ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
Convergence of marine megafauna movement patterns
in coastal and open oceans
A. M. M. Sequeira
, J. P. Rodríguez
, V. M. Eguíluz
, R. Harcourt
, M. Hindell
, D. W. Sims
, C. M. Duarte
D. P. Costa
, J. Fernández-Gracia
, L. C. Ferreira
, G. C. Hays
, M. R. Heupel
, M. G. Meekan
, A. Aven
, F. Bailleul
A. M. M. Baylis
, M. L. Berumen
, C. D. Braun
, J. Burns
, M. J. Caley
, R. Campbell
, R. H. Carmichael
, E. Clua
L. D. Einoder
, Ari Friedlaender
, M. E. Goebel
, S. D. Goldsworthy
, C. Guinet
, J. Gunn
, D. Hamer
N. Hammerschlag
, M. Hammill
, L. A. Hückstädt
, N. E. Humphries
, M.-A. Lea
, A. Lowther
, A. Mackay
E. McHuron
, J. McKenzie
, L. McLeay
, C. R. McMahon
, K. Mengersen
, M. M. C. Muelbert
, A. M. Pagano
B. Page
, N. Queiroz
, P. W. Robinson
, S. A. Shaffer
, M. Shivji
, G. B. Skomal
, S. R. Thorrold
S. Villegas-Amtmann
, M. Weise
, R. Wells
, B. Wetherbee
, A. Wiebkin
, B. Wienecke
, and M. Thums
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
turning angles
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
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.,
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 (
20350/digitalCSIC/8525). Information on how to access the individual datasets is included
in SI Appendix.
A.M.M.S. and J.P.R. contributed equally to this work.
To whom correspondence should be addressed. Email:
This article contains supporting information online at
1073/pnas.1716137115/-/DCSupplemental. PNAS Latest Articles
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 0150 m depth range (here
referred to as coastal ocean) (see Materials and Methods for de-
tails and SI Appendix,TablesS1S3).
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
) 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).
| Sequeira et al.
analysis of displacements (d
) scaled with time as a power law
) 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
exponent (with 0.51.0 indicating normal random to super-
diffusive, more directed, ballistic motion) was also detected. Higher
exponents were found for all displacements taking place in
the open ocean, where depths 150 m (d
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.
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
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
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
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
) 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.
| Sequeira et al.
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
UWA Oceans Institute, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA 6009, Australia;
School of Biological Sciences,
University of Western Australia, Crawley, WA 6009, Australia;
Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones
CientíficasUniversity of the Balearic Islands, E-07122 Palma de Mallorca, Spain;
Department of Biological Sciences, Macquarie University, Sydney, NSW
2109, Australia;
Ecology and Biodiversity Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS 7004, Australia;
Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth PL1 2PB, United Kingdom;
Ocean and Earth Science, National
Oceanography Centre Southampton, University of Southampton, Southampton SO14 3ZH, United Kingdom;
Centre for Biological Sciences, University of
Southampton, Southampton SO17 1BJ, United Kingdom;
Red Sea Research Center, King Abdullah University of Science and Technology, 23955-6900
Thuwal, Kingdom of Saudi Arabia;
Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060;
Australian Institute of
Marine Science, Indian Ocean Marine Research Centre (M096), University of Western Australia, Crawley, WA 6009, Australia;
School of Life and
Environmental Sciences, Deakin University, Warrnambool, VIC 3280, Australia;
Australian Institute of Marine Science, Townsville, QLD 4810, Australia;
University Programs, Dauphin Island Sea Lab, Dauphin Island, AL 36528;
Department of Marine Sciences, University of South Alabama, Mobile, AL 36688;
South Australian Research and Development Institute, West Beach, SA 5024, Australia;
South Atlantic Environmental Research Institute, FIQQ1ZZ Stanley,
Falkland Islands;
Falklands Conservation, FIQQ1ZZ Stanley, Falkland Islands;
Joint Program in Oceanography/Applied Ocean Science and Engineering,
Massachusetts Institute of Technology-Woods Hole Oceanographic Institution, Cambridge, MA 02139;
Biology Department, Woods Hole Oceanographic
Institution, Woods Hole, MA 02543;
Department of Biological Sciences, University of Alaska, Anchorage, AK 99508;
School of Mathematical Sciences,
Queensland University of Technology, Brisbane, QL 4000, Australia;
Australian Research Council Centre of Excellence for Mathematical and Statistical
Frontiers, Queensland University of Technology, Brisbane, QL 4000, Australia;
Marine Science Division, Department of Parks and Wildlife, Kensington, WA
6151, Australia;
Paris Science Lettre, Laboratoire dExcellence CORAIL, Centre de Recherche Insulaire et Observatoire de lEnvironnement 3278, Ecole
Pratique des Hautes Etudes, 66860 Perpignan, France;
Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina, NT
0810, Australia;
Antarctic Ecosystem Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and
Atmospheric Administration, La Jolla, CA 92037;
Centre dÉtudes Biologiques de Chizé, UMR 7372 CNRS-Université de La Rochelle, 79360 Villiers-en-Bois,
Depredation and By-Catch Mitigation Solutions (DBMS), Global Oceans, Hobart, Tasmania 7001, Australia;
Rosenstiel School of Marine and
Atmospheric Science, Abess Center for Ecosystem Science and Policy, University of Miami, Miami, FL 33149;
Department of Fisheries and Oceans, Maurice
Lamontagne Institute, Mont Joli, QC G5H3Z4, Canada;
Alaska Ecosystems Program, National Marine Mammal Laboratory, National Oceanic and
Atmospheric Administration Fisheries, Alaska Fisheries Science Center, Seattle, WA 98115;
Research Department, Biodiversity Section, Norwegian Polar
Institute, 9296 Tromsø, Norway;
Science Group, Department of Environment, Water and Natural Resources, Adelaide, SA 5001, Australia;
Sydney Institute
of Marine Science, Mosman, NSW 2088, Australia;
Instituto de Oceanografia, Universidade Federal do Rio Grande, Rio Grande, RS 96203-900, Brazil;
Alaska Science Center, US Geological Survey, Anchorage, AK 99508;
Biosecurity South Australia, Primary Industries and Regions South Australia, South
Australia, Adelaide, SA 5001, Australia;
Centro de Investigação em Biodiversidade e Recursos Genéticos (Research Centre in Biodiversity and Genetic
Resources), Associate Laboratory, Universidade do Porto, 4485-668 Vairão, Portugal;
Department of Biological Sciences, San Jose State University, San
Jose, CA 95192;
Guy Harvey Research Institute, Halmos College of Natural Sciences & Oceanography, Nova Southeastern University, Dania Beach, FL
Shark Research Program, Massachusetts Division of Marine Fisheries, New Bedford, MA 02740;
Marine Mammal Program, Office of Naval
Research, Arlington, VA 22203-1995;
Sarasota Dolphin Research Program, Chicago Zoological Society, c/o Mote Marine Laboratory, Sarasota, FL 34236; and
Australian Antarctic Division, Department of the Environment and Energy, Kingston, TAS 7052, Australia
1. Stearns SC (1976) Life-history tactics: A review of the ideas. Q Rev Biol 51:347.
2. Charnov EL (1976) Optimal foraging, the marginal value theorem. Theor Popul Biol 9:
3. Stephens D, Krebs J (1986) Foraging Theory (Princeton Univ Press, Princeton).
4. Schmidt-Nielsen K (1972) Locomotion: Energy cost of swimming, flying, and running.
Science 177:222228.
5. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB (2004) Toward a metabolic
theory of ecology. Ecology 85:17711789.
6. Nussbaum M C (1978) Aristotles De Motu Animalium: Text with Translation, Commentary,
and Interpretive Essays (Princeton Univ Press, Princeton).
7. Nathan R, et al. (2008) A movement ecology paradigm for unifying organismal
movement research. Proc Natl Acad Sci USA 105:1905219059.
8. Fauchald P (1999) Foraging in a hierarchical patch system. Am Nat 153:603613.
9. Kareiva P, Odell G (1987) Swarms of predators exhibit preytaxis if individual predators
use area-restricted search. Am Nat 130:233270.
10. Bartumeus F, Da Luz MGE, Viswanathan GM, Catalan J (2005) Animal search strate-
gies: A quantitative. Random-walk analysis. Ecology 86:30783087.
11. Turchin P (1998) Quantitative Analysis of Movement (Sinauer Associates, Sunderland,MA).
12. Viswanathan G, da Luz M, Raposo E, Stanley H (2011) The Physics of Foraging: An
Introduction to Random Searches and Biological Encounters (Cambridge Univ Press,
Cambridge, UK).
13. Morales JM, et al. (2010) Building the bridge between animal movement and pop-
ulation dynamics. Philos Trans R Soc Lond B Biol Sci 365:22892301.
14. Hays GC, Scott R (2013) Global patterns for upper ceilings on migration distance in sea
turtles and comparisons with fish, birds and mammals. Funct Ecol 27:748756.
15. Bale R, Hao M, Bhalla APS, Patankar NA (2014) Energy efficiency and allometry of
movement of swimming and flying animals. Proc Natl Acad Sci USA 111:75177521.
16. Singh NJ, Ericsson G (2014) Changing motivations during migration: Linking move-
ment speed to reproductive status in a migratory large mammal. Biol Lett 10:
17. Shaffer SA, Weimerskirch H, Costa DP (2001) Functional significance of sexual di-
morphism in Wandering Albatrosses, Diomedea exulans. Funct Ecol 15:203210.
18. Whitlock RE, et al. (2015) Direct quantification of energy intake in an apex marine
predator suggests physiology is a key driver of migrations. Sci Adv 1:e1400270.
19. Sims DW, et al. (2008) Scaling laws of marine predator search behaviour. Nature 451:
20. Garland T (1983) Scaling the ecological cost of transport to body-mass in terrestrial
mammals. Am Nat 121:571587.
21. Carbone C, Cowlishaw G, Isaac NJB, Rowcliffe JM (2005) How far do animals go?
Determinants of day range in mammals. Am Nat 165:290297.
22. Jetz W, Carbone C, Fulford J, Brown JH (2004) The scaling of animal space use. Science
23. Donovan ER, Gleeson TT (2008) Scaling the duration of activity relative to body mass
results in similar locomotor performance and metabolic costs in lizards. J Exp Biol 211:
24. Lidicker WZ, Jr, Stenseth NC (1992) To Disperse or Not to Disperse: Who Does it and
Why? Animal Dispersal (Springer, Dordrecht, The Netherlands), pp 2136.
25. Rubenstein DR, Hobson KA (2004) From birds to butterflies: Animal movement pat-
terns and stable isotopes. Trends Ecol Evol 19:256263.
26. Block BA, et al. (2011) Tracking apex marine predator movements in a dynamic ocean.
Nature 475:8690.
27. Hindell MA, et al. (2016) Circumpolar habitat use in the southern elephant seal: Im-
plications for foraging success and population trajectories. Ecosphere 7:e01213.
28. Hussey NE, et al. (2015) ECOLOGY. Aquatic animal telemetry: A panoramic window
into the underwater world. Science 348:1255642.
29. Hays GC, et al. (2016) Key questions in marine megafauna movement ecology. Trends
Ecol Evol 31:463475.
30. Barbraud C, Weimerskirch H (2001) Emperor penguins and climate change. Nature
31. Gallagher AJ, Hammerschlag N (2011) Global shark currency: The distribution, fre-
quency, and economic value of shark ecotourism. Curr Issues Tour 14:797812.
32. Queiroz N, et al. (2016) Ocean-wide tracking of pelagic sharks reveals extent of
overlap with longline fishing hotspots. Proc Natl Acad Sci USA 113:15821587.
33. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees.
J Anim Ecol 77:802813.
34. Jonsen ID, et al. (2013) State-space models for bio-loggers: A methodological road
map. Deep Sea Res Part II Top Stud Oceanogr 8889:3446.
35. Bost CA, et al. (2009) The importance of oceanographic fronts to marine birds and
mammals of the southern oceans. J Mar Syst 78:363376.
36. Villegas-Amtmann S, Costa DP, Tremblay Y, Salazar S, Aurioles-Gamboa D (2008)
Multiple foraging strategies in a marine apex predator, the Galapagos sea lion Za-
lophus wollebaeki. Mar Ecol Prog Ser 363:299309.
37. Humphries NE, et al. (2010) Environmental context explains Lévy and Brownian
movement patterns of marine predators. Nature 465:10661069.
38. Humphries NE, Weimerskirch H, Queiroz N, Southall EJ, Sims DW (2012) Foraging
success of biological Lévy flights recorded in situ. Proc Natl Acad Sci USA 109:
39. Steele JH, Henderson EW (1994) Coupling between physical and biological scales.
Philos T Roy Soc B 343:59.
40. Rodríguez JP, et al. (2017) Big data analyses reveal patterns and drivers of the
movements of southern elephant seals. Sci Rep 7:112.
41. Harley CDG, et al. (2006) The impacts of climate change in coastal marine systems.
Ecol Lett 9:228241.
42. Schipper J, et al. (2008) The status of the worlds land and marine mammals: Diversity,
threat, and knowledge. Science 322:225230.
43. Halpern BS, et al. (2008) A global map of human impact on marine ecosystems.
Science 319:948952.
44. Barnosky AD, et al. (2017) Merging paleobiology with conservation biology to guide
the future of terrestrial ecosystems. Science 355:eaah4787.
45. Post E, et al. (2013) Ecological consequences of sea-ice decline. Science 341:519524.
46. Nicholls RJ, Cazenave A (2010) Sea-level rise and its impact on coastal zones. Science
47. Dutton A, et al. (2015) SEA-LEVEL RISE. Sea-level rise due to polar ice-sheet mass loss
during past warm periods. Science 349:aaa4019.
| Sequeira et al.
... Within guilds of animals, activity space (i.e., space use) generally varies as a function of body weight (Turner et al. 1969;Hendriks 2007) because larger animals tend to move over larger spatial extents than smaller animals because of their greater energy requirements (McNab 1963;Lindstedt et al. 1986) and the relatively lower energetic cost of movement (Schmidt-Nielsen 1972). Allometry of activ-ity space across animal guilds is also influenced by a range of other factors, such as locomotive strategy, trophic level, social conditions, habitat dimensionality, and prey size (Harestad and Bunnel 1979;Carbone et al. 2004;Jetz et al. 2004;Tamburello et al. 2015;Rosten et al. 2016;Sequeira et al. 2018;Boratyński et al. 2020;Todd and Nowakowski 2021). ...
... Comparative studies of lake-and river-dwelling freshwater fishes have shown that while the relationship between body size and activity space produced homogenous slopes across environments, lake fish always had larger activity spaces than river fish of equal body mass (Minns 1995). Furthermore, movement patterns and activity spaces of endothermic marine animals are significantly influenced by whether they forage on or off the continental shelf, with species that occupy offshore pelagic habitats displaying more directed daily movements than their coastal counterparts (Sequeira et al. 2018). Species that feed at higher trophic levels need to forage over greater distances to find their relatively less abundant prey (Lindstedt et al. 1986). ...
... 2. Dimensionality of foraging habitat. Differences in the dimension of foraging habitat have been shown to affect activity space (Pearce et al. 2013;Sequeira et al. 2018) and to lower exponents of the body mass-home range relationship in mammals ). As the present study estimated two-dimensional activity space, it might have also underestimated the true home range of tertiary consumers species that feed in a three-dimensional habitat (Bestley et al. 2015;Udyawer et al. 2015;Lee et al. 2017). ...
Unifying models have shown that the amount of space used by animals (e.g., activity space, home range) scales allometrically with body mass for terrestrial taxa; however, such relationships are far less clear for marine species. We compiled movement data from 1,596 individuals across 79 taxa collected using a continental passive acoustic telemetry network of acoustic receivers to assess allometric scaling of activity space. We found that ectothermic marine taxa do exhibit allometric scaling for activity space, with an overall scaling exponent of 0.64. However, body mass alone explained only 35% of the variation, with the remaining variation best explained by trophic position for teleosts and latitude for sharks, rays, and marine reptiles. Taxon-specific allometric relationships highlighted weaker scaling exponents among teleost fish species (0.07) than sharks (0.96), rays (0.55), and marine reptiles (0.57). The allometric scaling relationship and scaling exponents for the marine taxonomic groups examined were lower than those reported from studies that had collated both marine and terrestrial species data derived using various tracking methods. We propose that these disparities arise because previous work integrated summarized data across many studies that used differing methods for collecting and quantifying activity space, introducing considerable uncertainty into slope estimates. Our findings highlight the benefit of using large-scale, coordinated animal biotelemetry networks to address cross-taxa evolutionary and ecological questions.
... They play a crucial role in maintaining biodiversity patterns and ecosystem function, transferring energy as they travel both vertically and horizontally through the ocean, often over large spatial and temporal scales (e.g. Catano et al., 2016;Hindell et al., 2020;Sequeira et al., 2018;Wang et al., 2019). It is therefore important that the distribution and abundance of marine predator populations are understood; yet, given their K-selected life history and high mobility, this requires largescale and long-term monitoring. ...
... However, few examples of comprehensive monitoring programmes exist in marine ecosystems and are often retrospective analyses (e.g. Hindell et al., 2020;Sequeira et al., 2018) despite the anthropogenic pressures that threaten the persistence of biodiversity, including large marine predators Schipper et al., 2008;Worm et al., 2006). Anthropogenic impacts are well-illustrated by the poor conservation status of cetacean and shark species, with a decline of more than 70% of sharks and rays since the 1970s and 37% of marine mammals at risk of extinction (Davidson et al., 2012). ...
Full-text available
Aim Large marine predators, such as cetaceans and sharks, play a crucial role in maintaining biodiversity patterns and ecosystem function, yet few estimates of their spatial distribution exist. We aimed to determine the species richness of large marine predators and investigate their fine‐scale spatiotemporal distribution patterns to inform conservation management. Location The Hauraki Gulf/Tīkapa Moana/Te Moananui‐ā‐Toi, Aotearoa/New Zealand. Methods We conducted a replicate systematic aerial survey over 12 months. Flexible machine learning models were used to explore relationships between large marine predator occurrence (Bryde's whales, common and bottlenose dolphins, bronze whaler, pelagic and immature hammerhead sharks) and environmental and biotic variables, and predict their monthly distribution and associated spatially explicit uncertainty. Results We revealed that temporally dynamic variables, such as prey distribution and sea surface temperature, were important for predicting the occurrence of the study species and species groups. While there was variation in temporal and spatial distribution, predicted richness peaked in summer and was the highest in coastal habitats during that time, providing insight into changes in distributions over time and between species. Main Conclusions Temporal changes in distribution are not routinely accounted for in species distribution studies. Our approach highlights the value of multispecies surveys and the importance of considering temporally variable abiotic and biotic drivers for understanding biodiversity patterns when informing ecosystem‐scale conservation planning and dynamic ocean management.
... Satellite tags are advantageous for measuring the efficacy of large, remote protected areas as data can be recorded independently of geopolitical zones, and in areas beyond national jurisdiction (White et al., 2017;MacKeracher et al., 2019;Sequeira et al., 2018). Emphasis in recent years has been placed on amalgamating sources of tracking data in order to optimize tagging outcomes and produce results on a global scale (see Sequeira et al., 2018;Harcourt et al., 2019, andHays et al., 2019). ...
... Satellite tags are advantageous for measuring the efficacy of large, remote protected areas as data can be recorded independently of geopolitical zones, and in areas beyond national jurisdiction (White et al., 2017;MacKeracher et al., 2019;Sequeira et al., 2018). Emphasis in recent years has been placed on amalgamating sources of tracking data in order to optimize tagging outcomes and produce results on a global scale (see Sequeira et al., 2018;Harcourt et al., 2019, andHays et al., 2019). Multi-year, continuous studies are beneficial for overcoming the often-limited duration of tagging studies (usually less than one year), by creating pooled resources that can be used to generate long-term studies of seasonality and species movements, migrations, residency and/or aggregations ('hotspots') on a global scale , whilst reducing tagging bias based on the tagging location (Maxwell et al., 2019;Francis et al., 2019;Queiroz et al., 2016), irregular post-release behaviour (Shipley et al., 2017a), and sex and age of tagged sharks (Andrzejaczek et al., 2018;Howey et al., 2016;Howey-Jordan et al., 2013;Wells et al., 2018). ...
Satellite telemetry as a tool in marine ecological research continues to adapt and grow and has become increasingly popular in recent years to study shark species on a global scale. A review of satellite tag application to shark research was published in 2010, provided insight to the advancements in satellite shark tagging, as well as highlighting areas for improvement. In the years since, satellite technology has continued to advance, creating smaller, longer lasting, and more innovative tags, capable of expanding the field. Here we review satellite shark tagging studies to identify early successes and areas for rethinking moving forward. Triple the amount of shark satellite tagging studies have been conducted during the decade from 2010 to 2020 than ever before, tracking double the number of species previously tagged. Satellite telemetry has offered increased capacity to unravel ecological questions including predator and prey interactions, migration patterns, habitat use, in addition to monitoring species for global assessments. However, <17% of the total reviewed studies directly produced results with management or conservation outcomes. Telemetry studies with defined goals and objectives produced the most relevant findings for shark conservation, most often in tandem with secondary metrics such as fishing overlap or management regimes. To leverage the power of telemetry for the benefit of shark species, it remains imperative to continue improvements to tag function and maximize the outputs of tagging efforts including increasing data sharing capacity and standardization across the field, as well as spatial and species coverage. Ultimately, this review offers a status report of shark satellite tagging and the ways in which the field can continue to progress.
... 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. ...
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.
... Because oceanic fronts influence prey distribution, studies of highly mobile marine predators often focus on movements and distribution in relation to fronts Sequeira et al., 2018). While this information provides valuable insight of when and where predators encounter prey and use frontal habitat, they often lack the resolution necessary to describe how the fronts are exploited (but see Arostegui et al., 2022). ...
Full-text available
Pelagic predators must contend with low prey densities that are irregularly distributed and dynamic in space and time. Based on satellite imagery and telemetry data, many pelagic predators will concentrate horizontal movements on ephemeral surface fronts-gradients between water masses-because of enhanced local productivity and increased forage fish densities. Vertical fronts (e.g. thermoclines, oxyclines) can be spatially and temporally persistent, and aggregate lower trophic level and diel vertically migrating organisms due to sharp changes in temperature, water density or available oxygen. Thus, vertical fronts represent a stable and potentially energy rich habitat feature for diving pelagic predators but remain little explored in their capacity to enhance foraging opportunities. Here, we use a novel suite of high-resolution biologging data, including in situ derived oxygen saturation and video, to document how two top predators in the pelagic ecosystem exploit the vertical fronts created by the oxygen minimum zone of the eastern tropical Pacific. Prey search behaviour was dependent on dive shape, and significantly increased near the thermocline and hypoxic boundary for blue marlin Makaira nigricans and sailfish Istiophorus platypterus, respectively. Further, we identify a behaviour not yet reported for pelagic predators, whereby the predator repeatedly dives below the thermocline and hypoxic boundary (and by extension, below the prey). We hypothesize this behaviour is used to ambush prey concentrated at the boundaries from below. We describe how habitat fronts created by low oxygen environments can influence pelagic ecosystems, which will become increasingly important to understand in the context of global change and expanding oxygen minimum zones. We anticipate that our findings are shared among many pelagic predators where strong vertical fronts occur, and additional high-resolution tagging is warranted to confirm this.
... Documenting spatiotemporal patterns of species presence is a crucial part of the conservation and management of marine top predators. Detailed spatial information about species presence often aids in the definition of new or distinct populations as well as the understanding of habitat use and movement patterns (e.g., Baird et al., 2010;Scofield et al., 2011;Sequeira et al., 2018). Continuous temporal data facilitate description of key patterns in animal activity, which is crucial for understanding foraging strategies and mitigating harmful anthropogenic interactions (Forney et al., 2011;Jones et al., 2019;Soldevilla et al., 2011). ...
Full-text available
Successful conservation and management of marine top predators rely on detailed documentation of spatiotemporal behavior. For cetacean species, this information is key to defining stocks, habitat use, and mitigating harmful interactions. Research focused on this goal is employing methodologies such as visual observations, tag data, and passive acoustic monitoring (PAM) data. However, many studies are temporally limited or focus on only one or few species. In this study, we make use of an existing long-term (2009-2019), labeled PAM data set to examine spatiotemporal patterning of at least 10 odontocete (toothed whale) species in the Hawaiian Islands using compositional analyses and modeling techniques. Species composition differs among considered sites, and this difference is robust to seasonal movement patterns. Temporally, hour of day was the most significant predictor of detection across species and sites, followed by season, though patterns differed among species. We describe long-term trends in species detection at one site and note that they are markedly similar for many species. These trends may be related to long-term, underlying oceanographic cycles that will be the focus of future study. We demonstrate the variability of temporal patterns even at relatively close sites, which may imply that wide-ranging models of species presence are missing key fine-scale movement patterns. Documented seasonal differences in detection also highlights the importance of considering season in survey design both regionally and elsewhere. We emphasize the utility of long-term, continuous monitoring in highlighting temporal patterns that may relate to underlying climatic states and help us predict responses to climate change. We conclude that long-term PAM records are a valuable resource for documenting spatiotemporal patterns and can contribute many insights into the lives of top predators, even in highly studied regions such as the Hawaiian Islands.
... This decouples step lengths from turning angles. In reality, step lengths and turning angles are often related, with longer step lengths having smaller turning angles, especially during pelagic migrations [50]. A way to consider this correlation is to draw from a single, two-dimensional empirical probability distribution at each time step in the turning angle-step length space (Fig. 3c). ...
Full-text available
Background Understanding the selection of environmental conditions by animals requires knowledge of where they are, but also of where they could have been. Presence data can be accurately estimated by direct sampling, sightings, or through electronic tag deployments. However, absence data are harder to determine because absences are challenging to measure in an uncontrolled setting. To address this problem, ecologists have developed different methods for generating pseudo-absence data relying on theoretical movement models. These null models represent the movement of environmentally naive individuals, creating a set of locations that animals could have been if they were not exhibiting environmental selection. Methods Here, we use four different kinds of null animal movement models—Brownian motion, Lévy walks, Correlated random walks, and Joint correlated random walks to test the ability and power of each of these null movement models to serve as appropriate animal absence models. We use Kolmogorov-Smirnov tests to detect environmental selection using two data sets, one of simulated animal tracks biased towards warmer sea surface temperatures, and one of 57 observed blue shark tracks of unknown sea surface temperature selection. Results The four different types of movement models showed minimal difference in the ability to serve as appropriate null models for environmental selection studies. Selection strength and sample size were more important in detecting true environmental selection. We show that this method can suffer from high false positive rates, especially in the case where animals are not selecting for specific environments. We provide estimates of test accuracy at different sample sizes and selection strengths to avoid false positives when using this method. Conclusion We show how movement models can be used to generate pseudo-absences and test for habitat selection in marine organisms. While this approach efficiently detects environmental selection in marine organisms, it cannot detect the underlying mechanisms driving this selection.
... 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). ...
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. ...
Full-text available
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.
Full-text available
Loggerhead sea turtle ( Caretta caretta ) nesting events are increasing in the western Mediterranean Sea, far from their usual nesting areas in the Mediterranean and Atlantic basins. The study of dispersal behaviour towards potential developmental areas of loggerhead post-hatchlings from this new nesting area is crucial to comprehend this colonization process and determining grounded conservation strategies. To fill this gap, was investigated, for the first time in the Mediterranean Sea, the dispersal strategies and habitat use based on data from 19 head-started loggerhead post-hatchlings released from the Spanish Mediterranean coast and satellite-tracked between 2016-2018. Turtles dispersed over large areas and showed active swimming phases, as they frequently dispersed against sea currents. Dispersal routes varied for each individual, although they consistently dispersed south-eastwards, especially during the coldest periods. Several post-hatchlings travelled through the Sicilian Channel to reach deep and warmer areas in the eastern Mediterranean basin. The most year-round suitable areas corresponded to the Ionian and Levant Seas. Therefore, conservation measures focused on this stage should be taken at least in these developmental areas.
Full-text available
The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with “big data”, that require no ‘a priori’ assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for “big data” techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.
Full-text available
BACKGROUND: The pace and magnitude of human-caused global change has accelerated dramatically over the past 50 years, overwhelming the capacity of many ecosystems and species to maintain themselves as they have under the more stable conditions that prevailed for at least 11,000 years. The next few decades threaten even more rapid transformations because by 2050, the human population is projected to grow by 3 billion while simultaneously increasing per capita consumption. Thus, to avoid losing many species and the crucial aspects of ecosystems that we need—for both our physical and emotional well-being—new conservation paradigms and integration of information from conservation biology, paleobiology, and the Earth sciences are required.
Full-text available
It is a golden age for animal movement studies and so an opportune time to assess priorities for future work. We assembled 40 experts to identify key questions in this field, focussing on marine megafauna, which include a broad range of birds, mammals, reptiles, and fish. Research on these taxa has both underpinned many of the recent technical developments and led to fundamental discoveries in the field. We show that the questions have broad applicability to other taxa, including terrestrial animals, flying insects, and swimming invertebrates, and, as such, this exercise provides a useful roadmap for targeted deployments and data syntheses that should advance the field of movement ecology.
In the last two decades it has become increasingly clear that the spatial dimension is a critically important aspect of ecological dynamics. Ecologists are currently investing an enormous amount of effort in quantifying movement patterns of organisms. Connecting these data to general issues in metapopulation biology and landscape ecology, as well as to applied questions in conservation and natural resource management, however, has proved to be a non-trivial task. This book presents a systematic exposition of quantitative methods for analyzing and modeling movements of organisms in the field. Quantitative Analysis of Movement is intended for graduate students and researchers interested in spatial ecology, including applications to conservation, pest control, and fisheries. Models are a key ingredient in the analytical approaches developed in the book; however, the primary focus is not on mathematical methods, but on connections between models and data. The methodological approaches discussed in the book will be useful to ecologists working with all taxonomic groups. Case studies have been selected from a wide variety of organisms, including plants (seed dispersal, spatial spread of clonal plants), insects, and vertebrates (primarily, fish, birds, and mammals).
An optimal search theory, the so-called Lévy-flight foraging hypothesis, predicts that predators should adopt search strategies known as Lévy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey. Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify Lévy behaviour has recently been questioned. Consequently, whether foragers exhibit Lévy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory’s central predictions. Here we use maximum-likelihood methods to test for Lévy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for Lévy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between Lévy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found Lévy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the Lévy-flight foraging hypothesis1, supporting the contention that organism search strategies naturally evolved in such a way that they exploit optimal Lévy patterns.
Day range (daily distance traveled) is an important measure for understanding relationships between animal distributions and food resources. However, our understanding of variation in day range across species is limited. Here we present a day range model and compare predictions against a comprehensive analysis of mammalian day range. As found in previous studies, day range scales near the \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $1/ 4$ \end{document} power of body mass. Also, consistent with model predictions, taxonomic groups differ in the way day range scales with mass, associated with the most common diet types and foraging habitats. Faunivores have the longest day ranges and steepest body mass scaling. Frugivores and herbivores show intermediate and low scaling exponents, respectively. Day range in primates did not scale with mass, which may be consistent with the prediction that three‐dimensional foraging habitats lead to lower exponents. Day ranges increase with group size in carnivores but not in other taxonomic groups.