles (epitome for the slowest animals on land but not at sea). Marine megafauna show a prevalence of movement patterns dominated by search behavior in coastal habitats compared with more directed, ballistic movement patterns when the animals move across the open ocean. The habitats through which they move will therefore need to be considered for effective conservation.
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Convergence of marine megafauna movement patterns
in coastal and open oceans
A. M. M. Sequeira
a,b,1,2
, J. P. Rodríguez
c,1
, V. M. Eguíluz
c
, R. Harcourt
d
, M. Hindell
e
, D. W. Sims
f,g,h
, C. M. Duarte
a,i
,
D. P. Costa
j
, J. Fernández-Gracia
c
, L. C. Ferreira
k
, G. C. Hays
l
, M. R. Heupel
m
, M. G. Meekan
k
, A. Aven
n,o
, F. Bailleul
p
,
A. M. M. Baylis
q,r
, M. L. Berumen
i
, C. D. Braun
s,t
, J. Burns
u
, M. J. Caley
v,w
, R. Campbell
x
, R. H. Carmichael
n,o
, E. Clua
y
,
L. D. Einoder
p,z
, Ari Friedlaender
j
, M. E. Goebel
aa
, S. D. Goldsworthy
p
, C. Guinet
bb
, J. Gunn
m
, D. Hamer
p,cc
,
N. Hammerschlag
dd
, M. Hammill
ee
, L. A. Hückstädt
j
, N. E. Humphries
f
, M.-A. Lea
e,ff
, A. Lowther
p,gg
, A. Mackay
p
,
E. McHuron
j
, J. McKenzie
p,hh
, L. McLeay
p
, C. R. McMahon
d,e,ii
, K. Mengersen
v,w
, M. M. C. Muelbert
e,jj
, A. M. Pagano
kk
,
B. Page
p,ll
, N. Queiroz
f,mm
, P. W. Robinson
j
, S. A. Shaffer
nn
, M. Shivji
oo
, G. B. Skomal
pp
, S. R. Thorrold
t
,
S. Villegas-Amtmann
j
, M. Weise
qq
, R. Wells
rr
, B. Wetherbee
ss
, A. Wiebkin
p,ll
, B. Wienecke
ss
, and M. Thums
k
Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved January 9, 2018 (received for review September 18, 2017)
The extent of increasing anthropogenic impacts on large marine
vertebrates partly depends on the animalsmovement patterns. Effec-
tive conservation requires identification of the key drivers of move-
ment including intrinsic properties and extrinsic constraints associated
with the dynamic nature of the environments the animals inhabit.
However, the relative importance of intrinsic versus extrinsic factors
remains elusive. We analyze a global dataset of 2.8 million locations
from >2,600 tracked individuals across 50 marine vertebrates evolu-
tionarily separated by millions of years and using different locomotion
modes (fly, swim, walk/paddle). Strikingly, movement patterns show a
remarkable convergence, being strongly conserved across species and
independent of body length and mass, despite these traits ranging
over 10 orders of magnitude among the species studied. This repre-
sents a fundamental difference between marine and terrestrial verte-
brates not previously identified, likely linked to the reduced costs of
locomotion in water. Movement patterns were primarily explained by
the interaction between species-specific traits and the habitat(s) they
move through, resulting in complex movement patterns when moving
close to coasts compared with more predictable patterns when mov-
ing in open oceans. This distinct difference may be associated with
greater complexity within coastal microhabitats, highlighting a critical
role of preferred habitat in shaping marine vertebrate global move-
ments. Efforts to develop understanding of the characteristics of ver-
tebrate movement should consider the habitat(s) through which they
move to identify how movement patterns will alter with forecasted
severe ocean changes, such as reduced Arctic sea ice cover, sea level
rise, and declining oxygen content.
global satellite tracking
|
probability density function
|
root-mean-square
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turning angles
|
displacements
Unifying theoretical frameworks that explain general princi-
ples of animal life-history (1), optimal foraging (2, 3), and
metabolic scaling in organisms (4, 5) facilitate the interpretation of
data and the generation of testable hypotheses. Animal movement
accounts for most of the energy budgets of vertebrates because it
underpins critical components of their behavior, such as feeding
and mating. Following the challenge posed by Aristotle millennia
ago in De Motu Animalium (On the Movement of Animals)(6),
efforts have been made to develop a unifying framework to study
movement (7). [Now we must consider in general the common
reason for moving with any movement whatever (for some animals
move by flying, some by swimming, some by stepping, some in
other comparable ways).(ref. 6, p. 24)] Such efforts have pro-
vided clarification that the primary challenge for understanding
animal movement lies in the identification of the key external
factors, internal states, and the motion and navigation capabilities
influencing movement (7). It is also known that animal movement
patterns are underpinned by common principles, such as opti-
malresource exploitation by predators (optimality paradigm;
refs. 2, 3, 8, and 9) or the use of more efficient search trajectories
(randomparadigm; refs. 1012). Overall, animal movement pat-
terns have been attributed to extrinsic factors, including the dynamic
nature of the environments they inhabit and constrained by intrinsic
properties (1319), including allometric and metabolic scaling with
day or home range and locomotion speed, particularly for terrestrial
animals (4, 15, 2023). However, the relative importance of extrinsic
versus intrinsic properties in determining the observed patterns of
movement of free-ranging animals remains ambiguous. To effec-
tively partition the relative contributions of extrinsic versus intrinsic
factors and effectively investigate whether a unifying framework
exists irrespective of location, scale of movement, and stage or phase,
a large-scale comparison across multiple species is needed (24, 25).
Rapid technological developments in animal-attached elec-
tronic tags (telemetry/biologging) have generated large tracking
datasets across an array of marine vertebrates, now available for
Significance
Understanding the key drivers of animal movement is crucial to
assist in mitigating adverse impacts of anthropogenic activities
on marine megafauna. We found that movement patterns of
marine megafauna are mostly independent of their evolu-
tionary histories, differing significantly from patterns for ter-
restrial animals. We detected a remarkable convergence in the
distribution of speed and turning angles across organisms
ranging from whales to turtles (epitome for the slowest ani-
mals on land but not at sea). Marine megafauna show a
prevalence of movement patterns dominated by search be-
havior in coastal habitats compared with more directed, bal-
listic movement patterns when the animals move across the
open ocean. The habitats through which they move will
therefore need to be considered for effective conservation.
Author contributions: R.H., M. Hindell, D.W.S., D.P.C., G.C.H., M.R.H., M.G.M., A.A., F.B., A.M.M.B.,
M.L.B., C.D.B., J.B., R.C., R.H.C., E.C., L.D.E., A.F., M.E.G., S.D.G., C.G., D.H., N.H., M. Hammill, L.A.H.,
M.-A.L., A.L., A.M., E.M., J.M., L.M., C.R.M., M.M.C.M., A.M.P., B.P., N.Q., P.W.R., S.A.S., M.S., G.B.S.,
S.R.T.,S.V.-A.,M.W.,R.W.,B.Wetherbee,A.W.,B.Wienecke,andM.T.provideddata;A.M.M.S.,
J.P.R., J.F.-G., L.C.F., and M.T. collated the datasets; A.M.M.S., J.P.R., V.M.E., and J.F.-G. con-
ducted the analyses with assistance from N.E.H. and K.M.; V.M.E., R.H., M. Hindell, D.W.S.,
C.M.D., D.P.C., G.C.H., M.R.H., M.G.M., M.J.C., J.G., and M.T. assisted with data interpre-
tation and discussions with contributions from all authors; and A.M.M.S. wrote the paper
with assistance from J.P.R., V.M.E., R.H., M. Hindell, D.W.S., C.M.D., D.P.C., L.C.F., G.C.H.,
M.R.H., M.G.M., and M.T. and contributions from all authors.
The authors declare no conflict of interest.
Data deposition: The data reported in this paper are archived in digital.csic (dx.doi.org/10.
20350/digitalCSIC/8525). Information on how to access the individual datasets is included
in SI Appendix.
1
A.M.M.S. and J.P.R. contributed equally to this work.
2
To whom correspondence should be addressed. Email: ana.sequeira@uwa.edu.au.
1073/pnas.1716137115/-/DCSupplemental.
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multiple regions, temporal scales, and habitats across the globe.
Such large datasets provide the foundational information re-
quired to discover commonalities in movement patterns across
species and environments and to assess the influence of a range
of intrinsic and extrinsic factors. Because marine vertebrates
have diverse life histories and include all extant vertebrate classes
except Amphibia, they provide an ideal group for the exploration
of the underlying principles that might govern animal movement.
Moreover, marine vertebrates range broadly in their movement
patterns, from species with small home ranges (centimeters to
kilometers) to highly migratory animals traveling hundreds to
thousands of kilometers while crossing entire ocean basins (26
movement will have broad-reaching application in understanding
movement in species from terrestrial vertebrates to aquatic in-
vertebrates (29). Moreover, marine vertebrates include many
threatened species that are particularly vulnerable to changing
environments (e.g., polar bears and penguins) (30) or to extractive
anthropogenic activities (e.g., whales, sharks, and seals), as well
as species with important economic value to human societies
(31, 32). Hence, understanding how marine vertebrates move is
critical to broadly understand the mechanisms of animal move-
ment as well as to assist developing effective conservation mea-
sures and predicting the potential impacts of global change
on populations.
Here, we synthesize movement data from the largest satellite
telemetry dataset yet assembled at a global scale for large marine
vertebrate species (termed megafauna) to quantify the relative
importance of both extrinsic and intrinsic factors as drivers of
movement and identify unifying patterns in marine megafauna
movement. Our dataset includes species that fly, swim, and walk/
paddle, with distributional ranges varying across tropical, tem-
perate, and polar regions, and comprising sharks, turtles, flying
and swimming birds, true and eared seals, cetaceans, sirenians,
and polar bears (Fig. 1A). We analyzed individual movement by
characterizing horizontal displacements as the shortest great-circle
distances between two consecutive locations and the turning an-
gles between them. We tested for differences in these attributes
among taxonomic groups (taxa, family, species), allometric scaling
(body length and mass), life history traits (e.g., breeding and for-
aging strategies), energy requirements, as well as locomotion mode,
region (polar, temperate, tropical), and coastal affinity, defined as
the fraction of displacements within the 0150 m depth range (here
referred to as coastal ocean) (see Materials and Methods for de-
tails and SI Appendix,TablesS1S3).
Results
The mean displacements per day (din km/d; effective speed),
referred to in the terrestrial animal movement literature as day
range(DR) (21) or daily movement range(20), were mostly
independent of body length and mass (SI Appendix,Fig.S1)both
among and within species groups. Exceptions include true seals
(body length only) and turtles, but the latter is simply associated
with the different mode of foraging of the largest turtle consid-
ered, the leatherback turtle, which constantly travels large distances
in search of prey. The other turtle species included in the analysis
tend to be neritic as adults, living most of their lives in shallow
coastal waters where they move little. The root-mean-square
Fig. 1. Representation of the global tracking dataset and scaling properties for all species analyzed. (A) Global map with trajectories obtained by satellite tracking for
all50species.(B) The scaling exponents (μ) obtained from the root-mean-square (d
RMS
) analysis of displacements for all species analyzed (species names indicated from
left to right in caption; squares indicate mean values, and bars show the SD). Histogram in Inset shows the number of individuals for the range of scaling exponents.
Long-nosed fur seal: common name for the South Australian population of New Zealand fur seal. Colors represent each of the nine guilds with data: cetaceans
(yellow), eared seals (blue), flying birds (green), penguins (cyan), polar bears (orange), sharks (dark green), sirenians (purple), true seals (red), and turtles (pink).
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analysis of displacements (d
RMS
) scaled with time as a power law
(d
RMS
T
μ
) with exponents (μ) mostly above the value of 0.5
(commonly associated with Brownian motion) (Fig. 1B), indicating
that most individuals moved superdiffusivelythat is, faster than
expected in a normal diffusion process. Using predetermined time
windows of 1 d, we compared the probability density functions
(PDFs) of the observed displacements for each individual using a
dimensionless coefficient of PDF spread (CS), defined as the ratio
between the second moment (average square displacement) and
the square of the first moment (square of the average displace-
ment). Our CS can be used for comparison across all individuals
irrespective of scale and provides an estimate of the spread of the
resulting PDF normalized by the square of the average displace-
ment (Fig. 2A). Generally, CS >> 1 indicates wide distributions
with heavy tails, such as a power law or lognormal. Lower values
generally highlight a narrower range of displacements identified in
the tracked movement resulting in a smaller spread of the PDFs.
Our CS results show high variability among individuals within and
among species (Fig. 2Band SI Appendix,TableS4), revealing
substantial within-species variability in movement patterns, with
increasing mean coefficient of variation from 22.94%, 38.10%, and
40.37% for species grouped within low, mixed, and high coastal
affinity, respectively (Fig. 2B). Lower CS values generally rep-
resented simpler, more linear paths, while higher values repre-
sented more varied, complex movement patterns (Fig. 2A).
Model fits from boosted regression trees (BRTs; ref. 33) to the
resulting CSs for all individuals showed that species group and
coastal affinity had the highest relative importance (74.0% and
21.5%, respectively) and identified an interaction (size =36.0)
between these two variables (SI Appendix,Fig.S2). This interaction
highlights that the different movement patterns in the coastal and
open ocean are not uniquely a species-specific trait and partly ac-
counts for the high variability in movement patterns among indi-
viduals within species (Fig. 2 and SI Appendix,Fig.S2). Moreover,
life-history traits, such as breeding and foraging strategies, as well
as allometric traits, such as body mass and length, were not shown
to greatly influence CS and were mostly removed during the sim-
plification procedure in the BRTs. Based on our modeling results,
CS increases with greater coastal affinity (Fig. 2B), indicating a
larger range of displacements observed when individuals move
mostly through coastal areas (i.e., including small and very large
displacements). An association between coastal affinity and the
d
RMS
exponent (with 0.51.0 indicating normal random to super-
diffusive, more directed, ballistic motion) was also detected. Higher
d
RMS
exponents were found for all displacements taking place in
the open ocean, where depths 150 m (d
RMS
exponent =0.791
0.112 ×ocean,whereocean is open oceanor coastal ocean,
P<0.001 for the linear model) (Fig. 3). This result is congruent
with simple, extensive, and directed movements in that habitat. It is
also supported by the finding of prevailing frequency of angles of 0°
that is, more directed, forward movement patterns in open ocean
(depths 150 m), and less directed patterns for displacements in the
coastal ocean (depths <150 m) with higher frequency of lateral and
backward angles (Fig. 3 and SI Appendix,Fig.S3).
The KolmogorovSmirnov analysis of the distances between the
cumulative distributions of displacements for each species pair was
used to compute a dendrogram that resulted in two main clusters
above and below California sea lions (a species with mixed coastal
affinity). The split of clusters was consistent with a split between
species moving in coastal and open oceans (i.e., high and low coastal
affinities as shown in the color scale for Fig. 4A). The resulting
dendrogram was unrelated to phylogeny, such that closely related
species were no more similar in their displacement patterns than
were distantly related ones. This result further reinforces that habitat
structure is an important driver of the movement patterns among
marine megafauna and reiterates that differences are not fully de-
pendent on intrinsic traits.
Discussion
Our integrated, multispecies study revealed that differences in
movement patterns of large marine vertebrates are primarily de-
fined by the species to which they belong (74% relative influence)
but underpinned by a strong interaction with the habitat through
which the animals move (open or coastal ocean; 21% relative
influence). This interaction partly accounts for the large variability
in movement patterns among individuals of the same species (Fig.
2andSI Appendix,Fig.S6) and is likely related to a combination
of directed- and resident-type movements that can occur over the
Fig. 2. Results of the analysis of displacements. (A) PDF of displacements (d, km) at the species level with 1-d time windows for species with low (mean <0.3;
Left), mixed (Center), and high (mean >0.7; Right) coastal affinity (Bottom), and example tracks for each group (Top; black and white scale bars represent 100 km;
black dotted lines: PDF for the example track shown). (B) Relationship between coefficient of PDF spread (CS) and coastal affinity (CA) obtained from the BRTs
(dashed black line; also shown in the Top Right Inset). To the top and right are histograms of CA and CS, respectively. Outlier green point: western gulls (0 CA
1; SI Appendix,TableS4). Average coefficients of variation: 22.94%, 38.10%, and 40.37% for CS, and 243.55%, 85.93%, and 11.48% for CA for low (blue), mixed
(green), and high (red) CA species, respectively. Solid black: mean ±SD among all species with a coefficient of variation of 37.20% for CS and 39.51% CA.
Sequeira et al. PNAS Latest Articles
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course of an individuals track in association with different behaviors
(e.g., transiting and foraging, resting, breeding) (34). Here, we used
an analysis of displacements with no prior assumptions for the
movement patterns observed and show that coastal and open ocean
habitats directly influence the horizontal movement patterns of
marine megafauna potentially in association with habitat complexity
and related prey availability. The patterns we found emerged con-
sistently across a diverse array of marine vertebrates and locomo-
tion modes, confirming that habitat structure is a powerful driver of
movement patterns. Species with movements that occurred mostly
in coastal environments displayed a greater variety of displacement
lengths (indicated by the generally higher CS; Fig. 2 and SI Ap-
pendix,Fig.S6), regardless of the intrinsic factors related to each
species. In contrast, species moving mostly off-shelf in deep, oceanic
habitat conformed to a relatively narrower range of displacements,
which indicated less variability in displacement lengths (generally
larger). We suggest that this difference in behavior between on- and
off-shelf movements is related to habitat complexity, with open
water habitats off the continental shelves being comparatively less
complex (i.e., more homogeneous physical habitat) despite their
highly dynamic nature. Open ocean species tend to use oceano-
graphic features, such as fronts, eddies, and currents, as foraging
and movement cues (e.g., ref. 35). This difference may explain the
convergence of movement behaviors characterized by more di-
rected movement with generally higher d
RMS
exponents among
species when moving off the continental shelf. In contrast, animals
moving over the shelf and in the coastal ocean experience a much
wider variety of structurally complex habitats (e.g., reef, seagrass)
that support a diverse suite of varying resources (e.g., prey, refuges)
and threats (e.g., predators, human disturbance), stimulating more
complex movement patterns and covering a larger range of dis-
placements (Fig. 2 and SI Appendix,Fig.S6). This complex mix of
features provides a rich and diverse array of opportunities for for-
aging, breeding, and other behaviors (36). Previous studies of
movement have also revealed different patterns exhibited by marine
fish and foraging albatrosses in a manner consistent with prey dis-
tribution between coastal shelf and oceanic areas (37, 38).
Our results reveal a remarkable convergence in movement pat-
terns among a large range of marine vertebrates, departing from
those reported for terrestrial animals in that the patterns we de-
tected were independent of body length and mass (despite these
Fig. 3. Comparison of movement patterns in open and coastal oceans. The
classification into open or coastal was based on the depth at which the dis-
placements occurred with depth 150 m classified as coastal ocean. (A)Dis-
tribution of turning angles (arrow points to 0°) in open (blue) and coastal
oceans (red). The circular plot reveals high frequency of 0° angles in open ocean
and a large number of angles between 90° and 270° angles (peaking at 180°;
i.e., returns) in coastal oceans. Black inner circle: uniform distribution of angles.
(B) Boxplot of d
RMS
exponents (μ) for individuals showing coastal affinity below
(blue) and above (red) 0.5 (μ=0.7840.085 ×coastal ocean;P<0.001).
Fig. 4. Analysis of distances between the CDFs of displacements for each pair of species. The colors shown correspond to the classification of each species as
having high (red) or low coastal affinity (blue) based on the proportion of their observed displacements occurring completely within coastal ocean for each
individual of the same species (SI Appendix, Table S4). (A) Dendrogram obtained from the distance matrix derived from the KolmogorovSmirnov analysis
showing two main branches (anchored by the line for California sea lions) broadly associated with low (<0.5; upper branch) and high (>0.5, lower branch)
coastal affinity. (B) Distance matrix (mirror image from diagonal) with darker colors indicating short distances (d
KS
) between the speciesCDFs. Cand Dshow
the CDF of displacements for species in the upper and lower branch of the dendrogram, respectively, highlighting distinct displacement regions (d in the x
axis) for the curves relative to species occurring mostly on open and coastal oceans, respectively.
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ranging 10 orders of magnitude among the species studied). By
contrast, for a variety of terrestrial species, the DR is known to
scale with body mass, following power laws with exponents around
0.25 (20, 21), with slight differences in the scaling for different
taxonomic groups associated with diet types and foraging habitats
(21). Our finding suggests that the fluid dynamics (air and water) of
the ocean environment has lessened some of the physical con-
straints that operate on landfor example, the similarity between
the density of seawater and animal bodies largely reduces the en-
ergetic costs of body mass displacement in the ocean compared with
land (15). Also, the marine environment is a 3D foraging habitat, a
factor that has also been found to decrease the scaling of DR for
primates (which use the 3D habitat offered by forest canopies) in
comparison with other terrestrial mammals (21). We found no
significant phylogenetic differences in the components of movement
analyzed here despite the evolutionary histories of these animals
spanning millions of years from turtles to polar bears with evo-
lutionary ages of 157 million y and 150,000 y, respectively.
Our comparative analysis of movement across this diverse
group of marine megafauna contributes two key underpinnings
to our understanding of their movements. First, internal factors
that affect movement are species-specific and independent of
the phylogenetic history or traits shared at higher taxonomic or
functional groupings (such as family or taxa) and of life-history
traits alone, such as breeding strategies. Second, we identified
that the key external factor influencing movement of marine
megafauna species is their interaction with coastal or open ocean
habitats. This finding was corroborated by the dendrogram based
on the KolmogorovSmirnov distances between the cumulative
distribution functions (CDFs) of displacements for each pair of
species (Fig. 4). As a consequence, there is a broad diversity of
individual movement patterns, ranging from random searching
patterns to more directed movement, largely in response to ex-
trinsic forcing (i.e., depending on whether they occur in an off-
shore or coastal environment). Such differences of scale are
consistent with differences in oceanographic processes and the
related biophysical coupling (39). We highlight, however, that
when available, the internal species-specific factors that may
affect movement patterns should be included in future models to
further assess how individual movement varies within species.
The study of the different movement behaviors of single species
has mostly been framed within the random or optimality para-
digms, but when applied in isolation, such theories fail to en-
compass all of the components associated with movement (7). We
propose that a more encompassing framework for understanding
animal movement, its connections with habitat, and the species-
specific traits that influence it, would define how, where, and why
animals move in three sequential levels of analysis. The first level
would focus on how animals move by analyzing the characteristics
of their displacements, for example, as we have done here using
predetermined time windows to understand and describe the ob-
served movement patterns. The second level would focus on
quantifying the drivers of movement and specifically on where
animals move, for example, by using models to estimate the rel-
ative importance of drivers throughout the range of habitats where
movement occurs (e.g., open versus coastal oceans or microhabi-
tats). If habitat information is not available, discretizing space into
low and high occupancy areas can also be a practical method (40).
The final level would focus on why animals move and involve
hypothesis testing for specific behaviors as commonly undertaken
using the random and the optimality paradigms (12). We expect
that a unifying framework of animal movement would consist of
the integration of these multiple assessments rather than on the
results from specific single assessments completed in isolation.
This hierarchical framework encompassing three levels of quan-
titative exploration to understand movement will provide a strong
basis from which to predict potential changes in animal movement
associated with forecasted severe environmental changes.
The common reason for movingsought by Aristotle and
many others since appears to be, at least for the vast group of
marine megafauna, largely associated with the contrasting use
of the coastal and open ocean habitats. The convergence of
movement patterns across marine vertebrates separated by mil-
lions of years of evolution and using fundamentally different
locomotion modes is remarkable. Given recurring changes in the
extent and location of continental shelves over the millennia, the
influence of habitat change on the evolution of species should
not be underestimated (4143). Indeed, the importance of un-
derstanding paleobiology in the conservation of terrestrial eco-
systems was recently identified (44). The importance of habitat
shaping the movement patterns of marine megafauna might also
be associated with habitat-specific ecological roles of these large
species and be key to identifying specific areas of behavioral
interest. Our study suggests that efforts to understand marine
megafauna movement through analysis of its evolutionary history
may yield fewer advances than a focus on understanding the hab-
itats through which animals move (e.g., movement phases in
coastal versus open ocean). Such a shift in focus, together with
the use of a more encompassing framework, will assist pre-
dicting the effects of changes already underway, for example,
with the reduction in Arctic shelf areas (45) and predicted sea
level rise during the next millennia (46, 47). The great be-
havioral plasticity of coastal marine vertebrates provides some
hope of their higher resilience in a rapidly changing coastal
marine environment.
Materials and Methods
Tracking Datasets. Our dataset spans three decades (19852015) and includes
a total of 2,557 individuals from 50 marine vertebrate species. Details are
given in SI Appendix,SI Materials and Methods.
Probabilistic Analysis of Displacements. We characterized movement patterns
from the time-series of displacements recorded in the spatial trajectories of
tagged animals. Displacements were measured as the shortest great circle
distance between two locations separated by a predetermined time-window
T(e.g., 1 d) along an individual track. Details are given in SI Appendix,SI
Materials and Methods.
Assessing Coastal Affinity. We considered coastal habitats to be those located
in emerging and submerged lands within depths of 0150 m and calculated
coastal affinity as the fraction of observed displacements completely oc-
curring within coastal habitats for each individual. Further details are given
in SI Appendix,SI Materials and Methods.
BRTs. We fitted BRT models to the final set of 2,303 individuals across 38 species
and the 12 predictor variables (SI Appendix, Tables S2S4). Modeling details
are given in SI Appendix,SI Materials and Methods.
Dendrogram of Movement. We calculated the KolmogorovSmirnov distance
between the set of displacements for each species pair when using a time
window of 1 d. We then used these distances to produce the dendrogram.
Details are given in SI Appendix,SI Materials and Methods.
ACKNOWLEDGMENTS. We are thankful to I. Jonsen for initial discussions and
all involved with the many aspects of fieldwork and data collection; details are
included in SI Appendix, Acknowledgments. Workshop funding was granted by
the University of Western Australia (UWA) Oceans Institute, the Australian In-
stitute of Marine Science (AIMS), and King Abdullah University of Science and
Technology (KAUST). A.M.M.S. was supported by Australian Research Council
Grant DE170100841 and an Indian Ocean Ocean Marine Research Centre (UWA,
AIMS, Commonwealth of Scientific and Industrial Research Organisation) fel-
lowship. J.P.R., V.M.E., and J.F.G. were supported by Agencia Estatal de Inves-
tigación (AEI, Spain) and Fondo Europeo de Desarrollo Regional (FEDER) through
project Spatiotemporality in Sociobological Interactions, Models and Methods
(SPASIMM) (FIS2016-80067-P AEI/FEDER, European Union), and by research
funding from KAUST. J.P.R. was supported by Ministerio de Educación, Cultura
y Deporte (Formación de Profesorado Universitario Grant, Spain). D.W.S. was
supported by the UK Natural Environment Research Council and Save Our Seas
Foundation. N.Q. was supported by Fundação para a Ciência e Tecnologia
(Portugal). M.M.C.M. was supported by a Coordenação de Aperfeiçoamento
de pessoal de Nível Superior fellowship (Ministry of Education).
Sequeira et al. PNAS Latest Articles
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ECOLOGY
a
UWA Oceans Institute, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA 6009, Australia;
b
School of Biological Sciences,
University of Western Australia, Crawley, WA 6009, Australia;
c
Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones
CientíficasUniversity of the Balearic Islands, E-07122 Palma de Mallorca, Spain;
d
Department of Biological Sciences, Macquarie University, Sydney, NSW
2109, Australia;
e
Ecology and Biodiversity Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS 7004, Australia;
f
Marine
Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth PL1 2PB, United Kingdom;
g
Ocean and Earth Science, National
Oceanography Centre Southampton, University of Southampton, Southampton SO14 3ZH, United Kingdom;
h
Centre for Biological Sciences, University of
Southampton, Southampton SO17 1BJ, United Kingdom;
i
Red Sea Research Center, King Abdullah University of Science and Technology, 23955-6900
Thuwal, Kingdom of Saudi Arabia;
j
Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060;
k
Australian Institute of
Marine Science, Indian Ocean Marine Research Centre (M096), University of Western Australia, Crawley, WA 6009, Australia;
l
School of Life and
Environmental Sciences, Deakin University, Warrnambool, VIC 3280, Australia;
m
Australian Institute of Marine Science, Townsville, QLD 4810, Australia;
n
University Programs, Dauphin Island Sea Lab, Dauphin Island, AL 36528;
o
Department of Marine Sciences, University of South Alabama, Mobile, AL 36688;
p
South Australian Research and Development Institute, West Beach, SA 5024, Australia;
q
South Atlantic Environmental Research Institute, FIQQ1ZZ Stanley,
Falkland Islands;
r
Falklands Conservation, FIQQ1ZZ Stanley, Falkland Islands;
s
Joint Program in Oceanography/Applied Ocean Science and Engineering,
Massachusetts Institute of Technology-Woods Hole Oceanographic Institution, Cambridge, MA 02139;
t
Biology Department, Woods Hole Oceanographic
Institution, Woods Hole, MA 02543;
u
Department of Biological Sciences, University of Alaska, Anchorage, AK 99508;
v
School of Mathematical Sciences,
Queensland University of Technology, Brisbane, QL 4000, Australia;
w
Australian Research Council Centre of Excellence for Mathematical and Statistical
Frontiers, Queensland University of Technology, Brisbane, QL 4000, Australia;
x
Marine Science Division, Department of Parks and Wildlife, Kensington, WA
6151, Australia;
y
Paris Science Lettre, Laboratoire dExcellence CORAIL, Centre de Recherche Insulaire et Observatoire de lEnvironnement 3278, Ecole
Pratique des Hautes Etudes, 66860 Perpignan, France;
z
Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina, NT
0810, Australia;
aa
Antarctic Ecosystem Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and
Atmospheric Administration, La Jolla, CA 92037;
bb
Centre dÉtudes Biologiques de Chizé, UMR 7372 CNRS-Université de La Rochelle, 79360 Villiers-en-Bois,
France;
cc
Depredation and By-Catch Mitigation Solutions (DBMS), Global Oceans, Hobart, Tasmania 7001, Australia;
dd
Rosenstiel School of Marine and
Atmospheric Science, Abess Center for Ecosystem Science and Policy, University of Miami, Miami, FL 33149;
ee
Department of Fisheries and Oceans, Maurice
Lamontagne Institute, Mont Joli, QC G5H3Z4, Canada;
ff
Alaska Ecosystems Program, National Marine Mammal Laboratory, National Oceanic and
gg
Research Department, Biodiversity Section, Norwegian Polar
Institute, 9296 Tromsø, Norway;
hh
Science Group, Department of Environment, Water and Natural Resources, Adelaide, SA 5001, Australia;
ii
Sydney Institute
of Marine Science, Mosman, NSW 2088, Australia;
jj
Instituto de Oceanografia, Universidade Federal do Rio Grande, Rio Grande, RS 96203-900, Brazil;
kk
Alaska Science Center, US Geological Survey, Anchorage, AK 99508;
ll
Biosecurity South Australia, Primary Industries and Regions South Australia, South
mm
Centro de Investigação em Biodiversidade e Recursos Genéticos (Research Centre in Biodiversity and Genetic
Resources), Associate Laboratory, Universidade do Porto, 4485-668 Vairão, Portugal;
nn
Department of Biological Sciences, San Jose State University, San
Jose, CA 95192;
oo
Guy Harvey Research Institute, Halmos College of Natural Sciences & Oceanography, Nova Southeastern University, Dania Beach, FL
33004;
pp
Shark Research Program, Massachusetts Division of Marine Fisheries, New Bedford, MA 02740;
qq
Marine Mammal Program, Office of Naval
Research, Arlington, VA 22203-1995;
rr
Sarasota Dolphin Research Program, Chicago Zoological Society, c/o Mote Marine Laboratory, Sarasota, FL 34236; and
ss
Australian Antarctic Division, Department of the Environment and Energy, Kingston, TAS 7052, Australia
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## Supplementary resource (1)

Data
March 2018
... Through assimilating comprehensive baseline data on the distributions of these four taxa, consistent long-term monitoring would allow us to evaluate the effects of climate change as it accelerates [11,17,78]. Key insights on distributions, movement patterns and behaviour can be obtained from direct observations, strandings, tracking (and logging) studies, as well as aerial drones [17,22,24,28,41]. However, the current study showed that bias exists in individual monitoring approaches regarding the coastal distributions of marine megafauna, supporting existing studies calling for caution in using single approaches to infer distributions, even when assimilating data from 1000s of animals across taxa [23,79]. ...
... Therefore, protecting these areas remains important, regardless of their value as conservation surrogates [12,16,80]. However, more focused effort to identify and protect non-breeding areas (including developmental, foraging and wintering habitats) is also required, which, in turn, would safeguard other key marine megafauna [12,15,24,30]. Ultimately, loggerhead sea turtles, at least in our study region, are viable umbrella species that could be used to help protect habitats used by other marine megafauna. ...
Article
Full-text available
Quantifying the capacity of protected area networks to shield multiple marine megafauna with diverse life histories is complicated, as many species are wide-ranging, requiring varied monitoring approaches. Yet, such information is needed to identify and assess the potential use of umbrella species and to plan how best to enhance conservation strategies. Here, we evaluated the effectiveness of part of the European Natura 2000 protected area network (western Greece) for marine megafauna and whether loggerhead sea turtles are viable umbrella species in this coastal region. We systematically surveyed inside and outside coastal marine protected areas (MPAs) at a regional scale using aerial drones (18,505 animal records) and combined them with distribution data from published datasets (tracking, sightings, strandings) of sea turtles, elasmobranchs, cetaceans and pinnipeds. MPAs covered 56% of the surveyed coastline (~1500 km). There was just a 22% overlap in the distributions of the four groups from aerial drone and other datasets, demonstrating the value of combining different approaches to improve records of coastal area use for effective management. All four taxonomic groups were more likely to be detected inside coastal MPAs than outside, confirming sufficient habitat diversity despite varied life history traits. Coastal habitats frequented by loggerhead turtles during breeding/non-breeding periods combined overlapped with 76% of areas used by the other three groups, supporting their potential use as an umbrella species. In conclusion, this study showed that aerial drones can be readily combined with other monitoring approaches in coastal areas to enhance the management of marine megafauna in protected area networks and to identify the efficacy of umbrella species.
... Moreover, traits corresponding to dispersal ability would enable species to colonize newly suitable environments (Guha et al., 2013;McCauley et al., 2015). Despite traits being valuable evidencebased tools for conservation and management (Estrada et al., 2016;MacLean and Beissinger, 2017;Sequeira et al., 2018;Polivka, 2020), empirical evidence on the links between species' traits and long-term geographical interactions in specific areas remains limited. ...
... The AAM affects growth rate and generation time and determines proliferation capability (Lester et al., 2004;Minte-Vera et al., 2016;Honsey et al., 2017). The HA influences species' site (in) fidelity, mobility and habitat requirements and thus the emigration of species (Carrasco et al., 2017;Sequeira et al., 2018;Polivka, 2020). Species with larger DRs and LRs are expected to physiologically tolerate a wider range of climate variation, which ultimately aids in establishment and colonization (Sunday et al., 2011). ...
Article
Full-text available
Accounting for biotic interactions is important for predicting species and ecosystem variation under changing climate but difficult to achieve in practice. The proportion of geographical overlap between species, called species geographical sensitivity (SGS), could be used to gauge the potential for species interactions. Species with increasingly high SGS could have the potential to experience more interactions with other species and vice versa, which might have important implications in ecological assessment, particularly at a community level, in the face of climate change. We compiled fish occurrences in the North Sea from 1983 to 2020 and calculated annual mean SGS (mSGS) to systematically evaluate their temporal changes and to estimate influences of species traits on the relative temporal changes in mSGS. The results showed that 36.3% of species significantly changed their mSGS over time, with high correlations between changes in species range size and overlap with other species. The species’ averaged mSGS before warming was highly correlated with the relative change in mSGS. Depth range, body length, and age at maturity together explained most variation in mSGS among these species. Contemporary climate change is expected to reorganize species distributions and interactions and substantially alter marine ecosystem functioning. Our assessment opens a new avenue for evaluating climate change impacts on species geographical interactions, and such geographical changes may be contingent on species traits.
... Species often use changes in these environmental features as cues for feeding, migration, etc.; therefore, these features are crucial to systematically evaluate when interpreting biologging data [52,53]. One interesting result of our model is the species-specific relationship to frontal features, incorporated into the models as ocean color and SST frontal gradient magnitude (FGM). ...
Article
Full-text available
The aftermath of the 2010 Deepwater Horizon oil spill highlighted the lack of baseline spatial, behavioral, and abundance data for many species, including imperiled marine turtles, across the Gulf of Mexico. The ecology of marine turtles is closely tied to their vertical movements within the water column and is therefore critical knowledge for resource management in a changing ocean. A more comprehensive understanding of diving behavior, specifically surface intervals, can improve the accuracy of density and abundance estimates by mitigating availability bias. Here, we focus on the proportion of time marine turtles spend at the top 2 m of the water column to coincide with depths where turtles are assumed visible to observers during aerial surveys. To better understand what environmental and oceanographic conditions influence time at surface, we analyzed dive and spatial data from 136 satellite tags attached to three species of threatened or endangered marine turtles across 10 years. We fit generalized additive models with 11 remotely sensed covariates, including sea surface temperature (SST), bathymetry, and salinity, to examine dive patterns. Additionally, the developed model is the first to explicitly examine the potential connection between turtle dive patterns and ocean frontal zones in the Gulf of Mexico. Our results show species-specific associations of environmental covariates related to increased time at surface, particularly for depth, salinity, and frontal features. We define seasonal and spatial variation in time-at-surface patterns in an effort to contribute to marine turtle density and abundance estimates. These estimates could then be utilized to generate correction factors for turtle detection availability during aerial surveys.
... Fine-scale, ephemeral features revealed by FTLE, such as fronts and eddies, provide physical structure in a moving medium, aggregating energy, nutrients and biomass into dynamic patches. As a result, the spatio-temporal scales of marine predators' movements and foraging decisions differ qualitatively from their terrestrial counterparts [34,99]. The scales investigated in this study are therefore necessary for quantifying marine predators' functional responses to environmental heterogeneity and developing a holistic understanding of how consumers optimize energy intake. ...
Article
Marine predators face the challenge of reliably finding prey that is patchily distributed in space and time. Predators make movement decisions at multiple spatial and temporal scales, yet we have a limited understanding of how habitat selection at multiple scales translates into foraging performance. In the ocean, there is mounting evidence that submesoscale (i.e. less than 100 km) processes drive the formation of dense prey patches that should hypothetically provide feeding hot spots and increase predator foraging success. Here, we integrated environmental remote-sensing with high-resolution animal-borne biologging data to evaluate submesoscale surface current features in relation to the habitat selection and foraging performance of blue whales in the California Current System. Our study revealed a consistent functional relationship in which blue whales disproportionately foraged within dynamic aggregative submesoscale features at both the regional and feeding site scales across seasons, regions and years. Moreover, we found that blue whale feeding rates increased in areas with stronger aggregative features, suggesting that these features indicate areas of higher prey density. The use of fine-scale, dynamic features by foraging blue whales underscores the need to take these features into account when designating critical habitat and may help inform strategies to mitigate the impacts of human activities for the species.
... Measuring the responses of marine ecosystems to a changing ocean can be particularly challenging in remote habitats such as polar regions, the deep-sea, or the high-seas (Howell et al., 2021;Kennicutt et al., 2014). To that end, animal biotelemetry, or the use of electronic devices to remotely measure the physiology, behavior, or energetic status of free-living animals (Cooke et al., 2004), has been proven useful to provide with information from these uncharted waters (Sequeira et al., 2018). Biotelemetry is a commonly applied method to investigate the movement ecology and behavior of marine fauna in relation to their environment. ...
Article
Full-text available
The ocean is a key component of the Earth's dynamics, providing a great variety of ecosystem services to humans. Yet, human activities are globally changing its structure and major components, including marine biodiversity. In this context, the United Nations has proclaimed a Decade of Ocean Science for Sustainable Development to tackle the scientific challenges necessary for a sustainable use of the ocean by means of the Sustainable Development Goal 14 (SDG14). Here, we review how Acoustic animal Tracking, a widely distributed methodology of tracking marine biodiversity with electronic devices, can provide a roadmap for implementing the major Actions to achieve the SDG14. We show that acoustic tracking can be used to reduce and monitor the effects of marine pollution including noise, light, and plastic pollution. Acoustic tracking can be effectively used to monitor the responses of marine biodiversity to human‐made infrastructures and habitat restoration, as well as to determine the effects of hypoxia, ocean warming, and acidification. Acoustic tracking has been historically used to inform fisheries management, the design of marine protected areas, and the detection of essential habitats, rendering this technique particularly attractive to achieve the sustainable fishing and spatial protection target goals of the SDG14. Finally, acoustic tracking can contribute to end illegal, unreported, and unregulated fishing by providing tools to monitor marine biodiversity against poachers and promote the development of Small Islands Developing States and developing countries. To fully benefit from acoustic tracking supporting the SDG14 Targets, trans‐boundary collaborative efforts through tracking networks are required to promote ocean information sharing and ocean literacy. We therefore propose acoustic tracking and tracking networks as relevant contributors to tackle the scientific challenges that are necessary for a sustainable use of the ocean promoted by the United Nations. The United Nations has proclaimed a Decade of Ocean Science for Sustainable Development. Here, we review how Acoustic animal Tracking (AT), a widely distributed methodology of tracking marine biodiversity with electronic devices, can provide a roadmap for implementing the major Actions to achieve the Sustainable Development Goals of life below water. This review provides a list of specific examples in how AT can help reaching most Targets by providing cutting‐edge scientific data.
... www.megamove.org; Block et al. 2011;Sequeira et al. 2018;Dunn et al. 2019;Queiroz et al. 2019;Davidson et al. 2020;). However, Sequeira et al. (2019) note that "a lack of organization is likely to create problems in the future by slowing the accessibility and use of large quantities of near real-time data that could be used scientifically to underpin a step change in how marine megafauna are managed." ...
Chapter
Marine mammalsMarine mammals move through dynamic and heterogeneous environments to fulfill maintenance functions. These movements can be studied with various techniques that yield different types of information, and this is increasingly revealing the diversity of movement behaviors among marine mammals. These behaviors vary extensively in their characteristics, from the restricted ranging of some dolphinDolphin species to long-distance seasonal migrationsMigration by species such as humpback and gray whalesGray whale, some of the longest migrations of any animal. As such, movements link places and processes across space and time and are therefore key to understanding the ecology of marine mammals. Given these connections, movement also exposes marine mammals to various natural and anthropogenic threatsAnthropogenic threats and a layer of conservationConservation, management,Management and policy actions across national and international jurisdictions. We review marine mammal movement ecologyMovement ecology in this context, using diverse examples to illustrate the implications of marine mammals’ movements for their conservationConservation and management as well as identifying opportunities therefore. Movement behaviors across different spatiotemporal scales present a difficult challenge for the conservationConservationof marine mammalsMarine mammals, since marine mammals are exposed to pressures and threatsThreats varying from localized effects to global effects such as climate changeClimate change, which are set within—but often beyond—the jurisdiction of many states. For example, species such as blue and humpback whalesHumpback whale migrate through the waters of several nations, and the critical habitatsCritical habitat of pelagic species such as elephant sealsSeal lie in Areas Beyond National Jurisdiction. However, both place-based conservationConservation approaches (such as Marine Protected AreasMarine Protected Area (MPA)) and pressure-based conservationConservation approaches (such as those promoted by multilateral agreementsAgreementsincluding the Convention on the Conservation of Migratory Species of Wild AnimalsConvention on the Conservation of Migratory Species of Wild Animals (CMS)) can integrate information on the movement ecologyMovement ecology of marine mammals, in increasingly dynamic ways. It is clear that “movescapesMovescapes” (the functional value of land- and seascapes to animals over space and time) are essential conservationConservation features, as recognized by Important Marine Mammal AreasImportant Marine Mammal Area (IMMA), for example. Further, as the patterns and consequences of connectivityConnectivity among discrete sites are elucidated, the preservation of connectivity is emerging as a key challenge and opportunity for the conservationConservationand managementManagementof marine mammalsMarine mammals. However, to achieve effective conservationConservation outcomes to so many pressing threatsThreats, marine mammal movescapeMovescapes data needs to be open, accessible, and actionable to inform design and implementation of conservationConservation measures connecting critical habitatsCritical habitatand migration corridorsMigration corridors to mitigate threats. To achieve success, an improved understanding of the needs of managers, policymakers, and governments on a national and international level is required from the start and needs to be championed by the data producers with relevant stakeholdersStakeholders along the way.
... The movement behavior of large marine vertebrates is strongly impacted by habitat complexity rather than evolutionary origin across a wide range of taxa [1]. Movements can be shaped by foraging opportunities and reproductive ecology [2,3], predator avoidance [4,5], and environmental needs [6], all of which are influenced by scale-dependent environmental factors. ...
Article
Full-text available
Background: Reef manta ray (Mobula alfredi) populations along the Northeastern African coastline are poorly studied. Identifying critical habitats for this species is essential for future research and conservation efforts. Dungonab Bay and Mukkawar Island National Park (DMNP), a component of a UNESCO World Heritage Site in Sudan, hosts the largest known M. alfredi aggregation in the Red Sea. Methods: A total of 19 individuals were tagged using surgically implanted acoustic tags and tracked within DMNP on an array of 15 strategically placed acoustic receivers in addition to two offshore receivers. Two of these acoustically monitored M. alfredi were also equipped with satellite linked archival tags and one individual was fitted with a satellite transmitting tag. Together, these data are used to describe approximately two years of residency and seasonal shifts in habitat use. Results: Tagged individuals were detected within the array on 96% of monitored days and recorded an average residence index of 0.39 across all receivers. Detections were recorded throughout the year, though some individuals were absent from the receiver array for weeks or months at a time, and generalized additive mixed models showed a clear seasonal pattern in presence with the highest probabilities of detection occurring in boreal fall. The models indicated that M. alfredi presence was highly correlated with increasing chlorophyll-a levels and weakly correlated with the full moon. Modeled biological factors, including sex and wingspan, had no influence on animal presence. Despite the high residency suggested by acoustic telemetry, satellite tag data and offshore acoustic detections in Sanganeb Atoll and Suedi Pass recorded individuals moving up to 125 km from the Bay. However, all these individuals were subsequently detected in the Bay, suggesting a strong degree of site fidelity at this location. Conclusions: The current study adds to growing evidence that M. alfredi are highly resident and site-attached to coastal bays and lagoons but display seasonal shifts in habitat use that are likely driven by resource availability. This information can be used to assist in managing and supporting sustainable ecotourism within the DMNP, part of a recently designated UNESCO World Heritage Site.
... Marine megafauna (sharks, marine reptiles, seabirds and mammals), many of which are threatened globally, occur commonly around O&G infrastructure . These species are often highly mobile, with movements ranging from short term (days) over distances of tens to hundreds of kilometres or longer, with some species travelling thousands of kilometres (Block et al., 2011;Sequeira et al., 2018). Their movements link various water masses with different oceanographic regimes, habitats and species compositions; therefore, the additional habitat provided by O&G infrastructure in the marine environment can influence seascape connectivity. ...
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