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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 animals’movement 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
|
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-
mal”resource exploitation by predators (“optimality paradigm”;
refs. 2, 3, 8, and 9) or the use of more efficient search trajectories
(“random”paradigm; refs. 10–12). 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 (13–19), including allometric and metabolic scaling with
day or home range and locomotion speed, particularly for terrestrial
animals (4, 15, 20–23). 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.
This article is a PNAS Direct Submission.
Published under the PNAS license.
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.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1716137115/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1716137115 PNAS Latest Articles
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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–
28). For these reasons, answering questions about marine animal
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 0–150 m depth range (here
referred to as “coastal ocean”) (see Materials and Methods for de-
tails and SI Appendix,TablesS1–S3).
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 superdiffusively—that 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 0.5–1.0 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 ocean”or “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 Kolmogorov–Smirnov 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 individual’s 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=0.784–0.085 ×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 Kolmogorov–Smirnov 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 species’CDFs. 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 land—for 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 Kolmogorov–Smirnov 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 moving”sought 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 (41–43). 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 (1985–2015) 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 0–150 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 S2–S4). Modeling details
are given in SI Appendix,SI Materials and Methods.
Dendrogram of Movement. We calculated the Kolmogorov–Smirnov 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
Científicas–University 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 d’Excellence CORAIL, Centre de Recherche Insulaire et Observatoire de l’Environnement 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 d’Études 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
Atmospheric Administration Fisheries, Alaska Fisheries Science Center, Seattle, WA 98115;
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
Australia, Adelaide, SA 5001, Australia;
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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