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Capture-mark-recapture methods have been extensively used to estimate abundance, demography, and life history parameters of populations of several taxa. However, the high mobility of many species means that dedicated surveys are logistically complicated and expensive. Use of opportunis-tic data may be an alternative, if modeling takes into account the inevitable heterogeneity in capture probability from imperfect detection and incomplete sampling, which can produce significant bias in parameter estimates. Here, we compare covariate-based open Jolly-Seber models (POPAN) and multi-state open robust design (MSORD) models to estimate demographic parameters of the sperm whale population sum-mering in the Azores, from photo-identification data collected opportunistically by whale-watching operators and researchers. The structure of the MSORD also allows for extra information to be obtained, estimating temporary emigration and improving precision of estimated parameters. Estimates of survival from both POPAN and MSORD were high, constant, and very similar. The POPAN model, which partially accounted for heterogeneity in capture probabilities, estimated an unbiased super-population of ~1470 whales, with annual abundance showing a positive trend from 351 individuals (95% CI: 234-526) in 2010 to 718 (95% CI: 477-1082) in 2015. In contrast, estimates of abundance from MSORD models that explicitly incorporated imperfect detection due to temporary emigration were less biased, more precise, and showed no trend over years, from 275 individuals (95% CI: 188-404) in 2014 to 367 (95% CI: 248-542) in 2012. The MSORD estimated short residence time and an even-flow temporary emigration, meaning that the probability of whales emigrating from and immigrating to the area was equal. Our results illustrate how failure to account for transience and temporary emigration can lead to biased estimates and trends in abundance, compromising our ability to detect true population changes. MSORD models should improve inferences of population dynamics, especially when capture probability is low and highly variable, due to wide-ranging behavior of individuals or to non-standardized sampling. Therefore, these models should provide less biased estimates and more accurate assessments of uncertainty that can inform management and conservation measures.
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Multi-state open robust design applied to opportunistic data
reveals dynamics of wide-ranging taxa: the sperm whale case
REBECCA M. BOYS ,
1,2,
CL
AUDIA OLIVEIRA,
1,2
SERGI P
EREZ-JORGE ,
1,2
RUI PRIETO ,
1,2
LISA STEINER,
3
AND M
ONICA A. SILVA
1,2,4
1
Okeanos R&D Centre - University of the Azores and IMAR Institute of Marine Research, 9901-862 Horta Portugal
2
MARE Marine and Environmental Sciences Centre, 9901-862 Horta Portugal
3
Whale Watch Azores (WWA), Estrada da Caldeira, 2, Horta 9900-089 Faial
4
Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543 USA
Citation: Boys, R. M., C. Oliveira, S. P
erez-Jorge, R. Prieto, L. Steiner, and M. A. Silva. 2019. Multi-state open robust
design applied to opportunistic data reveals dynamics of wide-ranging taxa: the sperm whale case. Ecosphere 10(3):
e02610. 10.1002/ecs2.2610
Abstract. Capturemarkrecapture methods have been extensively used to estimate abundance,
demography, and life history parameters of populations of several taxa. However, the high mobility of
many species means that dedicated surveys are logistically complicated and expensive. Use of opportunis-
tic data may be an alternative, if modeling takes into account the inevitable heterogeneity in capture proba-
bility from imperfect detection and incomplete sampling, which can produce signicant bias in parameter
estimates. Here, we compare covariate-based open Jolly-Seber models (POPAN) and multi-state open
robust design (MSORD) models to estimate demographic parameters of the sperm whale population sum-
mering in the Azores, from photo-identication data collected opportunistically by whale-watching opera-
tors and researchers. The structure of the MSORD also allows for extra information to be obtained,
estimating temporary emigration and improving precision of estimated parameters. Estimates of survival
from both POPAN and MSORD were high, constant, and very similar. The POPAN model, which partially
accounted for heterogeneity in capture probabilities, estimated an unbiased super-population of ~1470
whales, with annual abundance showing a positive trend from 351 individuals (95% CI: 234526) in 2010
to 718 (95% CI: 4771082) in 2015. In contrast, estimates of abundance from MSORD models that explicitly
incorporated imperfect detection due to temporary emigration were less biased, more precise, and showed
no trend over years, from 275 individuals (95% CI: 188404) in 2014 to 367 (95% CI: 248542) in 2012. The
MSORD estimated short residence time and an even-ow temporary emigration, meaning that the proba-
bility of whales emigrating from and immigrating to the area was equal. Our results illustrate how failure
to account for transience and temporary emigration can lead to biased estimates and trends in abundance,
compromising our ability to detect true population changes. MSORD models should improve inferences of
population dynamics, especially when capture probability is low and highly variable, due to wide-ranging
behavior of individuals or to non-standardized sampling. Therefore, these models should provide less
biased estimates and more accurate assessments of uncertainty that can inform management and conserva-
tion measures.
Key words: abundance; capturemarkrecapture; mobile taxa; multi-state open robust design model; opportunistic
data; photo-identication; POPAN model; population dynamics; sperm whales (Physeter macrocephalus); survival;
temporary emigration; transients.
Received 18 April 2018; revised 22 November 2018; accepted 9 January 2019. Corresponding Editor: Tanya Berger-Wolf.
Copyright: ©2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
E-mail: rebeccaboys@hotmail.com
www.esajournals.org 1March 2019 Volume 10(3) Article e02610
INTRODUCTION
Application of capturemarkrecapture (CMR)
methods to estimate life history parameters from
photo-identication data of naturally marked indi-
viduals has been extensively used on several taxa,
such as cetaceans (Hammond et al. 1990), mana-
tees (Langtimm et al. 2004), sharks (Arzoumanian
et al. 2005), and a variety of felids (Broekhuis and
Gopalaswamy 2016). Ideally, CMR studies would
involve extensive sampling effort across the geo-
graphic range of the target population (Kendall
and Nichols 2004). In addition, in the case of long-
lived species, sampling over multiple years is
usually required to efciently estimate survival or
recruitment. However, such effort is expensive
and logistically demanding. A cost-effective app-
roach could be the use of individual-identication
data that are opportunistically collected (Tezanos-
Pinto et al. 2013, Strandbr
aten Rødland and
Bjorge 2015, Bertulli et al. 2017).
The application of CMR methods to highly
mobile species can be challenging though. Differ-
ences in movement patterns and site delity
among individual animals over time can lead to
heterogeneous capture probabilities, violating
the standard assumptions of conventional mod-
els (Kendall et al. 1997). Heterogeneity may also
arise from the uneven distribution of survey
effort, if individuals are more likely to be
detected at some locations and times than others
which may be exacerbated in opportunistic sam-
pling. Ignoring individual heterogeneity in cap-
ture probability can affect accuracy and precision
of CMR estimates and may result in false trends
being observed (Pfaller et al. 2013, Sanders and
Trost 2013).
The most commonly used modeling approa-
ches to deal with individual heterogeneity and
imperfect detection are random-effects, nite-
mixture, and models with individual covariates.
Random-effects (Gimenez and Choquet 2010)
and nite-mixture (Pledger et al. 2010) models
are especially appropriate when heterogeneity
cannot be measured or when individual covari-
ates are not applicable (Gimenez et al. 2017).
When heterogeneity is adequately explained by
individual covariates, capture and survival prob-
abilities can be modeled as a function of these
covariates (Pollock 2002). Continuous time-vary-
ing individual covariates can be observable
attributes of individuals (e.g., age class or body
mass) or variables that allow inference about
hidden states (e.g., capture frequency data from
previous sampling periods; Pollock 2002). Con-
tinuous time-varying covariates are challenging
to model, but discrete time-varying individual
covariates, known as states (Gimenez et al.
2017), can be analyzed with multi-state models.
In essence, multi-state CMR models assume that
animals may be in a discrete set of states (dened
by geographic location, reproductive status, age,
etc.), some of which may be observable and
others unobservable, and individuals may transi-
tion between states over time (Schwarz et al.
1993, Lebreton and Pradel 2002).
Therefore, multi-state models provide a conve-
nient way of modeling heterogeneity caused by
temporary emigration, by implicitly assuming
that animals present in the study area are observ-
able, whereas unobservable individuals are those
temporary emigrants, absent from the study area
during a given period. These models can pro-
duce unbiased estimates of state-specic parame-
ters (abundance, apparent survival, capture
probability) and of the probability of animals
changing between states. A special case of these
models is the multi-state open robust design
(MSORD) that combines features of multi-state
models with Pollocks robust design sampling
strategy and implicitly accounts for imperfect
detection probability (Kendall et al. 1997, 2018,
Schwarz and Stobo 1997, Kendall and Bjorkland
2001, Ruiz-Gutierrez et al. 2016). Pollocks robust
design (Pollock 1982) consists of two or more sec-
ondary samples over relatively short intervals
(days to weeks) within each primary period (usu-
ally seasons or years). Multi-state open robust
design models benet from the extra information
in the secondary occasions to estimate abun-
dance for each state within each primary period
and to improve precision of survival and transi-
tion probabilities (Kendall and Bjorkland 2001).
An important assumption of MSORD models is
that animals may enter and exit the study area
once during each primary period (Kendall and
Bjorkland 2001), allowing the population to be
geographically open. Therefore, MSORD models
permit animals to arrive and depart the study
area at different times within a primary period,
accounting for transience and temporary emigra-
tion, and only assume equal capturability among
www.esajournals.org 2March 2019 Volume 10(3) Article e02610
BOYS ET AL.
those individuals present in the area during a
primary period.
Multi-state open robust design models can be
more complex and data-hungry than open mod-
els incorporating individual covariates but offer
greater exibility in modeling heterogeneity in
capture probabilities. Here, we apply both meth-
ods to a large sperm whale (Physeter macro-
cephalus) photo-identication dataset collected
opportunistically by whale-watching operators
and researchers around Faial and Pico islands, in
the Azores. Our aim was to examine the ability
of these methods to handle strong individual
heterogeneity and estimate the abundance and
demographic parameters of highly mobile taxa
from data collected opportunistically.
Sperm whales are widely distributed from the
tropics to near the ice edges in both hemispheres,
but males and females occupy distinct parts of
this range (Whitehead 2003). Females stay in
tropical and subtropical waters year-round where
they live in long-term social groups with their
immature offspring (Lyrholm and Gyllesten 1998,
Whitehead 2003). Males disperse from their natal
group as they approach puberty and gradually
move to higher latitudes reaching as far as polar
waters (Whitehead 2003). In their late twenties,
males start migrating periodically to the warm
waters inhabited by females to mate (Rice 1989).
The Azores is an important feeding, calving and
possibly mating ground for sperm whales in the
North Atlantic (Clarke 1981). Whales of both
sexes and all age classes use the area year-round,
but the majority of the observations consist of
social groups in late spring and summer (Silva
et al. 2014). Sperm whale social groups are noma-
dic (Whitehead et al. 2008), and the Azores
encompasses only a part of their range. Although
a few groups appear to regularly use the area,
none permanently remain there (Silva et al. 2006,
2014; Appendix S1: Fig. S3).
Photo-identication of sperm whales in the
Azores began in 1987, and since then, photo-
identication data are regularly collected by
whale-watching operators and researchers. So
far, there has been a single study applying CMR
methods to these data (Matthews et al. 2001),
using a two-sample closed model to estimate
annual abundance during summer (May
September), and an open model to estimate sur-
vival and birth rates. Unfortunately, these
estimates are likely biased because such classical
closed and open population models cannot ade-
quately account for the inevitable heterogeneity
in capture probabilities resulting from differ-
ences in sperm whale movements and uneven
sampling effort in space and time (Otis et al.
1978, Kendall et al. 1997).
In the present study, we investigate alternative
CMR methods that incorporate individual
heterogeneity and imperfect detection. We
applied the Schwarz and Arnason (1996) param-
eterization of the open Jolly-Seber model with an
individual covariate (hereafter called POPAN)
and MSORD models to the sperm whale photo-
identication catalogue collected opportunisti-
cally. We explored the potential of POPAN
models to account for transience and temporary
emigration by modeling survival and capture
probabilities as a function of previous capture
histories (PriorCapL; Cooch and White 2017). We
also applied the MSORD approach that explicitly
accounts for heterogeneity in capture probabili-
ties due to movement, using a model with one
observable state (P,present in the study area) and
one unobservable state (E,temporary emigrant), to
estimate population size, survival, average resi-
dence time, and temporary emigration.
METHODS
Study area and data collection
We analyzed 28 yr (19872015) of photo-iden-
tication data collected in the Azores (37°41°N,
25°31°W) by three main contributors: a whale-
watching operator, a research institution, and a
non-governmental organization (Appendix S1:
Table S1). Survey platforms, photographic equip-
ment, and data collection procedures differed
among data contributors and throughout the
study period (Appendix S1: Table S1; see Mat-
thews et al. 2001, Steiner et al. 2012, Silva et al.
2014 for further details), though most data were
obtained during 6- to 8-h daily trips. We
restricted the study area to the waters around the
islands of Faial and Pico (Fig. 1) where most
sampling effort was undertaken and discarded
photographs taken outside this area. Still,
because photo-identication data were not
obtained during random or systematic surveys,
sampling effort was unevenly distributed across
the study area.
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BOYS ET AL.
Photographic processing and matching
Sperm whales were individually identied
from photographs of the ventral side of the uke,
based on natural markings on the trailing edge.
Photographs were graded for quality based on
Arnbom (1987; ranges from Q1 =bad quality to
Q5 =excellent quality), irrespective of distinc-
tiveness of uke markings. Each whale was
assigned a distinctiveness value that ranged from
D0 =no markings to D5 =missing portion of
uke, based on certainty of future re-identica-
tion using the shape and marks on the trailing
edge of the uke (Dufault and Whitehead 1995,
Childerhouse et al. 1996). To minimize hetero-
geneity in captures due to misidentication of
non-distinctive ukes, only high-quality pho-
tographs (Q 3) of large sub-adult and adult
whales with distinct ukes (D 3) were used in
this study, as calves usually bear few distinct
marks. Potential matches were found using
Match and Phlex 1.3 software (Beekmans et al.
2005), and these probable matches were checked
visually by two independent observers. Adult
males, females, and sub-adults have not been
analyzed separately as they are indistinguishable
from uke photographs.
CMR dataset
The full dataset consisted of 4815 high-quality
photographs of 2342 distinctive individuals col-
lected on 1188 survey days between 1987 and
2015 (Appendix S1: Fig. S1). In our study, a cap-
ture event represents the photographic record of
an individually identied sperm whale. Analysis
of the encounter histories built from this dataset
showed that 76% of the whales identied were
only captured once (i.e., transients according to
Pradel et al. 1997), and the average number of
captures of the remaining whales was low (3.97).
As a result, capture probability (p) over the study
period was low (p=0.108, SD =0.02) and all
models tested provided a poor t to the data and
Fig. 1. Location of Azores and of sperm whales identied in the study area from 2009 to 2015.
www.esajournals.org 4March 2019 Volume 10(3) Article e02610
BOYS ET AL.
few identiable parameters (not shown here).
Therefore, we discarded data from years and
months with lower sampling effort and models
were t to a subset of data collected for approxi-
mately 2 months (Julyearly September) from
2009 to 2015.
Statistical modeling
As expected, results from the program CloseT-
est (Stanley and Burnham 1999) indicated that
the sperm whale population was not closed
(P0.001) within each 2-month sampling period
(Appendix S1: Table S2), likely, and at least par-
tially, due to the high proportion of transients.
Therefore, we used two different classes of open
models: the POPAN model (i.e., the Schwarz and
Arnason (1996) parameterization of the Jolly-
Seber model) with an individual covariate and a
MSORD model (Schwarz and Stobo 1997). All
models were tted in program MARK version
8.0 (Cooch and White 2017).
Transient animals were dened as those that
permanently emigrated from the study area after
initial capture, such that they were not avail-
able to be encountered in the future (Pradel et al.
1997). However, models including only live
captures cannot distinguish between death and
permanent emigration, meaning that a transient
individual will appear to have died after rst
capture. If this is not accounted for, then survival
estimates will be negatively biased for those
animals that remain in the study area
(Pradel et al. 1997). Nevertheless, since transients
are not captured again after the rst capture, the
negative bias on survival will only affect the
rst occasion. Therefore, a common way to
account for transience is to use a model that
allows for the estimate of survival from the rst
occasion to be different from the following occa-
sions. This can be done using time since marking
(TSM) models which generally provide satisfac-
tory results (Pradel et al. 1997, Cooch and White
2017). Since TSM models are not applicable in
POPAN due to the model structure, we used the
PriorCapL covariate function instead.
Similarly to TSM, this function distinguishes
individuals based on whether they have been
captured before or not, and estimates survival
separately for the interval after rst capture
and for subsequent intervals (Cooch and White
2017).
POPAN models
POPAN models were used to estimate the fol-
lowing parameters: (1) abundance of the super-
population (N
super
), which is the total number of
sperm whales using the study area in the sum-
mers of 20092015; (2) annual abundance (N
t
),
the abundance of sperm whales summering in
the study area in sampling year t; (3) apparent
survival probability (/
t
), hereafter survival,
which is the probability of whales surviving and
returning to the study area between sampling
years tand t+1; and (4) the probability that a
sperm whale from the super-population entered
the study area between years tand t+1(pent
t
).
Captures of individual sperm whales made
during the same year were pooled, and each year
was treated as a sampling occasion. Models were
built with the capture (p) and survival (/) proba-
bilities set as constant (.), time-dependent (t), or
varying as a function of prior capture. The indi-
vidual covariate function PriorCapL was applied
to indicate whether a whale was observed or not
on specied sampling occasions (Cooch and
White 2017). Here, PriorCapL was incorporated
in the models to enable survival (/) to be esti-
mated separately for transients and those whales
seen twice or more, thus avoiding the negative
bias on survival from transient individuals.
PriorCapL (t,j) applied to /took the value 1 if
the whale was captured on any previous occa-
sion t,t+1,...,jand 0 otherwise. PriorCapL was
also applied to the capture probability (p)to
account for some heterogeneity from temporary
emigration. In this case, PriorCapL(t) was 1 for
whales seen on the preceding occasion t1 and 0
for whales not seen on that occasion. Finally, pent
was modeled as constant or varying over
sampling years.
MSORD models
Multi-state open robust design models are
based on observations from multiple secondary
(typically within-season) sampling occasions
over multiple primary occasions (typically
years). The extra information on capture proba-
bilities from secondary periods allows estimation
of movement in and out of the study area, as well
as achieving unbiased and more precise parame-
ter estimates (Kendall and Bjorkland 2001).
However, MSORD models require large amounts
of data to obtain estimates for additional
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BOYS ET AL.
within-primary period parameters. Consequently,
MSORD models could only be t to a subset of
data of the same 2 months from 2011 to 2015. We
modeled these data as ve primary periods, cor-
responding to the years 20112015, where each
primary period was composed of three sec-
ondary occasions of 3 weeks (Appendix S1:
Table S4). The 3-week secondary occasions were
chosen to ensure sufcient captures and because
3 weeks is a compromise between the average
residence time estimated from a preliminary
analysis of the 20112015 dataset (33.1 d) and
residency estimated using the emigration and
re-immigration model with lagged identication
rates (Whitehead 2007) on the 19972004 dataset
(14.9 d; Silva et al. 2006). Similarly to the
POPAN models, the MSORD method also
assumes a super-population of individuals, part
of which may be present (P) in the study area and
available for detection (observable state) on a
given sampling occasion, while others may be
outside the area (temporary emigrants, E) and
therefore be unobservable. Using a two-state
model structure, we estimated the following
parameters describing the annual dynamics of
the sperm whale population: (1) the apparent
survival (S
t
) hereafter survival, the probability of
whales surviving between sampling years tand
t+1 for those occupying state P; (2) the transi-
tion probabilities (wPE
t) and (wEP
t), which indi-
cate the probability of whales transitioning from
being present (P) in the study area to being tem-
porary emigrants (E), and vice-versa, between
years tand t+1, conditional on survival.
Because of the multinomial nature of transition
probabilities, the probability that a whale
remains in the original state is simply derived by
subtraction; that is, wPP
t¼1wPE
t. In addi-
tion, we modeled the movement dynamics and
detectability of sperm whales present in the area
in each year: (3) the entry or arrival probability
(pent
j
), the probability that a whale arrives to the
study area in secondary period j; (4) the persis-
tence probability (uj), the probability of being
present in the study area at occasion j, given it
was present at occasion j1 (departure probabil-
ity =1u); (5) the capture probability (p
j
), the
probability of being detected at occasion j, given
it was present.
MSORD models assume that survival is the
same for animals occupying the observable and
unobservable state, so Sfor temporary emigrant
whales was set to equal that of whales present in
the area. To avoid bias in survival probability
from transient whales, Swas modeled as a func-
tion of time since marking (TSM) to allow a sepa-
rate survival estimate for newly and previously
captured whales (Pradel et al. 1997). The Prior-
CapL function was also applied to Sto under-
stand whether there were differences in estimates
based on the function applied. To investigate the
pattern of movement of sperm whales, we com-
pared four different emigration models: Marko-
vian (wPE
twEE
t) where the probability of
being a temporary emigrant depends on whether
or not the individual was present in the previous
year, random (wPE
t¼wEE
t) where the probabil-
ity of being a temporary emigrant is independent
of the individualsprevious availability, even-
ow (wPE
t¼wEP
t) where emigration out of and
immigration into the area occur with the same
probability, and no movement (wPE
t¼wEP
t¼0)
where there are no transitions into or out of the
area. We also modeled was constant (.) and vary-
ing across years (t). Arrival (pent), persistence (u),
and capture (p) probabilities for temporary emi-
grants (E) were xed to 0, since these whales
were not available for capture. For whales present
in the study area pent,uand pwere allowed to
vary between years and secondary occasions or
were set to constant. In addition, uwas also mod-
eled as a function of TSM to test whether the
probability of whales leaving the study area
within a year varied as a function of their time
since arrival.
Estimates of the total number of sperm whales
from the super-population visiting the study area
each summer (N
t
) and of their residence time (R
t
,
calculated as the average number of secondary
occasions whales remained in the study area)
were obtained as derived parameters of the
models.
The model with no emigration (wPE
t¼wEP
t¼
0) was deemed biologically unreasonable but
was used as a basis to assess the effects of time
and TSM dependence on other parameters
(S,pent,u,p). Following the selection of the most
parsimonious model based on only these param-
eters, models were then built to incorporate other
emigration types. In some models where param-
eters were time-dependent, the rst and second
or, ultimate and penultimate occasions were
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BOYS ET AL.
constrained equal to avoid confounding parame-
ters (Kendall et al. 1997).
Model assumptions and selection
Currently, there are no methods to assess
goodness of t (GOF) and estimate overdisper-
sion of models with individual covariates or
MSORD models. In the case of POPAN models, a
Cormack-Jolly-Seber (CJS) model was built with
the same dataset and GOF tests performed on
the most general data structure without covari-
ates (utp
t
pent
t
) using program RELEASE (Burn-
ham et al. 1987) in MARK. Model overdispersion
was examined by calculating the median ^
cfor
the same CJS global model.
Two different GOF tests were used to assess
the MSORD data. The rst was constructed as an
annual CJS model where secondary occasions
were pooled. This allowed for a time-dependent
model to be tested in RELEASE to examine cap-
ture heterogeneity (Test 2) and heterogeneity in
survival, for example, transience (Test 3; Cooch
and White 2017). Following this, transience was
accounted for in the CJS model and the median ^
c
approach used to estimate overdispersion. The
second GOF test was through the Pearson chi-
square test available in program ORDSURV
(Hines 2001). This tested whether the data were
appropriately structured to be modeled with
MSORD. This program also provides an indica-
tion of the type of temporary emigration in the
data by setting the emigration parameter to dif-
ferent values and comparing model t. Results
from ORDSURV GOF were then used to calcu-
late model overdispersion.
Model selection was based on the Akaike
information criterion corrected for the effective
sample size (AIC
c
; Burnham and Anderson 2002)
or on the quasi-likelihood AIC (QAIC
C
; Ander-
son et al. 1994) where overdispersion and ^
cwere
applied. Models with DAIC
C
(or DQAIC
C
)<2
were considered to have some support from the
data and were used to estimate parameters and
respective standard errors (SEs; Burnham and
Anderson 2002).
Estimating total population size
Abundance estimates from POPAN and
MSORD models only pertain to individuals with
sufcient distinct natural markings to allow their
identication and must therefore be corrected to
include unmarked individuals as well. Total pop-
ulation size (N
total
) of sperm whales in the area
during the sampling years was calculated by
dividing model-based abundance estimates (N)
by the proportion of marked animals (h). This
was calculated using only photographs of Q 3,
as the number of whales with recognizable
marks divided by the total number of whales.
The SEs of the corrected abundance estimates
were then calculated as:
SEðb
NtotalÞ¼ð
b
NtotalÞ2SEðb
NÞ2
ðb
NÞ2þ1h
nh
Log-normal condence intervals were calcu-
lated following Burnham et al. (1987), where Cis
C¼exp"Za
2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ln1þSEðb
NtotalÞ
b
Ntotal 2
s
and the lower condence limit is N
total
L=N
total
/
Cand the upper condence limit is N
total
U=
N
total
9C.
RESULTS
The majority of photographs were obtained
during encounters with social units and likely
include both adult females and immatures of
both sexes. Although a few adult males may
have been mixed with social units, the popula-
tion estimates presented here should pertain to
the female and immature component of the
sperm whale population.
POPAN: model assumptions and selection
A total of 539 individual sperm whales were
photo-identied during the summer months
between 2009 and 2015. The number of whales
identied per year ranged between 48 in 2013
and 91 in 2015 (mean =68.5, SD =16.32). Only
122 of the 539 individuals had been captured in
previous years, meaning that most captures
(77%) were of animals seen once.
Not surprisingly, the full time-dependent CJS
model tted the data poorly (^
c=3.09;v
2
=49.46,
df =16, P =0.001). Lack of t was due to signi-
cant heterogeneity in survival probabilities (Test
3: P =0.000) in agreement with the high propor-
tion of transients found in the data, but not in
capture probabilities (Test 2: P =0.181). Account-
ing for the transient effect by stratifying the
www.esajournals.org 7March 2019 Volume 10(3) Article e02610
BOYS ET AL.
survival parameter with a TSM model reduced
the overdispersion and resulted in a good tting
model (^
c=1.13; v
2
=133.87, df =118, P 0.10).
We assumed ^
c=1.13 to be a liberal estimate of
the overdispersion value of our POPAN models,
since the application of PriorCapL function on
survival and capture probabilities is expected to
account for part of the excess of variation in the
data from transients, as well as temporary emi-
grants (G. White, Colorado State University, per-
sonal communication). The most parsimonious
model had 86% of the support in the data and
was used for parameter inference. This model
had apparent survival and capture probabilities
varying as a function of whether or not the indi-
vidual was previously captured (PriorCapL) and
constant probability of entry into the study area
(Table 1).
POPAN: estimates of abundance and apparent
survival
Estimates of the model-based annual abun-
dance of sperm whales summering in the study
area varied between 250 and 545 (Appendix S1:
Table S5). Abundance for the rst year (2009)
could not be reliably estimated due to confound-
ing survival and capture parameters. The esti-
mated proportion of identiable individuals
varied between 0.64 and 0.78 (SD =0.05) per
year. The corrected abundance estimates account-
ing for the unidentiable sperm whales ranged
from 351 (95% CI: 234526) in 2010 to 718 (95%
CI: 4771082) in 2015 (Fig. 2). These estimates
showed an increasing trend until 2013, after
which it leveled off.
The POPAN model estimated the total size of
the super-population of sperm whales at 1062
individuals (95% CI: 8771286), which was 1468
(95% CI: 12031791) when corrected for the
unidentiable individuals. This abundance esti-
mate represented all sperm whales summering
in the study area from 2009 to 2015, including
transient individuals and those that may have
died. Although calves were not included in the
analysis, those that became juveniles during the
study period and possessed distinctive marks
could also be included in this estimate. The prob-
ability that a sperm whale from the super-popu-
lation entered the study area between years was
0.078 (SE =0.012).
The mean apparent annual survival probability
of sperm whales was 0.95 (SE =0.07), while the
apparent survival for newly captured whales was
0.33 (SE =0.05). Applying the Pradel et al. (1997)
formula to these estimates yields a proportion of
transients in the sperm whale population of 66%.
MSORD: model assumptions and selection
A total of 426 individual sperm whales were
identied in the summers of 20112015. The
number of whales captured per secondary occa-
sion ranged between 20 (in the third occasion of
2014) and 46 (in the rst occasion of 2014;
mean =34.3, SD =8.77). The number of recap-
tured individuals per secondary occasion varied
from 1 to 20, and 81% of individuals were only
captured once.
The full time-dependent CJS model showed
poor t to the data (^
c=3.95; v
2
=31.62, df =8,
P=0.00). As with the POPAN results, Test 2 was
Table 1. Summary of best tting POPAN models t to sperm whale data ranked by the lowest Akaike
information criterion corrected for the effective sample size (AIC
c
) values.
Model
No Structure AIC
c
Delta
AIC
c
AIC
c
weight
Model
likelihood
No.
parameters Deviance
1/(PriorCapL) p(PriorCapL) pent (.) 631.51 0.00 0.86 1.00 6 1345.63
2/(PriorCapL) p(.) pent (t3 =t4) 636.65 5.14 0.07 0.08 9 1346.69
3/(PriorCapL) p(PriorCapL) pent (t1 =t2, t5 =t6) 637.01 5.50 0.06 0.06 9 1346.33
4/(PriorCapL) p(.) pent (.) 640.27 8.76 0.01 0.01 5 1334.81
5/(PriorCapL) p(.) pent (t4 =t5) 640.80 9.29 0.01 0.01 9 1342.54
6/(t)p(.) pent (.) 652.50 20.99 0.00 0.00 11 1335.03
7/(.) p(2a) pent (t) 652.82 21.31 0.00 0.00 10 1332.61
8/(t)p(t)pent (t) 653.02 21.51 0.00 0.00 20 1353.78
9/(.) p(.) pent (t) 653.57 22.06 0.00 0.00 9 1329.78
Notes: Model parameters are /, apparent survival probability; p, capture probability; pent, probability of entry; where
PriorCapL, previous capture function; t, time-dependent; and ., constant.
www.esajournals.org 8March 2019 Volume 10(3) Article e02610
BOYS ET AL.
not signicant (P =0.20), while Test 3 was signif-
icant (P =0.001). Incorporating TSM on survival
improved overall model t and reduced overdis-
persion (^
c=0.98; v
2
=115.5, df =118, P 0.20).
Models with TSM and PriorCapL gave similar
results, but as models with covariates cannot be
adjusted for overdispersion, we decided to
model transience with TSM. Also, results from
ORDSURV indicated that Markovian emigration
poorly t the data (^
c=13.09; v
2
=549.751,
df =42, P 0.00), while both constant and ran-
dom temporary emigration t reasonably with
low overdispersion (constant: ^
c=1.53; v
2
=
36.814, df =24, P 0.05; random: ^
c=1.75;
v
2
=36.748, df =21, P =0.05). Models were
adjusted for overdispersion using the constant ^
c
from ORDSURV.
The best tting MSORD model had 42% of the
support of the data and was only three times bet-
ter supported than the second and third candi-
date models (Table 2). The top model included a
different survival probability for newly and pre-
viously captured whales (TSM effect), even-ow
temporary emigration that varied over time,
time-dependent probability of whales entering
the study area within-primary periods and con-
stant between primary periods. The probability
of whales remaining (u) in the study area and
probability of capture (p) were constant within
and between primary periods. The top model
was used for parameter inference (Table 2).
MSORD: estimates of abundance, survival, and
temporary emigration
The survival estimate for the whales captured
more than once was 0.93 (SE =0.11). Annual
abundance varied from 183 (95% CI: 117249) in
2013 to 270 (95% CI: 173368) in 2011 (Appen-
dix S1: Table S6). Model-based abundance esti-
mates were adjusted by the proportion of
marked whales from the corresponding years to
give total abundances ranging between 275 (95%
CI: 188404) in 2014 and 367 (95% CI: 248543) in
2012 (Fig. 2).
The top model in the candidate set included
time-dependent even-ow temporary emigration.
Fig. 2. Estimated total abundance and 95% log-normal condence intervals based on best tting POPAN
model and multi-state open robust design (MSORD) model.
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BOYS ET AL.
Models with no emigration also had some sup-
port from the data, but we found little support for
models with random or Markovian emigration.
The probability of emigrating from and immigrat-
ing into the study area varied between 0.22
(SE =0.20) in 20142015 and 0.66 (SE =0.17) in
20132014. After applying the Pradel et al. (1997)
method, we estimated that 56% of the sperm
whale population consisted of transient animals.
MSORD: estimates of within-year dynamics
Within a year, the probability of sperm whales
entering the study area between secondary sam-
pling occasions varied from 0.32 (SE =0.027) to
0.40 (SE =0.028) and the probability of remaining
in the study area was constant at 0.053
(SE =0.025). The average residence time within a
primary period was 1.06 (SE =0.02), where one
unit represented a 3-week period, and the proba-
bility of persistence was a function of time since
arrival. The capture probability was constant
between secondary occasions at 0.44 (SE =0.10).
DISCUSSION
By combining MSORD models and individual
covariates in standard open models, we estimated
key parameters of the population dynamics of
sperm whales summering in the Azores, which
would not be possible with conventional analyti-
cal approaches. POPAN estimated a super-popu-
lation abundance of about 1500 sperm whales
using the Azores as part of their summer habitat.
However, not all whales visit the area every sum-
mer; the MSORD estimates suggest that the
annual population comprises about 20% of the
super-population. Apparent survival rates from
both models were high and constant over time, as
expected for a long-lived mammal. The sperm
whale population in the study area is character-
ized by short residence times, with an even-ow
of animals entering and leaving the area in con-
secutive years.
Our results highlight the ability of MSORD
models to estimate demographic parameters
with reliability and precision, when there is sev-
ere heterogeneity in capture probabilities due to
non-standardized sampling and wide-ranging
behavior of animals. This method could be appli-
cable to CMR studies of wide-ranging taxa and
may be especially suited for the analysis of data
collected opportunistically.
Comparing modeling approaches: POPAN vs.
MSORD
Even if some model assumptions were not
fully met, diagnostic tests indicated that both
POPAN and MSORD models tted the data well
Table 2. Summary of best tting multi-state open robust design models t to sperm whale data ranked by the
lowest QAIC
c
.
Model
No Model structure QAIC
c
Delta
QAIC
c
QAIC
c
weights
Model
likelihood
No
parameters QDeviance
1S(tsm) Ψ(EVENt) pent (t.) φ(..) p(..) 1178.41 0.00 0.42 1.00 10 1157.98
2S(tsm) Ψ(0) pent (t.) φ(t.) p(.t1 =t2 t) 1180.61 2.20 0.14 0.33 10 1160.18
3S(tsm) Ψ(0) pent (t.) φ(..) p(..) 1181.06 2.65 0.11 0.27 6 1168.90
4S(tsm) Ψ(0) pent (..) φ(..) p(..) 1181.83 3.42 0.08 0.18 5 1171.71
5S(tsm) Ψ(0) pent (t.) φ(.t)p(.t1 =t2) 1182.46 4.05 0.06 0.13 13 1155.75
6S(tsm) Ψ(EVEN.) pent (t.) φ(..) p(..) 1182.84 4.43 0.05 0.11 7 1168.63
7S(tsm) Ψ(0) pent (.t)φ(..) p(..) 1183.46 5.06 0.03 0.08 9 1165.11
8S(tsm) Ψ(EVEN.) pent (t.) φ(t.) p(.t) 1184.54 6.13 0.02 0.05 12 1159.93
9S(tsm) Ψ(RANDOM.) pent (t.) φ(..) p(..) 1184.71 6.30 0.02 0.04 7 1170.49
10 S(tsm) Ψ(MARKOVIAN.) pent (t.) φ(..) p(..) 1184.75 6.34 0.02 0.04 8 1168.47
11 S(tsm) Ψ(0) pent (..) φ(.t)p(.t) 1184.86 6.45 0.02 0.04 13 1158.15
12 S(tsm) Ψ(0) pent (.t)φ(t.) p(.t) 1184.95 6.54 0.02 0.04 14 1156.12
13 S(tsm) Ψ(EVENt) pent (t.) φ(..) p(.t) 1185.50 7.09 0.01 0.03 14 1156.67
14 S(tsm) Ψ(EVENt) pent (t.) φ(t.) p(.t) 1185.68 7.27 0.01 0.03 15 1154.73
15 S(tsm) Ψ(EVENt) pent (t.) φ(.tp(.t) 1186.32 7.92 0.01 0.02 16 1153.25
Notes: Model parameters are S, survival probability; Ψ, transition probability; pent, probability of entry; φ, probability of
remaining; p, capture probability; tsm, time since marking; Ψ(0), no movement; Ψ(EVEN), even-ow emigration; Ψ(RANDOM),
random emigration; Ψ(MARKOVIAN), Markovian emigration; t, time-dependent; and ., constant. For pent,φ,and pparameters,
the rst annotation in brackets refers to within the primary period and the second annotation to between primary periods.
www.esajournals.org 10 March 2019 Volume 10(3) Article e02610
BOYS ET AL.
and variance was within acceptable limits.
Nonetheless, there were important differences in
the estimates between the two types of models,
resulting from their different abilities to handle
temporary emigration.
Estimates of annual abundance from the
MSORD were lower and had smaller condence
intervals than those based on POPAN. Moreover,
POPAN estimates showed an increasing trend
over time, whereas those of MSORD varied only
slightly between years. The best tting MSORD
model indicated high rates of temporary emigra-
tion in the sperm whale population, with an
even-ow of animals into and out of the area.
MSORD models account for the temporary
unavailability of individuals and estimate only
the number of animals observable in the study
area in a given sampling period. POPAN models
ignore temporary emigration and estimate the
total abundance of individuals from the super-
population found in the study area during at
least one sampling period and the probability
that an animal from the super-population
entered the study area at each sampling occasion
(Pollock et al. 1990, Arnason and Schwarz 1995,
1999, Schwarz and Arnason 1996). Thus, in cases
of even-ow temporary emigration, we expect
capture probabilities from POPAN to be nega-
tively biased and estimates of annual abundance
to be positively biased with respect to the num-
ber of animals in the sampled area in a given
sampling period (Pollock et al. 1990, Arnason
and Schwarz 1995, 1999, Schwarz and Arnason
1996). Our attempts to eliminate this bias by
modeling capture probabilities as a function of
previous capture histories with the PriorCapL
function were unsuccessful, and capture proba-
bilities estimated in POPAN models were sub-
stantially lower than those of MSORD.
Furthermore, capture probabilities decrease with
increasing rates of temporary emigration (Ken-
dall et al. 1997), which may explain the increas-
ing trend in abundance in the rst years of the
study when emigration rates were higher. These
results illustrate how failure to explicitly account
for imperfect and incomplete detectability can
strongly inuence population size estimates and
eventually lead to detection of false population
trends.
An advantage of the POPAN model compared
to the MSORD is its ability to provide unbiased
estimates of the total number of individuals
using the study area throughout the survey per-
iod (Arnason and Schwarz 1995, 1999, Schwarz
and Arnason 1996). The super-population esti-
mates may be especially useful in studies of
migratory animals where the main interest is
determining the number of individuals going
through a specic area. Apparent survival rates
from POPAN and MSORD models were very
similar and the difference could simply be due to
the different study periods analyzed. Except in
the case of Markovian emigration, survival rates
from POPAN models are robust to heterogeneity
in detection probability (Kendall et al. 1997) and
estimates of survival from our models should be
unbiased although their precision may be lower.
In the case of MSORD, survival would only be
affected by temporary emigration if there is more
than one entry and exit per primary period
(Kendall et al. 2013) which, if occurring, would
not be detectable with only three sampling occa-
sions. The presence of transient individuals nega-
tively biases survival estimates (Pradel et al.
1997). Our results indicate that using the Prior-
CapL function to model survival as a function of
previous captures should give reliable estimates
of survival probability, providing a suitable alter-
native to TSM models that can handle transients
in POPAN models.
Population size and survival probability
Although true values for this sperm whale
population are unknown, the fact that the
MSORD model accounts for individual move-
ments, whereas the POPAN model does not,
leads us to suggest that the MSORD is more
robust (Pfaller et al. 2013, Ruiz-Gutierrez et al.
2016). The MSORD model estimated that 275
367 sperm whales used the area around Faial
and Pico islands each summer. This estimate
includes all whales that visited the area in a
given summer regardless of their residence time,
including transiting individuals. Abundance esti-
mates from the MSORD varied slightly between
years but showed no annual trend, suggesting a
fairly constant number of individuals using the
area each summer. The super-population size
estimated from POPAN was ~1470 whales,
which summered in the area over the 7-year
study period. It should be stressed that our study
area encompasses a small fraction of the Azorean
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BOYS ET AL.
waters, so the number of sperm whales visiting
the archipelago is expected to be greater than the
estimates presented here. The assumption of
individual capture probabilities being indepen-
dent is violated in social species, such as the
sperm whale (Hammond 1986, Wells et al. 1987).
We did not account for this violation, and
although the effect on abundance estimates
should be negligible, the variance of parameter
estimates may be negatively biased, resulting in
higher precision than reality (Gupta et al. 2017).
Using a standard POPAN formulation, Mat-
thews et al. (2001) estimated that about 450900
female and immature whales visited the central
islands of the Azores each summer from 1988 to
1990, increasing to 18002500 animals in 1991
1994. The two-sample closed estimator used by
these authors indicated that 340900 whales were
present at any given time. Compared to our
study, Matthews et al. (2001) covered a wider
geographic area and slightly longer season (May
to mid-September), but >85% of their whale iden-
tications were from the same area and months
as ours. Although care should be taken when
comparing these results, the POPAN estimates
reported by Matthews et al. (2001) point to a
much larger population than our MSORD esti-
mates, which could indicate that the number of
whales summering in the area declined between
study periods. Instead, we believe the difference
in the estimates between the two studies reects
the inability of POPAN models to deal with high
temporary emigration (Kendall et al. 1997), over-
estimating population size. Additionally, esti-
mates from the closed models were probably
biased by violation of the closure assumption, as
acknowledged by the authors (Matthews et al.
2001).
The boundaries of the sperm whale population
sighted in the Azores and connectivity to other
populations in the North Atlantic are not well
known. The information currently available sug-
gests that this population may have its core
habitat within Macaronesian waters (Azores,
Madeira and Canary Islands; Steiner et al. 2015).
The only abundance estimates within this region
are from the Canary Islands. An acoustic line-
transect survey conducted in autumn and winter
in the territorial waters of this archipelago gave
an absolute abundance of 224 sperm whales
(95% log-normal CIs: 120418; Fais et al. 2016).
These authors suggest that the Canary Islands
may be acting as a population sink due to high
rates of mortality from ship strikes in the area.
Thus, information on the size, structure, and pro-
ductivity of the population inhabiting Macarone-
sia is urgently needed. Application of MSORD
models to sperm whale photo-identication data
from multiple sites could provide information on
movement rates of the population and enable
estimation of global and site-specic demo-
graphic parameters (Nichols et al. 2007, Cha-
banne et al. 2017).
In our study, models incorporating PriorCapL
or TSM effects on survival provided the best t
to data. These models enabled separating
transients from temporary emigrants, producing
estimates of apparent survival that should
approximate well to true survival of whales
(Lebreton et al. 1992). All the best tting
POPAN and MSORD models indicated that sur-
vival of sperm whales was constant over time,
with estimates of 0.95 (95% log-normal CIs:
0.560.99) and 0.93 (95% log-normal CIs: 0.74
1), respectively, for POPAN and MSORD. These
estimates were higher than previous estimates
reported in the study area (Matthews et al.
2001), which we expect were negatively biased
by the large percentage of transient whales in
the dataset that were not accounted for in the
modeling process.
Our estimates are consistent with known adult
survival rates of sperm whales in Southern Aus-
tralia (Evans and Hindell 2004) and in the East-
ern Caribbean (Gero and Whitehead 2016), and
are considerably higher than those found in
Japan, where the population is subject to signi-
cant mortality in shing gear (Evans and Hindell
2004).
Survival rates of mammals tend to be high and
constant for most of their adulthood, whereas
juvenile survival is usually lower and tends to
increase as the animals approach maturity (Gail-
lard et al. 1998). We could not t separate models
for females and immatures because they were
indistinguishable from uke photographs and
the criteria used to identify them in the eld were
not consistent among data providers. Thus, the
survival probabilities reported here may be
slightly overestimating the true survival of
immature sperm whales and underestimating
survival of adult females.
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BOYS ET AL.
Inter- and intra-annual movement dynamics
POPAN models cannot inform about move-
ments of animals. Conversely, MSORD models
enable investigating movement dynamics through
the estimation of transition probabilities between
states representing the presence or absence of ani-
mals in an area. The models that best described
the temporary emigration of sperm whales
included time-dependent even-ow. This means
that the probability of whales temporarily leaving
the area between consecutive primary periods
was the same as the probability of whales immi-
grating into the area, but movement rates varied
between years. About 41% of the whales encoun-
tered in the study area in 2012 were not observed
in 2013, the same proportion of individuals not
encountered in 2012 but observed the following
year. The symmetric ow rate of whales increased
to 66% between 2013 and 2014 and decreased to
22% in 20142015. However, inter-annual varia-
tions in movement rates should be interpreted
cautiously because the condence intervals on the
transition parameters were wide.
Temporary emigration meant some individu-
als were not observable during the two-month
sampling period in a given year, either because
they were outside the sampled area or because
they were present but were not detected. Our
study site is relatively small compared to the
overall range of the population, which makes it
unsurprising that a large proportion of the pop-
ulation is away during a primary period. This is
supported by the analysis of the full CMR data-
set (Appendix S1: Figs. S2, S3) spanning
28 years and covering most of the spring and
summer. Of the 2342 individuals identied, only
34% were captured more than once and most
recaptures occurred within the same year and
month of the initial capture; only 17% of the
whales were captured in four or more years. A
few social units, however, seem to return to the
area in consecutive years over an extended per-
iod (average 8 years), usually in the same
month. Interestingly, three of the 15 matches to
the Canary Islands were of whales captured in
the study site every year between 2009 and
2015, which shows that even whales that exhib-
ited inter-annual delity to the study site also
used distant habitats. Several individuals
(n=92) were photographically matched
between the Azorean islands (Steiner et al.
2015), sometimes within the same year, indicat-
ing that some temporary emigrant whales could
also have been elsewhere in the archipelago.
Nonetheless, some whales could have been pre-
sent in the study area at the time of sampling
but were not captured, overestimating tempo-
rary emigration. Foraging sperm whales spend
a large proportion of their time submerged and
tend to spread out over a large area (Whitehead
2003), which makes it difcult to detect and sys-
tematically photograph all members of a group.
Models with no movement that disregard tem-
porary emigration (Cooch and White 2017) also
received some support in the data. Lack of tem-
porary emigration in this CMR dataset is highly
unlikely, and we suspect this result to be the con-
sequence of low capture probabilities in some
sampling periods leading to the confounding of
survival and transition parameters (Schaub et al.
2004).
The MSORD is a useful tool to infer intra-
annual movement patterns in the absence of
direct measurements. The estimated average resi-
dence time, of adult females and immatures
within the study area, was just over 3 weeks,
slightly higher than the 15 d estimated from
lagged identication rates (Whitehead 2007) for
the 19972004 period (Silva et al. 2006). We note
that our estimate of residence time is coarse, con-
ditioned by the necessity to aggregate data from
multiple sampling occasions to increase capture
probability (see Challenges of modeling opportunis-
tic photoidentication data).
The distribution of sperm whales in the Azores
is strongly correlated with primary productivity,
suggesting that prey availability is an important
driver of local movements and habitat use
(Tobe~
na et al. 2016). The displacement of sperm
whales from the study area may be a direct
response to changes in food resources, feeding
success, or be mediated by increased density and
intraspecic competition (Whitehead and Ren-
dell 2004, Whitehead et al. 2008, Cantor et al.
2017). Other factors may inuence the movement
dynamics of sperm whales over periods of days
to weeks, including occasional presence and
harassment by adult males attempting to mate
(Sundaresan et al. 2007, Craig et al. 2014) or
repeated disturbance from whale-watching boats
(Gordon and Steiner 1992, Magalh~
aes et al. 2002,
Christiansen et al. 2013). All these factors may
www.esajournals.org 13 March 2019 Volume 10(3) Article e02610
BOYS ET AL.
affect the estimates of residence time, probability
of remaining and temporary emigration patterns.
As intrinsic and extrinsic drivers of movement
patterns are expected to vary both within and
between years, so should the probability of indi-
viduals persisting and entering the area, but this
was not observed in our models. In fact, the
probability of whales remaining in the study area
was low and constant, while the entry probabil-
ity varied slightly between secondary periods
(0.320.40) but was constant over years. While
these results are consistent with the even-ow of
whales into and out of the area, lack of temporal
variability in these parameters may be the conse-
quence of an insufcient sample size for model-
ing parameters describing both the inter- and
intra-annual population dynamics (Kendall and
Bjorkland 2001, White et al. 2006). Improved esti-
mation of intra-seasonal movements can be
achieved in the future by focusing only on those
parameters that model dynamics within primary
periods (Ruiz-Gutierrez et al. 2016).
Conservation implications
Whale-watching has become an increasingly
important economic activity in the Azores. Dur-
ing the summer, 23 boats operate in our study
area, each making two daily trips of approxi-
mately 3 h (Oliveira et al. 2007). The Azores is
an important foraging, calving, and nursing area
for sperm whales (Clarke 1981), raising concerns
about the detrimental effects of whale-watching
on foraging and reproductive success of the pop-
ulation. Females accompanied by calves incre-
ased aerial behavior and mean blow interval
when approached by whale-watching boats
(Magalh~
aes et al. 2002). Such short-term changes
in behavior could translate into increased ener-
getic costs and reduced foraging and nursing
times (Williams et al. 2006). Negative effects will
likely be higher for individuals frequently
exposed to whale-watching interactions because
repeated behavioral disruption can result in a
constant imbalance of bioenergetic budget
(Bejder et al. 2006, Christiansen et al. 2013).
Our results indicate that approximately 300
sperm whales summer in the study area every
year. Despite a relatively small population, the
short residency time and low inter-annual de-
lity mean that exposure to whale-watching activ-
ities in the area is likely limited within and
across years for most individuals. However,
cumulative effects due to whale-watching distur-
bance may occur in some social units that visit
the area nearly every year and remain there for a
few months. Furthermore, sperm whales may
also be exposed to whale-watching activities out-
side the study area, scaling up potential negative
impacts. Finally, even though exposure levels
may be low, disturbance from whale-watching
may result in behavioral and physiological
changes that might affect individualshealth and
vital rates, and have implications to the dynam-
ics of this population. As it is yet unknown how
this may reect on the super-population demo-
graphics, local dedicated studies are needed to
assess potential effects of whale-watching to this
population.
Challenges of modeling opportunistic photo-
identification data
Data from opportunistic platforms, such as that
collected by whale-watching vessels, are often
agged as a panacea for lack of long-term dedi-
cated monitoring programs of animal popula-
tions, with the advantage of being low cost.
However, application of CMR models to oppor-
tunistic data is challenging and our study pro-
vides a good example of some of those difculties.
One problem with opportunistic data is that
sampling is often insufcient and detection prob-
abilities are low. When numbers of captures and
recaptures are low and vary across sampling
occasions, precision and accuracy of CMR esti-
mators are generally poor (Burnham et al. 1987,
Pollock et al. 1990, Lebreton et al. 1992). In our
case, this led us to discard 21 years of data col-
lected year-round, to restrict the analyses to
times with more whale encounters and ensure a
sufcient number of captures and recaptures for
reliable estimation. Another way to overcome
this problem, such as described here for MSORD
models, is to aggregate data across sampling
occasions to increase the number of captures and
recaptures to generate estimates. This solution
comes with several costs. First, some information
will be lost. In our case, by pooling data into 3-
week secondary periods, we were only able to
get a coarse estimate of the residency and within-
seasonal movements of whales. Second, it may
force the combination of data that are more
heterogeneous with respect to detection
www.esajournals.org 14 March 2019 Volume 10(3) Article e02610
BOYS ET AL.
processes, requiring complex models for detec-
tion probability and, consequently, more data
(Litt and Steidl 2010). The aggregation process
should be driven by biological information and
weigh the benet of increased data for modeling
against model complexity (Litt and Steidl 2010).
Opportunistic platforms typically do not sam-
ple the entire study area at every sampling occa-
sion. If animals remain outside the sampled area
during the study, they are simply excluded from
all the estimates. Issues arise when animals
occupy well-dened home ranges, but these
change between primary periods. When this
occurs, incomplete surveying can bias estimates
of temporary emigration because some individu-
als may remain unobservable for the duration of
a primary period but not in others (Sanders and
Trost 2013). In the case of sperm whales, there is
no evidence that individuals or groups exhibit
delity to specic regions within the study area
and we expected whales to move randomly in
and out of surveyable areas, and, therefore, be
exposed to sampling every primary period.
Despite these challenges, our study demon-
strates the feasibility and value of opportunistic
data to improve demographic estimates when
combined with robust statistical models, such as
MSORD. The substantial advantage of this
method is in its ability to estimate the size and
various parameters describing the inter- and
intra-annual dynamics of populations with imper-
fect detectability. We believe MSORD models
could provide relevant information on the demog-
raphy of many wide-ranging species where data
are regularly collected by the public through eco-
tourism activities (Davies et al. 2012, Dennhardt
et al. 2015, Bertulli et al. 2017). We do not advo-
cate that opportunistic data could or should
replace data collection under well-designed CMR
studies. Rather, CMR analyses of opportunistic
data may be carried out alongside and comple-
ment those based on dedicated monitoring
schemes and may be used to investigate specic
aspects of the ecology of the target population.
Future work should focus on exploring other
methods, such as spatially explicit CMR models,
and integrating photo-identication data from
other geographic areas and ancillary information
(e.g., from telemetry or focal follows) to obtain
robust demographic estimates and understand the
dynamics of the population. Finally, a possibility
could be the use of a custom-built model (e.g.,
Conn et al. 2011) for this dataset, to specically
model any biases caused by heterogeneity and
incorporate in the model likelihood, the unmarked
animals and misidentication parameters, which
could improve estimates of uncertainty.
ACKNOWLEDGMENTS
We acknowledge IFAW for providing photo-identi-
cation data from the early period of the study (1987
1993), Biosphere Expeditions and clients of Whale
Watch Azores for making data collection possible. We
thank Sara Magalh~
aes, Tiago S
a, Jo~
ao Medeiros, Yves
Cuenot, Pablo Chevallard Navarro, and numerous vol-
unteers that over the years helped with data collection
and organization of the photo-identication catalogue.
We are deeply grateful to Gary White, Bill Kendall, Jim
Hines, James Nichols, Paul Conn, and Olivier Gimenez
for offering guidance and advice on CMR modeling.
We thank Jonathan Gordon for his comments on an
earlier version of the manuscript. We are thankful to
the three anonymous reviewers for providing very
helpful comments.
This work was supported by Fundac
ß
~
aoparaaCi
^
encia
e Tecnologia (FCT), Azores 2020 Operational Pro-
gramme, and Fundo Regional da Ci^
encia e Tecnologia
(FRCT) through research projects FCT-Exploratory pro-
ject (IF/00943/2013/CP1199/CT0001), WATCH IT
(Acores-01-0145-FEDER-000057), and MISTIC SEAS II
(GA11.0661/2017/750679/SUB/ENV.C2) co-funded by
FEDER, COMPETE, QREN, POPH, ESF, Portuguese
Ministry for Science and Education, and EU-DG/ENV.
The Azores 2020 Operational Programme is funded by
the community structural funds ERDF and ESF. We also
acknowledge funds provided by FCT to MARE, through
the strategic project UID/MAR/04292/2013. Rebecca M
Boys is supported by an Estagiar L scholarship, Cl
audia
Oliveira by a research assistant contract from WATCH
IT and M
onica A Silva by an FCT-Investigator contract
(IF/00943/2013), and Rui Prieto by an FCT postdoctoral
grant (SFRH/BPD/108007/2015).
M
onica A Silva conceptualized the project, acquired
funding, administered, and supervised the project. Lisa
Steiner, Cl
audia Oliveira, Rebecca M Boys, and M
onica
A Silva involved in data curation. Rebecca M Boys,
M
onica A Silva, Sergi P
erez-Jorge, and Cl
audia Oliveira
involved in formal analysis, investigation, and method-
ology. Rebecca M Boys preparation and visualization of
the data. Rebecca M Boys, M
onica A Silva, Sergi P
erez-
Jorge, Rui Prieto wrote the original draft of the manu-
script. Rebecca M Boys, M
onica A Silva, Rui Prieto,
Sergi P
erez-Jorge, Cl
audia Oliveira, and Lisa Steiner
wrote, reviewed, and edited the manuscript.
www.esajournals.org 15 March 2019 Volume 10(3) Article e02610
BOYS ET AL.
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SUPPORTING INFORMATION
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... D1C2 -MM_ABU_CMR: The only reliable estimates of the absolute abundance for the population of sperm whales in the Azores are those reported byBoys et al. (2019). These authors used photo idenification data of adult females and immatures of both sexes collected opportunistically in the coastal waters around Faial and Pico in the summer months (July-August) between 2011 and 2015 and applied a multi-state open robust model (MSORD) to estimate demographic and movement parameters of the population. ...
... Therefore, these estimates are not for the MU of sperm whales using the coastal waters of the Azores but only for the part of the MU that uses the waters around Faial and Pico islands. Estimates of abundance varied between years ranging from 367 (95% CI = 230-585) individuals in 2012 to 275 (95% CI = 174-436) in 2014(Boys et al., 2019). However, no clear trend was apparent in the data and the more precise estimate (with the lower CV), i.e. 45 adult female and immature CV = 0.19 calculated in 2011, is proposed as a baseline value for the parameter. ...
... However, no clear trend was apparent in the data and the more precise estimate (with the lower CV), i.e. 45 adult female and immature CV = 0.19 calculated in 2011, is proposed as a baseline value for the parameter. As there are no other updated abundance estimates, GES of the MU cannot be assessed.D1C3 -MM_DEM_SR: The only reliable estimates of survival rates available for the population of sperm whales in the Azores are also those reported byBoys et al. (2019). Annual survival probability of sperm whales did not vary over the study period and the average survival rate for the period 2011-2015 is proposed as the baseline value for the parameter (i.e. ...
Technical Report
Full-text available
This document, the MRR, includes the description of the criteria and species assessed, along with compilation of the results obtained during the implementation of the pilot monitoring programmes under the MSFD for marine birds, mammals and turtles in the three Macaronesian archipelagos (Azores, Madeira and Canary Islands) but also from other additional data available from other projects or governmental management programs. This report will be the basis for the MS, Portugal and Spain, to fulfil the obligations of the MSFD article 17 implementation.
... We proceeded this way to guarantee the presence of all vultures captured throughout the year to avoid the potential bias of considering the fraction of birds that use the landfill in a given season (see Discussion;and Smith and Anderson 1987, Hargrove and Borland 1994, O'Brien et al. 2005. Despite this, and following other studies based on year-round captures and where part of the data was omitted (Peach et al. 2001, Boys et al. 2019, we performed the same models using half-year (six months) of captures as a sampling occasion in order to explore the effect on our estimates of shortening the pooling interval (see Appendix S3 for the results of this analysis). ...
... Transience and trap-dependence are common sources of bias when estimating survival and capture probabilities in CMR models (Pradel et al. 1993(Pradel et al. , 1997. For the POPAN model the only available tool for assessing both heterogeneity sources is the PriorCapL function in MARK program and has hardly been used for these purposes (Boys et al. 2019). Particularly for transience, several parameterizations have been developed to unravel the underlying biological meaning of this phenomenon when modelling and it has been suggested that is due to differences in age classes, presence of true transients, a permanent emigration due to marking effect or the cost of first reproduction Pradel 2019, Oro andDoak 2020). ...
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Globally, vultures are one of the most threatened of all groups of birds. European vulture populations are benefited by several anthropogenic food sources such as landfills. Current European Union directives aim to decrease the amount of organic matter dumped in landfills, reducing this important food source for some vulture species. In this context, we assessed the effect of the reduction of organic waste available and accessible for scavengers in a landfill on the visitation probability and abundance of a local Eurasian Griffon Vulture Gyps fulvus population in Central Catalonia (NE Iberian Peninsula), using a long-term dataset of captured-marked-recaptured individuals in the period 2012–2018. Our results indicated a decrease in the visitation probability due to a significant reduction of organic matter dumped into the landfill after a waste treatment centre was built (0.82 to 0.76) that may cause a permanent emigration of vultures in response to food reduction. However, the estimated annual abundance of vultures tended to grow over time due to the positive trend that regional vulture populations have experienced in recent decades. These results suggest that population processes occurring at regional scales are more relevant to vulture populations than local waste management measures. A reduction in locally available food can make a site less attractive, but species with high dispersal capacity such as vultures may overcome this issue by moving to other suitable sites. Although Griffon Vultures obtain most of the food from domestic and wild ungulates, a regional application of European directives could threaten an important alternative feeding source, especially in food shortage seasons where landfills could be supporting the energetic requirements of the species. Conservation strategies should be planned to counteract the possible negative effects of new European directives on scavenger populations.
... Along the Portuguese coast, high concentrations of heavy metals have been found in the livers of bottlenose dolphins Tursiops truncates and common dolphins Delphinus delphis (Zhou et al. 2001, Carvalho et al. 2002. Disturbance from whale watching in the Azores may cause detrimental effects to sperm whale social units that are regular visitors and spend long periods off these islands, with potential effects on the population dynamics (Boys et al. 2019). Additionally, the rate of dolphin by-catch in the poleand-line tuna (Thunnini tribe) fishery in the Azores has varied considerably between years, and while dolphins caught were reported to be released alive, the fate of these released individuals is unknown (Cruz et al. 2018). ...
... For example, deep-diving species such as sperm whales Physeter macrocephalus and beaked whales (Ziphiidae) are seldom seen over the continental shelf, which hinders their study in most areas of the Atlantic (MacLeod & Mitchell 2006). However, they are relatively common near the Macaronesia archipelagos, enabling sustained research over medium-to-long time periods (e.g., Prieto et al. 2013, Boys et al. 2019. In contrast, the continental shelf along the coastal regions in this study can stretch tens of kilometres into the sea, limiting the occurrence of deep-diving species close to shore Trends in cetacean research in the Eastern North Atlantic B. Cartagena-Matos et al. and increasing logistical costs associated with the study of these animals (Kiszka et al. 2007, Viddi et al. 2010). ...
... Along the Portuguese coast, high concentrations of heavy metals have been found in the livers of bottlenose dolphins Tursiops truncates and common dolphins Delphinus delphis (Zhou et al. 2001, Carvalho et al. 2002. Disturbance from whale watching in the Azores may cause detrimental effects to sperm whale social units that are regular visitors and spend long periods off these islands, with potential effects on the population dynamics (Boys et al. 2019). Additionally, the rate of dolphin by-catch in the poleand-line tuna (Thunnini tribe) fishery in the Azores has varied considerably between years, and while dolphins caught were reported to be released alive, the fate of these released individuals is unknown (Cruz et al. 2018). ...
... For example, deep-diving species such as sperm whales Physeter macrocephalus and beaked whales (Ziphiidae) are seldom seen over the continental shelf, which hinders their study in most areas of the Atlantic (MacLeod & Mitchell 2006). However, they are relatively common near the Macaronesia archipelagos, enabling sustained research over medium-to-long time periods (e.g., Prieto et al. 2013, Boys et al. 2019. In contrast, the continental shelf along the coastal regions in this study can stretch tens of kilometres into the sea, limiting the occurrence of deep-diving species close to shore and increasing logistical costs associated with the study of these animals (Kiszka et al. 2007, Viddi et al. 2010). ...
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... Multistate CMR models, in which individuals can occupy either observable or unobservable states and transition between these states over time, allow for heterogeneity in capture probability resulting from temporary emigration to be incorporated in the models (e.g. Chabanne et al. 2017, Boys et al. 2019. The application of Bayesian versions of CMR models (Rankin et al. 2016) may also prove useful for addressing temporary migration and individual heterogeneity in recapture probabilities, improving model fit and the reliability of parameter estimates. ...
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Many large marine species are vulnerable to anthropogenic pressures, and substantial declines have been documented across a range of taxa. Many of these species are also long-lived, have low individual resighting rates and high levels of individual heterogeneity in capture probability, which complicates assessments of their conservation status with capture-�mark-�recapture (CMR) models. Few studies have been able to apply CMR models to whale sharks Rhincodon typus, the world’s largest fish. One of their aggregation sites off Mafia Island in Tanzania is characterised by unusually high residency of this Endangered species, making it an ideal target for CMR methods. Three different CMR models were fitted to an 8 yr photo-identification data set to estimate abundance, population trend and demographic parameters. As anticipated, resighting rates were unusually high compared to other aggregations. Different CMR models produced broadly similar parameter estimates, showing a stable population trend with high survivorship and limited recruitment. Tagging and biopsy sampling for concurrent research did not negatively affect those sharks’ apparent survival or capture probabilities. Scenario-based power analyses showed that only pronounced abundance trends (±30%) would be detectable over our study period, at a 90% level of probability, even with the relatively high precision in yearly abundance estimates achieved here. Other, more transient whale shark aggregations, with reduced precision in abundance estimates, may only be able to confidently detect a similar trend with CMR models after 15-20 yr of observations. Precautionary management and long-term monitoring will be required to assist and document the recovery of this iconic species.
... This confirms that sperm whales are not truly resident at Kaik oura but immigrate and emigrate from the study area over time (Childerhouse et al., 1995) and justifies the use of Robust Design models over alternatives which assume that individuals are always available to be sampled. Robust Design models were also considered to be more appropriate for sperm whale populations in the Azores, compared with open Jolly-Seber modeling (Boys et al., 2019). This is because, similar to Kaik oura, the study site at the Azores represents only a portion of the population's range (Silva et al., 2014). ...
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Kaikōura, New Zealand, is one of the few places worldwide where sperm whales can be routinely found close to the coast. Although whales are present nearly all year round, no individuals are truly resident. In this study, we analyzed photo‐identification data collected over 27 years to investigate long‐term trends in inshore abundance. We contrasted two analytical approaches: Cormack‐Jolly‐Seber (CJS) mark‐recapture models, and the “Robust Design,” which can incorporate temporary emigration of individuals. CJS models for open populations showed a significant decline in the annual number of individual males, from 89, 95% CI [60, 133] in 1991, to 40, 95% CI [33–49] in 2017. The Robust Design models indicated that the trend was driven by a decline in abundance of whales using Kaikōura during summer, from 54, 95% CI [18, 156] in 1990, to 23, 95% CI [16, 33] in 2017. In contrast, there was no evidence for change in the numbers of whales using Kaikōura during winter. Incorporating temporary emigration had the most support, indicating Robust Design was more appropriate for estimating abundance. The results of Pradel recruitment models suggest that recruitment to the population using the study area during summer declined during the late 1990s/early 2000s, potentially explaining the decline in abundance.
... For the MSORD models, daily resighting histories were used as secondary sampling occasions within the primary weekly time period of this study. MSORD models estimate state-specific survival and transition probabilities among primary time periods, and state-specific arrival, persistence, and detectability within primary time periods, which have been used to address temporary emigration of individuals not observable within sampling periods (Boys et al. 2019). To be consistent with the multistate models, daily resightings within the weeks that each individual was ascribed as resident were assigned to a state of residency for the MSORD models; all other resightings were assigned to the mobile state. ...
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Effective adaptive management of aquaculture-based fisheries enhancement programs requires iterative feedback on the impact of stocking activities. For estuarine finfishes, postrelease survival is particularly challenging to assess where recapture rates are low or difficult to obtain. We describe a novel approach to assess short-term apparent survival of hatchery-reared fish stocked into open estuarine systems and address postrelease behavioral states to quantify weekly survival of common snook ( Centropomus undecimalis ) after one year of monitoring. Following a weekly spatial and temporal replicate-release design for two experimental releases, 1922 juvenile snook (133–281 mm fork length) were marked with passive integrated transponder (PIT) tags and released among two regions of Phillippi Creek, Florida. Marine-adapted PIT tag antenna arrays detected 79% of released individuals and provided daily resighting histories for analysis with multistate mark-recapture models. Resighting histories were best explained by short-term differences in apparent survival among the first few weeks, and long-term patterns in detectability driven by residency behaviors. Weekly apparent survival rates increased from between 0.25 and 0.52 after the first week to >0.9 after week five. Fork length positively influenced survival for both releases and water height positively influenced detectability for the fall release. The highest survival was observed for individuals released in lower Phillippi Creek in the spring, suggesting lower reaches of tidal creek systems provide ideal release locations for juvenile snook. Further application of this approach will help refine optimal release locations, times, and procedures, promote adaptive management of enhancement programs, and maximize the benefits of strategic, science-based stocking on receiving populations.
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Collisions with ships are one of the main modern threats to whale survival. Several solutions exist to reduce the risk of collision, but the compliance of the shipping industry with them is often limited. This interdisciplinary thesis aims at understanding the economic, logistic, and ecological gaps that hinder the shipping industry’s compliance. The research question is the following: How to integrate human and ecological dimensions in a standardized process to better manage whale-ship collisions? To answer this question, this thesis aims at (1) defining a standardized assessment process for mitigation solutions; (2) investigating the economic and logistic dimensions needed to achieve a holistic assessment of the whale-ship collision issue. The International Maritime Organization has the potential to improve whale protection from ship strikes, and we investigate its risk assessment framework, namely the Formal Safety Assessment. Based on the identified gap within this framework, our research first explores the notion of acceptable risk within the shipping industry and conservation science. Then, we investigate the preferences of the shipping industry for mitigation solutions, and study the economic benefits of avoiding collisions, through avoided costs and risk evaluation criterion. By creating a bridge between economics and ecology, this manuscript improves the mutual understanding of the shipping industry and conservation science. This work could be used as guidelines for the proposal of solutions, leading to an increased compliance of the shipping companies, and, therefore, an improved protection of whales.
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There has been a globally growing interest in cetacean-based tourism resulting in increasing cumulative impacts on many wildlife species. In the Azores archipelago, the local whale watching industry has drastically evolved over the last two decades, especially targeting a population of sperm whales that use the habitat as important feeding ground and can be found year-round. Some individuals return annually to the area and stay over several weeks to breed, which makes them especially vulnerable to human-induced disturbance. Hence, this study aimed to contribute to the establishment of a conservation framework to ensure the future sustainability of this industry by including the comprehensive evaluation of cumulative exposure of sperm whales to whale watching boats in the area into the management of human activities. For the first time spatially-explicit capture-recapture (SECR) models have been developed to quantify the cumulative interaction time between photo-identified individual sperm whales and whale watching boats in the area. The study provided baseline estimates of sperm whale encounter probabilities which were integrated together with the whale watching intensity in the area to estimate spatial variations of exposure on individual-level. Model estimates revealed that whale watching activities were mainly concentrated in two distinct areas north of Faial and south of Pico island, consequently exposure levels were found to be significantly higher in respective ‘core’ areas of disturbance. Furthermore, results indicated seasonal variations in daily individual exposure levels with a peak in June, where the most repeated interactions between the same individual and whale watching boats took place. The present findings stress the importance of taking the individual exposure into consideration when it comes to the management of potentially harmful human activities and support a precautionary approach.
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Variation between and within individuals in life history traits is ubiquitous in natural populations. When affecting fitness-related traits such as survival or reproduction, individual heterogeneity plays a key role in population dynamics and life history evolution. However, it is only recently that properly accounting for individual heterogeneity when studying population dynamics of free-ranging populations has been made possible through the development of appropriate statistical models. We aim here to review case studies of individual heterogeneity in the context of capture-recapture models for the estimation of population size and demographic parameters with imperfect detection. First, we define what individual heterogeneity means and clarify the terminology used in the literature. Second, we review the literature and illustrate why individual heterogeneity is used in capture-recapture studies by focusing on the detection of life-history tradeoffs, including senescence. Third, we explain how to model individual heterogeneity in capture-recapture models and provide the code to fit these models (https://github.com/oliviergimenez/indhet_in_CRmodels). The distinction is made between situations in which heterogeneity is actually measured and situations in which part of the heterogeneity remains unobserved. Regarding the latter, we outline recent developments of random-effect models and finite-mixture models. Finally, we discuss several avenues for future research.
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Population structure must be considered when developing mark-recapture (MR) study designs as the sampling of individuals from multiple populations (or subpopulations) may increase heterogeneity in individual capture probability. Conversely, the use of an appropriate MR study design which accommodates heterogeneity associated with capture-occasion varying covariates due to animals moving between ‘states’ (i.e. geographic sites) can provide insight into how animals are distributed in a particular environment and the status and connectivity of subpopulations. 2.The Multistate Closed Robust Design was chosen to investigate: 1) the demographic parameters of Indo-Pacific bottlenose dolphins (Tursiops aduncus) subpopulations in coastal and estuarine waters of Perth, Western Australia; and 2) how they are related to each other in a metapopulation. Using four years of year-round photo-identification surveys across three geographic sites, we accounted for heterogeneity of capture probability based on how individuals distributed themselves across geographic sites and characterized the status of subpopulations based on their abundance, survival and interconnection. 3.MSCRD models highlighted high heterogeneity in capture probabilities and demographic parameters between sites. High capture probabilities, high survival and constant abundances described a subpopulation with high fidelity in an estuary. In contrast, low captures, permanent and temporary emigration and fluctuating abundances suggested transient use and low fidelity in an open coastline site. 4.Estimates of transition probabilities also varied between sites, with estuarine dolphins visiting sheltered coastal embayments more regularly than coastal dolphins visited the estuary, highlighting some dynamics within the metapopulation. 6.Synthesis and applications. To date, bottlenose dolphin studies using mark-recapture approach have focussed on investigating single subpopulations. Here, in a heterogeneous coastal-estuarine environment, we demonstrated that spatially structured bottlenose dolphin subpopulations contained distinct suites of individuals and differed in size, demographics and connectivity. Such insights into the dynamics of a metapopulation can assist in local-scale species conservation. The MSCRD approach is applicable to species/populations consisting of recognizable individuals and is particularly useful for characterizing wildlife subpopulations that vary in their vulnerability to human activities, climate change or invasive species. This article is protected by copyright. All rights reserved.
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Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates. Therefore, the effect of social organization on bias in population estimation could be removed by using POPAN with specific parameter combinations, to obtain population size estimates in a social species.
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Marine spatial planning and ecological research call for high-resolution species distribution data. However, those data are still not available for most marine large vertebrates. The dynamic nature of oceanographic processes and the wide-ranging behavior of many marine vertebrates create further difficulties, as distribution data must incorporate both the spatial and temporal dimensions. Cetaceans play an essential role in structuring and maintaining marine ecosystems and face increasing threats from human activities. The Azores holds a high diversity of cetaceans but the information about spatial and temporal patterns of distribution for this marine megafauna group in the region is still very limited. To tackle this issue, we created monthly predictive cetacean distribution maps for spring and summer months, using data collected by the Azores Fisheries Observer Programme between 2004 and 2009. We then combined the individual predictive maps to obtain species richness maps for the same period. Our results reflect a great heterogeneity in distribution among species and within species among different months. This heterogeneity reflects a contrasting influence of oceanographic processes on the distribution of cetacean species. However, some persistent areas of increased species richness could also be identified from our results. We argue that policies aimed at effectively protecting cetaceans and their habitats must include the principle of dynamic ocean management coupled with other area-based management such as marine spatial planning.
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Sperm whale (Physeter macrocephalus) populations were expected to rebuild following the end of commercial whaling. We document the decline of the population in the eastern Caribbean by tracing demographic changes of well-studied social units. We address hypotheses that, over a ten-year period of dedicated effort (2005–2015), unit size, numbers of calves and/or calving rates have each declined. Across 16 units, the number of adults decreased in 12 units, increased in two, and showed no change in two. The number of adults per unit decreased at -0.195 individuals/yr (95% CI: -0.080 to -0.310; P = 0.001). The number of calves also declined, but the decline was not significant. This negative trend of -4.5% per year in unit size started in about 2010, with numbers being fairly stable until then. There are several natural and anthropogenic threats, but no well-substantiated cause for the decline.
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Many ecological theories and species conservation programmes rely on accurate estimates of population density. Accurate density estimation, especially for species facing rapid declines, requires the application of rigorous field and analytical methods. However, obtaining accurate density estimates of carnivores can be challenging as carnivores naturally exist at relatively low densities and are often elusive and wide-ranging. In this study, we employ an unstructured spatial sampling field design along with a Bayesian sex-specific spatially explicit capture-recapture (SECR) analysis, to provide the first rigorous population density estimates of cheetahs (Acinonyx jubatus) in the Maasai Mara, Kenya. We estimate adult cheetah density to be between 1.28 ± 0.315 and 1.34 ± 0.337 individuals/100km2 across four candidate models specified in our analysis. Our spatially explicit approach revealed ‘hotspots’ of cheetah density, highlighting that cheetah are distributed heterogeneously across the landscape. The SECR models incorporated a movement range parameter which indicated that male cheetah moved four times as much as females, possibly because female movement was restricted by their reproductive status and/or the spatial distribution of prey. We show that SECR can be used for spatially unstructured data to successfully characterise the spatial distribution of a low density species and also estimate population density when sample size is small. Our sampling and modelling framework will help determine spatial and temporal variation in cheetah densities, providing a foundation for their conservation and management. Based on our results we encourage other researchers to adopt a similar approach in estimating densities of individually recognisable species.
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Understanding population dynamics, and how it is influenced by exogenous and endogenous factors, is important to the study and conservation of species. Moreover, for migratory species, the phenology and duration of use of a given location can also influence population structure and dynamics. For many species, breeding abundance, survival, and reproductive performance, as well as phenology of nesting, are often the most accessible, and therefore practical, elements of their life history to study. For a population of hawksbill sea turtles (Eretmochelys imbricata), we modeled population change for nesters and total adult females, survival, and breeding probability, from 25 years of intensive tagging data. We modeled breeding probability as a function of the number of years since last breeding, and tested for differences between neophyte and experienced nesters. For each year, we also estimated the number of clutches deposited per female, and phenology of use, for neophytes and experienced nesters. In order to implement the analysis we developed a novel generalized multistate open robust design mark‐recapture modeling framework, with parameters for survival and transition probabilities, and for each primary period, state structure and arrival, persistence, and detection probabilities. Derived parameters included abundance of observable and unobservable components of the population, residence time, expected arrival and departure periods, and per‐period intensity of study area use. Abundance of nesters increased over most of the time series. Survival probability was 0.935 (se = 0.01). Virtually all hawksbills skipped at least one year of nesting. Breeding probability increased by skipping a second year, but then decreased thereafter. Subsequent breeding probability was lower for neophyte nesters than for experienced nesters, but the effect was weaker than the effect of years since breeding. Clutch frequency varied by year, with no discernable pattern of differences between neophytes and experienced nesters. Mean arrival and departure dates also varied, with a slight shift of nesting activity to earlier in the season. The multistate open robust design model developed here provides a flexible framework for modeling the dynamics of structured migratory populations, and the phenology and duration of their seasonal use of study areas. This article is protected by copyright. All rights reserved.
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Knowledge of abundance and survival of humpback whales, white-beaked dolphins and minke whales are essential to manage and conserve these species in Icelandic coastal shelf waters. Our main goal was to test the feasibility of employing inexpensive research methods (data collected by trained-scientist volunteers onboard opportunistic vessels) to assess abundance and apparent survival. No previous studies in Iceland have investigated these two demographic parameters in these three cetacean species using open capture-recapture models accounting for imperfect and possibly heterogeneous detection. A transient effect was accounted for whenever required to estimate the population of resident individuals. Identification photographs were collected by scientist-trained volunteers for 7 years (2006–2013) from onboard commercial whale-watching vessels in the coastal waters of Faxaflói (southwest coast, ~ 4400 km²) and Skjálfandi (northeast coast, ~ 1100 km²), Iceland. We estimated an average abundance of 83 humpback whales (Mn; 95% confidence interval: 54–130) in Skjálfandi; 238 white-beaked dolphins (La; [163–321]) in Faxaflói; and 67 minke whales (Ba; [53–82]) in Faxaflói and 24 (14–31) in Skjálfandi. We also found that apparent survival was constant for all three species (Mn: 0.52 [0.41–0.63], La: 0.79 [0.64–0.88], Ba-Faxaflói: 0.80 [0.67–0.88], Ba-Skjálfandi: 0.96 [0.60–0.99]). Our results showed inter-annual variation in abundance estimates which were small for all species, and the presence of transience for minke whales. A significant increase in abundance during the study period was solely found in minke whale data from Skjálfandi. Humpback whales and white-beaked dolphins showed lower apparent survival rates compared to similar baleen whale and dolphin populations. Our results show data collected by trained-scientist volunteers can produce viable estimates of abundance and survival although bias in the methods we employed exist and need to be addressed. With the continued increase in anthropogenic pressures on our three target populations in Iceland our results can be used by relevant stakeholders to develop appropriate conservation strategies in the region.
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While population sizes and structures naturally fluctuate over time, rapid within-generation changes are usually driven by shifts in habitat quality and (or) abrupt mortality. We evaluate how sperm whales (Physeter macrocephalus L., 1758 = Physeter catodon L., 1758) responded to the dynamic habit off the Galápagos Islands over 30 years, relating it to variation in prey availability and whaling operations in the tropical Pacific. In the 1980s, males and females were commonly sighted foraging and socializing in the northwest of the archipelago. Sightings decreased during the 1990s; by the 2000s, they became very rare: occasional single foraging males were sighted and females abandoned the archipelago. In the 2010s, whales return to the southern waters, in large groups with apparently more breeding males and calves. The waxing and waning of Galápagos sperm whales are likely caused by environmental shifts together with ripple effects of whaling. Their patchy prey are influenced by variation in sea temperature and productivity, which drives movements of whales in and out of the archipelago. Whaling may have aggravated these movements by leaving an attractive surplus of prey in coastal waters depleted of whales. These findings highlight the magnitude of spatiotemporal scales used by sperm whales and the consequent challenges of assessing population dynamics of long-lived, mobile pelagic species.