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Differential population responses of native
and alien rodents to an invasive predator,
habitat alteration and plant masting
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Research
Cite this article: Fukasawa K, Miyashita T,
Hashimoto T, Tatara M, Abe S. 2013 Differential
population responses of native and alien
rodents to an invasive predator, habitat
alteration and plant masting. Proc R Soc B 280:
20132075.
http://dx.doi.org/10.1098/rspb.2013.2075
Received: 10 August 2013
Accepted: 3 October 2013
Subject Areas:
ecology
Keywords:
predator– prey interaction, bottom-up effect,
top-down effect, mesopredator release,
Bayesian state-space model, Amami Island
Author for correspondence:
Keita Fukasawa
e-mail: [email protected], fukasawa@
nies.go.jp
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2013.2075 or
via http://rspb.royalsocietypublishing.org.
Keita Fukasawa1, Tadashi Miyashita2, Takuma Hashimoto3, Masaya Tatara4
and Shintaro Abe5
1
Center for Environmental Biology and Ecosystem, National Institute for Environmental Studies, 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan
2
Graduate School of Agricultural and Life Sciences, University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657,
Japan
3
Japan Wildlife Research Center, 3-3-7 Kotobashi, Sumida-ku, Tokyo 130-8606, Japan
4
Biodiversity Center of Japan, Ministry of the Environment, 5597-1 Kenmarubi, Kamiyoshida, Fujiyoshida,
Yamanashi 403-0005, Japan
5
Naha Nature Conservation Office, Ministry of the Environment, Okinawa Tsukansha Building 4F,
5-21 Yamashita-cho, Naha, Okinawa 900-0027, Japan
Invasive species and anthropogenic habitat alteration are major drivers of
biodiversity loss. When multiple invasive species occupy different trophic
levels, removing an invasive predator might cause unexpected outcomes
owing to complex interactions among native and non-native prey. Moreover,
external factors such as habitat alteration and resource availability can affect
such dynamics. We hypothesized that native and non-native prey respond
differently to an invasive predator, habitat alteration and bottom-up effects.
To test the hypothesis, we used Bayesian state-space modelling to analyse
8-year data on the spatio-temporal patterns of two endemic rat species and
the non-native black rat in response to the continual removal of the invasive
small Indian mongoose on Amami Island, Japan. Despite low reproductive
potentials, the endemic rats recovered better after mongoose removal than
did the black rat. The endemic species appeared to be vulnerable to predation
by mongooses, whose eradication increased the abundances of the endemic
rats, but not of the black rat. Habitat alteration increased the black rat’s carrying capacity, but decreased those of the endemic species. We propose that
spatio-temporal monitoring data from eradication programmes will clarify
the underlying ecological impacts of land-use change and invasive species,
and will be useful for future habitat management.
1. Introduction
Invasive alien species and anthropogenic habitat alteration are the primary drivers of global biodiversity loss [1]. Invasive predators are the major cause of
endemic prey extinction [2–5], and predicting the potential consequences of
non-native predator species is essential to effectively mitigate their negative
impacts. Furthermore, habitat alteration frequently occurs in combination with
invasive species [6] and can change predator–prey systems in multiple ways
[7], through direct effects on the predator and/or prey populations and by altering the intensity of predator–prey interactions [7]. Because the responses of a
community with both native and invasive species depend on the intensities of
these effects, each effect must be individually analysed to predict whether a
particular management action will lead to the desired restoration outcome.
Ecosystems harbouring multiple alien species are now ubiquitous. Often, a
non-native predator may interact with both non-native and native prey within
an ecosystem [8]. In such a situation, predicting which type of prey will
dominate following the removal of the non-native predator is crucial [8–11],
because the response of the prey community to the predator removal is contextdependent [12–14]. Theoretical studies have indicated that eradicating
& 2013 The Author(s) Published by the Royal Society. All rights reserved.
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non-native predator
Amami spiny rat:
?
endemic prey
black rat:
non-native prey
Ryukyu long-haired rat:
endemic prey
Figure 1. Possible trophic structure of the small Indian mongoose, Amami spiny rat, Ryukyu long-haired rat and black rat on Amami Island. The unidirectional
(black) and bidirectional (grey) arrows indicate predator– prey and competition interactions, respectively.
non-native apex predators results in the outbreak of non-native
prey and induces severe declines in native prey populations if
the non-native prey are either superior competitors or are
mesopredators of the native prey (termed the mesopredator
release effect) [9]. Such an unwanted outcome occurs when
top-down forces, rather than bottom-up forces, control the
non-native prey populations [10,15]. Moreover, anthropogenic
habitat alteration is likely to alter the relative advantage of
native versus non-native competitors [16]. However, to the
best of our knowledge, there have been no empirical studies
demonstrating the outcome of predator eradication in such
potentially context-dependent conditions.
Because invasive species abundance and anthropogenic
habitat alteration often spatially correlate to one other, their
effects on a population or community are difficult to distinguish. The eradication of invasive species in heterogeneous
landscapes, however, could provide a unique opportunity to
tease apart these factors at a large scale. In this study, we examined a predator–multiple-prey system consisting of an invasive
predator undergoing eradication (the small Indian mongoose,
Herpestes auropunctatus), a non-native prey species (the black
rat, Rattus rattus) and two endemic prey species (the Amami
spiny rat, Tokudaia osimensis, and the Ryukyu long-haired rat,
Diplothrix legata) on Amami Island, southern Japan (figure 1).
The small Indian mongoose is an opportunistic predator [17]
that poses a major threat to island endemics worldwide [18] and
is ranked among the world’s 100 worst invasive species [19].
All three rat species are omnivores that depend on plant seeds
and insects [20,21] and share the same guild on the island.
The black rat is a highly noxious invasive species worldwide,
whose impact is often associated with the decline or extinction
of native rodents, flightless invertebrates, ground-dwelling
reptiles, land birds and burrowing seabirds [22] caused by intraguild predation and/or competition. While there has been no
evidence of intraguild predation and competition between
native rats and the black rat, such interactions could threaten
the conservation of native rats (figure 1). Endemic rat species
on Amami Island have lower fecundity than black rats
(see §2a), a trait that is generally adaptive in an island ecosystem
with intense intraspecific competition [23]. The top-down forces
imposed by mongooses on rat species [24] as well as other
endemic species [25] appear to have been ameliorated by an
island-scale eradication programme beginning in 2000.
The dominant tree species, Castanopsis sieboldii (Fagaceae),
whose seeds are the major rat diet in winter [21,26], has a
masting seed crop [27] that affects bottom-up regulation in
the ecosystem [15]. The landscape of Amami Island has
been altered by human activity, including urbanization,
agriculture and forestry [28], and the spatial structure can
be summarized by a simple gradient of habitat alteration
(see §2b). These unique characteristics of Amami Island provide a good opportunity to understand landscape-scale
trophic control on endemic and non-native prey species.
We collected data on the spatio-temporal patterns of relative abundance for the four species from 2002 to 2009 and
estimated multiple factors, i.e. the top-down effects of the
mongoose, anthropogenic habitat alteration and seed masting, that might affect the population growth rates of the
endemic and non-native prey species.
We applied a Bayesian state-space model [29–31] to extract
the relationship between the population growth rates and the
various limiting factors on the three rat species, considering
both process error and observation error. Process error is the
stochastic fluctuation in population growth rate that cannot
be explained by the deterministic relationship of the model.
Observation error (measurement error) is that associated
with the observation of the population [32]. We also evaluated
the intraguild interactions (i.e. competition or intraguild predation) among the three rat species by assessing the lagged
correlation between the process error at time t and the relative
abundance at time t 2 1. We tested the hypotheses that:
(i) variation in the population growth rate among the three
rat species can be explained by their different susceptibilities
to predation, masting and anthropogenic habitat alteration;
(ii) invasive predators, anthropogenic habitat alteration and
their interactive effect suppress the endemic rat populations
Proc R Soc B 280: 20132075
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small Indian mongoose:
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more than black rat populations; and (iii) bottom-up limitations are stronger on black rats than on endemic rat species.
(a) Study system
(b) Dataset
In this study, we analysed data from April 2002 through to
March 2010. The mongoose eradication project has recorded
(c) Bayesian state-space models
Bayesian state-space models have been used to analyse nonGaussian and nonlinear ecological processes from time-series
data [31,40 –42]. State-space models can be divided into state processes and observation processes. State process, denoted as nt
(t ¼ 0, 1, . . . , T ), is an unobservable vector that expresses stochastic evolution of ecological states (e.g. relative population density).
When analysing population dynamics, a first-order Markov
process, ntjnt2 1, is applied generally. The observation process,
denoted as yt, is an observable vector that is related to nt but
fluctuates with observation error. These processes have uncertainties, which are described as a set of three probability
distribution functions ( pdfs):
gt ðnt jnt1 ; hÞ
state process pdf;
g0 ðn0 ; nÞ
ft ðyt jnt ; cÞ
initial state pdf and
observation process pdf,
where h, n and c are vectors of model parameters. In this study,
we applied Bayesian state-space models to obtain the smoothed
relative abundance of mongooses as an index of top-down effects
and to estimate dynamics of the rat species.
(d) Relative abundance of mongooses
We regarded the relative abundance of mongooses as an index of
top-down forces. Catch per unit effort (CPUE, number of captured individuals per capture effort) obtained by trapping is a
population index often used for small mammals and is related
to relative abundance [43 – 45]. However, the assumption of a
linear relationship between them is valid only when the probability of capture is low [46], because probability of capture
will saturate with increasing capture effort when the capture
probability is near 1. In addition, the population index includes
observation error, especially where the trapping effort or relative
abundance were low, which may obscure the actual top-down
effect. To filter such observation errors and to estimate the relative density of mongooses throughout all grid cells from the
capture and effort data, we applied a Bayesian state-space
Proc R Soc B 280: 20132075
Amami Island (288190 N, 1298220 E, 712 km2) is a subtropical
island belonging to the Ryukyu Archipelago in southwesternmost Japan. The climate is warm and wet but seasonal. Annual
mean temperature and annual precipitation are 21.68C and
2837.7 mm, respectively. The coldest and hottest months are January and July, respectively, with mean temperatures of 14.88C
and 28.78C. The landscape of the island has been modified by
human activities, such as forestry, agriculture and urbanization.
Extensive clear-cut forestry for pulpwood production was conducted from the 1950s through the early 1990s [28], and most
of the island’s forests are secondary growth. Primary natural
forest remains on only 3% of the island’s area. There are over
60 000 residents on Amami Island, and sugar cane production
is a major industry. Nine per cent of the island’s vegetation has
been converted to urban area and agricultural fields. The dominant non-agricultural vegetation is forest, and C. sieboldii is the
dominant tree species in both natural and secondary forests.
This tree produces a masting seed crop [27], and on Amami
Island, consecutive island-scale lean crop (denoted ‘LC’
hereafter) years occurred in 2004 and 2005 [33].
The endemic Amami spiny rat (endemic to Amami Island)
and Ryukyu long-haired rat (endemic to Amami Island and
Ryukyu Island) have lower fecundity than the black rat. Litter
sizes of the three species are 1 – 7, 2 – 5 and 2 – 10, respectively
[21]. The breeding season of the Amami spiny rat is limited to
October – December [21]. The Ryukyu long-haired rat is the
largest native rat in Japan [21].
The small Indian mongoose was introduced in 1979, and it
poses a significant predatory threat to the endemic vertebrates
[24,34]. Although the main diet of mongooses is insects and
mammalian prey occupied only 20% of mongoose pellets on
Amami Island [24], there is evidence that mongooses feed on
rats. Abe [35] found that 24% of the stomach contents of mongooses was mammal tissue, most of which was black rat,
probably because of its high abundance. The Amami spiny rat
[36] and Ryukyu long-haired rat [37] were also found in
mongoose stomach and in faecal contents.
With increasing social awareness of the ecological threats
posed by the mongoose, the local government began a control
project in 1993. The Ministry of the Environment of Japan has
been operating the mongoose eradication project since 2000
[24]. Mongooses have been trapped, and the population has
declined dramatically. Fukasawa et al. [38] estimated that the
mongoose population in 2000 was 6141 individuals (95% credible interval, CI: 5415 – 6817). In 2011, that population had
declined to 196 (95% CI: 42 – 408). By-catches of both native
and alien rats occur incidentally, and these data were used to
estimate the relative abundances of the rat species. Both live
traps and kill traps have been used to eradicate the mongooses.
However, we analysed data from live traps only, because the
use of kill traps was prohibited within the range of the endemic
rats to avoid lethal by-catch, and thus by-catches of native
rats are actually uncommon (mean annual number of kill-trap
by-catches of Amami spiny rat and Ryukyu long were 1.25
and 5.38, respectively). Whether native or non-native, all rats
captured in live traps were released.
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2. Material and methods
trapping effort and the number of mongooses and rats captured,
including the locations and dates of all traps. Trapping effort was
defined as the number of traps multiplied by the number of
nights of trapping per year (called trap-days or TDs, hereafter).
Annual TDs of live traps differed among years, ranging from
55 864 to 175 991 TDs per year (see the electronic supplementary
material, figure S3). The total numbers of by-catches of black rat,
Amami spiny rat and Ryukyu long-haired rat from April 2002
through to March 2010 were 6056, 1320 and 177, respectively.
We pooled the number of live-trap catches in a fiscal year
(from April to March) in each geographical grid cell of 10 3000
longitude and 10 latitude, for a total of 159 grid cells 8 years.
Spatio-temporal patterns of trapping effort are shown in the
electronic supplementary material, figure S3. The habitat alteration
index (HAI) was derived from vegetation maps using an ordination
technique. First, we calculated the area ratios of primary natural
forest, secondary forest, farmland and urban area for each grid
cell using ARCGIS v. 10.0 (ESRI Inc., Redlands, CA, USA). Next,
we performed detrended correspondence analysis (DECORANA
[39]) for the four landform ratios. Axis I of the detrended correspondence analysis explained well the gradient of land-use activity
(from native forests to urban areas; see the electronic supplementary
material, figure S1, which was defined by a HAI. In 2004 and
2005, island-scale scarcity of C. sieboldii nuts occurred on Amami
Island [33]. To estimate the effects of LC years, we considered
a binary explanatory variable, representing 2004 and 2005 as
the value 1. Data are deposited in the Dryad repository at http://
datadryad.org/resource/doi:10.5061/dryad.2hb12.
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(e) Modelling rat dynamics and observations
n½i; t ¼ an½i; t 1 þ b0 þ b1 LC½t 1 þ b2 HAI½i þ b3 M½i; t 1
þ b4 HAI½i M½i; t 1 þ r½i þ v½t 1 þ e½i; t 1;
where a is a measure of density dependence, b0 is the intercept
and b1 2 b4 are the coefficients of the factors mentioned earlier.
The range of a is (0, 1), and when a ¼ 1, the population
dynamics is density independent. The r[i] and v[t] are spatially
and temporally structured process errors, respectively. It is a
Markov random field model for r[i] that allows for spatial autocorrelation in b0. Conditional variance of r[i] and variance of
v[t] is denoted as sr and sv, respectively. The e[i, t 2 1] is an
unstructured process error with a normal distribution centred
on 0: e[i,t 2 1] N(0, sn 2).
Although the by-catch data used to estimate the state-space
model were obtained by non-lethal trapping using live-traps,
kill-traps were also used in the mongoose eradication project.
The lethal by-catches of the native rats were few and thus
negligible, but black rats appeared to have suffered a sufficient
number of lethal by-catches to potentially affect their population
growth rate. We introduced the trapping effort of kill traps,
TDs per area (/km2/10 000) denoted Ek, as an additional term,
gEk[i, t 2 1], in the process model of the black rat.
The distribution of the initial state, n0 ¼ (n[1, 0], n[2, 0], . . . ,
n[I, 0]), was assumed to be spatially autocorrelated and was
described by a Gaussian Markov random field [48 –50]:
!
P
2
j[di n[ j; 0] s0
n[i; 0]jn[i; 0] N
;
;
ki
ki
where n[2i, 0] is all the elements of n0 except n[i, 0] and di and ki
are the class of neighbours (defined as grid cells sharing the same
edge or vertex) of i and their number (eight, except for boundary
grid cells), respectively. The s20 is a conditional variance associated with spatial divergence among neighbouring grid cells.
Note that the mean of n0 was constrained to be always zero,
and the subsequent state vectors n1:T are relative values scaled
by the mean initial log-abundance.
The observation process describes the relationship between
the number of captures and the relative abundance. As with
the mongoose model, we assumed that the number of individuals captured, y[i, t], follows a Poisson distribution, and the
mean is proportional to the product of the relative abundance
exp(n[i, t]) and power-transformed trapping effort E[i, t], with
a lognormally distributed overdispersion term, exp(el[i, t]):
b
yit PoissonðexpðaÞ exp (nit )E½i; t expðel ½i; tÞÞ;
where exp(a) is a proportional coefficient and b is an exponent of
the trapping effort. When b ¼ 1, the relationship between relative
abundance and CPUE is linear. We defined Eit as TDs/100 000
(dividing by a large value improved convergence of the
estimate). Variance of el[i, t] was defined as sl.
We estimated carrying capacity of the three rat species
along the gradient of anthropogenic habitat alteration. Using the
(f ) Bayesian inference
In a Bayesian context, the inference objective for a state-space
model draws samples from the joint posterior distribution for
the state variables n0:T and unknown parameters u ¼ (h, n, c)
conditional on the observed time-series data y1:T. Denoting the
prior pdf of the model parameters as p(u), the joint posterior
pdf can be written as
(
)
T
Y
pðn0:T ; ujy1:T Þ / pðuÞg0 ðn0 jnÞ
gt ðnt jnt1 ; hÞft ðyt jnt ; cÞ :
t¼1
Posterior samples were obtained by Gibbs sampling using
WINBUGS v. 1.43 [51]. For all parameters of the three rat species,
we used vague prior distributions to make inferences using only
information from the data: N(0, 1000) for b0, b1, b2, b3, b4 and a.
Uniform(0, 1.001) for a and b, and Uniform(0, 100) for sr, sv, sn,
sm and sl. Note that setting 1.001 (not 1) as the upper bound of
the uniform priors is a rule of thumb to avoid a numerical overflow of the WINBUGS. The posterior summaries were derived
from three-chain Gibbs sampling for 1 000 000 iterations after discarding 1 000 000 samples as ‘burn-in’. The posterior summaries
of all the parameters are shown in the electronic supplementary
material, table S1 and the posterior densities are shown in the
electronic supplementary material, figures S4 – S6.
As a proxy of mongoose predation, we used the posterior
median of mongoose relative abundance estimated by the
state-space model. To evaluate the extra variation derived
from uncertainty in estimated relative abundance, we iteratively
estimated the rat models 50 times using randomly selected
posterior samples of mongoose relative abundance, instead of
the posterior median. The models with randomly selected
posterior samples yielded results that were qualitatively consistent with those of the model with a posterior median (see the
electronic supplementary material, figure S7). We concluded
our result was robust to the uncertainty owing to using an
estimate of mongoose relative abundance.
(g) Detecting intraguild interactions
To detect intraguild interactions among the rat species, we evaluated the posterior Pearson’s correlation coefficient between
process error from year t 2 1 to t, r[i] þ v[t 2 1] þ e[i, t 2 1], and
the relative abundance of another species in year t 2 1, nit2 1. The
correlation coefficient was calculated for each of the posterior
samples. Although including interspecific interactions in the process model is desirable in a statistical sense, such modelling was
not used here, because the posterior probability failed to converge.
3. Results
From 2002 to 2009, mongooses were removed from Amami
Island, and the annual CPUE decreased from 14.8 to 0.32
(figure 2a). Temporal patterns of the CPUEs of native and
non-native rats responded differently to the mongoose
decline. While the CPUE of the endemic rat species increased
exponentially, a similar increase was not found in the black
rat. With low levels of anthropogenic habitat alteration, the
CPUE of the two endemic rat species increased faster
4
Proc R Soc B 280: 20132075
We incorporated the indices of top-down forces of the invasive
predator (M), habitat alteration (HAI), their interaction (HAI M)
and bottom-up forces (LC) on rat populations into a densitydependent stochastic population model and implemented it as
a Bayesian state-space model to estimate the model parameters.
As the state process, we applied the Gompertz population
model [47]. The logarithm of the relative abundance of rats in
the ith grid cell in the tth year, n[i, t], is described by a dynamic
linear model:
Gompertz population model, the log[carrying capacity] at an arbitrary HAI can be calculated as ‘(b0 þ b2HAI)/(12 a) a ’. However,
this carrying capacity is a relative value when the overall mean of
log[relative abundance] in 2002 is set to 0. As a more informative
representation, we scaled the relative abundance to the number of
captures with 100 000 TDs, ‘(b0 þ b2HAI)/(1 2 a) þ a’, which is
the mean log[number of captures] at the carrying capacity of
each rat species.
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model (appendix A). We defined the posterior median of the
mongoose’s relative abundance as the mongoose predatory
index, denoted M[i, t].
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15
10
(b)
0
10
5
0
(c)
15
10
5
(d)
4. Discussion
0
2
1
0
2002
2004
2006
2008
year
Figure 2. Temporal changes in catch per unit effort of (a) small Indian mongoose, (b) black rat, (c) Amami spiny rat and (d) Ryukyu long-haired rat. For the
rat species, the solid and dashed lines indicate the areas of mild (habitat alteration index, HAI 0) and intensive (HAI . 0) habitat alteration, respectively.
(figure 2c,d), whereas the CPUE of the black rat was higher in
the areas of intensive habitat alteration than in the areas of
low habitat alteration (figure 2b). (See the electronic supplementary material, figure S8 for the spatial patterns of the
mongoose, and the three rat species that were used to parametrize the dynamic state-space models.) The estimated
relationship between CPUE and mongoose relative abundance was almost linear. The posterior mean of bm was
0.86, and the 95% CI ranged from 0.77 to 0.96. Estimates of
b (see the electronic supplementary material, table S1)
showed that the CPUEs of the three rat species were also
approximately linear correlated to the relative abundances.
The Bayesian state-space model revealed that the growth
rate of each prey species could be explained by different factors. The 95% CIs of b2 of the black rat and Amami spiny
rat did not overlap, and the endemic Amami spiny rat was
more vulnerable to the invasive predator than was the
non-native black rat (table 1). The 95% CI of the top-down
coefficient for the Amami spiny rat fell below zero, which
indicated that the population growth rate declined with an
increase in mongoose abundance. The top-down coefficient
for the Ryukyu long-haired rat was also negative, although
the uncertainty was large. However, the median of the
top-down coefficient for the black rat was almost zero.
Although eradication of invasive predators occasionally
results in an outbreak of another invasive species [8,9,
52– 54], the occurrence of such an indirect effect is contextdependent [14,55]. In our case, populations of two endemic
rat species rapidly increased with the decline of invasive
mongooses, whereas that of the invasive black rat did not.
A Bayesian state-space model clearly showed the different
susceptibilities of the rat species to top-down forces and
anthropogenic habitat alteration.
The reason that black rats did not break out in response to
mongoose eradication seems simple; the predation impact of
the mongoose on the black rat was much lower than on the
native rats (table 1). This result is not surprising, as there
are many cases in which the biological control of black rats
by introduced mongooses has failed [56]. Although rodents
are one of the major foods of mongooses, and are frequently
found in mongoose stomachs in their invaded ranges [57,58],
including on Amami island [35], the top-down pressure on
the black rat population appeared to be trivial. This result
might be owing to microhabitat segregation of the mongoose
and black rat (ground-dwelling versus arboreal nesting) [59],
and/or to some unknown adaptive behaviour against mongoose predation that has not been acquired by the endemic
rats, which have not coexisted with mammalian predators
on the islands until recently.
The extent to which the interactive effects of invasive
species and habitat degradation threaten biodiversity in
nature is an important issue in ecology and conservation
biology [6,60,61]. Our analysis revealed that habitat alteration
can decrease the carrying capacity of endemic rats and halt
the recovery of endemics from suppression by an invasive
predator (figure 3), but the interaction effect with predatory
impact was not significant (table 1). This finding is consistent
with those of previous studies focusing on the interaction
effects of invasive species and habitat alteration [62,63]. Importantly, as habitat alteration intensified, the relative rank of the
carrying capacity of the black rat increased (figure 3), probably
because the black rat can eat agricultural crops and rubbish,
which are abundant in human-dominated landscapes, whereas
endemic rats are purely forest-dwellers. Thus, the natural
Proc R Soc B 280: 20132075
catch per unit effort (CPUE, /1000 trap-days)
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Habitat alteration positively affected black rats, and the
95% CI fell above 0, whereas the effects on the two native
rat species tended to be negative, and the 90% CIs fell
below 0 (table 1 and the electronic supplementary material,
table S1). The effects of masting and the interaction of the
invasive predator and habitat alteration were not significant
for any of the rat species.
Figure 3 shows the carrying capacity of the three rat
species along the gradient of habitat alteration. The carrying
capacity of the black rat increased with increasing anthropogenic habitat alteration, but inverse patterns were observed
for the Amami spiny rat and the Ryukyu long-haired rat;
their carrying capacities were higher in natural forests.
None of the pairwise correlations between the process
error of a particular species and the precedent relative
abundance of another species was significant (table 2). In
particular, the correlation between the black rat and Amami
spiny rat or Ryukyu long-haired rat was small, indicating no
detectable effect of invasive black rats on native rat species.
(a)
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Table 1. Posterior mean (95% credible interval, CI) of the population growth coefficients for the three rat species.
black rat
invasive predator (mongoose)
a
Amami spiny rat
0.0144 (20.00403, 0.0303)
a
anthropogenic habitat alteration
invasive predator anthropogenic
0.125 (0.0293, 0.244)
0.00505 (20.00349, 0.0134)
habitat alteration
bottom-up factor (lean crop year)
20.199 (22.48, 1.72)
Ryukyu long-haired rat
20.401 (20.732, 20.143)a
20.242 (20.562, 0.0129)
20.0701 (20.182, 0.0311)
b
1.19 (21.01, 3.50)
20.176 (20.445, 0.0434)
20.471 (21.03, 0.0467)b
0.0732 (20.0404, 0.208)
22.31 (27.76, 3.39)
Table 2. Posterior mean and 95% credible interval (CI) of Pearson’s correlation coefficient between process error and the precedent relative abundance.
precedent relative abundance
Amami spiny rat
ln(carrying capacity)
process error
Amami spiny rat
Ryukyu long-haired rat
0.154 (20.324, 0.524)
0.247 (20.108, 0.557)
0.143 (20.238, 0.478)
0.303 (20.207, 0.693)
black rat
0.133 (20.0399, 0.304)
0.125 (20.161 – 0.398)
black rat
Amami spiny rat
Ryukyu long-haired rat
10
5
0
–5
–4
–3
intact forest
Ryukyu long-haired rat
–2
–1
0
1
land-use activity index
2
urban area
Figure 3. Posterior means of log-scale carrying capacity for the three rat
species along the land-use gradient. Error bars indicate posterior standard
deviations. (Online version in colour.)
forests remaining on Amami Island appear to be a core area for
the restoration of the endemic rat species. Fortunately, these
forests are located far from the release point of the mongoose,
and the predatory impact of the invasive mongoose has been
relatively low thus far [34]. If the central area of the topdown control by mongooses had overlapped with the natural
forests, then the endemic rat species might have been more
threatened. The spatial distributions of habitat alteration and
invasive species, and their overlap, are important to predict
how these drivers will impact native species.
We found little evidence for intraguild interactions (i.e.
intraguild predation and competition) among rat species
(table 2). This lack appeared to be an important factor that
enabled the endemic rats to recover after the mongoose
removal, with no apparent effects of the black rat. While
many studies have examined the mesopredator release effects
of invasive rats on seabird colonies [12], evidence for predation
on insular rodents by invasive rats is quite limited [64]. The
black rat
20.133 (20.366, 0.109)
0.00120 (20.233, 0.232)
0.0415 (20.183, 0.269)
predatory impact of the black rat on Amami Island is also unlikely to be a major limiting factor on endemic rat populations.
As for interspecific competition, many pre–post eradication
surveys revealed a competitive impact of black rats on
island-endemic small mammals [64,65], but we found no
such evidence. Dietary segregation, spatial segregation [66]
and temporal resource variation [67,68] may explain the lack
of competitive interactions among rodent species.
Our state-space modelling of rat species explicitly considered
top-down and bottom-up effects, as well as anthropogenic habitat alteration and density dependence. However, we did not
include diffusion processes of populations because of the failure
of parameter convergence in Gibbs sampling. However, it seems
reasonable to assume that diffusion from more favourable to
less favourable sites is common, and this could reduce the difference in apparent population growth rates between sites with
different environmental conditions. Thus, our model probably
underestimated the environmental effects, and the qualitative
conclusions are unlikely to change.
When attempting to eradicate invasive species, the monitoring of not only invasive species under eradication but
also native and other non-native species is essential to evaluate ecosystem recovery [11]. We showed that it is possible
to estimate from monitoring data the strength of interspecific interactions and extrinsic factors that may limit the
dynamics of multiple species if the monitoring system is
properly designed in space and time. This knowledge offers
valuable insights into ecosystem management. In the case
of Amami Island, we have shown clearly that to conserve
the Amami spiny rat, not only is eradication of mongooses
required, but also the protection of natural and secondary
forests, and an outbreak of the invasive black rat is unlikely
to occur. Beyond the current eradication attempt, the
approach presented here will help decision-making in
adaptive management of communities and ecosystems composed of complex interaction webs that include multiple
alien and native species.
Proc R Soc B 280: 20132075
Significant effect whose 95% CI does not overlap with zero.
Marginal effect whose 90% CI does not overlap with zero.
b
rspb.royalsocietypublishing.org
variable
6
Downloaded from http://rspb.royalsocietypublishing.org/ on June 18, 2017
Acknowledgements. We thank Ken Ishida for information on seed mast-
grid cells. vm[t 2 1] and em[i, t 2 1] are assumed to follow a
hierarchical Gaussian with mean zero:
vm ½t 1 N(0; s2mv );
em ½i; t 1 N(0; s2m );
Appendix A. Bayesian smoothing for the relative
abundance of mongooses
m½i; 0 ¼ mm0 þ mm ½i;
Funding statement. This research was supported by the Mongoose Eradi-
To estimate the relative density of mongooses throughout all
grid cells from the capture and effort data, we applied a
Bayesian state-space model. Although the relative abundance
of mongooses is thought to be determined by intrinsic growth
rate, diffusion and removal by humans, we used a convenient
approach that considered only a spatio-temporal autoregressive
process with spatially structured process error [69].
The process model describes spatio-temporal change
in the relative abundance of mongooses. Letting m[i, t]
be the log-scale relative abundance of mongooses in year
t (t ¼ 1,2, . . . ,T ) at site i (i ¼ 1,2, . . . ,I ), we used a first-order
autoregressive process as follows:
m½i; t ¼ m½i; t 1 þ r þ rm ½i þ vm ½t 1 þ em ½i; t 1;
where r is the apparent mean log population growth rate,
rm[i] is the spatially structured process error, vm[t 2 1] is
the temporally structured process error and em[i, t 2 1] is
the unstructured process error. The spatio-temporal structure
of the process error allows us to accommodate the heterogeneous population dynamics mediated by intrinsic growth
and removal. The spatial structure of rm[i] was expressed
by a Gaussian Markov random field [48,49]:
!
P
2
j[d½i rm ½ j smr
rm [i]jrm [i] N
;
;
k½i
k½i
where rm[2i] is all the elements of rm except rm[i]. d[i] and k[i]
are a class of neighbours (defined as grid cells sharing the same
edge or vertex) of i and their number (eight except for boundary grid cells), respectively. s2mr is the conditional variance
associated with spatial divergence among the neighbouring
where mm0 is overall mean of m0 and mm[i] is spatial heterogeneity. For mm[i], a Gaussian Markov random field with
conditional variance s2mm was used in the same manner as rm.
The observation model describes the stochastic relationship between the number of mongooses captured (Cm[i, t])
and the relative abundance (m[i, t]). We assumed that the
mean number of mongooses captured, lm[i, t], is proportional
to mongoose relative abundance and to exponentiated trapping effort, with fluctuation owing to local environmental
conditions. lm[i, t], can thus be described as follows:
lm ½i; t ¼ expðm½i; tÞE½i; tbm expðeml ½i; tÞ;
where E[i, t] is capture effort (1000), bm is an exponent corresponding to the saturation of capture probability with
increasing capture effort and eml[i, t] is an overdispersion
term owing to local environmental fluctuations expressed
as a Gaussian distribution with mean 0 and variance sml 2.
The observed number of captures was assumed to follow a
Poisson distribution with mean lm[i, t].
Posterior samples were obtained by Gibbs sampling using
the WINBUGS v. 1.43 [51]. Vague prior distributions were
used for all the model parameters: N(0, 1000) for r and m0,
Uniform(0, 1.001) for bm, and Uniform(0, 100) for smr, smv,
sm, smm and sml. Note that setting 1.001 (not 1) as the
upper bound of the uniform prior is a rule of thumb to
avoid a numerical overflow of the WINBUGS. The posterior
summaries were derived from three-chain Gibbs sampling
for 100 000 iterations after discarding 100 000 samples as
‘burn-in’. We defined the posterior median of exp(m[i, t])
as the mongoose predatory index, denoted M[i, t].
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