Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

Differential population responses of native and alien rodents to an invasive predator, habitat alteration and plant masting Keita Fukasawa, Tadashi Miyashita, Takuma Hashimoto, Masaya Tatara and Shintaro Abe Proc. R. Soc. B 2013 280, 20132075, published 6 November 2013

Supplementary data

"Data Supplement" http://rspb.royalsocietypublishing.org/content/suppl/2013/11/04/rspb.2013.2075.DC1.h tml

References

This article cites 56 articles, 7 of which can be accessed free

Subject collections

Articles on similar topics can be found in the following collections

http://rspb.royalsocietypublishing.org/content/280/1773/20132075.full.html#ref-list-1

ecology (1477 articles)

Email alerting service

Receive free email alerts when new articles cite this article - sign up in the box at the top right-hand corner of the article or click here

To subscribe to Proc. R. Soc. B go to: http://rspb.royalsocietypublishing.org/subscriptions

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

Differential population responses of native and alien rodents to an invasive predator, habitat alteration and plant masting rspb.royalsocietypublishing.org

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]om, [email protected] 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.

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

2 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

?

rspb.royalsocietypublishing.org

small Indian mongoose:

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

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.

3

rspb.royalsocietypublishing.org

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.

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

(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.

rspb.royalsocietypublishing.org

model (appendix A). We defined the posterior median of the mongoose’s relative abundance as the mongoose predatory index, denoted M[i, t].

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

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)

5

5

rspb.royalsocietypublishing.org

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)

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

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 rspb.royalsocietypublishing.org on December 26, 2013

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].

References 1.

2.

3.

4.

Sala OE et al. 2000 Global biodiversity scenarios for the year 2100. Science 287, 1770–1774. (doi:10. 1126/science.287.5459.1770) Dick JTA, Platvoet D. 2000 Invading predatory crustacean Dikerogammarus villosus eliminates both native and exotic species. Proc. R. Soc. Lond. B 267, 977–983. (doi:10.1098/rspb.2000.1099) Savidge JA. 1987 Extinction of an island forest avifauna by an introduced snake. Ecology 68, 660–668. (doi:10.2307/1938471) Blackburn TM, Cassey P, Duncan RP, Evans KL, Gaston KJ. 2004 Avian extinction and mammalian

5.

6.

7.

introductions on oceanic islands. Science 305, 1955 –1958. (doi:10.1126/science.1101617) Clavero M, Garcı´a-Berthou E. 2005 Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110. (doi:10.1016/j.tree.2005.01.003) Didham RK, Tylianakis JM, Gemmell NJ, Rand TA, Ewers RM. 2007 Interactive effects of habitat modification and species invasion on native species decline. Trends Ecol. Evol. 22, 489– 496. (doi:10. 1016/j.tree.2007.07.001) Tylianakis JM, Didham RK, Bascompte J, Wardle DA. 2008 Global change and species interactions in

terrestrial ecosystems. Ecol. Lett. 11, 1351–1363. (doi:10.1111/j.1461-0248.2008.01250.x) 8. Zavaleta ES, Hobbs RJ, Mooney HA. 2001 Viewing invasive species removal in a whole-ecosystem context. Trends Ecol. Evol. 16, 454–459. (doi:10. 1016/S0169-5347(01)02194-2) 9. Courchamp F, Langlais M, Sugihara G. 1999 Cats protecting birds: modelling the mesopredator release effect. J. Anim. Ecol. 68, 282–292. (doi:10. 1046/j.1365-2656.1999.00285.x) 10. Russell JC, Lecomte V, Dumont Y, Le Corre M. 2009 Intraguild predation and mesopredator release effect

Proc R Soc B 280: 20132075

cation Projects on the Island of Amami Oshima and by the Global Environment Research Fund (D-1101, Leader: Koichi Goka) of the Ministry of the Environment, Japan, in 2012.

where s2mv and s2m are the variances of the temporal and unstructured error terms, respectively. As the initial state, we also introduced a spatially structured random field and considered heterogeneity in relative abundance among cells. Let m0 ¼ (m[1, 0], m[2, 0], . . . , m[I, 0]) be the vector of initial state of each cell. We denoted them as follows:

7

rspb.royalsocietypublishing.org

ing. Hirohisa Kishino offered helpful suggestions on the statistical modelling. Taku Kadoya, Yuya Watari and Fumio Yamada provided helpful comments on an earlier version of this manuscript. Trapping was performed by the Amami Mongoose Busters. This paper is dedicated to the memory of Go Ogura, a respected mongoose researcher at the University of the Ryukyus.

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

12.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

38. Fukasawa K, Hashimoto T, Tatara M, Abe S. 2013 Reconstruction and prediction of invasive mongoose population dynamics from history of introduction and management: a Bayesian state-space modelling approach. J. Appl. Ecol. 50, 469–478. (doi:10.1111/ 1365-2664.12058) 39. Oksanen J, Minchin PR. 1997 Instability of ordination results under changes in input data order: explanations and remedies. J. Veg. Sci. 8, 447–454. (doi:10.2307/3237336) 40. Royle JA, Ke´ry M. 2007 A Bayesian state-space formulation of dynamic occupancy models. Ecology 88, 1813 –1823. (doi:10.1890/06-0669.1) 41. Newman KB, Ferna´ndez C, Thomas L, Buckland ST. 2009 Monte Carlo inference for state–space models of wild animal populations. Biometrics 65, 572–583. (doi:10.1111/j.1541-0420.2008.01073.x) 42. New LF, Matthiopoulos J, Redpath S, Buckland ST. 2009 Fitting models of multiple hypotheses to partial population data: investigating the causes of cycles in red grouse. Am. Nat. 174, 399–412. (doi:10.1086/603625) 43. Bowman J, Forbes G, Dilworth T. 2001 Landscape context and small-mammal abundance in a managed forest. For. Ecol. Manag. 140, 249–255. (doi:10.1016/S0378-1127(00)00315-7) 44. Caut S, Angulo E, Courchamp F. 2008 Dietary shift of an invasive predator: rats, seabirds and sea turtles. J. Appl. Ecol. 45, 428–437. (doi:10.1111/j. 1365-2664.2007.01438.x) 45. Santos-Filho M, Peres C, da Silva D, Sanaiotti T. 2012 Habitat patch and matrix effects on smallmammal persistence in Amazonian forest fragments. Biodivers. Conserv. 21, 1127–1147. (doi:10.1007/s10531-012-0248-8) 46. Skalski JR, Ryding KE, Millspaugh JJ. 2005 Wildlife demography: analysis of sex, age and count data. Burlington, MA: Elsevier Academic Press. 47. Knape J, de Valpine P. 2012 Are patterns of density dependence in the global population dynamics database driven by uncertainty about population abundance? Ecol. Lett. 15, 17 –23. (doi:10.1111/j. 1461-0248.2011.01702.x) 48. Besag J, Kooperberg C. 1995 On conditional and intrinsic autoregressions. Biometrika 82, 733–746. (doi:10.1093/biomet/82.4.733) 49. Latimer AM, Wu S, Gelfand AE, Silander Jr JA. 2006 Building statistical models to analyze species distributions. Ecol. Appl. 16, 33 –50. (doi:10.1890/ 04-0609) 50. Iijima H, Nagaike T, Honda T. 2013 Estimation of deer population dynamics using a Bayesian statespace model with multiple abundance indices. J. Wildl. Manage. 77, 1038–1047. (doi:10.1002/ jwmg.556) 51. Lunn DJ, Thomas A, Best N, Spiegelhalter D. 2000 WINBUGS: a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10, 325 –337. (doi:10.1023/a:1008929526011) 52. Johnson CN, Isaac JL, Fisher DO. 2007 Rarity of a top predator triggers continent-wide collapse of mammal prey: dingoes and marsupials in Australia. Proc. R. Soc. B 274, 341–346. (doi:10.1098/rspb.2006.3711)

8

Proc R Soc B 280: 20132075

13.

24. Yamada F. 2002 Impacts and control of introduced small Indian mongoose on Amami Island, Japan. In Turning the tide: the eradication of invasive species (eds CR Veitch, MN Clout), pp. 389–392. Auckland, New Zealand: IUCN. 25. Watari Y, Nishijima S, Fukasawa M, Yamada F, Abe S, Miyashita T. In press. Evaluating the ‘recoverylevel’ of endangered species without prior information before alien invasion. Ecol. Evol. (doi:10.1002/ece3.863) 26. Abe H, Ishii N, Itoo T, Kaneko Y, Maeda K, Miura S, Yoneda M. 1994 A guide to the mammals of Japan. Hadano, Kanagawa, Japan: Tokai University Press. 27. Kubota Y, Narikawa A, Shimatani K. 2007 Litter dynamics and its effects on the survival of Castanopsis sieboldii seedlings in a subtropical forest in southern Japan. Ecol. Res. 22, 792 –801. (doi:10. 1007/s11284-006-0329-9) 28. Sugimura K, Yamada F, Miyamoto A. 2003 Population trend, habitat change and conservation of the unique wildlife species on Amami Island, Japan. Glob. Environ. Res. 7, 79 –89. 29. Buckland ST, Newman KB, Thomas L, Koesters NB. 2004 State-space models for the dynamics of wild animal populations. Ecol. Model. 171, 157 –175. (doi:10.1016/j.ecolmodel.2003.08.002) 30. de Valpine P, Rosenheim JA. 2008 Field-scale roles of density, temperature, nitrogen, and predation on aphid population dynamics. Ecology 89, 532–541. (doi:10.1890/06-1996.1) 31. Moore JE, Barlow J. 2011 Bayesian state-space model of fin whale abundance trends from a 1991 –2008 time series of line-transect surveys in the California Current. J. Appl. Ecol. 48, 1195–1205. (doi:10.1111/j.1365-2664.2011.02018.x) 32. de Valpine P, Hastings A. 2002 Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57 –76. (doi:10.1890/00129615(2002)072[0057:fpmipn]2.0.co;2) 33. Ishida K. 2010 Ecosystem management on Amamioshima Island considering the community process with multiple invasive alien species. In Database of Grants-in-Aid for Scientific Research, Japan. See http://kaken.nii.ac.jp/pdf/2010/seika/jsps/12601/ 18310151seika.pdf (in Japanese). 34. Watari Y, Takatsuki S, Miyashita T. 2008 Effects of exotic mongoose (Herpestes javanicus) on the native fauna of Amami-Oshima Island, southern Japan, estimated by distribution patterns along the historical gradient of mongoose invasion. Biol. Invasions 10, 7–17. (doi:10.1007/s10530-007-9100-6) 35. Abe S. 1992 What does mongoose prey on the Amami Island? Chirimos 3, 1 –18 (in Japanese). 36. Watari Y. 2009 Mongoose wa nani wo tabete irunoka? Gairai seibutsu no shokusei bunseki kekka no tadashii mikata. Shinrin Gizyutsu 803, 30 –31 (in Japanese). 37. Naha Nature Conservation Office. 2011 Annual report of the small Indian mongoose eradication project on the island of Amami O¯ shima, FY 2010. Naha Nature Conservation Office, Ministry of the Environment, Japan. (in Japanese with English abstract)

rspb.royalsocietypublishing.org

11.

on long-lived prey. Ecol. Model. 220, 1098–1104. (doi:10.1016/j.ecolmodel.2009.01.017) Bull L, Courchamp F. 2009 Management of interacting invasives: ecosystem approaches. In Invasive species management: a handbook of principles and techniques (eds M Clout, P Williams), pp. 232 –247. New York, NY: Oxford University Press. Rayner MJ, Hauber ME, Imber MJ, Stamp RK, Clout MN. 2007 Spatial heterogeneity of mesopredator release within an oceanic island system. Proc. Natl Acad. Sci. USA 104, 20 862 –20 865. (doi:10.1073/ pnas.0707414105) Na´jera-Tillman E, King P, Baird AC, Breen BB. 2009 Effect of pest-management operations on the abundance and size-frequency distribution of the New Zealand endemic frog Leiopelma hochstetteri. N.Z. J. Zool. 36, 389 –400. (doi:10.1080/03014223. 2009.9651471) Bonnaud E, Zarzoso-Lacoste D, Bourgeois K, Ruffino L, Legrand J, Vidal E. 2010 Top-predator control on islands boosts endemic prey but not mesopredator. Anim. Conserv. 13, 556–567. (doi:10.1111/j.14691795.2010.00376.x) Tompkins DM, Veltman CJ. 2006 Unexpected consequences of vertebrate pest control: predictions from a four-species community model. Ecol. Appl. 16, 1050 –1061. (doi:10.1890/10510761(2006)016[1050:UCOVPC]2.0.CO;2) Byers JE. 2002 Impact of non-indigenous species on natives enhanced by anthropogenic alteration of selection regimes. Oikos 97, 449–458. (doi:10. 1034/j.1600-0706.2002.970316.x) Cavallini P, Serafini P. 1995 Winter diet of the small Indian mongoose, Herpestes auropunctatus, on an Adriatic island. J. Mammal 76, 569–574. (doi:10. 2307/1382365) Barun A, Hanson CC, Campbell KJ, Simberloff D. 2011 A review of small Indian mongoose management and eradications on islands. In Island invasives: eradication and management (eds CR Veitch, MN Clout, DR Towns), pp. 17– 25. Gland, Switzerland: IUCN. Lowe S, Browne M, Boudjelas S, De Poorter M. 2000 100 of the world’s worst invasive alien species: a selection from the world’s worst invasive alien species. Auckland, New Zealand: ISSG, SSC and IUCN. Okinawa Prefectural Board of Education. 1981 Report of investigation on actual condition of Ryukyu long-haired rat Diplothrix legatus. Naha, Japan: Okinawa Prefectural Board of Education (in Japanese). Ohdachi S, Ishibashi Y, Iwasa M, Saitoh T. 2010 The wild mammals of Japan. Kyoto, Japan: Shoukadoh Book Sellers. Towns D, Atkinson I, Daugherty C. 2006 Have the harmful effects of introduced rats on islands been exaggerated? Biol. Invasions 8, 863–891. (doi:10. 1007/s10530-005-0421-z) Adler GH, Levins R. 1994 The island syndrome in rodent populations. Q. Rev. Biol. 69, 473– 490. (doi:10.1086/418744)

Downloaded from rspb.royalsocietypublishing.org on December 26, 2013

64.

65.

66. 67.

68.

69.

identity matters. Biol. Conserv. 143, 1079–1086. (doi:10.1016/j.biocon.2010.01.020) Harris D. 2009 Review of negative effects of introduced rodents on small mammals on islands. Biol. Invasions 11, 1611–1630. (doi:10.1007/ s10530-008-9393-0) Stokes VL, Banks PB, Pech RP, Spratt DM. 2009 Competition in an invaded rodent community reveals black rats as a threat to native bush rats in littoral rainforest of south-eastern Australia. J. Appl. Ecol. 46, 1239–1247. (doi:10.1111/j.1365-2664. 2009.01735.x) Hutchinson GE. 1961 The paradox of the plankton. Am. Nat. 95, 137–145. (doi:10.1086/282171) Kotler BP, Brown JS. 1988 Environmental heterogeneity and the coexistence of desert rodents. Annu. Rev. Ecol. Syst. 19, 281–307. (doi:10.1146/ annurev.es.19.110188.001433) Harris DB, Gregory SD, Macdonald DW. 2006 Space invaders? A search for patterns underlying the coexistence of alien black rats and Gala´pagos rice rats. Oecologia 149, 276–288. (doi:10.1007/ s00442-006-0447-7) Cressie N, Wikle CK. 2011 Statistics for spatiotemporal data. Hoboken, NJ: John Wiley & Sons.

9

Proc R Soc B 280: 20132075

58. Roy SS. 2002 An ecological basis for control of the mongoose Herpestes javanicus in Mauritius; is eradication possible? In Turning the tide: the eradication of invasive species (eds CR Veitch, MN Clout), pp. 266– 273. Auckland, New Zealand: IUCN. 59. Baldwin PH, Schwartz CW, Schwartz ER. 1952 Life history and economic status of the mongoose in Hawaii. J. Mammal 33, 335 –356. (doi:10.2307/ 1375771) 60. Gurevitch J, Padilla DK. 2004 Are invasive species a major cause of extinctions? Trends Ecol. Evol. 19, 470 –474. (doi:10.1016/j.tree.2004.07.005) 61. Didham RK, Tylianakis JM, Hutchison MA, Ewers RM, Gemmell NJ. 2005 Are invasive species the drivers of ecological change? Trends Ecol. Evol. 20, 470 –474. (doi:10.1016/j.tree.2005.07.006) 62. Hermoso V, Clavero M, Blanco-Garrido F, Prenda J. 2010 Invasive species and habitat degradation in Iberian streams: an analysis of their role in freshwater fish diversity loss. Ecol. Appl. 21, 175 –188. (doi:10.1890/09-2011.1) 63. Karraker NE, Arrigoni J, Dudgeon D. 2010 Effects of increased salinity and an introduced predator on lowland amphibians in Southern China: species

rspb.royalsocietypublishing.org

53. Prugh LR, Stoner CJ, Epps CW, Bean WT, Ripple WJ, Laliberte AS, Brashares JS. 2009 The rise of the mesopredator. Bioscience 59, 779–791. (doi:10. 1525/bio.2009.59.9.9) 54. Letnic M, Dworjanyn SA. 2011 Does a top predator reduce the predatory impact of an invasive mesopredator on an endangered rodent? Ecography 34, 827–835. (doi:10.1111/j.1600-0587.2010. 06516.x) 55. Ruscoe WA et al. 2011 Unexpected consequences of control: competitive vs. predator release in a fourspecies assemblage of invasive mammals. Ecol. Lett. 14, 1035 –1042. (doi:10.1111/j.1461-0248.2011. 01673.x) 56. Hays WST, Conant S. 2007 Biology and impacts of Pacific Island invasive species. I. A worldwide review of effects of the small Indian mongoose, Herpestes javanicus (Carnivora: Herpestidae). Pac. Sci. 61, 3– 16. (doi:10.1353/psc.2007.0006) 57. Lewis D, van Veen R, Wilson B. 2011 Conservation implications of small Indian mongoose (Herpestes auropunctatus) predation in a hotspot within a hotspot: the Hellshire Hills, Jamaica. Biol. Invasions 13, 25– 33. (doi:10.1007/ s10530-010-9781-0)

Differential population responses of native and alien rodents to an invasive predator, habitat alteration and plant masting.

Invasive species and anthropogenic habitat alteration are major drivers of biodiversity loss. When multiple invasive species occupy different trophic ...
576KB Sizes 0 Downloads 0 Views