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Like human immunodeficiency virus type 1 (HIV-1), simian immunodeficiency virus of chimpanzees (SIVcpz) can cause CD4+ T cell loss and premature death. Here, we used molecular surveillance tools and mathematical modeling to estimate the impact of SIVcpz infection on chimpanzee population dynamics. Habituated (Mitumba and Kasekela) and non-habituated (Kalande) chimpanzees were studied in Gombe National Park, Tanzania. Ape population sizes were determined from demographic records (Mitumba and Kasekela) or individual sightings and genotyping (Kalande), while SIVcpz prevalence rates were monitored using non-invasive methods. Between 2002-2009, the Mitumba and Kasekela communities experienced mean annual growth rates of 1.9% and 2.4%, respectively, while Kalande chimpanzees suffered a significant decline, with a mean growth rate of -6.5% to -7.4%, depending on population estimates. A rapid decline in Kalande was first noted in the 1990s and originally attributed to poaching and reduced food sources. However, between 2002-2009, we found a mean SIVcpz prevalence in Kalande of 46.1%, which was almost four times higher than the prevalence in Mitumba (12.7%) and Kasekela (12.1%). To explore whether SIVcpz contributed to the Kalande decline, we used empirically determined SIVcpz transmission probabilities as well as chimpanzee mortality, mating and migration data to model the effect of viral pathogenicity on chimpanzee population growth. Deterministic calculations indicated that a prevalence of greater than 3.4% would result in negative growth and eventual population extinction, even using conservative mortality estimates. However, stochastic models revealed that in representative populations, SIVcpz, and not its host species, frequently went extinct. High SIVcpz transmission probability and excess mortality reduced population persistence, while intercommunity migration often rescued infected communities, even when immigrating females had a chance of being SIVcpz infected. Together, these results suggest that the decline of the Kalande community was caused, at least in part, by high levels of SIVcpz infection. However, population extinction is not an inevitable consequence of SIVcpz infection, but depends on additional variables, such as migration, that promote survival. These findings are consistent with the uneven distribution of SIVcpz throughout central Africa and explain how chimpanzees in Gombe and elsewhere can be at equipoise with this pathogen.
Gombe National Park is located in northwestern Tanzania, along the eastern shore of Lake Tanganyika. The park’s southern border is located 15 km north of Kigoma. The park covers 35 km2 of rugged terrain, rising from the lakeshore in the west (770 meters above sea level; m.a.s.l.) to the crest of the rift escarpment in the east (1300 to 1600 m.a.s.l.) [15], [17], [18]. As of January 2009, the park provides habitat for 96–100 chimpanzees in three communities: Mitumba (25), Kasekela (57), and Kalande (14–18). Most research has focused on the Kasekela community, which Goodall began studying in 1960 [17]. Efforts to habituate the Mitumba community began in the 1980s and by the mid-1990s most Mitumba chimpanzees could be observed within a distance of 20–30 meters [18]. Efforts to habituate the Kalande community started with a six-month project by C. Gale (December 1968–June 1969), followed by additional attempts in the 1970s and 1980s, which were not successful. However, a monitoring program initiated by E. Greengrass (February 1999–August 2000) and F. Grossmann (September 2000–March 2002) has continued to the present. In this program, researchers have not attempted to habituate chimpanzees, but have instead focused on nest transect surveys (1999–2002), monitoring of phenology trails (2002 – present), and opportunistic sightings of chimpanzees and other wildlife (1999 – present). Since 2002, Tanzanian field assistants trained by Greengrass and Grossmann have continued the monitoring, conducting regular searches of the area for chimpanzees and other wildlife. Fecal and urine sample collection in Gombe began in 2000, with collection of feces starting in Kalande in late 2001. For habituated apes, fecal and urine samples were collected under direct observation [2], [10]; however, this was not possible for most Kalande apes, who were sampled by collecting stool from the forest floor near night nests. When possible, field assistants also collected samples during direct observation, but because of the brief observation times at Kalande, only few such opportunities occurred. Fecal samples (∼20 g) were placed into 50 ml conical tubes, and mixed with equal amounts of RNAlater (Ambion). If the sample was collected under direct observation, the name (if known) or age-sex class was recorded. Time, date, location, and name of collector were also recorded. Specimens from Kasekela were frozen on the day of collection, while specimens from Mitumba and Kalande remained at ambient temperature until transported to the field lab in Kasekela (usually within one week of collection). Samples were shipped at ambient temperatures, then stored at −80°C upon receipt. Between 2000 and 2009, a total of 1,536 fecal samples were collected from all three Gombe communities, 1,153 of which have been reported previously [2]. During the same time period, 341 fecal samples were collected from 26 individuals who resided in Kalande (Table S1). Three Kalande apes transferred to Kasekela or Mitumba during the study years and were previously reported (Ch-071, Ch-098, Ch-099). A fourth female, Ch-108, was sampled in Kasekela, but was apparently visiting rather than transferring, as she has since been sampled in Kalande. All individuals were identified by microsatellite genotyping. A median 4.5 samples were collected for each Kalande chimpanzee (range = 1–75). Table 1 summarizes the number of samples collected in Kalande for each year since 2001. Fecal DNA was extracted as described previously [2], [10], [11] and quantified using real-time PCR [31]. All individuals for whom fecal DNA was available were microsatellite genotyped at autosomal loci as well as typed for sex and mitochondrial haplotype [32], [33]. A total of 116 individuals from the three communities were genotyped at a minimum of 8 of 11 microsatellite loci and were tested for relatedness. We used the likelihood-based program CERVUS 2.0 [34] to identify parent-offspring relationships (Tables S5 and S6). We first examined individuals within the same mitochondrial haplotype for mother-offspring relationships since mitochondrial DNA is matrilineally inherited. Females were only considered candidate mothers if they shared at least one microsatellite allele at each locus. Simulations were run using 100,000 cycles, 1% error rate, and confidence levels of 80% and 95%. The sampling proportions for the simulations were determined by including all genotyped females of a given haplotype with an additional 50% unsampled female candidates included to account for any ungenotyped females from the Kalande community. When a probable mother-offspring relationship was identified, we used the possible mother as the “known parent” in CERVUS to identify potential fathers amongst all sampled males (n = 49) from the three communities using the same simulation conditions. A male was only considered a probable father if, given the genotype of the corresponding mother, he did not have microsatellite allelic mismatches with the genotype of the presumed offspring (Table S6). In some cases, CERVUS assigned a particular candidate as “most likely,” even though a statistically significant parent was not identified. To further validate parent-offspring relationships and identify siblings we also used the microsatellite genotypes to perform KINSHIP analyses [35] (Tables S5 and S6). These analyses tested whether dyads were maternally or paternally related compared to the null hypothesis that they were unrelated. We used KINSHIP to calculate a likelihood ratio for the primary (related) and null hypotheses for each dyad. Given the availability of long-term demographic data in Gombe, we were able to include the identity of known mothers and fathers for numerous individuals within the population, which improved the likelihood calculations. Nonetheless, when individuals did not have identified parents, KINSHIP was unable to differentiate between maternal and paternal lineages among autosomal loci. We also used KINSHIP to estimate the relatedness of individuals, R, defined as the probability that the same allele found in two individuals is identical by descent, taking into account the frequency of the allele in the population [35]. For diploid, sexually reproducing species, R should be 0.5 for parent-offspring and full-sibling relationships, and 0.25 for half-sibling and grandparent-grandoffspring relationships. Departures from these expected values may occur when calculating R from a relatively small number of loci, such as the 8 to 11 loci that were used here (which were nonetheless sufficient to correctly assign close relationships, e.g., parent, half-sibling; [36]). Thus, we obtained calculated estimates for R that were close to (but not precisely equal to) 0.5 for parental relationships (mother-offspring: n = 12, median = 0.43, range = 0.21–0.62; father-offspring: n = 7, median = 0.40, range = 0.24–0.74) and close to zero for unrelated individuals (n = 17, median = 0.06, range = −0.22–0.38). Tables S5 and S6 summarize all CERVUS and KINSHIP results, with particular focus on SIVcpz-infected chimpanzees from Kalande. These results are conservative in that we only report results for dyads that are (i) within the same mitochondrial haplotype and also lack microsatellite allelic mismatches; and/or (ii) significant relationships from CERVUS and the corresponding KINSHIP analyses for these dyads; as well as (iii) results for any dyad for which KINSHIP found a strongly significant relationship (P1. Annual fertility is b and the reduction in fertility from SIVcpz infection is given by 0<α≤1. The annual rate of increase is the sum of average annual survivorship and annual fertility [e.g., 46]. Average survivorship and fertility are both mixtures for infected and uninfected rates with the mixing fraction given by f. An identity from survival analysis says that annual survival probability . The annual rate of increase in our mixed population thus was: Setting λ = 1, we can solve for the prevalence, denoted f*, at which deterministic population stationarity is maintained. This value is: Whenever , the average birth rate exceeds the death rate and the population increases (in the absence of stochasticity). The average adult female mortality rate is approximately μ = 0.04. The interbirth interval for daughters is about 11 years and recruitment to age at first reproduction is approximately 50%. This makes “annual fertility” (which for this minimal model is a composite of fertility and recruitment) approximately b = 0.05. We employed a fully event-driven stochastic model of infection dynamics and demography. Using the direct method of Gillespie [47], we ran the event-driven model from the following master equations: where is the number of susceptible individuals of the ith sex ( is its time derivative), is the number of infected individuals of the jth sex ( is its time derivative), is the mortality rate of the ith sex, b is the birth rate (fertility times recruitment), is the effective contact rate of transmission from sex j to sex i, is the mortality multiplier (the degree to which SIVcpz infection increases annual mortality risk), and is the fertility multiplier (the degree to which SIVcpz infection decreases annual fertility). Migration does not appear in the master equations (though it does in the stochastic realizations) since the expected net migration is zero. Estimating the probability of SIVcpz transmission per coital act. Preliminary evidence indicated that SIVcpz is transmitted primarily by sexual contact [2]. We estimated the effective contact rate [48], [49] for the epidemic model using the approach outlined by Morris [50]. Specifically, in a structured population, the effective contact rate for transmission from the jth class to the ith class in the model is where is the contact rate (i.e., number of copulations) of class i, is the conditional probability that individuals of class i have contact with class j individuals (which for the case of a two-sex heterosexual model is for all i,j), is the probability of transmission from j to i conditional on contact, and is the population size of class j individuals. We used two approaches to estimate τ, the probability of transmission. First, given the possibility that SIVcpz transmission may occur at similar rates to HIV-1, we used the estimate of per-coital-act transmission probabilities for HIV-1 as determined by Gray and colleagues [23] in sero-discordant couples in Rakai, Uganda, using a generalized linear model with a complementary log-log-link. We used the unadjusted (overall) estimate of transmission, which is = 0.0011. Second, given the possibility that differences in mating patterns and physiology between humans and chimpanzees may result in different transmission rates, we estimated the probability of SIVcpz infection per coital act for Gombe chimpanzees. For a two-sex, completely heterosexual network, the basic reproduction number is the geometric mean of the off-diagonal elements of a 2×2 next generation matrix [48]. In particular, where τ is the probability of transmission per coital act, CFM is the expected number of contacts of infectious males with susceptible females, CMF is the expected number of contacts of infectious females with susceptible males, Si is the expected number of susceptibles of the ith sex, ρ is the degree to which SIVcpz infection increases mortality, and μi is the mortality rate of the ith sex. The epidemiological contact rate between individuals of sex i and sex j is given by Morris [50]: where ci is the expected number of matings for sex i, πij is the conditional probability that, given an observed i mating, it will be with class j (obviously πij = 1 for a purely heterosexual model like this), and Tj is the population size of class j. We solve equation 1 for τ yielding: We estimated these values using empirically determined SIVcpz prevalence, mating and mortality data from the habituated chimpanzees of the Kasekela community. We can estimate R0 from the prevalence data using standard epidemiological theory [48], [49] as the inverse of the equilibrium fraction susceptible. The prevalence for the Kasekela community has remained remarkably stable for the years 2002 to 2009. We therefore used the median prevalence (10.8%) as an estimate of the current equilibrium fraction. This yielded R0 = 1.21. The number of susceptible individuals in the population was calculated based on the number of individuals of each sex that were observed copulating each calendar year. Individuals were considered susceptible if they were not infected at the start of the calendar year. All males that were 8 years old at the start of the calendar year, or who turned 8 during the calendar year, were included. At Gombe, males begin copulating with full intromission as early as four years old, and have been observed to ejaculate by age nine [51]. To be conservative, we included males starting at age eight, when testicular enlargement is usually first noticeable [51]. All females who had fully tumescent sexual swellings or who were recorded copulating during the calendar year were included. We estimated CFM and CMF using behavioral data (2002–2007) to estimate copulation rates (copulation data from 2008–2009 are not yet available for analysis). Because we are interested in how frequently individuals copulate in years when they are sexually active, we included only years in which the individual was recorded mating at least once. We calculated each individual's annual copulation rate as the number of completed copulations recorded for that individual during his or her focal follows, divided by that individual's total observation time (in hours) as a focal subject for that year. We estimated the total number of copulations for each year by multiplying the hourly copulation rate by 12 hours of activity per day times 365.25 days per year (98.7% of all copulations recorded from 1976 through 2007 occurred between 6:00 am and 6:00 pm). We then took the mean of each individual's annual copulation rates to calculate the individual's overall copulation rate. During the six-year study period, most males were sexually active for all six years (median = 6 years, range = 1 to 6 years). Males copulated a median 0.04/hr (range: 0.01–0.17/hr), or 209 times per year (range = 100–356/yr; n = 14 males). In contrast to males, female chimpanzees generally do not copulate when pregnant or lactating, and thus may not copulate at all for several years at a time, from the start of a pregnancy to the weaning of the infant. Thus, from 2002–2007, each female was sexually active during a median of one year (range = 1–5). Moreover, even in years in which females are sexually active, their copulation rates vary according to their ovarian cycle. Females generally copulate only during the 14 days of the 35-day cycle during which they have a fully tumescent sexual swelling [52]. Females may also copulate more frequently during conception cycles than nonconception cycles; Emery Thompson [52] found that parous females at two sites in Uganda copulated a median 0.40/hr in nonconceptive cycles (n = 18) and 0.85/hr in conceptive cycles. During focal follows in years in which they were sexually active, Gombe females copulated a median 0.41/hr (range: 0.083–1.0/hr; n = 21 females). This rate may be an overestimate, because females with full sexual swellings attract or are attracted to parties with many males, making it easier for observers to follow them. Assuming females copulated a this rate (0.41/hr) rate during the median 28.6% of days per year on which they had full swellings, females were estimated to have copulated a median 508 times per year. The mortality multiplier, ρ, was previously estimated to be approximately 10 to 16 [2]. To err on the conservative side, we used 10 as our highest value. We also used ρ = 5, because this value lies towards the lower end of the 95% confidence interval for ρ [2]. Annual mortality was set at μF = 0.04 for females and μM = 0.05 for males, based on the mean of adult age-specific mortality for each sex [53]. Together, these values yielded τ = 0.0015 for ρ = 10, and τ = 0.00077 for ρ = 5. Intercommunity migration. Both immigration and dispersal were incorporated into the stochastic model. A key question was whether migration could mitigate stochastic small-population effects of extinction, both of chimpanzee populations and of SIVcpz infections. It is expected that migration will rescue populations if there is more immigration than dispersal on average. However, in a closed collection of populations, the two will tend to balance. Consequently, we parameterized migration so that the expected net migration rate was zero. We modeled single populations with (i) emigration occurring at a rate proportional to the size of the female population and (ii) immigration occurring at fixed rates from outside the population. Migration between communities is typically observed in adolescent females [51], although as noted in the Result section, older females also sometimes migrate. Including secondary transfers, females of known and estimated ages at Gombe had a mean age of migration of 15 years. Because our models are not age-structured, to parameterize migration, we followed a common practice for such models, by assuming that sojourn times are exponentially distributed with a rate parameter of the inverse of the mean time to event [54]. We thus parameterized the migration rate as 1/15 = 0.067. At Gombe, 30% of females who transferred when their infectious status was known were SIVcpz infected. SIVcpz prevalence rates at field sites in Cameroon have ranged from 5% to 35% [11], [55], suggesting that Gombe is towards the higher end of those prevalence rates. To model the range of likely conditions for chimpanzees, we thus ran both low (pF = 5%) and high (pF = 30%) probabilities that incoming females are infected with SIVcpz. Running simulations. Simulations were constructed in Matlab using code modified from Keeling and Rohani [56]. We ran 10,000 replicate simulations, using the demographic parameters described above. Simulated populations were based on the observed number of sexually active individuals in Kasekela, which included all males age eight years and older, based on minimum age of producing ejaculate, and all females who were observed with fully tumescent sexual swellings and/or were observed copulating during that year. The number of initially infected males and females were taken as Poisson random numbers with rate parameters of λ = 1 and λ = 3, respectively. The initial number of susceptible individuals was then the difference between Poisson random numbers with rate parameters of λF = 18 and λM = 14 and the realized values of initially infected females and males. We ran simulations under 12 sets of different starting conditions, in which we varied the following parameters: migration (with or without); τ, the probability of transmission per coital act (high, τ = 0.0015, medium, τ = 0.0011, and low, τ = 0.0008, with the high and low values based on SIVcpz data and the medium value based on HIV-1 data), ρ (high, ρ = 10, or low, ρ = 5), and when migration was included, with probabilities that immigrating females would be infected of 5% and 30%. In the estimates derived from SIVcpz data, the value of τ varies directly with the value for ρ. In contrast, the estimate derived from HIV-1 data is independently derived, so we ran simulations with both high and low values of ρ with that estimate of τ. Newly derived SIVcpz sequences have been deposited in GenBank under accession numbers {"type":"entrez-nucleotide","attrs":{"text":"GU992204","term_id":"291501813","term_text":"GU992204"}}GU992204 and {"type":"entrez-nucleotide","attrs":{"text":"GU992205","term_id":"291501815","term_text":"GU992205"}}GU992205.
