Lemur species-specific metapopulation responses to habitat loss and fragmentation
Lemur species-specific metapopulation responses to habitat loss and fragmentation
Travis S. Steffens 0 1
Shawn M. Lehman 1
0 Planet Madagascar , Toronto, Ontario , Canada , 2 Department of Anthropology, University of Toronto , Toronto, Ontario , Canada
1 Editor: Elke Zimmermann , Tierarztliche Hochschule Hannover , GERMANY
Determining what factors affect species occurrence is vital to the study of primate biogeography. We investigated the metapopulation dynamics of a lemur community consisting of eight species (Avahi occidentalis, Propithecus coquereli, Microcebus murinus, Microcebus ravelobensis, Lepilemur edwardsi, Cheirogaleus medius, Eulemur mongoz, and Eulemur fulvus) within fragmented tropical dry deciduous forest habitat in Ankarafantsika National Park, Madagascar. We measured fragment size and isolation of 42 fragments of forest ranging in size from 0.23 to 117.7 ha adjacent to continuous forest. Between June and November 2011, we conducted 1218 surveys and observed six of eight lemur species (M. murinus, M. ravelobensis, C. medius, E. fulvus, P. coquereli, and L. edwardsi) in the 42 fragments. We applied among patch incidence function models (IFMs) with various measures of dispersal and a mainland-island IFM to lemur species occurrence, with the aim of answering the following questions: 1) Do lemur species in dry deciduous forest fragments form metapopulations? 2) What are the separate effects of area (extinction risk) and connectivity/ isolation (colonization potential) within a lemur metapopulation? 3) Within simulated metapopulations over time, how do area and connectivity/isolation affect occurrence? and 4) What are the conservation implications of our findings? We found that M. murinus formed either a mainland-island or an among patch metapopulation, M. ravelobensis formed a mainland-island metapopulation, C. medius and E. fulvus formed among patch metapopulations, and neither P. coquereli or L. edwardsi formed a metapopulation. Metapopulation dynamics and simulations suggest that area was a more consistent positive factor determining lemur species occurrence than fragment isolation and is crucial to the maintenance of lemur populations within this fragmented landscape. Using a metapopulation approach to lemur biogeography is critical for understanding how lemur species respond to forest loss and fragmentation.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: Financial support for this work was
provided by the following intuitions and
organizations: Sigma Xi Grants in Aid of Research
(https://www.sigmaxi.org), American Society of
Primatology (Conservation Small Grant; https://
www.asp.org), Calgary and Edmonton Valley Zoos
(https://www.calgaryzoo.com and https://www.
edmonton.ca/attractions_events/edmonton-valleyzoo.aspx), Primate Conservation, Inc. (http://www.
Endemic to Madagascar, lemurs are the most endangered mammal group in the world, 94% of
lemur species are threatened with extinction [
] largely due to habitat loss and fragmentation
primate.org), The Explorers Club (Exploration
Fund; https://explorers.org), and the University of
Toronto School of Graduate Studies Travel Grant to
TS, and the Natural Sciences and Engineering
Research Council of Canada (Discovery Grant;
of the forests in Madagascar [
]. While there is some disagreement on precisely how much
forest has been lost in Madagascar , researchers estimate that between 40 and 52 percent of the
forest cover has been converted to non-forested habitat between the 1950s and 2010 [2±4].
The processes of habitat loss and fragmentation create landscapes with discrete fragments of
]. In western Madagascar, the forest is mostly rare tropical dry deciduous forest, and
Ankarafantsika National Park contains one of the largest remaining intact portions of
continuous dry forest. Dry forest is extremely sensitive to fire [
], which has resulted in a high degree
of forest loss and increased habitat fragmentation in this area [
]. Indeed, satellite imagery
shows that habitat loss and fragmentation continue in these tracks of continuous forest [
Therefore, even in protected areas such as Ankarafantsika, lemurs could be subject to
increased habitat fragmentation.
The effects of forest loss and fragmentation on primate species occurrence are well studied
[8±14]. Habitat loss is simply the removal of habitat from a landscape and habitat
fragmentation is the separation of habitat into smaller less connected portions [
]. Typically, primate
species occurrence decreases with increased forest loss [10,13,16±19]. Conversely, landscape
connectivity/isolation appears to have little to no effect on individual primate occurrence
when compared to fragment area [13,20±23]. The effect of habitat fragmentation separate
from habitat loss on primate occurrence is not well understood [24±26]. To better understand
how fragmentation impacts primate species, researchers need to further assess how
connectivity, independent of habitat loss, affects primate occurrence .
Metapopulation dynamics offers multiple models for determining the population viability
of lemur species in remnant forest patches. There are different types of single species
metapopulation models with variable characteristics, including but not limited to the mainland-island
] and an among patch metapopulation model where colonization is not
influenced by a mainland. A mainland-island metapopulation occurs where a patch or
population within a fragmented landscape is particularly large (mainland) and is surrounded by
smaller patches. The mainland has a large population of individuals that is unlikely to become
extinct . However, extinction risk is confounded by patch size [
], with smaller patches
having a relatively higher extinction risk than larger patches. Because of its large size the
mainland produces an unlimited supply of migrants called propagule rain (Hanski, 1994a). The
mainland's unlimited supply of migrants is independent of the number of patches occupied
within the system [
]. The colonization potential or isolation of island patches is related to
their distance from the mainland [
]. A mainland-island metapopulation may help explain
the source-sink dynamics observed in some metapopulations [
]. Similar to a
mainlandisland model, in an among patch model extinction probability of a patch is a function of the
area of that patch [
]. However, colonization is a negative exponential function of the
distance to the nearest occupied patch plus a species-specific dispersal parameter [
Researchers have used metapopulation theory as a conservation tool to predict species
persistence in a fragmented landscape under varying conservation strategies [
], to assess
extinction risk , to determine factors impacting species occurrence [
], to determine
species minimal critical forest patch size [
], and for population viability analyses [
studies have investigated the impact of fragment size and isolation within suspected
metapopulations but they did not use metapopulation dynamics per se [
]. Despite metapopulation
dynamics being widely recognized as a useful approach to determine how individual species
respond to habitat loss and fragmentation [
], there is no research on metapopulation
dynamics in lemurs and few studies on primates [
]. In one example of a study on
primates, Chapman et al. [
] examined forest fragments along the periphery of Kibale National
Park, Uganda and fitted a mainland-island incidence function model to occurrence data on
four primate species. The metapopulation models accounted for a substantial amount of
2 / 26
variation in each species occurrence. However, the authors found low confidence in the
estimated coefficients for the models. For both Procolobus badius (red colobus) and Colobus
guereza (black and white colobus), Chapman et al. [
] found a strong area effect on occurrence but
little influence of connectivity on each species occurrence. For Cercopithecus ascanius
(redtailed monkey) fragment size or distance did not affect occurrence while Chapman et al. [
found Pan troglodytes (chimpanzees) were an unsuitable species for the application of
metapopulation dynamics because of their highly mobile nature. In another example of a
metapopulation study on primates, Lawes et al. [
] examined a fragmented portion of Podocarpus forest
in KwaZulu-Natal Province, South Africa, and applied a mainland-island incidence function
model to Cercopithecus mitis labiatus occurrence and additional land use and environmental
factors. Lawes et al. [
] found that the best-fit model incorporated only area as a factor
determining C. m. labiatus occurrence. Model fit was not improved by the inclusion of isolation,
land use, or other environmental factors.
Preliminary biogeography research on lemur species indicates considerable differences in
lemur responses between continuous and fragmented forests [10,36±38]. For example,
Steffens and Lehman [
] found that contrary to a previous study of two species of mouse lemurs
(Microcebus murinus and M. ravelobensis) in continuous forest [
], there were significant,
positive correlations between density and abundance for both species in forest fragments.
Knowing that there are differences in biogeographic patterns in some species in continuous
versus fragmented habitat raises the question: How will other species will respond to
increased habitat fragmentation? Thus, understanding spatial variations in lemur responses
to forest fragmentation is critical to a more informed understanding of their conservation
The goal of this study is to investigate the vulnerability of eight lemur species to habitat loss
and fragmentation in a fragmented landscape in Ankarafantsika National Park. Vulnerability
was determined using different stochastic patch occupancy models (incidence function models
(IFM)). Following to previous research on mammal patch occupancy in tropical environments
], we do not employ standard hypothesis testing. Rather, we compare IFMs to answer the
following questions: 1) Do lemur species in dry deciduous forest fragments form
metapopulations? 2) What are the relative effects of area (extinction risk) and connectivity/isolation
(colonization potential) within a lemur metapopulation? 3) Within simulated metapopulations over
time, how do area and connectivity/isolation affect occurrence? and 4) What are the
conservation implications of our findings?
Study site and study species
We conducted this study in an approximately 3000 ha fragmented landscape consisting of 42
relatively homogeneous forest fragments surrounded by a relatively homogeneous grassland
matrix within the western boundary of Ankarafantsika National Park (ANP), Madagascar
(Fig 1). To the north, east, and south of the fragments there is continuous forest (Fig 1). We
received permission to conduct our study from the Ministère de l'Environnement, de
l'Ecologie et des Forêts, and Madagascar National Parks (Permit Number: 089/11/MEF/SG/DGF/
DCB.SAP/SCB). ANP is approximately 135,800 ha, and consists of a mosaic of approximately
72,670 ha of dry deciduous forest and grassland [
]. The climate is mostly dry with mean
yearly rainfall of 1,000±1,500 mm occurring mostly in the rainy season between November
and April . There are eight species of lemurs in ANP (Table 1). We conducted this study in
the dry season and early part of the wet season (June±November) of 2011 to facilitate access,
3 / 26
Fig 1. Study site and distribution of forest within Madagascar. a) Location of study site within Madagascar. b) Location of the study site
within Ankarafantsika National park. c) Close up of study site showing the fragmented landscape, consisting of 42 fragments of dry
deciduous forest separated by a mainly homogeneous matrix of grassland. Survey fragments are represented in dark grey and continuous
forest in light grey and grassland in white.
4 / 26
Median-dispersal distance was calculated as seven times the square root of the mean reported home range from each study.
and because there is increased visibility due to reduced foliage. All species were active during
the entire study except C. medius, which is in torpor between April and October [
To determine patch occupancy we used a single season visual survey along a single line
transect within each of the 42 habitat fragments. We placed one survey transect along the
longest axis of each fragment while going through the center of the fragment except in Fragment
12A where we placed the transect along the longest axis of the largest portion of the fragment,
and fragment three where we had two transects. During survey walks each researcher walked
slowly (approximately one km/hour), scanning and listening for all lemur species. During
diurnal surveys one or two researchers scanned both sides of the transect simultaneously. Two
researchers walked together during all nocturnal surveys and each researcher focused on one
side of the transect for the entire duration of the survey. Each team member used
high-powered flashlights and headlamps during nocturnal surveys to observe eye shine. Each team
member carried binoculars to facilitate species identification and a laser range finder to
measure distance metrics (below). We conducted diurnal and nocturnal surveys as follows: early
diurnal surveys between 06:19 and 09:07 hours, late diurnal surveys between 14:39 and17:18
hours, early nocturnal surveys between 18:00 and 21:27 hours, and late nocturnal surveys
between 02:17 and 5:55 hours. To ensure temporal independence for each survey, we only
conducted one of each survey type (diurnal and nocturnal) per 24-hour period in each transect.
To ensure spatial independence we alternated the direction of each transect walk. We surveyed
all fragments at least twice during early June and between October and November to ensure an
accurate assessment of the occurrence of C. medius, who can be in torpor between April and
]. In total, we conducted between 11 and 18 diurnal, and 11 and 21 nocturnal
surveys in each fragment (S1 Table). Prior to the surveys, we trained the core team members
on identifying each species within Ampijoroa field station. When we conducted surveys we
ensured that there was at least one core team member experienced in identifying each of the
eight different species on the survey. When team members observed a group or individual
lemur, the team spent up to 15 minutes measuring and recording the following information:
observer to animal distance, perpendicular distance of animal to transect, GPS location of the
5 / 26
observer, angle of animal from transect, time, date, researchers names, which side the animal
was detected, transect number, walk number, height animal was found, tree height animal was
found, animal activity, group size, group spread, and species identity. Each of the species was
easily identified by size, except for the two Microcebus species. These cryptic species are
difficult to visually identify. Therefore, we used a suite of characteristics to determine the species
identity of the two Microcebus species. We determined a positive identification of M. murinus
only when the team observed all of the following characteristics: grey/brown fur, small body
size, and a short tail that was thick at the base. We determined a positive identification of M.
ravelobensis when the team observed all of the following characteristics: rufus fur, and a long
tail that was thin at the base. We found it difficult to identify 41% of Microcebus sightings to
the species level during surveys. Because of the cryptic nature of the Microcebus spp., in this
study we conducted analysis on the sightings for M. murinus and M. ravelobensis when we
were confident of their identification and second analysis combining all Microcebus spp.
sightings. If we did not observe a species during any survey of a fragment, we considered it absent
for that fragment.
Question 1: Do lemur species in dry deciduous forest fragments form
In an incidence function model (IFM), incidence is a measure of the probability of species
occurrence within a patch and is a function of both patch extinction probability and
colonization potential [
]. It is difficult to measure patch extinction probability and colonization
potential directly [
]. To determine a species extinction probability within a patch or patch
network, it is necessary to acquire long-term data on mortality of individual primates, who are
long lived, within patches of varying sizes. To determine colonization potential of a patch or
patch network, it is necessary to know a species dispersal abilities between patches over more
than one year. For primates this requires difficult to acquire long-term data on species
Using an IFM researchers can determine incidence in a metapopulation model without
data on species extinction or colonization rates. From an IFM, we can infer the extinction
probability of a patch and its colonization potential using simple occurrence data (presence/
absence) gathered from a single-survey period among patches within a fragmented landscape
]. An IFM uses area as a proxy for extinction risk and isolation as a proxy for
colonization potential. Thus, it describes the probability that a species occurs (incidence) within a
patch as a function of both the area (extinction risk) and isolation (colonization potential) of
that patch [
]. The only additional data required are the sizes and locations of each patch
and knowledge of the median-dispersal range of a species within the landscape. The benefit of
an IFM is that it is more realistic because it incorporates patch area and isolation directly
measured from the landscape and it can be easily parameterized based on occurrence data of
species within a fragmented landscape at one particular point in time.
The incidence function models we used are spatially explicit models that have some
simplifying assumptions including the following: that the patch has a size but no shape, the quality of
the patch is constant, and the matrix is relatively uniform. The data needed for a
metapopulation IFM include at least a single survey of patch occupancy within a network of patches, the x
and y coordinates of each patch to determine the distance between each patch, patch area,
and the species-specific dispersal ability within the landscape [
]. We selected our study site
because it suits many assumptions of the model including having mostly homogeneous forest
fragments of varying size separated by mostly homogeneous matrix of grassland. It is relatively
easy to gather occurrence data for primates and to measure patch area and distances using
6 / 26
current GPS and GIS technology. However, it is very difficult to know the dispersal ability of
primates within a landscape.
We chose to investigate three different models including: 1. an among-patch incidence
function model with rescue effect (including four associated sub-models: IFM, IFMproxy,
IFMproxy2, IFMlit), 2. a mainland-island incidence function model without rescue effect
(MI-IFM), and 3. a null model where lemur species occurrence varies randomly with respect
to patch area and isolation (Null). The four among-patch sub-models are similar but differ in
the way the dispersal parameter (α) was defined (see below).
For each model, we input data on patch occupancy for each species and Microcebus spp.
combined, a measure of species-specific dispersal distance (α), patch area, and isolation (see
below). We then took a linearized version of the IFM sub-models (see Eq (9) below) and the
MI-IFM model (see Eq (15) below) and ran binomial generalized linear models (GLM) with a
logit link function using the glm function in R [
], for each species and Microcebus spp.
combined (adapted from [
]). For each GLM we ran we input species occurrence as the response
variable and species-specific dispersal distance (α), patch area, and isolation as the predictor
The IFM sub-models with rescue effect takes the following form [
where Ji in Eq (1) is patch incidence in patch i defined in terms of extinction and colonization
rates, Ei is the extinction probability, Ci is the colonization probability, Si is a measure of
connectivity for patch i, and Ai is the area of patch i. Mi is a measure of connectivity within a
landscape. Ji in Eq (5) is patch incidence in patch i defined in terms of patch size Ai and patch
connectivity Si. It is difficult to estimate extinction and colonization directly [
it is possible to calculate e and y (parameters estimated from the data that relate to extinction
and colonization probabilities respectively; see Question 2: What are the Separate Effects of
Area (Extinction Risk) and Connectivity/Isolation (Colonization Potential) within a Lemur
Metapopulation? for more explanation) with data on patch occupancy pj, patch size A, and
connectivity S collected during a single time period survey . Connectivity is estimated
using the following:
where is α inverse of the median species-specific dispersal distance and dij is a distance matrix
among patches. It is possible to change Eq (5) by applying a linear model for the log-odds of
The mainland-island incidence function model without rescue effect takes the following
form (MI-IFM; [
where q and ß are two parameters and Di is the distance from the mainland to each island.
Assuming that all the species are common on the mainland, where Ci approaches one when Di
approaches zero than q = 1 and Eq (11) can be simplified further [
It is possible to linearize Eq (13) by applying the log-odds of incidence:
A logit transformation then results in:
bDi b2 log
b1D b2 log
Patch occupancy. We determined patch occupancy of each fragment using the methods
described above. We considered a species as present if we visually or acoustically (one
instance) recorded their presence within a fragment. We considered a species as absent if we
did not visually observe or acoustically confirm their presence within a fragment.
Dispersal distance. To determine connectivity within each IFM sub-model, we needed to
determine the dispersal parameter (α). However, there is limited data on dispersal ability in
most primates, especially lemurs. For the species in this study, dispersal distance has only been
estimated only in M. murinus [
] and to a lesser degree of accuracy in M. ravelobensis .
Therefore, we ran multiple IFMs incorporating different dispersal parameters (α). For each
species, we ran three IFM sub-models using different measures for dispersal, except for M.
murinus and M. ravelobensis for which we ran four IFM sub-models with different measures
for dispersal. For the first IFM sub-model, we determined which α fit the survey data by
running all possible values for α and selecting the one that provided the lowest deviance (IFM;
]). For the second IFM sub-model, we used a proxy for median-dispersal distance based on
a function of the home range size of each species (IFMproxy) [
]. Bowman et al. [
that median-dispersal distance could be estimated as the linear dimension (square root) of the
mean home range multiplied by a factor of 7. Therefore, we took the mean reported home
range for each species and determined its dispersal ability with the following formula:
7 median dispersal distance
Alpha (α) is calculated as:
median dispersal distance
The proxy for dispersal using the formula from Bowman et al. [
] overestimated the
median-dispersal of the two known species (M. murinus and M. ravelobensis; Table 1).
Therefore, for the third IFM sub-model (IFMproxy2), we created a second proxy where we took the
linear dimension of the mean reported home range:
Because lemurs like many arboreal primates may be dispersal limited [
] the value derived
from formula (16) may overestimate median-dispersal distance. The dispersal distance values
derived using formula (18) better fit the known dispersal distances for M. murinus and M.
ravelobensis (Table 1). For the final IFM sub-model (IFMlit: M. murinus and M. ravelobensis
only), we used the largest median-dispersal distance reported for each species regardless of
sex (M. murinus = 251 m [
]; M. ravelobensis = 54 m [
]. For Microcebus spp. combined we
included the all the sub-models as above but used both proxy dispersal estimates for both
species (i.e. IFMproxyMM, IFMproxyMR, IFMproxy2MM, and IFMproxy2MR).
Patch area and isolation. To measure the area (ha) of each fragment we first walked the
perimeter of each fragment recording the track with a handheld global positioning device
(Garmin GPS map 60csx). We input the track into QGIS (2012; n = 38). If obstructions
prevented a complete walk of the fragments perimeter (n = 4; Fragments 37, 39, 40, 42), we traced
the fragments perimeter from a high resolution DigitalGlobe™ satellite image, via Google
Earth™ taken during the study (10/8/2011). We input each polygon in QGIS and used the field
calculator tool to determine the area of these fragments. Because an IFM does not incorporate
shape of a fragment we estimated the edge-to-edge distance between each fragment by first
calculating the center-to-center distance between each fragment using the ArcGIS Spatial Join
tool and subtracting that by the radius of each fragment pairing assuming a circular shape for
Model comparison. To determine which model was the most likely among the models/
sub-models we tested we calculated corrected Akaike's information criterion with a correction
for finite sample sizes (AICc) and then calculated AIC weights (wi; [
]). We considered the
model with the highest wi as the model with the highest likelihood of being selected among the
models/sub-models we tested [
]. We considered models with AICc values within two of the
model with the lowest AICc as potential candidate models.
9 / 26
Question 2: What are the separate effects of area (extinction risk) and
connectivity/isolation (colonization potential) within a lemur
To determine if the incidence probability was positively related to area and connectivity (Si)
we ran univariate GLM analysis on each species incidence probability against area and
connectivity for the candidate model with the lowest AICc selected in question 1. We determined the
incidence probability for each patch (Ji) based on among patch models (Eq 1) or the
mainland-island model (Eq 10). However, to calculate incidence probability (Ji), we needed to
determine the extinction probability (Ei) and colonization probability (Ci) for each patch. For
the among patch models, with known patch sizes, patch occupancies, and connectivity, we
used a generalized linear binomial model with logit link function (Eq (9)) to determine the
coefficients ß0 and ß1 = x and to separate e (a parameter related to extinction probabilities)
from y (a parameter related to colonization probabilities) to calculate the extinction and
colonization probabilities for each patch using the following steps:
~e min Ax^
It is not possible to calculate e and y from the GLM directly (Eq 9) because any possible
combinations of pairs of e and y giving the estimated eby are equally good [
], we separated e and y from eby by fixing e as the smallest patch where a species was
present as the area that extinction probability equals 1 (Eq 20) and dividing to determine y (Eq
21). We calculated the extinction probability (Ei) and colonization probability (Ci) and
subsequently incidence probability (Ji)of each patch using Eqs (2) and (4) respectively.
For the mainland-island model, we also used a generalized linear binomial model from a
single survey of patch occupancy on Eq (15). Using this equation, we could determine the
coefficients ß0 = μ, ß1 = ß and ß2 = x to calculate the colonization and extinction probabilities with
Eqs (11) and (12) respectively. We then input the values from the colonization and extinction
probabilities into Eq (10) to determine the incidence probability for each patch.
Using a Shapiro Wilk's test we assessed normality for the following independent variables:
area, species specific connectivity measures for the among patch models (Si), and the edge of
fragment to continuous forest distance for the mainland-island models (DCF) [
found that some variables needed transformation to meet the assumption of normality (e.g.
log 10 for area, log 10 for Si estimations for E. fulvus, and square root for the edge of fragment
to continuous forest distance (DCF).
Question 3: Within simulated metapopulations over time, how do area and
connectivity/isolation affect occurrence? What are the conservation
To see if there was a difference in how area affected occurrence compared to connectivity, we
simulated metapopulation dynamics for each species over time based on the extinction and
colonization probabilities derived from the IFM selected in question 1 in R following Oksanen
]. We ran two sets of simulations. The first set represents a worst-case scenario where we
ran a simulation separating out the five largest fragments and a second simulation where we
10 / 26
separated out the five most connected/closest fragments. The second set represents the
opposite scenario where fragments that are smaller and least connected/furthest were removed
from the simulation. For each species and Microcebus spp. combined, we then ran the two sets
of two simulations for 200 time steps (equivalent of 200 years). In the first half of the
simulation all 42 fragments contribute to the metapopulation. At time step 101, we continue
simulations on the five removed fragments (i.e. five largest, five smallest, five most connected, five
least connected) and on the remaining 37 fragments for 99 more time steps. This method
allows the ability to model what happens to lemur species occupancy in the five removed and
remaining 37 fragments independently of each other. We used the number five because this
represented a realistic conservation scenario where in a single event five of the largest, smallest,
least/furthest, or most connected/closest fragments could be lost.
We sampled 42 fragments within the study landscape with a median size of 2.16 and range of
0.23±117.70 ha. The mean distance between centroids of each fragment was 2.82 ± 1.36 km
with a range of 0.15 km to 6.48 km. The proxy for median-dispersal distance using Eq (16)
ranged from 538 m (M. ravelobensis) to 3080 m (P. coquereli) and using Eq (18) ranged from 77 m
(M. ravelobensis) and 400 m (P. coquereli: Table 1). Patch occupancy differed among species.
Smaller-bodied Cheirogaleids occurred in the largest number of fragments while the remaining
three larger species occurred in the fewest (Table 1). However, a linear regression of frequency
of occupancy versus body size yielded no relationships (adjusted R2 = 0.33, P = 0.14). Transect
and survey data are summarized in S1 Table. We did not observe any A. occidentalis or E.
mongoz individuals during our study. Therefore, we did not include these species in the analysis.
Question 1: Do lemur species in dry deciduous forest fragments form
The probability of occurrence differed among species (Fig 2). Both Microcebus species had the
highest probability of occurrence in the landscape followed by C. medius. E. fulvus had the
lowest probability of occurrence within the landscape.
We found differences in model selection results between species (Table 2). For C. medius,
the among patch sub-model where dispersal was determined based on the IFM had the lowest
AICc (IFM: Table 2). No other models had AICc values within two of the lowest model for C.
medius. For both M. murinus and M. ravelobensis separately and combined the
mainlandisland model had the lowest AICc (Table 2). However, for M. murinus and both Microcebus
spp. combined we found additional candidate models within two AICc values of the lowest
model including the among patch (IFM) model and the null model (Table 2). These results
suggest that when these species data are combined it is likely that they do not form either a
mainland island or an among patch metapopulation (Table 2). For both P. coquereli and L.
edwardsi, there were no models with values lower than or within two of the null model
suggesting these species do not form either type of metapopulation that we tested for. However, for P.
coquereli the among patch model was close at 2.31 AICc away from the null model (Table 2).
For E. fulvus, the model with the lowest AICc value was IFM followed by IFMproxy as the only
other model within two AICc values (Table 2). Therefore, we found support for among patch
metapopulations in C. medius and E. fulvus and mainland-island metapopulations in M.
murinus and M. ravelobensis. However, we found no support for the formation of metapopulations
for P. coquereli or L. edwardsi. We found limited support for a mainland-island
metapopulation when the Microcebus data was combined at the genus level.
11 / 26
Fig 2. Probability of occurrence among patches for four lemur species in a fragmented landscape. Colors represent the probability of occurrence: red reflects
the highest probability of occurrence for a species within a fragment and white the lowest. The probability of occurrence is based on the fitted incidence function
model with α parameterized from the data (IFM) for C. medius, M. murinus, and E. fulvus and the IFM with α determined from the literature (IFMlit) for M.
ravelobensis. The size of each circle represents the size of each fragment relative to one another. The position of fragments is based on Universal Transverse
Mercator (UTM) coordinate system. Northing is equivalent to latitude and easting is equivalent to longitude.
Question 2: What are the separate effects of area (extinction risk) and
connectivity/isolation (colonization potential) on a lemur metapopulation?
We found that log10 area was a significant positive contributor to incidence probability for all
species for all models (Table 3). However, the results for connectivity (Si) were more
complicated. For the among patch models, connectivity (Si) was not a significant contributor to
incidence probability for C. medius (Table 3). The square root of connectivity (sqrt(Si)) was a
significant positive contributor to incidence probability for sub-model IFM for E. fulvus
(Table 3). For the mainland-island models, the square root of the distance to continuous forest
12 / 26
a IFM = incidence function model where α was parameterized based on occupancy data from one survey period; MI-IFM = mainland-island incidence function model;
IFMproxy, IFMproxyMM, and IFMproxyMR = IFM where α was calculated as a proxy for dispersal ability based on the square root of the mean home range multiplied
by seven reported for each species in the literature (MM represents M. murinus and MR represents M. ravelobensis); IFMproxy2, IFMproxy2MM, and
IFMproxy2MR = IFM where α was calculated as a proxy for dispersal ability based on the square root of the mean home range reported for each species in the literature;
IFMlit = IFM where α was based on the literature for species where data has been reported on dispersal ability. K = number of parameters in the model.
13 / 26
(sqrt(DCF)) was a significant negative contributor to incidence for M. murinus, M.
ravelobensis, and Microcebus spp. combined (Table 3).
Question 3: Within simulated metapopulations over time, how do area and
connectivity/isolation affect occurrence?
Fragment area has a greater influence than fragment isolation on overall species occurrence.
Removal of the five largest fragments via simulation caused all species to decline in occurrence
(Fig 3A). The most extreme example was C. medius, which became extinct in the remaining
fragments. None of the species showed any appreciable change in occurrence among the five
largest fragments when they were separated from the remaining 37, although occurrence of C.
medius did vary between three and five in the five largest fragments. Removing the five smallest
fragments (Fig 3B) caused no noticeable decline in species occurrence in the 37 remaining
fragments. However, separation of the five smallest fragments from the remaining 37 caused a
decline in occurrence of all species in the five smallest fragments. Removal of the five most
connected/closest fragments via simulation resulted in no obvious changes in occurrence for
any species except M. ravelobensis which declined following the removal of the five closest
fragments to the continuous forest (Fig 4A). Occurrence for all four species declined within the
five most connected fragments following separation from the remaining 37 patches. Removal
of the five least connected/furthest fragments in no change in occurrence for E. fulvus but
caused a declining trend in occurrence for C. medius and to a lesser degree M. murinus
followed by M. ravelobensis following removal of the five least connected fragments (Fig 4B). In
the five least connected/furthest fragments there was little difference before and after
separation from the remaining 37.
14 / 26
Fig 3. Simulations of metapopulation dynamics for four lemur species over 200 time steps in a fragmented landscape when the five
largest and five smallest fragments are removed. Simulated species occurrence over time using a Markov chain process. The five largest (A)
and five smallest (B) fragments (black lines), respectively are removed from the rest of the fragments (n = 37; red lines) at time period 101
(vertical line). After this point we ran simulations, to time period 200, separately to demonstrate the impact of either removing the largest (A)
or smallest (B) fragments (black). We ran simulations using the IFM with α parameterized from the data (IFM) for C. medius, M. murinus, and
E. fulvus and the IFM with α determined from the literature (IFMlit) for M. ravelobensis.
15 / 26
Fig 4. Simulations of metapopulation dynamics for four lemur species over 200 time steps in a fragmented landscape when the five most
connected and five least connected fragments are removed. Simulated species occurrence over time using a Markov chain process. We
removed the five most connected/closest (A) and five least connected/furthest (B) fragments, respectively (black lines) from the rest of the
fragments (n = 37; red lines) at time period 101(vertical line). After this point we ran simulations, to time period 200, separately to demonstrate
the impact of either removing the most/closest (A) or least/furthest (B) connected fragments (black). We ran simulations using the IFM with α
parameterized from the data (IFM) for C. medius, M. murinus, and E. fulvus and the IFM with α determined from the literature (IFMlit) for M.
16 / 26
Using a single-season survey of patch occupancy, we found that six lemur species respond
differently to area and connectivity/isolation in a fragmented landscape. Simulations of
metapopulation dynamics provide support that lemur species occurrence was consistently positively
related to area but had mixed results for connectivity. For both Microcebus species, occurrence
was negatively related to connectivity, while E. fulvus occurrence was positively related to
connectivity. Based on the simulation results, we suggest that the most connected fragments are
not as important to the maintenance of the metapopulation as would be predicted by
metapopulation theory [
]. For example, when we separated the most connected/closest fragments
from the remaining fragments in the system only the simulated occurrence for M. ravelobensis
declined within the most connected fragments. However, when we separated the least
connected/furthest fragments our simulations showed declines in species occurrence for C.
medius, M. murinus, and M. ravelobensis.
We would like to identify some potential caveats that could impact the interpretation of our
results. The first is the known difficulty in visually assessing Microcebus spp. in our study area.
We acknowledge this difficulty and warn readers to consider the implications of
misidentification when interpreting the results. Our research teams are currently using mark-recapture
methods combined with genetic analysis to determine if visual surveys provide an accurate
representation of species determination in mouse lemurs. It is not only difficult to visually
determine species identity but there is also the potential for hybridization between the two
Microcebus spp. We observed numerous occurrences of individuals that had what appeared to
be mixed characteristics. In total we were unsure in the identification of 41% of our Microcebus
spp. sightings. Further genetic research is needed to determine if hybridization is occurring
within the landscape. The second caveat is the fact that C. medius goes into torpor between
April and October. In an attempt to determine the occurrence of C. medius we re-surveyed
each fragment (two more times) where C. medius had not been observed when C. medius was
coming out of torpor. We found that C. medius are extremely active and very conspicuous
when coming out of torpor. However, it is important to note that the limited number of
surveys we conducted when C. medius was potentially active may impact our results for this
Question 1: Do lemur species in dry deciduous forest fragments form
Metapopulation dynamics represents an ideal approach for conservation biogeography under
the following circumstances: when the habitat is in discrete patches, when ecological processes
occur at the local and metapopulation scale, when habitat within the discrete spatial unit is
large enough for local breeding populations, and when the patches are relatively permanent
]. The level of habitat loss and fragmentation in Madagascar provides an ideal situation in
which to apply a metapopulation approach to studying lemur populations: there is only a
fraction of habitat remaining, forest patches (fragments) are discrete units separated by a
non-habitat matrix, many patches are large enough to maintain local breeding populations, and local
populations are connected to one another through dispersal, thus creating metapopulations.
We found support for only the null model (where occurrence was randomly related to
patch area and connectivity) for P. coquereli and L. edwardsi, suggesting these species did not
form metapopulations within our study site. Both of these species only occurred within a small
number of large fragments within the landscape (three for P. coquereli and two for L. edwardsi)
and appear to be more impacted by habitat loss and fragmentation than the other species we
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It is possible that the existing populations of the two species within the fragments declined
and became locally extinct over time because fragment size was too small to support local
populations. The majority of the fragments are smaller than home ranges reported for P. coquereli
]. Warren and Crompton [
] found that for L. edwardsi, yearly home range size was 0.81±
1.70 ha and Rasoloharijaona et al. [
] found median home range size for L. edwardsi ranged
between 0.98 ha (females) and 1.0 ha (males). These home range sizes suggests that L. edwardsi
should be able to tolerate smaller fragments, however their mean horizontal distance travelled
per day was quite large. Warren and Crompton [
] found that L. edwardsi could travel as
much as 463 m (horizontal travel distance) per day. Few fragments in our study site had linear
dimensions greater than 463 m. L. edwardsi occurrence appears to differ in continuous versus
fragmented habitats. For example, Craul et al. [
] found L. edwardsi to occur in 13 of 17
continuous forest sites surveyed but only two of six habitat fragments, a finding that supports the
hypothesis that this species is not tolerant to habitat loss and fragmentation. We found both P.
coquereli and L. edwardsi to occur only in larger fragments (the three largest for P. coquereli
and the largest and fourth largest for L. edwardsi), and both were absent in all fragments
smaller than 11.58 ha. Therefore, the explanation of fragments being too small for survival is
plausible for both P. coquereli and L. edwardsi. L. edwardsi may also be particularly sensitive to
anthropogenic disturbance. Rabesandratana et al. [
] undertook a survey of L. edwardsi at 10
sites in Ankarafantsika National Park, but not in the vicinity of our study. Although this lemur
species was present at 9 of the 10 sites, density estimates were low for sites subject to
anthropogenic disturbance (e.g., near villages and areas for palm exploitation). Thus, the proximity of
our research site to local villages may indicate the deleterious effects of anthropogenics on L.
Minimum area requirements, limited numbers of large trees, and hunting pressure may
also relate to the absence of A. occidentalis in the forest fragments. In eastern littoral forests,
] only found Avahi meridionalis in forest fragments larger than 75 ha and no
correlation between fragment area and Avahi density. Although A. occidentalis have small home
ranges (median range between 1.57 ha± 1.79 ha, [
]) and rely on low quality abundant leaves,
suggesting that they would be able to inhabit relatively small fragments, it is possible that the
number and distribution of large trees impacts Avahi occurrence [
]. Norscia [
] found that
the percentage of large trees above 3.2 cm DBH was significantly positively related to Avahi
density. Illegal hunting does occur within the park, and the although the most commonly
hunted species are the larger-bodied and more common P. coquereli and E. fulvus, GarcÂõa and
] did identify remains of A. occidentalis from a hunt within the park. This lemur
species is also thought to be particularly sensitive to seasonal burning of grasslands adjacent to
forest fragments, which is undertaken by local people to promote fresh browse for domestic
Both Microcebus spp. appear to form mainland-island metapopulations. Dispersal ability
differs between M. murinus and M. ravelobensis in continuous forest [39,54±56] with M.
ravelobensis possibly more dispersal limited than M. murinus with estimated 0.05 km [
] and 0.25
] median-dispersal distances respectively. We found that our estimates of dispersal
ability determined from the metapopulation models were vastly higher than those reported for
each species (10 km for M. murinus and 0.27 km for M. ravelobensis). However, the trend of
M. murinus having a higher dispersal ability remained. It should be noted that dispersal
derived from incidence function models can be inaccurate [
]. It appears that M. ravelobensis
is more dispersal limited than M. murinus in both continuous and fragmented forest. If M.
ravelobensis is more dispersal limited then why do both species exhibit similar patterns of
occurrence within the landscape? M. murinus prefers higher elevation and drier forests than
M. ravelobensis [
]. However, our study site was chosen for its relative homogeneity. In a
18 / 26
study in the same landscape Steffens and Lehman [
] found that abundance in both M.
murinus and M. ravelobensis were related to similar factors such as dendrometrics, fragment area,
and isolation. In a continuous forest, Burke and Lehman [
] found differences between M.
murinus and M. ravelobensis in the capture rates of each species and the body mass of female
M. ravelobensis between the edge and interior habitat. They captured more M. ravelobensis and
fewer M. murinus along the edge than in the interior habitat. They found female M.
ravelobensis along the edge had greater body mass than those in the interior habitat. Therefore, both
species may form mainland-island metapopulations but for different reasons. We suggest M.
murinus is more capable of dispersing to further fragments than M. ravelobensis, but due to
greater edge tolerance M. ravelobensis is more capable of surviving in fragments, at least in the
short term. This would imply that M. murinus may be forming a source-sink metapopulation.
For both C. medius and E. fulvus, the most likely among patch incidence function
submodel were where we estimated the dispersal parameter (α) using the survey data (IFM). For
E. fulvus the sub-model where dispersal was based on the linear dimension of their mean
home range (IFMproxy2) was nearly as likely as the IFM model. Based on the support for
these models and lack of support for the mainland-island model for both lemur species it
appears they are sufficiently capable of moving between fragments to colonize extinct
fragments without needing to rely on the mainland for more migrants. C. medius and E. fulvus
were the only two frugivores in the study site. Larger-bodied species tend to have greater
dispersal ability than smaller species [
] and frugivorous primates tend to have larger home
ranges than folivorous primates [
]. Thus, home range size may predict dispersal ability
] and frugivores have greater dispersal ability than folivores. However, E. fulvus occurred in
fewer fragments than C. medius. C. medius with its small body mass and hibernation patterns,
may be better suited to survive in more fragments than E. fulvus. For example, we found C.
medius in fragments as small as 1.69 ha. Within this landscape there were likely few fragments
large enough for E. fulvus to live in, which required E. fulvus to move between fragments. E.
fulvus may be transient within patches that are smaller than they would normally need to be
able to survive. For example, we found E. fulvus in one fragment that was smaller (4.16 ha)
than their reported home range yet absent in four fragments that were within their reported
home range. E. fulvus is smaller in body mass but occurs in larger groups than P. coquereli
which is why they both have similar home range sizes (Table 1; [
]). However, E. fulvus
occurred in seven fragments and P. coquereli only two fragments. P. coquereli may be limited
by edge effects that reduce habitat suitability, hunting avoidance, or predator avoidance
] where E. fulvus may be more edge tolerant. A study on P. coquereli distribution
found that they are a capable of living in degraded habitat . Further study is needed to
determine why P. coquereli appears capable of living in degraded habitats but also appears
edge avoidant. Regarding E. fulvus Lehman et al. [
] found that contrary to predictions
Eulemur rubriventer was edge tolerant. Lehman  suggests that another species of Eulemur,
E. rubriventer, behaved more like a folivore/frugivore than a strict folivore. It is possible that E.
fulvus behaved the same way. However, we need further study to determine if E. fulvus is more
folivorous or frugivorous within the fragments and to determine what is the availability of
fruiting trees within the fragments.
Unlike any of the other species observed within the fragments, C. medius is capable of
extended hibernation [
]. Like other Cheirogaleus species, C. medius consumes large amounts
of high-sugar fruits prior to hibernation in order to build up fat reserves [
]. Therefore, C.
medius may be able to survive only in fragments that have a high availability of fruit during
this crucial period. Tree holes used by C. medius must be carefully selected in order to allow
sufficient maintenance of body temperature during the months that they hibernate [
fruit availability, tree holes may be a limiting resource for C. medius in the fragments.
19 / 26
Question 2: What are the separate effects of area (extinction risk) and
connectivity/isolation (colonization potential) on a lemur metapopulation?
Area. We found that fragment area was positively related to each species probability of
occurrence, regardless of the selected candidate model. In metapopulation dynamics as local
populations grow and reach their carrying capacity, individuals are forced to leave the patch to
find a new suitable patch [
]. An empty patch is considered colonized when an immigrant
arrives and subsequently survives in that patch. The quality and size of the habitat determines
survival of an individual in a previously unoccupied patch [
]. However, assessing habitat
quality is more difficult than measuring area of a patch. Hanski [
] argued that the ratio of
habitat quality versus area contributions to species occurrence is dependent on certain
speciesspecific factors. For primates, many biogeographic studies found that area was the largest
predictor of primate species occurrence [
]. For example, Lawes et al. [
] found that
area, rather than isolation and habitat disturbance, was the only factor that impacted
occurrence in C. m. labiatus in a fragmented landscape. We need future research to evaluate the
relative contribution of habitat quality versus area to lemur species occurrence.
Connectivity/Isolation. The metapopulation model predicts that connectivity should
have a significant positive effect on lemur species occurrence. E. fulvus was the only species to
show a positive relationship in occurrence probability and connectivity. Contrary to model
predictions, connectivity had no significant effect on occurrence probability for C. medius and
a negative effect on occurrence probability for M. murinus and M. ravelobensis. Migration
between fragments is risky for arboreal lemurs. For example, species travelling through the
matrix have increased predation risk [
] and there is the possibility of arriving at an
unsuitable fragment requiring further migration. In our study, E. fulvus, although capable of
migrating to any fragment and using matrix elements between fragments, appears to stay within the
largest and most connected fragments. Although occurring in multiple fragments, C. medius
tended to occur near the largest fragment (Fragment 3 and S2 Table) or the continuous forest.
Therefore, they are either not limited by dispersal in a fragmented landscape or they form an
intermediate metapopulation (combination of different metapopulations e.g. among patch
and mainland-island metapopulations). If C. medius do represent an intermediate
metapopulation, then they are likely to be able to move between fragments but one or more of the larger
fragments would act as a mainland source of more colonists [
]. For example, if Fragment 3
acted as a second mainland source, this pattern would explain a lack of difference in
occurrence probability between more and less connected fragments. For both Microcebus species,
the differences in occurrence probability were negative, meaning that the probability of
occurrence was lower in closer than in further fragments. One explanation for the negative
relationship with connectivity is that the two species of Microcebus have higher occurrence probability
in more isolated fragments because they are not area limited and are able to survive, possibly
in the long-term, within the smallest fragments regardless of isolation. Other studies have
recorded Microcebus species in all but the smallest (<1 ha) fragments [79±81]. However, these
studies did not include as many small (<1 ha) fragments as our study. We found Microcebus
to occur in fragments as small as 0.23 ha. It is possible that there are source-sink dynamics
occurring within the study landscape, where large patches and possibly the nearby continuous
forest provide a constant source of potential immigrants for smaller patches [
]. For example,
we only observed one Microcebus individual in the smallest fragment as well as numerous
sightings of mouse lemurs within matrix elements between fragments (Steffens unpublished
data), suggesting that occupancy in this patch is ephemeral and thus maintained through
colonization via the matrix. Ganzhorn and Schmid [
] also found a potential source-sink
relationship occurring for M. murinus in secondary forests within a fragmented landscape. They
20 / 26
observed poorer conditions (smaller, fewer trees and warmer temperatures) within the
secondary versus primary forest and within the secondary forest they never re-captured any
individuals but were able to recapture seven within the continuous forest.
Question 3: Within simulated metapopulations over time, how do area and
connectivity/isolation affect occurrence?
One of the advantages of a metapopulation approach is that it is possible to model extinction
(area) and colonization (connectivity/isolation) probabilities, which allows for the simulation
of their effects on occurrence over time. Our simulation results confirm that area is
consistently positively related to lemur species occurrence. The simulations on removing the most
connected/closest or least connected/furthest may help us understand Microcebus spp.
metapopulation dynamics within the landscape. M. ravelobensis occurrence declined when the closest
fragments were removed but M. murinus occurrence did not. This result supports our previous
suggestion that M. ravelobensis is more dispersal limited. They appear to need the closer
fragments as stepping-stones to the other fragments whereas M. murinus isn't impacted by the
proximity of the closest fragments due to their greater dispersal ability.
Question 4: What are the conservation implications of our findings?
Visual inspection of satellite imagery from 1984 to 2017 [
] shows that human activity has
maintained levels of habitat fragmentation in our study site with little to no forest recovery.
However, in the same time period forest along the periphery of Ankarafantsika National Park
has declined from human activity. The results of our study suggest that maintaining or
increasing fragment area within the study site will have the greatest positive benefit to the most lemur
species. All analyses suggest that large fragments are crucial to maintaining lemur species
metapopulations. Landscape connectivity should be an important variable for each lemur
species but our results show that only E. fulvus occurrence was positively related to connectivity.
Connectivity was negatively related to occurrence probability of M. murinus and M.
ravelobensis and was not significantly related to occurrence probability of C. medius. To have the
maximum benefit for the most species in the short-term we recommend that conservation efforts
focus on maintaining or increasing fragment size rather than increasing fragment
connectivity. Additionally, increasing fragment size will have the secondary benefit of improving
fragment connectivity as the distance between fragments will decrease as fragment size increases.
However, we do recommend improving fragment connectivity for conservation measures
focusing on E. fulvus as it is an important seed disperser in the landscape [
] and we found
their occurrence to be positively related to fragment connectivity. If source-sink dynamics are
occurring within the landscape for M. murinus and M. ravelobensis, then we also specifically
recommend increasing the area of potential sink fragments to reduce the likely mortality of
individuals moving from source populations in large fragments.
A metapopulation approach is useful for determining the combined and separate effects of
area and connectivity/isolation on lemur species occurrence in a fragmented landscape. This
study shows that four lemur species (C. medius, M. ravelobensis, M. murinus, and E. fulvus)
form metapopulations in fragmented landscapes. Within their metapopulations, lemurs are
impacted by both habitat area and isolation but our study shows that area strongly affects
species occurrence and that isolation has species-specific neutral, negative, and positive impacts
on lemur species occurrence. We identified dispersal ability and edge tolerance as potentially
explaining differences in metapopulation dynamics. It is possible that source-sink dynamics at
21 / 26
the studied scale are impacting populations of M. murinus and M. ravelobensis within the
landscape. The two most frugivorous lemurs, E. fulvus and C. medius, may be able to maintain
stable metapopulations likely through dispersal and their ability to survive within the largest
fragments. To maintain metapopulations for each species, we recommend strategies that
reduce further habitat loss and isolation among fragments in the landscape. We should pay
special attention to connecting the largest fragments allowing seed dispersers, such as E. fulvus
and C. medius to increase the area of potential seed deposition.
S1 Table. Transect length and survey data of 42 fragments in a 3,000 ha fragmented
landscape. The number of surveys conducted uring the day (diurnal), night (nocturnal), and total.
And the number of associated sightings of all lemur species.
S2 Table. Lemur species occurrence. DCF represents distance to continuous forests; CM
represents Cheirogaleus medius occurrence; MM represents Microcebus murinus occurrence; MR
represents Microcebus ravelobensis occurrence; PC represents Propithecus coquereli occurrence;
EF represents Eulemur fulvus occurrence; LE represents Lepilemur edwardsi occurrence.
The authors would like to thank the following: The Department of Anthropology at the
University of Toronto, Jarred Heinrich, Vincent Dorie, Fernando Mercado Malabet, the
Department of Paleontology at the University of Antananarivo, Madagascar Institut pour la
Conservation des Ecosystèmes Tropicaux (MICET/ICTE), Madagascar National Parks, the
residents of Andranohobaka and Maevatanimbary, the field assistants Mamy Razafitsalama,
Rindra Rakotoarvony, Jean Paul, Velontsara, Nada, Rollin, and Alpha, and the reviewers who
provided valuable feedback that greatly improved our manuscript.
Conceptualization: Travis S. Steffens, Shawn M. Lehman.
Formal analysis: Travis S. Steffens.
Funding acquisition: Travis S. Steffens, Shawn M. Lehman.
Investigation: Travis S. Steffens.
Methodology: Travis S. Steffens.
Resources: Shawn M. Lehman.
Supervision: Shawn M. Lehman.
Writing ± original draft: Travis S. Steffens, Shawn M. Lehman.
Writing ± review & editing: Travis S. Steffens, Shawn M. Lehman.
22 / 26
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