The use of population modelling is becoming an increasingly common tool in reintroduction planning and assessment (South et al., Reference South, Rushton and Macdonald2000; Armstrong & Ewen, Reference Armstrong and Ewen2002; Armstrong & Reynolds, Reference Armstrong, Reynolds, Ewen, Armstrong, Parker and Seddon2012; Parlato & Armstrong, Reference Parlato and Armstrong2012). Population models can be particularly helpful in assessing reintroduction success. Although initial reintroduction success is often measured by quantifying post-release survival and reproduction (Britt et al., Reference Britt, Welch, Katz, Iambana, Porton and Junge2004; Goossens et al., Reference Goossens, Setchell, Tchidongo, Dilambaka, Vidal, Ancrenaz and Jamart2005; Maran et al., Reference Maran, Põdra, Põlma and Macdonald2009; Tavecchia et al., Reference Tavecchia, Viedma, Martínez-Abraín, Bartolomé, Gómez and Oro2009; King et al., Reference King, Chamberlan and Courage2012), the ultimate goal of a reintroduction is to re-establish a viable, self-sustaining population (IUCN, 2002; Beck et al., Reference Beck, Walkup, Rodrigues, Unwin, Travis and Stoinski2007). The probability of the long-term persistence of a re-established population is best measured through modelling of population viability (Seddon et al., Reference Seddon, Armstrong and Maloney2007, Reference Seddon, Strauss, Innes, Ewen, Armstrong, Parker and Seddon2012). Another major role of population models is in guiding reintroduction decision-making (Armstrong & Reynolds, Reference Armstrong, Reynolds, Ewen, Armstrong, Parker and Seddon2012), including assessing potential reintroduction sites (Cramer & Portier, Reference Cramer and Portier2001; Schadt et al., Reference Schadt, Revilla, Wiegand, Knauer, Kaczensky and Breitenmoser2002) or potential release stock (Robert, Reference Robert2009), estimating the number of release stock necessary or the required duration of the release period (Slotta-Bachmayr et al., Reference Slotta-Bachmayr, Boegel, Kaczensky, Stauffer and Walzer2004; Armstrong & Seddon, Reference Armstrong and Seddon2008; Gusset et al., Reference Gusset, Jakoby, Müller, Somers, Slotow and Grimm2009; Schaub et al., Reference Schaub, Zink, Beissman, Sarrazin and Arlettaz2009), evaluating the impacts on the source population (Bustmante, Reference Bustmante1996; Somers, Reference Somers1997; Todd et al., Reference Todd, Jenkins and Bearlin2002; Kohlmann et al., Reference Kohlmann, Schmidt and Garcelon2005; Dimond & Armstrong, Reference Dimond and Armstrong2007), and comparing potential management strategies (Armstrong et al., Reference Armstrong, Castro and Griffiths2007; Wakamiya & Roy, Reference Wakamiya and Roy2009; Martínez-Abraín et al., Reference Martínez-Abraín, Regan, Viedma, Villuendas, Bartolomé, Gómez and Oro2011).
Population viability models are, however, highly sensitive to the quality of the input data (South et al., Reference South, Rushton and Macdonald2000; Asbjørnsen et al., Reference Asbjørnsen, Sæther, Linnell, Engen, Andersen and Bretten2005). Accurate estimations of demographic parameters are particularly difficult to obtain for long-lived species (Harcourt, Reference Harcourt1995; Gaillard et al., Reference Gaillard, Festa-Bianchet and Yoccoz1998; Robbins & Robbins, Reference Robbins and Robbins2004), and for the majority of reintroduced populations of any species because of low sample sizes (Nichols & Armstrong, Reference Nichols, Armstrong, Ewen, Armstrong, Parker and Seddon2012). Consequently reintroduction programmes for several long-lived primate species (e.g. Yaeger, Reference Yaeger1997; Tutin et al., Reference Tutin, Ancrenaz, Paredes, Vacher-Vallas, Vidal and Goossens2001; Goossens et al., Reference Goossens, Setchell, Tchidongo, Dilambaka, Vidal, Ancrenaz and Jamart2005; Strum, Reference Strum2005; King & Courage, Reference King, Courage and Soorae2008; Peignot et al., Reference Peignot, Charpentier, Bout, Bourry, Massima and Dosimont2008) have yet to utilize population viability models in planning or assessment. A review of literature on modelling reintroduced populations (Armstrong & Reynolds, Reference Armstrong, Reynolds, Ewen, Armstrong, Parker and Seddon2012) analysed 89 papers, of which 46 concerned mammals but only one concerned a primate (Swart & Lawes, Reference Swart and Lawes1996). This may to some extent be because of the geographical bias of publications, with most relating to projects in Europe, North America, New Zealand or Australia, where primates do not occur, and many models may not be published (Armstrong & Reynolds, Reference Armstrong, Reynolds, Ewen, Armstrong, Parker and Seddon2012). The model we develop here, for reintroduced western lowland gorillas Gorilla gorilla gorilla, appears to be the first published attempt to assess the long-term viability of a reintroduction programme for a long-lived and slow-reproducing threatened primate.
The western lowland gorilla is categorized as Critically Endangered on the IUCN Red List (Walsh et al., Reference Walsh, Tutin, Baillie, Maisels, Stokes and Gatti2008) based on a projected 80% decline in the wild over three generations. The major causes of decline are commercial hunting and mortality caused by the Ebola virus (Tutin et al., Reference Tutin, Stokes, Boesch, Morgan, Sanz and Reed2005; Walsh et al., Reference Walsh, Tutin, Baillie, Maisels, Stokes and Gatti2008), with habitat loss and degradation, and possibly climate change, expected to become major threats (Walsh et al., Reference Walsh, Tutin, Baillie, Maisels, Stokes and Gatti2008). Gorillas can live for > 40 years, usually do not reproduce until they are at least 10 years old, and females produce only one surviving offspring about every 5 years (Harcourt & Stewart, Reference Harcourt and Stewart2007).
Two populations of western lowland gorillas are in the process of being re-established in the Batéké Plateau region of central Africa, one each in the neighbouring countries of the Republics of Congo and Gabon (King, Reference King2004; King & Courage, Reference King and Courage2007, Reference King, Courage and Soorae2008; Pearson & King, Reference Pearson and King2008). The first releases occurred in 1996 and 2001, respectively, in the two countries, and post-release monitoring data have recently been analysed to quantify demographic parameters, to allow an assessment of initial reintroduction success (King et al., Reference King, Chamberlan and Courage2012). This analysis illustrated that the reintroduction programme had been successful in terms of post-release survival, reproduction, and dispersal, with quantitative measures of these parameters being similar to comparable measures for wild populations (King et al., Reference King, Chamberlan and Courage2012). To assess longer-term success we use the demographic data for the reintroduced populations (King et al., Reference King, Chamberlan and Courage2012) and published data on wild and a few captive gorilla populations to develop a population model. We use the model to investigate how possible scenarios could affect the viability of the two reintroduced populations, and how population models can inform reintroduction management decisions for long-lived species.
The two reintroduced western lowland gorilla populations are located in the Lesio-Louna Reserve of Congo and the Batéké Plateau National Park of Gabon (Fig. 1). Pre-release preparations and release implementation are described elsewhere (King et al., Reference King, Chamberlan and Courage2012). Both reintroduction sites have collaborative protected area management projects that arose from the development of the reintroduction programme, and the sites and the reintroduced populations have benefited from long-term post-release monitoring and surveillance (King, Reference King2008; King & Courage, Reference King, Courage and Soorae2008; King et al., Reference King, Chamberlan and Courage2012). Consequently, hunting pressure, which was identified as the main cause of the local extirpation of gorillas, has been dramatically reduced (King, Reference King2008; King et al., Reference King, Chamberlan and Courage2012). A total of 51 gorillas (24 males, 27 females) were released between 1996 and 2006, 25 in Congo and 26 in Gabon, comprising 43 rehabilitated wild-born orphans, and one in situ and seven ex situ captive-born (King et al., Reference King, Chamberlan and Courage2005, Reference King, Chamberlan, Pearson and Courage2009, Reference King, Chamberlan and Courage2012). In April 2009 total population sizes were 23 in Congo (comprising 15 wild-born and one in situ captive-born aged 8–22 years, plus seven first-generation offspring aged 6 months–5 years) and 25 in Gabon (16 wild-born and six ex situ captive-born aged 7–13 years, plus three first-generation offspring aged 2 months–1.5 years). Further releases are expected at both sites (King & Courage, Reference King, Courage and Soorae2008).
We used Vortex v. 9.94 (Lacy et al., Reference Lacy, Borbat and Pollak2003) to develop the population model for the reintroduced gorilla populations. Vortex is appropriate for modelling species with low fecundity and long lifespans (Miller & Lacy, Reference Miller and Lacy2005) and is the most commonly used software in published reintroduction models (Armstrong & Reynolds, Reference Armstrong, Reynolds, Ewen, Armstrong, Parker and Seddon2012).
The demographic input parameters we used were based primarily on a combination of the results of the post-release monitoring of both reintroduced gorilla populations (King et al., Reference King, Chamberlan and Courage2012), compared with data on wild, or occasionally captive, western and eastern Gorilla berengei gorillas (Table 1). The calculation of some input parameters required further analysis of the post-release monitoring data from the reintroduced populations, using the same dataset analysed by King et al. (Reference King, Chamberlan and Courage2012), notably those relating to environmental variability (EV). This was calculated following Miller & Lacy (Reference Miller and Lacy2005), using the equation: s EV = √s 2 EV = √ (s 2 TOT – s 2 DS), where s EV = the standard deviation because of EV, s 2 EV = the variance because of EV, s 2 TOT = the total variance across the data, and s 2 DS = the sampling variance because of demographic stochasticity (DS) = (p*(1−p))/(x−1), where p = the mean annual mortality rate and x = the mean annual initial population size. As reproduction, like mortality, is also binary, we used the same method for calculating environmental variability in annual birth rates. We calculated this for 2003–2008, with one year (2006) excluded as it was an outlier showing an abnormally high birth rate that was clearly a function of the low sample size rather than of environmental variability (four females from a single group gave birth within 2 months of each other, from a total of seven females of breeding age with breeding opportunities within the population at that time).
We defined population extinction as only one sex remaining. Inbreeding depression was included in the baseline scenario using the default values in Vortex (3.14 lethal equivalents, with 50% because of lethal alleles). Environmental variation was considered to affect survival and reproduction independently. Reproduction was not considered to be density dependent, and carrying capacity was set at a high level (1,000 individuals per population) to avoid modelling density dependent impacts on population size (although the legally-defined reintroduction sites have a lower carrying capacity, in reality these sites are not isolated from surrounding habitat and we were interested to know the full potential for population growth regardless of legal habitat boundaries). The phenomenon of adult male dispersal from reproductive groups to become solitary and non-reproductive in the longterm (Harcourt & Stewart Reference Harcourt and Stewart2007; King et al., Reference King, Chamberlan and Courage2012) was simulated in the model by specifying the reproductive system as long-term polygyny, and quantified through the mate monopolization parameter (Table 1).
The two reintroduced populations (in Congo and in Gabon) were modelled separately. The initial population sizes, structures and gene diversities were imported from studbooks for each population, as at April 2009 (excluding one recently born infant from each population whose sex was unknown at the time of the model development). We conducted a sensitivity analysis on the Congo baseline model, to identify the key vital rates requiring better estimates, by investigating the impact on the mean stochastic growth rate of using high and low values for various input parameters. For both populations we simulated the baseline model of no further releases, and five scenarios of varying reinforcement strategies (Table 2). The first reinforcement scenario (R1) modelled the inclusion of the gorillas in the pre-release phase of the programme in each country as at April 2009, whereas subsequent reinforcement scenarios modelled hypothetical future releases based approximately on recent rates of arrivals of new gorillas at the rehabilitation centres (King et al., Reference King, Chamberlan and Courage2005, Reference King, Chamberlan, Pearson and Courage2009). A probable scenario of reinforcement of the Congo population was chosen as a baseline for investigating the potential impacts of various catastrophes (Table 3). The first three catastrophe scenarios modelled potential disease outbreaks proposed by primate veterinarians for mountain gorillas Gorilla berengei berengei (Miller & Lacy, Reference Miller and Lacy2005), and the fourth was intended to model potential outbreaks of an Ebola-like virus. Each scenario was run for 1,000 iterations over 200 years (rather than the more frequently used 100 years because of the relatively long generation time of the species).
The results we recorded for each simulation were deterministic population growth rate (deterministic r), stochastic population growth rate (stochastic r), probability of extinction over the 200 year model period (P(E)), mean number of individuals in surviving populations (extant N), and gene diversity (as a percentage of original diversity), plus standard deviations (SDs) as measures of variability.
Sensitivity testing illustrated that the population model was highly sensitive to changes in the input parameters for annual birth rates, for the number of lethal equivalents, and for female annual mortality rates, especially for adults (Table 4). For example, a value of 0.18 for the annual birth rate rather than 0.20 as in the baseline model reduced the mean stochastic r from 0.004 to −0.003, resulting in an increase in the probability of extinction over 200 years from 9.2 to 29.3%. Conversely, increasing the birth rate to 0.22 resulted in a mean stochastic r of 0.010 and an extinction probability of 2.6%.
* B, baseline value
The baseline model resulted in a deterministic population growth rate (r) of 0.016. In the baseline scenario of no population supplementation, the mean stochastic population growth rates (r) were 0.004 ± SD 0.053 and 0.005 ± SD 0.048 for the Congo and Gabon populations, respectively. Over 200 years this resulted in extinction probabilities of 9.2 and 4.9%, mean extant population sizes of 82 ± SD 73.7 and 104 ± SD 80.3, and mean gene diversities of 77.0 ± SD 11.7 and 80.2 ± SD 10.5% for the Congo and Gabon populations, respectively.
For both populations the model predicted that a single reinforcement with the gorillas in the pre-release phase of the programme in each country as at April 2009 (scenario R1) would have a considerable impact on the viability of the populations compared to the baseline scenario, reducing the probability of extinction over 200 years from 9.2 to 4.0% in Congo, and from 4.9 to 2.2% in Gabon (Table 5). Each subsequent reinforcement scenario modelled also improved viability, with both populations showing a 0% probability of extinction and a mean retention of genetic diversity of > 90% with scenarios R4 and R5 (Table 5).
1Mean annual population growth rate; 2Probability of extinction; 3Mean extant population size
The four modelled catastrophe scenarios each had major impacts on population persistence (Table 6). Compared to the probability of extinction of 1.8% for the baseline R2 scenario used, the four modelled catastrophe scenarios increased the probability of extinction to between 13.5 and 99%. Gene diversity was also reduced.
1Mean annual population growth rate; 2Probability of extinction; 3Mean extant population size
Population viability analysis
Our primary goal was to evaluate the long-term success of the western lowland gorilla reintroduction programme on the Batéké Plateau. The results from the baseline population viability analysis suggest that the reintroduced gorilla populations have a reasonable chance of persistence (91 and 95% over 200 years, Congo and Gabon populations respectively) but that this probability could be significantly improved by further releases or reinforcements. However, our sensitivity analysis shows that this prediction can be dramatically altered through apparently small modifications of the input parameters to the model, particularly in birth rates, female mortality rates, and inbreeding depression estimates, and also through the inclusion of hypothetical catastrophic events. Some small modifications in demographic input parameters can increase the probability of persistence considerably, as does reducing the impact of inbreeding depression. Conversely, the inclusion of hypothetical catastrophes led to predictions of likely population extinction in all but one scenario.
In addition to population persistence over a specified time-frame, another aspect of population viability is the maintenance of adequate genetic diversity over the course of several generations (Lacy, Reference Lacy1997; Frankham et al., Reference Frankham, Ballou and Briscoe2002; Goossens et al., Reference Goossens, Funk, Vidal, Latour, Jamart and Ancrenaz2002; Armstrong & Seddon, Reference Armstrong and Seddon2008). Although some reintroduced populations have been established from < 10 founders (Taylor et al., Reference Taylor, Jamieson and Armstrong2005), and small founder populations do not necessarily lead to severe inbreeding depression (Jamieson et al., Reference Jamieson, Tracy, Fletcher and Armstrong2007), most geneticists consider that a large founder population is necessary to ensure sufficient genetic diversity, to avoid the potentially negative effects of inbreeding depression and to the capacity to adapt to environmental change in the long term (Frankham, Reference Frankham2005; Traill et al., Reference Traill, Brook, Frankham and Bradshaw2010; Groombridge et al., Reference Groombridge, Raisin, Bristol, Richardson, Ewen, Armstrong, Parker and Seddon2012; Jamieson & Lacy, Reference Jamieson, Lacy, Ewen, Armstrong, Parker and Seddon2012; Keller et al., Reference Keller, Biebach, Ewing, Hoeck, Ewen, Armstrong, Parker and Seddon2012). Genetic goals in population management often include the retention of 90% of genetic diversity over a specified time period (Frankham et al., Reference Frankham, Ballou and Briscoe2002).
Based on the current composition of both reintroduced populations the baseline model predicted a retention of c. 80% of genetic diversity over 200 years. The fourth reinforcement scenario (of current reinforcement plans plus three subsequent reinforcements of three females and two males each time) was sufficient in both cases to achieve a 90% retention of genetic diversity. We did not include genetic management, which is used for small captive populations to ensure maximum retention of genetic diversity (Earnhardt et al., Reference Earnhardt, Thompson and Schad2004), in our model. With the relatively intensive post-release monitoring techniques practised at both sites (King et al., Reference King, Chamberlan and Courage2012), some level of genetic management may be possible through population manipulation. Given that within gorilla society a few males dominate reproduction, and some fail to reproduce (Harcourt & Stewart, Reference Harcourt and Stewart2007), genetic management in the reintroduced gorilla populations could be effected by manipulating each male's opportunities to breed.
Modelling and reintroduction management
The modelling showed that the populations have the capacity to persist for 200 years, with the probability of persistence and the retained genetic diversity increasing if the populations are gradually reinforced with new individuals over subsequent years. However, the exercise also shows that events beyond the control of management, particularly catastrophes but also factors such as the impacts of inbreeding depression, could jeopardize the populations and lead to their extinctions if they are frequent or severe enough. This conclusion is not unexpected and our results support the current directions in the management of the reintroduction programme, rather than suggesting significant modifications. The quantitative nature of the results does, however, highlight the magnitude of the potential negative impacts of disease-based catastrophes and inbreeding depression, suggesting firstly that reintroduction managers should ensure that pathogen surveillance protocols are regularly updated and applied, and secondly that realistic taxon-specific measurements of inbreeding depression should ideally be more widely available for modelling purposes, given their significant influence for predicting extinction risk (O'Grady et al., Reference O'Grady, Brook, Reed, Ballou, Tonkyn and Frankham2006).
The continued monitoring of the reintroduced populations will facilitate refinement of the model, particularly for highly sensitive input parameters such as birth rates and female mortality rates, and improve confidence in its predictions and its relevance to decision-making. The model could then provide guidance on issues such as the optimum number of individuals required for reinforcement of the reintroduced populations, and whether genetic management through the manipulation of male opportunities to breed is a strategy worth considering. Integration of such an adjustable model into the decision-making process could, if well structured and defined, lead to an adaptive management approach to reintroduction management (Armstrong et al., Reference Armstrong, Castro and Griffiths2007; McCarthy et al., Reference McCarthy, Armstrong, Runge, Ewen, Armstrong, Parker and Seddon2012).
Monitoring and modelling slow-reproducing long-lived species
In reintroduction programmes for slow-reproducing long-lived species, post-release monitoring needs to be undertaken over a relatively long time-frame, to gather even simple data on post-release survival and reproduction, which can give an indication of the initial success of the programme (King et al., Reference King, Chamberlan and Courage2012). To assess long-term success an evaluation of population viability is needed, which requires the development of a population model (Armstrong & Reynolds, Reference Armstrong, Reynolds, Ewen, Armstrong, Parker and Seddon2012; Seddon et al., Reference Keller, Biebach, Ewing, Hoeck, Ewen, Armstrong, Parker and Seddon2012). For long-lived species the collection of the necessary demographic data, particularly mortality rates, could take decades. We were fortunate that our study species has a close relative, the eastern gorilla, one of the best-studied primates (Harcourt & Stewart, Reference Harcourt and Stewart2007; Robbins et al., Reference Robbins, Gray, Kagoda and Robbins2009). We were therefore able to compare our post-release monitoring data with large published datasets for eastern gorillas to verify that our data were realistic and to fill in gaps, particularly concerning adult mortality rates. If such data to develop a realistic population model are not available, reintroduction programmes for other long-lived species would have to wait for sufficient demographic data to be collected either through post-release monitoring or through demographic studies of wild populations.
We thank the Ministry of Forest Economy of the government of Congo, the Ministry of Water and Forests and the National Agency for National Parks of the government of Gabon, and The Aspinall Foundation, UK, for their long-term commitment to and funding of the reintroduction and protected area management projects. We also thank the Wildlife Conservation Society (USA), Florent Ikoli, Mbani Akangala Mankarika and Roland Missilou-Boukaka (Congolese government), and Adrien Noungou, Pierre Ngavoura, Mamadou Ntsoumou and René Bazin Assaly (Gabonese government), for their contribution to protected area management and conservation on the Batéké Plateau. The information presented here results from 2 decades of dedicated work by numerous project staff without whom this programme would not have been possible, and to all of whom we are grateful.
Tony King plans, implements and evaluates reintroduction and conservation projects, particularly in the Republic of Congo and in Madagascar. He is committed to finding locally-relevant strategies for the conservation of threatened species and their habitats. Christelle Chamberlan has managed conservation projects in the Congo and Madagascar for the past 10 years. She has previously studied elephants and buffaloes in Odzala National Park, Congo, rehabilitated orphan chimpanzees in Congo, and worked with mountain gorillas in Rwanda. Amos Courage has been involved in the gorilla reintroduction programme since the first releases in 1996.