# Addressing the impact of canine distemper spreading on an isolated tiger population in northeast Asia

Dawei Wang, James Smith, Francesco Accatino, Jianping Ge, Tianming Wang

► To cite this version:

Dawei Wang, James Smith, Francesco Accatino, Jianping Ge, Tianming Wang. Addressing the impact of canine distemper spreading on an isolated tiger population in northeast Asia. Integrative Zoology, 2023, 15 p. 10.1111/1749-4877.12712. hal-04055682

HAL Id: hal-04055682

https://hal.inrae.fr/hal-04055682

Submitted on 21 Jul 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
1 **Addressing the impact of canine distemper spreading on an isolated
tiger population in northeast Asia

3 Running title: PVA of Amur tiger population

4 Dawei Wang¹ | James L.D. Smith² | Francesco Accatino³ | Jianping Ge¹ | Tianming
5 Wang¹

6 ¹Ministry of Education Key Laboratory for Biodiversity Science and Engineering,

7 NFGA Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard &

8 College of Life Sciences, Beijing Normal University, Beijing, 100875, China

9 ²Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota,
10 St. Paul, MN 55108, USA

11 ³UMR SADAPT, INRAE, AgroParisTech, Université Paris-Saclay, 22 place de
12 l'agronomie, CS 80022, 91120 PALAISEAU Cedex, France

13

14 **Corresponding author:** Tianming Wang, Email: wangtianming@bnu.edu.cn

15 **Open Research statement:** Simulated data is available in Science Data Bank at
16 https://doi.org/10.57760/sciencedb.02627.

17 **Acknowledgments:** This work was supported by the National Natural Science
18 Foundation of China (31971539), the National Science and Technology Basic Resources
19 Survey Program of China (2019FY101700) and a scholarship from the China
20 Scholarship Council (202106040062).

21 **Author contributions:** T.W. and D.W. conceived the ideas and designed the study; D.W.
22 involved in the development of the PVA model, writing-original draft, and visualization.
23 J.L.D.S., F.A. and J.G advised on the analyses. All authors contributed critically to the
24 drafts, and gave final approval for publication.

25 **Conflict of interest statement:** The authors declare no conflict of interest.
**Abstract**

The continuation of the isolated Amur tiger (Panthera tigris altaica) population living on the China-Russia border is facing serious challenges due to factors such as its small size (including 38 individuals) and canine distemper virus (CDV). To assess options to reduce the impact of these negative factors, we use a population viability analysis (PVA) metamodel, which consists of a traditional individual-based demographic model linked to an epidemiological model, to assess options for controlling the impact of negative factors through domestic dog management in protected areas, increasing connectivity to the neighboring large population (including more than 400 individuals) and habitat expansion. Without intervention, under inbreeding depression of 3.14, 6.29 and 12.26 lethal equivalents, our metamodel predicted the extinction within 100 years is 64.4%, 90.6% and 99.8%, respectively. In addition, the simulation results showed that dog management or habitat expansion independently will not ensure tiger population viability for the next 100 years, and connectivity to the neighboring population would only keep the population size from rapidly declining. However, when the above three conservation scenarios are combined, even at the highest level of 12.26 lethal equivalents inbreeding depression, population size will not decline, the probability of extinction will be < 5.8%. Our findings highlight that protecting the Amur tiger necessitates a multifaceted synergistic effort. Our key management recommendations for this population underline the importance of reducing CDV threats and expanding tiger occupancy to its former range in China, but re-establishing habitat connectivity to the neighboring population is an important long-term objective.

**Keywords:** Amur tiger; Canine distemper virus; Habitat connectivity; Metamodel; Population viability analysis (PVA)

**1 INTRODUCTION**
The tiger (*Panthera tigris*), a flagship species in Asia, has declined to fewer than 5,000 individuals remaining in less than 7% of the surface of its historical range (Joshi *et al.* 2016; Jhala *et al.* 2021). Among the five extant subspecies, the Amur or Siberian tiger (*P. t. altaica*) is the northernmost (*Wang et al.* 2018). It suffered a severe decline in the mid-20th century, and currently has a low level of genetic diversity (Henry *et al.* 2009; Dou *et al.* 2016). Fewer than 550 wild Amur tigers exist; they are distributed in two populations separated by urban conglomerates and highways (Miquelle *et al.* 2007; Wang *et al.* 2018). The larger population is spread widely in the Sikhote-Alin mountains, Russia, and the smaller isolated Changbai-Primorye population (< 40 tigers) is restricted to the China-Russia border (Hebblewhite *et al.* 2014; Feng *et al.* 2017). The Changbai-Primorye population represents the main source for recovery of the Amur tiger in much of its former range in northeast China (Miquelle *et al.* 2010; Wang *et al.* 2016; Qi *et al.* 2021).

Unfortunately, this population is currently facing multiple threats, including insufficient prey resources (Wang *et al.* 2018), fragmentation and degradation of habitat (Hebblewhite *et al.* 2014; Wang *et al.* 2016), poaching (Robinson *et al.* 2015), and inbreeding depression (Henry *et al.* 2009; Ning *et al.* 2021). Furthermore, recent studies have shown that tiger populations are facing an emerging threat from canine distemper virus (CDV). CDV is a nearly globally distributed RNA virus (Deem *et al.* 2000; Adhikari *et al.* 2020) that has caused significant decline in carnivore populations such as Serengeti lions (*P. leo*) (Roelke-Parker *et al.* 1996; Weckworth *et al.* 2020) and Ethiopian wolves (*Canis simensis*) (Gordon *et al.* 2015). As early as 2000, CDV was reported in the Sikhote-Alin Amur tiger population (Quigley *et al.* 2010; Gilbert *et al.* 2015), and CDV was a factor responsible for a rapid decline of the Sikhote-Alin population (Miquelle *et al.* 2015). Although not yet detected in the Changbai-Primorye population, studies have shown that CDV has been spreading within sympatric population of Amur leopard (*P. pardus orientalis*) (Sulikhan *et al.* 2018). The
consequences of CDV spread to Changbai-Primorye Amur tiger population could be devastating. The multi-host nature of CDV poses a great threat to felid populations that coexist with other hosts. For the Sikhote-Alin and Changbai-Primorye tiger populations, free roaming domestic dogs that are occasionally killed by tigers (Sugimoto et al. 2016; Dou et al. 2019) constitutes a potential reservoir of infection for tigers (Craft et al. 2009), who provide 3-6% of the biomass consumed of tigers (Gu et al. 2018; Dou et al. 2019). Few studies have assessed the impact of CDV on a critically small endangered tiger population.

Currently, China and Russia are cooperating to conserve this population through joint monitoring (Feng et al. 2017). Additionally, in 2016, the Chinese government established a National Park, that expanded three Amur tiger and leopard reserves in China to create a ~15,000 km² Northeast Tiger Leopard National Park. This expansion is linked to the Land of Leopard Reserve, Russia, which has formed an 18,000 km² landscape for large felids. However, human development has effectively blocked continuous genetic exchange between Changbai-Primorye and the Sikhote-Alin population, increasing the risk of inbreeding depression in the Changbai-Primorye population (Henry et al. 2009; Sorokin et al. 2016).

Given the isolation and low genetic diversity of this population and the specific threats of CDV, a population viability analysis (PVA) is urgently needed for the Changbai-Primorye tiger population. Previous PVAs of the Amur tiger assessed the impact of habitat connectivity and fragmentation (Carroll & Miquelle 2006; Tian et al. 2011), and the influence of CDV on the larger Sikhote-Alin population (Gilbert et al. 2020). However, when CDV and other wildlife diseases are incorporated in PVA models they are usually treated as a general random effect and operate at the same time scale as the PVA. Epidemic disease such as CDV have dynamics that are often strongly related to the specific host populations and they operate at a rapid temporal scale not captured in a typical PVA analysis using an annual time step (Smith et al. 2009; Shoemaker et al.
107 2014). Therefore, in this study, we modeled the viability of the Changbai-Primorye tiger
108 population combining a traditional PVA (Lacy & Miller 2020) with a separate CDV
109 epidemiological model (Lacy et al. 2020). This metamodel approach is commonly used
110 to simulate the spread of epidemic diseases within wild populations (Bradshaw et al.
111 2012).

112 Here, we assess the probability of the persistence of the Changbai-Primorye
113 population for the next 100 years. We (i) use a metamodel modelling approach to
114 determine the impact of CDV infection, lack of connectivity and restricted habitat
115 which threaten the long-term viability of this population and (ii) assess the most
116 effective combinations of management strategies that increase CDV infection control,
117 expand habitat into its former range in China and establish population connectivity with
118 the larger Sikhote-Alin population. Our results provide guidance for conservation
119 policies to enhance the viability of the Changbai-Primorye population.

## 2 MATERIALS AND METHODS

### 2.1 Study area

122 The smaller Changbai-Primorye tiger population occurs in the Changbai Mountains
123 in the Jilin and Heilongjiang provinces of China and in the Land of Leopards National
124 Park in southwestern Primorye Province (Figure 1). The Changbai Mountains are one of
125 the highest priority Conservation Areas in China. This landscape consists of a large
126 network of habitat patches, which are connected with the Land of Leopards National
127 Park in Russia (Hebblewhite et al. 2012). In 2015, based on long-term monitoring, we
128 estimated the Changbai-Primorye population contained at least 38 tigers (13 adult
129 females, 10 adult males, 3 unidentified adults, and 12 sub-adults/cubs) occupying 9,000
130 km² along the China-Russia border (Feng et al. 2017). The elevations of the rugged
131 landscape ranges from 5m to 1477 m and has a temperate continental monsoon climate
132 that supports a temperate coniferous broad-leaved mixed forest. Due to long-term
133 deforestation, many low-elevation forests in these areas have been transformed into
secondary deciduous forests (Wang *et al.* 2016). The main prey species of Amur tigers in this area are wild boar (*Sus scrofa*), sika deer (*Cervus nippon*), Siberian roe deer (*Capreolus pygargus*), and domesticated species, such as cows and dogs (Kerley *et al.* 2015). The area has been subjected to cattle grazing, ginseng planting, frog farming, and edible fern collection for decades, particularly on the Chinese side (Feng *et al.* 2021).

## 2.2 Modelling overview

We developed a metamodel using two spatially implicit submodels to assess the extinction risk of the tiger population (Figure 2): an age- and sex-structured, individual-based stochastic demographic model based on Vortex software (version 10.5.0) (Lacy & Miller 2020) and an individual-based epidemiological model that simulates epizootics such as CDV built with Outbreak software (version 2.11.0) (Lacy *et al.* 2020). The two submodels were connected by the Metamodel Manager (version 1.0.6) (Pollak & Lacy 2020), which transfers information back and forth between submodels as “open data” that are updated by each before being passed on. All scenarios were projected for 100 years, with 500 model runs, each one with a specific draw of parameter values from its probability distributions or specified; the results of population size and genetic diversity were averaged across the runs. The population survival probability was calculated as the percentage of simulations ending with population survival.

### 2.2.1 Tiger demographic model

We used Vortex to build a demographic model for the tiger population with one year as a time step. Cubs and juveniles do not maintain their own territories (Tian *et al.* 2011) and male territories overlap with those of 1-3 females, while the overlap between females is relatively low (Hernandez-Blanco *et al.* 2015; Xiao *et al.* 2016). Thus, in this study, the habitat carrying capacity was measured in terms of female tiger home ranges; the initial habitat of 9,000 km² can accommodate approximately 23 females with an average home range of approximately 400 km² (Hernandez-Blanco *et al.* 2015). The
gestation period of the Amur tiger is 95-100 days, and the time of caring for the cubs is approximately 18.8 months (Kerley et al. 2003). So, we used 2 years both for time that young were dependent on their mother and the interbirth interval. If a female tiger died, all of her currently dependent offspring also died. For the mortality rates of the Amur tiger, we refer to mortality data from 1992 to 2012 in theikhote-Alin Mountains, Russia (Robinson et al. 2015). Poaching has been a severe threat to wild tiger survival (Kenney et al. 1995), but because of the Changbai-Primorye population's strong protection and transboundary cooperation, we used a mortality rate of subadults and adults as the sum of the natural mortality rate (5.1%) and 50% of theikhote-Alin population's poaching death rate (10.3%) (Robinson et al. 2015). In addition, inbreeding depression has been confirmed to exist in wild tiger populations (Smith & Mcdougal 1991), and the Amur tiger population in China has reached a moderate level of inbreeding (Ning et al. 2021), but accurate lethal equivalents to measure the severity of inbreeding depression have not been available. So we conservatively set the base line lethal equivalents to 3.14 (Ralls et al. 1988) and then examine the impact of higher lethal equivalents rated (6.29 and 12.26) more recently estimated by O'Grady et al. (2006). Other model parameters are from previously reported tiger studies (see Table 1, Appendix S1 for the details and main parameter input).

### 2.2.2 Epidemiological modelling

We developed an individual-based epidemiological model with one day as a time step using Outbreak, to simulate CDV transmission in the tiger population in the low-risk outbreak year. The model incorporates complex processes such as interindividual transmission, pathogen environmental transmission, incubation period, infection period and infection outcome. In the model, there are two main ways for tigers to become infected with CDV: (i) contact with environmental disease sources, such as predation on small and medium-sized wild predators and dogs carrying the virus or (ii) intraspecific transmission through social contact. According to Gilbert et al. (2014), the
probability of CDV infection of Amur tigers by feeding on infected domestic dogs or small carnivores is 1.4%, and the average daily infection probability is 0.003836% (Appendix S2: Table S1). We set the probability of infection in the interaction process as 1.4% of the cumulative prevalence rate of CDV within one year (see Table 2, Appendix S2 for the details and main parameter input). Furthermore, to simulate CDV's cyclical high-risk prevalence (Roscoe 1993), we applied 5 years of cyclically high infection risk to the model (Gilbert et al. 2014). In the high risk outbreak year, we set an additional mortality rate of 11.48% in the "catastrophes" module of Vortex (Table 1).

## 2.3 Sensitivity analysis

We developed a set of scenarios to assess how uncertainty about parameter values affect metamodel outcomes. We tested 9 key parameters in the sensitivity analysis. For lethal equivalents (LEs), in addition to a base line rate of 3.14, we examined values of 6.29 and 12.26 estimated through meta-analyses of multiple wild species (O'Grady et al. 2006; Kenney et al. 2014). To evaluate the impact of temporal high-risk cycles of CDV infection (CCI) we used cycles of 3, 7 and no high infection cycle. For poaching intensity (PI) we tested poaching mortality rates of 0 and 10.3% as reported by Robinson et al. (2015) (see Appendix S1 for the details). For the other 6 parameters, mortality rate after CDV infection (MCI), successfully breeding female proportion (BFP), infant-cub (0-1 year old) mortality rate (IMR), adult (>3 years old) female mortality rate (FMR), adult (>3 years old) male mortality rate (MMR), and carrying capacity (K), we increased or decreased the baseline value by 40% (He et al. 2020). A total of 19 scenarios were created for this analysis. We varied each key parameter while holding all other parameters to baseline values. The outputs of each sensitive scenario were compared to the baseline scenario: mean stochastic population growth rate (R), mean population number at the 100th year (N), population genetic diversity at the 100th year (GD), probability of extinction (PE), and mean time of extinction (TE). The sensitivity index (Pulliam et al. 1992) of the mean stochastic population growth rate for
each simulation parameter was calculated as follows:

$$S_R = |(\Delta R / R) / (\Delta P / P)|$$

$\Delta R / R$ represents the variable ratio of the mean stochastic population growth rate over 217 100 years; $\Delta P / P$ represents the variable ratio of the parameters. The sensitivity index is 218 proportional to the influence of the parameters on the output of the model. 219

## 2.4 Tests of alternative management actions

Based on the recent threats faced by the Amur tiger, we developed three scenarios 221 to assess how different management actions and their combinations affect the 222 probability of extinction of the Changbai-Primorye population (Appendix S4: Table S1 223 and Appendix S3). 224

### Scenario A: CDV control

For Scenario A, we reduced the risk of CDV infection in Amur tigers by managing 226 free-ranging domestic dogs (e.g., vaccination, hereafter referred to as “dog control”). 227 The probability of the tiger being infected with CDV due to predation on dogs was 228 reduced to 0, the probability of CDV transmission between individuals in low-risk years 229 was 0.99%, the daily average pathogenic environmental infection probability was 230 0.002712%, and the mortality rate of CDV infection in high-risk years was 83.8%. 231 These parameters were derived from published studies on the influence of CDV on 232 Amur tigers (Gilbert et al. 2014, 2015). 233

### Scenario B: habitat connectivity

For Scenario B, we constructed a larger population (the initial population contains 235 454 individuals and the habitat can hold 502 adult female tigers) in the model, based on 236 the population characteristics of the neighboring larger Sikhote-Alin population. Then 237 we established habitat connectivity between large and small populations by exchanging 238 individuals between populations. According to the research of Henry et al. (2009), 239 before the complete separation of the two populations, the probability of individuals in 240 the large population dispersing to the small population is 0.22%, and the probability of 241
242 individuals in the small population dispersing to the large population is 1.3%. In the
243 model we use this pair of dispersion rates.

244 Scenario C: habitat expansion

245 Scenario C was divided into two options. For the first option scenario C45, tiger
246 habitat increased to 18,000 km² within 20 years, which could accommodate 45 female
247 tigers, based on the plan of Wang *et al.* (2018) that designated protected habitat
248 becomes occupied. For the second option scenario C100, tiger habitat increased to
249 40,000 km² in 30 years and can accommodate 100 female tigers, based on Hebblewhite
250 et al.'s (2012) estimate of potential recoverable core habitat in China (see Appendix S3).

251 **2.5 Inbreeding depression**

252 To avoid underestimating the degree of inbreeding depression in wildlife
253 populations, in addition to the inbreeding depression test for 3.14LEs, we also examined
254 the impact of 6.29 LEs based on Kenney et al. (2014) and 12.26 LEs based on O'Grady
255 et al. (2006), to assess population trends under different cases of inbreeding depression.
256 We set the absence of any management alternatives as the baseline for each inbreeding
depression scenario. In the simulation of 3.14 LEs, the inbreeding only affected the
257 first-year survival. In the simulation of 6.29 LEs, 3.94 LEs were used to impact
258 fecundity and 2.35 LEs to reduce first-year survival. In the simulation of 12.26 LEs, we
259 added 5.97 LEs for altering survival from age 1 to sexual maturity based on 6.29 LEs.
260

261 **3 RESULTS**

262 **3.1 Baseline scenario**

263 The baseline scenario (no management actions) predicted that under inbreeding
depression of 3.14 LEs, the small population would decrease at a mean rate of 0.018 per
264 year, and would have a 64.4% extinction probability within 100 years. The mean time of
265 extinction was 66 years, the mean population size was 7 individuals and the population
266 genetic diversity would decline to 57.0% of its original level (Table 3).
267

268 **3.2 Sensitivity analysis**
The results showed that the three parameters with the highest sensitivity indices were BFP, MCI and IMR (Table 3, Figure S1 in Appendix S4). The effect of different parameter variations on the simulation were as follows: for LEs of 6.29 and 12.26, the extinction probability at 100 years was 90.6% and 99.8%, respectively and mean population size declined to 1 and 0, respectively (Table 3). For CCI, the longer the interval between high infection rates, the slower the population declines. However, when the infectious cycle increased to 7 years, the population extinction probability was still high (43%), but when cycle of CDV high infection risk was removed, the probability of extinction dropped to 0, and the population slowly grew from an initial size of 38 to 58 individuals after 100 years. For PI, when the mortality caused by poaching was set at 0% and 10.3%, the extinction probability was 1.8% and 99.8%, respectively. When poaching was fully eradicated, the population grew slowly, reaching a size of 56 after 100 years. The population exhibited a growing trend when MCI, IMR, and FMR were reduced by 40% and BFP was increased by 40%. For MMR and K, their increase or decrease had little influence on the population (Table 3).

## 3.2 Tests of alternative management actions

Just reducing domestic dogs (scenario A), even in the mildest inbreeding depression category (3.14 LEs), resulted in a negative growth rate (-0.006), the population extinction probability was 40.6%, and the population size declined to 16 within the 100 years. In the other two more severe inbreeding depression cases, the population is at a high risk of extinction, with extinction probability above 75% (Figure 3 and Table S2 in Appendix S4).

Increasing habitat connectivity (scenario B), with inbreeding depression at 3.14 and 6.29 LEs, resulted in a mean stochastic growth rate was 0.013 and 0.008, the mean population genetic diversity all declined to 89%, the population size reached 43 and 37, and the extinction probability within 100 years dropped to 1.4% and 4.2%, respectively. However, in the case of 12.26 LEs, the mean stochastic growth rate was 0 and the
extinction probability was 14.4% (Figure 3 and Table S2 in Appendix S4).

Expanding the tiger's range (scenario C) alone was inadequate. For both schemes (C45, C100), the simulation results were essentially the same as the baseline under the different inbreeding depression, except for a slight difference in population size in the 100th year. The population remained at high risk of extinction (Figure 3 and Table S2 in Appendix S4).

In summary, as the value of LEs increases, the benefits of implementing just one of the scenarios was largely ineffective. Habitat connectivity provided the biggest benefit. Implementing both habitat expansion and reducing the number of dogs in the forest have smaller benefits, but increasing habitat is the least beneficial (Figure 3). We chose the expansion scheme of 45 females (C45) as the representative of the habitat expansion scenario and simulated four combinations of the three scenarios overlapping each other. We found that the benefits of the four combinations were much higher than those of single management action (Appendix S4: Table S2, Table S3), regardless of the LEs (Figure 4). In the combination of scenario A and C at three levels of LEs, the population size reached 44, 13 and 0 within the 100th year, the genetic diversity was 71.3%, 66.7%, 0.00%, the mean stochastic growth rate was -0.002, -0.018, and -0.049, and the population extinction probability was 26.4%, 57.8%, and 99.8%, respectively; in the combination of scenarios B and C with the 3 possible levels of LEs, the population size was 73, 56, and 11, and the genetic diversity was 90.5%, 89.7%, and 88.4%, respectively, the mean stochastic growth rate was 0.008, 0.003, and -0.016, and the extinction probability was 2.2%, 3.4%, and 44.2% within 100 years, respectively; in the combination of scenario A and B of three levels of LE, the population size was 59, 56, 32, the genetic diversity in the 100th year was 93.3%, 93.3%, and 90.0%, and the mean stochastic growth rate was 0.030, 0.024, and 0.004, respectively. And there was no risk of population extinction within 100 years in the 3.14 and 6.29 LEs but the extinction probability with 12.26 LEs was 5.6%; when the three actions were combined, the
323 population size was 110, 97, 41, genetic diversity in the 100th year was 93.8%, 93.8%,
324 91.1%, and the mean stochastic growth rate was 0.021, 0.016, and 0.005, respectively.
325 There was no risk of extinction in the 3.14 and 6.29 LEs cases, but the extinction
326 probability with 12.26 LEs was 5.6% (Appendix S4: Table S3).
327

4 DISCUSSIONS

328 By incorporating a metamodel, we were better able to capture the potential
329 dynamics of the rapid spread of CDV within the Changbai-Primorye tiger population
330 and thus obtain a more realistic prediction of the impact of CDV on tigers. Compared
331 with previous PVA tiger studies (Carroll & Miquelle 2006; Tian et al. 2011), the
332 contributions of our study are as follows: (i) the metamodel allowed us to incorporate
333 the detailed, rapid dynamics of CDV into a tiger PVA, (ii) we incorporated recent
334 estimates of inbreeding depression that may be more realistic than those used in
335 previous models, and (iii) under these different levels of inbreeding depression and a
336 realistic model of CDV, we were able to explore management options for recovery of
337 the Changbai-Primorye tiger population. Through the simulation of different
338 management measures and their combinations, our results clearly show the importance
339 of habitat connectivity.
340

4.1 Insights from sensitivity assessment

341 Our results highlighted that with an outbreak of CDV, this population may require a
342 combination of management strategies to insure its persistence. The extinction
343 probability was greater than 60% under all realistic baseline estimations of lethal
344 equivalents without any intervention. Sensitivity analysis examined the impact of
345 variation in key demographic parameters on the model output results. Variation in the
346 proportion of breeding females was the most sensitive parameter in our sensitivity
347 analysis but based on observations in Nepal and Thailand (James L.D. Smith pers.
348 comm.) there is actually little variation in proportion of females that get pregnant. In
349 contrast, variation in female and infant-juvenile mortality does occur in wild
populations and can have a strong negative impact of recruitment. In tigers and other large felids, female survival is a major determinant of population viability (Kenney et al. 2014). This finding is consistent with PVA studies of other mammals such as the fennec fox (Vulpes zerda), the Asian elephant (Elephas maximus) (He et al. 2020; Franklin et al. 2021). Therefore, it is critical to ensure female tigers have good breeding habitat and low human-caused mortality to help increase their survival rates (Miquelle et al. 2015).

Sensitivity analysis also highlighted that poaching intensity, cycles of CDV high infection risk and mortality rate after CDV infection all had critical impacts on the probability of extinction. Without the threat of poaching and CDV, the current population would most likely remain viable for the next 100 years.

The sensitivity analysis showed that changes in environmental carrying capacity did not reduce the probability of extinction. This could be because the Changbai-Primorye population has been kept below the environmental capacity for long periods of time due to multiple threats (Robinson et al. 2015; Wang et al. 2018; Ning et al. 2021), and the influence of inbreeding depression did not decline with habitat expansion. Even if with a large enough habitat, it is difficult for the population to spread to any additional habitat due to the high extinction risk within the population.

Given the difficulty in estimating rates of inbreeding depression in the wild and recent debate as to the extent to which purging reduces inbreeding depression (Armstrong et al. 2021), further research is needed to estimate current levels of inbreeding depression in tigers and other species. Alternatively, we may have over-estimated mortality of adult females in our model and that is restricting recovery.

Kenney et al. (2014) estimated a much higher annual rate of adult female survival than we used in our model. Future modeling of this population will benefit from efforts to improve estimation of this parameter through our current intensive camera trapping.

Simultaneously, it is critical to monitor CDV in both tigers and domestic species.

## 4.2 Management interventions
With the newly established the Northeast Tiger and Leopard National Park in China, tigers are now expanding their range into northeast China. However, the distribution further from the Russian border is still spotty and the number of tigers is low (Wang et al. 2016; Qi et al. 2021). Most of the habitat expansion in China is by young males. Female tigers and their young are confined to the China-Russia border and breeding habitat is still estimated at < 6,000 km² (Wang et al. 2018; Qi et al. 2021). At this juncture, urgent actions are needed.

Our study revealed that maintaining connectivity among the two populations was essential and a prerequisite for preventing drastic reduction of population. The simulation of different management scenarios showed that habitat connectivity was the only measure that effectively increased growth rates in the tiger population. Moreover, the scenarios combining habitat connectivity performed relatively well in population projections. In contrast, dog control, habitat expansion and this combination resulted in the population reaching its maximum size in less than 40 years and then gradually declining. It is thus clear that gene exchange among populations plays a key role in the recovery and long-term population persistence.

A true barrier now exists due to continuing development along the Razdolnaya River basin, which prevents movements of tigers between the Changbai landscape and the southern Sikhote-Alin landscape; the populations show clear genetic differentiation (Sorokin et al. 2016). Furthermore, a recent study reported poor health status for the Changbai-Primorye population as well as 50% of individual relationships were cousins or half-sibs (Ning et al. 2021). Miquelle et al. (2015) used least-cost distance analyses to identify a single potential corridor to retain connectivity for the two subpopulations, but no evidence demonstrates that this corridor is actually used by tigers. When habitat connectivity is difficult to achieve, a more immediate, and practical strategy, is translocation of individuals from the Sikhote-Alin population. A similar movement of Texas panthers into the Florida panther population (*P. c. coryi*) resulted in a dramatic
population recovery (Johnson et al. 2010; Hostetler et al. 2013). Similarly, the reintroduction of tigers in Panna, India, resulted in rapid population recovery (Sankar et al. 2010).

CDV infection is regarded as the main threat to large felids conservation not only in Asia but also worldwide (Adhikari et al. 2020). Domestic dogs are a proven source of CDV for wild animals and transmit the CDV to the tiger population through direct predation by tigers or indirectly through interaction with other wildlife, which can increase the mortality risk of tigers (Gilbert et al. 2014; Dou et al. 2019). However, currently, domestic dogs have widely invaded into the Amur tiger's habitat (Gilbert et al. 2020). According to our camera-trapping data and field surveys, domestic dogs are still entering the parkland and committed wildlife attacks in 2021; on the Chinese side, unvaccinated domestic dogs had a high level of positive antibodies for CDV due to exposure to the disease (37% in 2020 domestic dogs serum tests in 2018, unpublished data). Although dog management practices were not the most effective when compared to habitat connectivity, and domestic dogs may not be the only source of CDV infection (Gilbert et al. 2020), dog management was the easiest and safest method to achieve among all conservation actions, and when combined with other measures, it can effectively improve population trajectories by increasing the population recovery speed. Given CDV is preventable, strict vaccination of domestic dogs with the commercial attenuated vaccine is needed. To ensure the long-term success of tiger conservation, we suggest that the local government implement policies aimed at gradually controlling dogs, if not completely prohibiting all dogs in the tiger's core range.

Although the establishment of national park has expanded the habitat of Amur tigers, through the monitoring of camera trap data in the past few years, the core range of Amur tigers still stays at the Sino-Russian border and does not spread to China on a large scale. As the results in our simulations show, the expansion of habitat did not significantly increase the population size, especially in the case of severe inbreeding
431 depression. This may be due to the fact that the Amur tiger population face many threats,
432 such as low initial population size, inbreeding depression and CDV, which cause the
433 population size to remain below the carrying capacity for a long time. Even with
434 sufficiently large habitats, it is difficult for tiger population to spread due to the high
435 risk of extinction. The same result was obtained when assessing the extinction risk of
436 tigers in central India (Thatte *et al.* 2018). Although our model did not indicate that
437 habitat expansion alone would reduce the probability of extinction, habitat expansion
438 has several benefits that should not be overlooked. Habitat expansion would increase
439 structurally and spatial diversity of the landscape, which in turn might decrease the
440 spread of CDV. Also shifting from people intensive logging activities to conservation
441 management may reduce the impact of humans and their dogs serving as a vector for
442 CDV. In India, a topographically and vegetatively diverse landscape also reduced the
443 risk of extinction (Thatte *et al.* 2018).

444 In summary, our findings highlight the critical need for integrated conservation
445 strategies and actions that link wildlife populations, landscape planning, CDV research,
446 and international cooperation while also meeting human development goals. Our
447 modeling of the potential impact of CDV highlights the critical need for integrated
448 conservation strategies that link and expand wildlife populations, institute broad
449 landscape planning, and include continuous surveys of potential CDV outbreaks.
450 Fortunately, transborder cooperation in monitoring and management is already in place.

451 **4.3 Methodological considerations**

452 Metamodelling requires estimating a number of parameters to build each
453 submodel with an acceptable degree of realism, and hence there is a degree of
454 uncertainty associated with their results (Shoemaker *et al.* 2014). However, metamodels
455 can assist managers and policymakers in making decisions for the conservation of
456 endangered populations in an uncertain environment (Reed *et al.* 2002; Lawson *et al.*
457 2021). Therefore, this analysis provides the relative benefits of various possible actions
458 rather than an absolute and accurate prediction of future population trends. Inbreeding
459 could have an impact on the survival and reproduction of adult tigers, especially in the
460 face of changing environmental conditions (Coltman *et al.* 1999; Fox & Reed 2011),
461 and population connectivity may slightly mitigate inbreeding depression in populations.

462 However, the above two considerations were not included in the model because of the
463 difficulty of accurately estimating the relevant parameters and their limited influence on
464 the overall population trends. In addition, since prey resources are also an important
465 factor affecting tiger population continuation and management and policy decisions
466 operate on short-term projections, it is necessary to construct more complex and
467 shorter-term projections that include prey species in the future.

468

469 REFERENCES

470 Adhikari RB, Shrestha M, Puri G, Regmi GR & Ghimire TR (2020). Canine Distemper
471 Virus (CDV): an emerging threat to Nepal's wildlife. *Applied Science and
472 Technology Annals* **1**, 149-54.

473 Armstrong EE, Khan A, Taylor RW *et al*. (2021). Recent Evolutionary History of
474 Tigers Highlights Contrasting Roles of Genetic Drift and Selection. *Molecular
475 Biology and Evolution* **38**, 2366-79.

476 Bradshaw CJA, McMahon CR, Miller PS *et al*. (2012). Novel coupling of
477 individual-based epidemiological and demographic models predicts realistic
478 dynamics of tuberculosis in alien buffalo. *Journal of Applied Ecology* **49**,
479 268-77.

480 Carroll C & Miquelle DG (2006). Spatial viability analysis of Amur tiger Panthera tigris
481 altaica in the Russian Far East: the role of protected areas and landscape matrix
482 in population persistence. *Journal of Applied Ecology* **43**, 1056-68.
483 Coltman DW, Pilkington JG, Smith JA & Pemberton JM (1999). Parasite-mediated selection against inbred Soay sheep in a free-living, island population. *Evolution* **53**, 1259–67.

486 Craft ME, Volz E, Packer C & Meyers LA (2009). Distinguishing epidemic waves from disease spillover in a wildlife population. *Proceedings of the Royal Society B: Biological Sciences* **276**, 1777–85.

489 Deem SL, Spelman LH, Yates RA & Montali RJ (2000). Canine distemper in terrestrial carnivores: A review. *Journal of Zoo and Wildlife Medicine* **31**, 441–51.

491 Dou H, Yang H, Feng L, Mou P, Wang T & Ge J (2016). Estimating the population size and genetic diversity of Amur tigers in Northeast China. *PLOS ONE* **11**, e0154254.

494 Dou H, Yang H, Smith JLD, Feng L, Wang T & Ge J (2019). Prey selection of Amur tigers in relation to the spatiotemporal overlap with prey across the Sino-Russian border. *Wildlife Biology* **2019**, 1–11.

497 Feng L, Shevtsova E, Vitkalova A *et al*. (2017). Collaboration brings hope for the last Amur leopards. *Cat News* **65**, 20.

499 Feng R, Lü X, Xiao W *et al*. (2021). Effects of free-ranging livestock on sympatric herbivores at fine spatiotemporal scales. *Landscape Ecology* **36**, 1441–1457.

501 Fox CW & Reed DH (2011). Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. *Evolution* **65**, 246–58.

503 Franklin AD, Lacy RC, Bauman KL, Traylor-Holzer K & Powell DM (2021). Incorporating drivers of reproductive success improves population viability analysis. *Animal Conservation* **24**, 386–400.
Gilbert M, Miquelle DG, Goodrich JM *et al*. (2014). Estimating the potential impact of canine distemper virus on the Amur tiger population (*Panthera tigris altaica*) in Russia. *PLOS ONE* 9, e110811.

Gilbert M, Soutyrina SV, Seryodkin IV *et al*. (2015). Canine distemper virus as a threat to wild tigers in Russia and across their range. *Integrative Zoology* 10, 329–43.

Gilbert M, Sulikhan N, Uphyrkina O *et al*. (2020). Distemper, extinction, and vaccination of the Amur tiger. *Proceedings of the National Academy of Sciences* 117, 31954–62.

Gordon CH, Banyard A, Hussein A *et al*. (2015). Canine distemper in endangered Ethiopian wolves. *Emerging Infectious Diseases* 824–32.

Gu J, Yu L, Hua Y *et al*. (2018). A comparison of food habits and prey preferences of Amur tiger (*Panthera tigris altaica*) at the southwest Primorskii Krai in Russia and Hunchun in China. *Integrative Zoology* 13, 595–603.

He C, Du J, Zhu D & Zhang L (2020). Population viability analysis of small population: a case study for Asian elephant in China. *Integrative Zoology* 15, 350–62.

Hebblewhite M, Miquelle DG, Robinson H *et al*. (2014). Including biotic interactions with ungulate prey and humans improves habitat conservation modeling for endangered Amur tigers in the Russian Far East. *Biological Conservation* 178, 50–64.

Hebblewhite M, Zimmermann F, Li Z *et al*. (2012). Is there a future for Amur tigers in a restored tiger conservation landscape in Northeast China? *Animal Conservation* 15, 579–92.

Henry P, Miquelle D, Sugimoto T, McCULLOUGH DR, Caccone A & Russello MA
529 (2009). In situ population structure and ex situ representation of the endangered
530 Amur tiger. *Molecular Ecology* **18**, 3173–84.

531 Hernandez-Blanco JA, Naidenko SV, Chistopolova MD *et al*. (2015). Social structure
532 and space use of Amur tigers (*Panthera tigris altaica*) in Southern Russian Far
533 East based on GPS telemetry data. *Integrative Zoology* **10**, 365–75.

534 Hostetler JA, Onorato DP, Jansen D & Oli MK (2013). A cat's tale: the impact of
535 genetic restoration on Florida panther population dynamics and persistence.
536 *Journal of Animal Ecology* **82**, 608–20.

537 Jhala Y, Gopal R, Mathur V *et al*. (2021). Recovery of tigers in India: Critical
538 introspection and potential lessons. *People and Nature* **3**, 281–93.

539 Johnson WE, Onorato DP, Roelke ME *et al*. (2010). Genetic restoration of the Florida
540 panther. *Science* **329**, 1641–5.

541 Joshi AR, Dinerstein E, Wikramanayake E *et al*. (2016). Tracking changes and
542 preventing loss in critical tiger habitat. *Science Advances* **2**, e1501675.

543 Kenney JS, Allendorf FW, McDougal C & Smith JLD (2014). How much gene flow is
544 needed to avoid inbreeding depression in wild tiger populations? *Proceedings of
545 the Royal Society B: Biological Sciences* **281**, 20133337.

546 Kenney JS, Smith JL, Starfield AM & McDougal CW (1995). The long-term effects of
547 tiger poaching on population viability. *Conservation Biology* **9**, 1127–33.

548 Kerley LL, Goodrich JM, Miquelle DG, Smirnov EN, Quigley HB & Hornocker MG
549 (2003). Reproductive Parameters of Wild Female Amur (Siberian) Tigers
550 (*Panthera tigris altaica*). *Journal of Mammalogy* **84**, 288–98.

551 Lacy RC & Miller PS (2020). Vortex: A stochastic simulation of the extinction process.
552 Version 10.5. Available from URL: https://scti.tools/downloads/.

553 Lacy RC, Pollak JP, Miller PS, Hungerford L & Bright P (2020). Outbreak. Version

554 2.10. Available from URL: https://scti.tools/downloads/.

555 Lawson AJ, Folt B, Tucker AM, Erickson F & McGowan CP (2021). Decision context

556 as a necessary component of population viability analysis assessments: response

557 to Chaudhary and Oli 2020. *Conservation Biology* **35**, 1683–85.

558 Miquelle DG, M. Goodrich J, L. Kerley L *et al*. (2010). Chapter 32 - Science-Based

559 Conservation of Amur Tigers in the Russian Far East and Northeast China. In:

560 *Tigers of the World (Second Edition)*, Noyes Series in Animal Behavior,

561 Ecology, Conservation, and Management (eds R Tilson & PJ Nyhus) pp. 403–23.

562 William Andrew Publishing, Boston.

563 Miquelle DG, Pikunov D, Y.M. D *et al*. (2007). 2005 Amur Tiger Census. *Cat News* **46**,

564 14–15.

565 Miquelle DG, Smirnov EN, Zaumyslova OYu, Soutyrina SV & Johnson DH (2015).

566 Population dynamics of Amur tigers (*Panthera tigris altaica*) in Sikhote-Alin

567 Biosphere Zapovednik: 1966–2012. *Integrative Zoology* **10**, 315–28.

568 Ning Y, Roberts NJ, Qi J *et al*. (2021). Inbreeding status and implications for Amur

569 tigers. *Animal Conservation*.

570 O'Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW & Frankham R (2006).

571 Realistic levels of inbreeding depression strongly affect extinction risk in wild

572 populations. *Biological Conservation* **133**, 42–51.

573 Pollak JP & Lacy RC (2020). Metamodel manager. Version 1.0.6. Available from URL:

574 https://scti.tools/downloads/.
575 Pulliam HR, Dunning Jr. JB & Liu J (1992). Population Dynamics in Complex

576 Landscapes: A Case Study. *Ecological Applications* **2**, 165–77.

577 Qi J, Gu J, Ning Y *et al*. (2021). Integrated assessments call for establishing a

578 sustainable meta-population of Amur tigers in northeast Asia. *Biological

579 *Conservation* **261**, 109250.

580 Quigley KS, Evermann JF, Leathers CW *et al*. (2010). Morbillivirus Infection in a Wild

581 Siberian Tiger in the Russian Far East. *Journal of Wildlife Diseases* **46**, 1252–6.

582 Ralls K, Ballou JD & Templeton A (1988). Estimates of Lethal Equivalents and the

583 Cost of Inbreeding in Mammals. *Conservation Biology* **2**, 185–93.

584 Reed JM, Mills LS, Dunning JB *et al*. (2002). Emerging Issues in Population Viability

585 Analysis. *Conservation Biology* **16**, 7–19.

586 Robinson HS, Goodrich JM, Miquelle DG, Miller CS & Seryodkin IV (2015). Mortality

587 of Amur tigers: The more things change, the more they stay the same.

588 *Integrative Zoology* **10**, 344–53.

589 Roelke-Parker ME, Munson L, Packer C *et al*. (1996). A canine distemper virus

590 epidemic in Serengeti lions (*Panthera leo*). *Nature* **379**, 441–5.

591 Roscoe DE (1993). Epizootiology of canine distemper in new jersey raccoons. *Journal*

592 *of Wildlife Diseases* **29**, 390–5.

593 Sankar K, Qureshi Q, Nigam P *et al*. (2010). Monitoring of reintroduced tigers in

594 Sariska Tiger Reserve, Western India: preliminary findings on home range, prey

595 selection and food habits. *Tropical Conservation Science* **3**, 301–18.

596 Shoemaker KT, Lacy RC, Verant ML *et al*. (2014). Effects of prey metapopulation

597 structure on the viability of black-footed ferrets in plague-impacted landscapes:
598 a metamodelling approach. *Journal of Applied Ecology* **51**, 735–45.

599 Smith JLD & Mcdougal C (1991). The contribution of variance in lifetime reproduction
d00 to effective population-size in tigers. *Conservation Biology* **5**, 484–90.

601 Smith KF, Acevedo-Whitehouse K & Pedersen AB (2009). The role of infectious
diseases in biological conservation. *Animal Conservation* **12**, 1–12.

603 Sorokin PA, Rozhnov VV, Krasnenko AU, Lukarevskiy VS, Naidenko SV &
604 Hernandez-Blanco JA (2016). Genetic structure of the Amur tiger (*Panthera
tigris altaica*) population: Are tigers in Sikhote-Alin and southwest Primorye
605 truly isolated? *Integrative Zoology* **11**, 25–32.
606

607 Sugimoto T, Aramilev VV, Nagata J & McCullough DR (2016). Winter food habits of
608 sympatric carnivores, Amur tigers and Far Eastern leopards, in the Russian Far
609 East. *Mammalian Biology* **81**, 214–8.

610 Sulikhan NS, Gilbert M, Blidchenko EY *et al.* (2018). Canine distemper virus in a wild
611 Far Eastern leopard (*Panthera pardus orientalis*). *Journal of Wildlife Diseases*
612 **54**, 170–4.

613 Thatte P, Joshi A, Vaidyanathan S, Landguth E & Ramakrishnan U (2018). Maintaining
tiger connectivity and minimizing extinction into the next century: Insights from
614 landscape genetics and spatially-explicit simulations. *Biological Conservation*
615 **218**, 181–91.
616

617 Tian Y, Wu J, Smith AT, Wang T, Kou X & Ge J (2011). Population viability of the
618 Siberian Tiger in a changing landscape: Going, going and gone? *Ecological
619 Modelling* **222**, 3166–80.

620 Wang T, Andrew Royle J, Smith JLD *et al*. (2018). Living on the edge: Opportunities
621 for Amur tiger recovery in China. *Biological Conservation* **217**, 269–79.

622 Wang T, Feng L, Mou P *et al*. (2016). Amur tigers and leopards returning to China:

623 direct evidence and a landscape conservation plan. *Landscape Ecology* **31**,

624 491–503.

625 Weckworth JK, Davis BW, Dubovi E *et al*. (2020). Cross-species transmission and

626 evolutionary dynamics of canine distemper virus during a spillover in African

627 lions of Serengeti National Park. *Molecular Ecology* **29**, 4308–21.

628 Xiao W, Feng L, Mou P *et al*. (2016). Estimating abundance and density of Amur tigers

629 along the Sino–Russian border. *Integrative Zoology* **11**, 322–32.
<table><caption>**Table 1.** Parameters input for the baseline scenario of the tiger population model in Vortex.</caption><thead><tr><th>Parameter</th><th>Baseline value</th></tr></thead><tbody><tr><td>Lethal equivalents</td><td>3.14</td></tr><tr><td>Percent due to recessive lethal alleles (%)</td><td>50</td></tr><tr><td>Breeding system</td><td>Polygynous</td></tr><tr><td>Age of first offspring (female/male)</td><td>3/4</td></tr><tr><td>Maximum age of reproduction</td><td>15</td></tr><tr><td>Maximum lifespan</td><td>15</td></tr><tr><td>Maximum number of broods per year</td><td>1</td></tr><tr><td>Maximum number of progenies per brood</td><td>6</td></tr><tr><td>Share of males at birth (%)</td><td>50</td></tr><tr><td>Cubs depend on the mother (years)</td><td>2</td></tr><tr><td>Successfully breeding female proportion (%)</td><td>70</td></tr><tr><td>Breeding male proportion (%)</td><td>70</td></tr><tr><td>Litter size (%)</td><td></td></tr><tr><td>1 offspring</td><td>10</td></tr><tr><td>2 offspring</td><td>38</td></tr><tr><td>3 offspring</td><td>38</td></tr><tr><td>4 offspring</td><td>10</td></tr><tr><td>5 offspring</td><td>3</td></tr><tr><td>6 offspring</td><td>1</td></tr><tr><td>Mortality of infant-cubs (%)</td><td>40 (0-1)</td></tr><tr><td>Mortality of juveniles (%)</td><td>20 (1-2)</td></tr><tr><td>Mortality of subadults (%)</td><td>18.368 (2-3)</td></tr><tr><td>Mortality of adult females (%)</td><td>6.273 (&gt;3)</td></tr><tr><td>Mortality of adult males (%)</td><td>15.539 (&gt;3)</td></tr><tr><td>Initial population size</td><td>38</td></tr><tr><td>Number of infant-cubs (female/male)</td><td>2/2</td></tr><tr><td>Number of juveniles (female/male)</td><td>2/2</td></tr><tr><td>Number of subadults (female/male)</td><td>2/2</td></tr><tr><td>Number of adults (female/male)</td><td>14/12</td></tr><tr><td>Carrying capacity (K)</td><td>23 (adult females)</td></tr><tr><td>Catastrophes (high risk of CDV)</td><td></td></tr><tr><td>Frequency (%)</td><td>20</td></tr><tr><td>Reproduction</td><td>1</td></tr><tr><td>Survival (%)</td><td>88.52</td></tr></tbody></table>
631 **Table 2.** Parameters input for the baseline scenario of the canine distemper epidemiological

632 model in Outbreak.

633

<table><thead><tr><td>Parameter</td><td>Baseline value</td></tr></thead><tbody><tr><td>Probability that an individual never becomes susceptible (%)</td><td>0</td></tr><tr><td>Earliest age of susceptibility (days)</td><td>15-30</td></tr><tr><td>Transmission probability from an infectious mother to a newborn</td><td>1</td></tr><tr><td>Time for maternally derived immunity to protect an offspring (days)</td><td>150-180</td></tr><tr><td>Number of individuals that an individual encounters per day</td><td>0.03</td></tr><tr><td>Transmission probability among individuals during an encounter (%)</td><td>1.40</td></tr><tr><td>Daily transmission probability from the environment (%)</td><td>0.003836</td></tr><tr><td>Duration of the incubation period (days)</td><td>2-7</td></tr><tr><td>Duration of the infectious period (days)</td><td>30-60</td></tr><tr><td>Probability of recovering and acquiring permanent immunity (%)</td><td>60</td></tr><tr><td>Mortality rate after CDV infection (%)</td><td>40</td></tr></tbody></table>
<table><caption>**Table 3.** Baseline candidate model results. R(SD): mean (standard deviation) of stochastic population growth rate across all years and iterations; N (SD): mean (standard deviation) of number of tigers at year 100th; GD (SD): initial genetic diversity (heterozygosity) remaining in population at year 100th; PE: probability of extinction, defined as only 1 sex remains at year 100th; TE: mean time of extinction (in years) for all iterations that went extinct within 100 years; SR: The sensitivity index of the mean stochastic population growth rate for each simulation parameter.</caption><thead><tr><th>Scenario</th><th>R (SD)</th><th>N (SD)</th><th>GD (SD)</th><th>PE</th><th>TE</th><th>S<sub>R</sub></th></tr></thead><tbody><tr><td>Baseline</td><td>-0.018 (0.186)</td><td>7(13)</td><td>0.570 (0.1700)</td><td>0.644</td><td>66</td><td></td></tr><tr><td>Lethal equivalents (LEs)</td><td></td><td></td><td></td><td></td><td></td><td>0.691</td></tr><tr><td>6.29</td><td>-0.030 (0.192)</td><td>1 (4)</td><td>0.549 (0.189)</td><td>0.906</td><td>63</td><td></td></tr><tr><td>12.26</td><td>-0.056 (0.191)</td><td>0 (0)</td><td>0 (0)</td><td>0.998</td><td>40</td><td></td></tr><tr><td>The cycles of CDV high infection risk (CCI)</td><td></td><td></td><td></td><td></td><td></td><td>1.989</td></tr><tr><td>NA</td><td>0.057 (0.105)</td><td>58 (9)</td><td>0.738 (0.093)</td><td>0.000</td><td>-</td><td></td></tr><tr><td>3</td><td>-0.037 (0.214)</td><td>1 (5)</td><td>0.493 (0.176)</td><td>0.928</td><td>55</td><td></td></tr><tr><td>7</td><td>-0.008 (0.171)</td><td>14 (18)</td><td>0.585 (0.178)</td><td>0.430</td><td>70</td><td></td></tr><tr><td>Poached intensity (PI)</td><td></td><td></td><td></td><td></td><td></td><td>2.798</td></tr><tr><td>0%</td><td>0.037 (0.125)</td><td>56 (17)</td><td>0.758 (0.092)</td><td>0.018</td><td>62</td><td></td></tr><tr><td>100%</td><td>-0.064 (0.244)</td><td>0 (0)</td><td>0 (0)</td><td>0.998</td><td>36</td><td></td></tr><tr><td>Mortality rate after CDV infection (MCI)</td><td></td><td></td><td></td><td></td><td></td><td>3.791</td></tr><tr><td>+40%</td><td>-0.042 (0.226)</td><td>0 (3)</td><td>0.454 (0.238)</td><td>0.966</td><td>51</td><td></td></tr><tr><td>-40%</td><td>0.013 (0.138)</td><td>33 (20)</td><td>0.664 (0.147)</td><td>0.128</td><td>74</td><td></td></tr><tr><td>Successfully breeding female proportion (BFP)</td><td></td><td></td><td></td><td></td><td></td><td>5.228</td></tr><tr><td>+40%</td><td>0.017 (0.151)</td><td>39 (23)</td><td>0.674 (0.150)</td><td>0.116</td><td>66</td><td></td></tr><tr><td>-40%</td><td>-0.059 (0.218)</td><td>0 (0)</td><td>0 (0)</td><td>1.000</td><td>40</td><td></td></tr><tr><td>Infant-cub (0-1 year old) mortality rate (IMR)</td><td></td><td></td><td></td><td></td><td></td><td>3.743</td></tr><tr><td>+40%</td><td>-0.043 (0.204)</td><td>0 (1)</td><td>0.482 (0.284)</td><td>0.982</td><td>52</td><td></td></tr><tr><td>-40%</td><td>0.011 (0.163)</td><td>33 (24)</td><td>0.659 (0.140)</td><td>0.202</td><td>71</td><td></td></tr><tr><td>Adult (&gt;3 years old) female mortality rate (FMR)</td><td></td><td></td><td></td><td></td><td></td><td>3.342</td></tr><tr><td>+40%</td><td>-0.040 (0.211)</td><td>1 (3)</td><td>0.412 (0.253)</td><td>0.954</td><td>53</td><td></td></tr><tr><td>-40%</td><td>0.009 (0.157)</td><td>30 (20)</td><td>0.648 (0.150)</td><td>0.168</td><td>70</td><td></td></tr><tr><td>Adult (&gt;3 years old) male mortality rate (MMR)</td><td></td><td></td><td></td><td></td><td></td><td>0.470</td></tr><tr><td>+40%</td><td>-0.021 (0.194)</td><td>5 (10)</td><td>0.497 (0.222)</td><td>0.732</td><td>63</td><td></td></tr><tr><td>-40%</td><td>-0.014 (0.173)</td><td>11 (15)</td><td>0.614 (0.183)</td><td>0.536</td><td>68</td><td></td></tr><tr><td>Carrying capacity (K)</td><td></td><td></td><td></td><td></td><td></td><td>0.275</td></tr><tr><td>+40%</td><td>-0.015 (0.176)</td><td>14 (21)</td><td>0.624 (0.166)</td><td>0.526</td><td>65</td><td></td></tr><tr><td>-40%</td><td>-0.019 (0.209)</td><td>2 (6)</td><td>0.428 (0.223)</td><td>0.844</td><td>60</td><td></td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Current range of the Amur tiger in Northeast China and Far East Russia. Small tiger population is areas with confirmed tiger breeding activity within the last 10 years. C100 and C45 indicate two habitat expansion schemes (see method for details). NTLNP and LLNP is the Northeast Tiger and Leopard National Park of China and the Land of Leopard National Park of Russia, respectively. Yellow arrow represents potential important ecological corridors for tigers between small and large populations.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Schematic flowchart of the Amur tiger-CDV metamodel structure used for this analysis. The PVA program acts as the system model (solid outline) to simulate individual survival and reproduction based on individual and population state variables (shown in italics) passed from other models. Epidemiological model (dashed outlines) simulates individual transitions in disease status. The central information flow control program passes state variables between the system and modifier models at appropriate time steps. Rounded rectangles represent software components, and parallelograms identify model output variables from Vortex.</figcaption></figure>
a

<figure><img src="image_4.png" /></figure>

b

<figure><img src="image_5.png" /></figure>

c

<figure><img src="image_6.png" /></figure>

d

<figure><img src="image_7.png" /><figcaption>**Figure 3.** Estimates of stochastic population growth (a), population size (b), genetic diversity (c) and survival probability (d) for the small Amur tiger population under different management alternatives and lethal equivalents (LEs) over 100 years based on the average of 500 model runs. Scenario A: dog control in the Amur tiger habitat; Scenario B: establish habitat connectivity between large and small populations; Scenario C45: habitat expanded to accommodate 45 females; Scenario C100: habitat expanded to accommodate 100 females.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Changes in population size, genetic diversity, and survival probability of small Amur tiger population under different combinations of management actions and lethal equivalents (LEs) over 100 years based on the average of 500 model runs. Scenario A: dog control in the Amur tiger habitat; Scenario B: establish habitat connectivity between large and small populations; Scenario C: habitat expanded to accommodate 45 females.</figcaption></figure>

666
667
668
669
670
671