Open Access

Genetic censusing identifies an unexpectedly sizeable population of an endangered large mammal in a fragmented forest landscape

BMC Ecology201515:21

https://doi.org/10.1186/s12898-015-0052-x

Received: 9 July 2015

Accepted: 15 July 2015

Published: 25 August 2015

Abstract

Background

As habitat degradation and fragmentation continue to impact wildlife populations around the world, it is critical to understand the behavioral flexibility of species in these environments. In Uganda, the mostly unprotected forest fragment landscape between the Budongo and Bugoma Forests is a potential corridor for chimpanzees, yet little is known about the status of chimpanzee populations in these fragments.

Results

From 2011 through 2013, we noninvasively collected 865 chimpanzee fecal samples across 633 km2 and successfully genotyped 662 (77%) at up to 14 microsatellite loci. These genotypes corresponded to 182 chimpanzees, with a mean of 3.5 captures per individual. We obtained population size estimates of 256 (95% confidence interval 246–321) and 319 (288–357) chimpanzees using capture-with-replacement and spatially explicit capture–recapture models, respectively. The spatial clustering of associated genotypes suggests the presence of at least nine communities containing a minimum of 8–33 individuals each. Putative community distributions defined by the locations of associated genotypes correspond well with the distribution of 14 Y-chromosome haplotypes.

Conclusions

These census figures are more than three times greater than a previous estimate based on an extrapolation from small-scale nest count surveys that tend to underestimate population size. The distribution of genotype clusters and Y-chromosome haplotypes together indicate the presence of numerous male philopatric chimpanzee communities throughout the corridor habitat. Our findings demonstrate that, despite extensive habitat loss and fragmentation, chimpanzees remain widely distributed and exhibit distinct community home ranges. Our results further imply that elusive and rare species may adapt to degraded habitats more successfully than previously believed. Their long-term persistence is unlikely, however, if protection is not afforded to them and habitat loss continues unabated.

Keywords

Habitat fragmentation Genetic census Ecological corridor Chimpanzee Population estimate Pan troglodytes Mark recapture

Background

Habitat loss and fragmentation are key threats to the survival of many species [1], with global deforestation resulting in the majority of remaining forest lying within 1 km of a forest edge [2]. Fragmentation can isolate populations, thereby reducing genetic diversity and population viability, which may result in local extinctions [35]. As wildlife populations face increasing anthropogenic threats, there is growing urgency to better understand how species respond to environmental disturbances. Although degraded habitats are often thought to have limited conservation value, many threatened species inhabit such environments [6]. Riparian forest fragments in particular can offer suitable habitat, providing dense resources to support wildlife [7, 8]. In addition, fragmented forests can sustain connectivity by linking larger populations, thereby enhancing gene flow and population viability [911]. Therefore, the potential of fragmented habitats to support viable populations must be carefully considered alongside the peril they pose to wildlife.

Large-bodied, wide-ranging mammals such as great apes are among the taxa most affected by growing habitat fragmentation. These species often live in unprotected areas, which are particularly vulnerable to forest loss and fragmentation [12, 13]. In East Africa, deforestation has led to increasing habitat fragmentation and poses a primary threat to the survival of eastern chimpanzees, Pan troglodytes schweinfurthii [14]. Eastern chimpanzees inhabit lowland and montane forest, woodland, savanna, and swamp forest habitats throughout various parts of East and Central Africa, with much of their current range occurring outside protected areas [14]. Three-quarters of chimpanzees in Tanzania are estimated to live outside national parks [15]. In Uganda, logging has led to a 37% reduction in forest cover between 1990 and 2010 [1, 16], and much of this deforestation occurred outside protected areas, leaving chimpanzees in such habitats vulnerable to local extinction [2, 17]. Similar patterns have also been reported for chimpanzees in West Africa [12, 18].

Because chimpanzees are an endangered species [19], it is essential to better understand their ability to persist in fragmented and degraded habitats. Moreover, precise estimates of the sizes and distributions of remaining populations are needed in order to establish research priorities and conservation management strategies. Such estimates can be challenging to obtain, however. Chimpanzee habituation allows for direct monitoring and hence precise censuses, but is a lengthy process which is necessarily restricted to small numbers of individuals, and may not be ethically appropriate or logistically feasible for many populations [20, 21]. Nest count surveys can be used to estimate the distribution and abundance of unhabituated chimpanzee populations. However, these survey methods may be inaccurate and lack the precision necessary to determine trends in population size [2224]. Such studies are also arduous to carry out, as commonly used nest count methods rely on data regarding nest decay rates and nest building and re-use rates, which can be highly variable and are often unknown locally [22, 23, 25, 26]. Recently, camera trapping and passive acoustic monitoring have also been utilized to census apes [2729]. However, these techniques are still in their infancy, while methods for efficiently automating individual identification are still in development [15, 30].

The challenges of accurately and precisely enumerating chimpanzee populations are similar to those posed by surveys of other rare and elusive mammal populations, including bears [31], gorillas [3234], African elephants [35], Eurasian otters [36], and giant pandas [37]. These challenges have led to the widespread implementation of genetic censusing (e.g., in chimpanzees [3840]), which relies on the characterization of individual DNA profiles derived from noninvasively collected samples [41]. The minimum number of individuals using the surveyed area is determined by the number of unique profiles, and resampling frequency can be used to estimate the number of animals that went undetected [42, 43].

Standard approaches for genetic censusing have relied upon accumulation curves and Bayesian estimators, along with more recent “capture with replacement” (capwire) models [4345]. However, the population size estimates these methods provide cannot be converted to density estimates except by collecting ancillary data or making restrictive assumptions [46, 47]. Density is generally a valuable parameter because it can be compared across populations of varying size and geographic scope, and used as an indicator for behavioral ecology and conservation questions relating to, for example, resource density, group structure and dynamics, and hunting pressure [31, 48, 49]. Recently developed spatially explicit capture–recapture (SECR) models allow the density of geographically open populations to be estimated directly from spatially-referenced detections of individuals, by modeling probability of detection as a (usually decreasing) function of the distance between detectors or areas searched and individuals’ centers of activity [5053]. SECR models are robust to spatial gaps in data collection [50, 52], which are common when sampling elusive species in degraded or mixed habitats.

In western Uganda, the approximately 1,200-km2 landscape of the Northern Albertine Rift separating the Budongo and Bugoma Forests illustrates such a degraded mosaic habitat. The government-owned Budongo and Bugoma Forest Reserves are each inhabited by over 600 chimpanzees, together composing approximately one-quarter of the estimated total chimpanzee population in Uganda (5,000 individuals [54]). The corridor between these forest blocks is a human-dominated landscape comprising mosaic riparian forest with villages, agricultural lands, and natural grasslands [55]. Most forests in this habitat are privately owned, but a few small government-owned forest reserves are present. The small forests in this region have been targeted for potential corridor enhancement given the vital role they may play for gene flow in numerous species throughout this region [56].

Despite the conservation potential of this habitat, few studies have examined the population size and distribution of its chimpanzees. A nationwide chimpanzee census used a nest count survey of forest fragments near the Bugoma Forest to extrapolate an estimate of ~70 chimpanzees in the corridor region [54]. Later, McLennan [55] found evidence of chimpanzees throughout the corridor habitat and estimated a total regional population of up to 260 individuals, an extrapolation derived from the estimated density of one chimpanzee community (Bulindi) in the corridor area [55]. Given the potentially vital role of this chimpanzee population in maintaining gene flow among chimpanzees of the Northern Albertine Rift, it is important to better understand the size and distribution of this population. The goal of this study was to use genetic censusing techniques to estimate the population size and distribution of this corridor population of chimpanzees in western Uganda. To do so, we estimated chimpanzee density using a spatially explicit model, as well as estimating abundance using both capwire and spatially explicit models. We further examined the number and spatial distribution of putative chimpanzee communities by analyzing the clustering of co-sampled genotypes. Additionally, because chimpanzees typically exhibit male philopatry and female dispersal, we examined the clustering of Y-chromosome haplotypes, which are paternally inherited and therefore can be used to reveal community affiliations [38, 40, 57].

Methods

Study area

Data were collected in Hoima and Masindi Districts, Uganda, in the corridor region between the Budongo and Bugoma Forests (1°37′–1°68′N and 31°1′–31°6′E; Figure 1). Both forests are classified as medium-altitude, moist semi-deciduous forests [58, 59]. The Budongo Forest Reserve covers 428 km2, while the Bugoma Forest Reserve measures 411 km2 [54, 60]. The region between these forests, which broadly measures approximately 40 km long by 30 km wide, is a mosaic habitat composed of agricultural land, villages, riparian forest fragments, and grasslands. These riparian forests occur mainly along the Waki, Hoima, and Rwamatonga Rivers and their tributaries [55]. Pollen and climatic data indicate that the Budongo Forest has been a standalone forest block for thousands of years, and the region to its south likely existed as a natural mosaic habitat throughout that time [61]. In recent decades, however, human populations have grown substantially, leading to the extensive conversion of unprotected riparian forests for commercial and subsistence agriculture [16, 62].
Figure 1

Map of the study area in Uganda. The inset map displays the landscape’s location within Uganda. Green indicates forest cover during the study period.

Genetic census methods

Chimpanzee fecal samples were collected noninvasively throughout the study area from October through December 2011 and October 2012 through September 2013. Samples were collected throughout the region, with a focus on searching riparian forest fragments for evidence of chimpanzees. Information on chimpanzee presence was also provided by McLennan [55] and by informal discussion with local inhabitants. It was not practical to employ strictly systematic survey methods in this human-dominated habitat comprising mainly privately owned farms and villages. Instead, search effort in forest fragments was centered around village boundaries, which typically encompass settlements, farmland, and privately owned forests. In accordance with local customs, prior to searching a forest fragment we first gained permission from the chairperson of the village in which the forest fragment was located, and from individuals who identified themselves as landowners of the forest fragment. We used satellite imagery to identify the forest fragments located within the boundaries of a given village, and visited accessible and permitted forest fragments within the boundaries of that village. We divided the study area into a grid of 1 km by 1 km cells and recorded when any part of each cell was searched (Figure 2).
Figure 2

Map of search effort over the study area. One-km2 grid cells are overlaid over the corridor region between the Budongo and Bugoma Forests. Gray shading indicates relative search effort in each cell, with the number of search occasions (days) binned. Search effort was not available in the Bulindi area, where samples were collected during concurrent long-term research.

Chimpanzee fecal samples were typically easy to identify because of (1) their locations under chimpanzee nests and along trails, (2) their characteristic size, shape, and odor, and (3) the absence of other sympatric large-bodied nonhuman primates. Although olive baboons (Papio anubis) produce dungs that can superficially resemble those of chimpanzees (pers. obs.), they have been eradicated from many parts of the study area. When we suspected that a fecal sample was produced by a baboon, a small portion of the dung was collected for genetic analysis, while the remainder was collected separately and washed later that day in a 1-mm mesh sieve. Fecal samples of baboons were easily distinguished from those of chimpanzees by their differing odor and dietary components when washed through a sieve [63]. Any sample suspected to originate from a baboon rather than a chimpanzee was thus discarded following washing (n = 5).

Target sample sizes were determined by roughly estimating the spatial area of a putative chimpanzee community home range, based on direct and indirect evidence of chimpanzee presence, then multiplying by the previously estimated density of chimpanzees in the Bulindi study community within the corridor region (0.66 chimpanzees per km2 [55]). This estimate was then tripled to determine a target number of samples to be collected within that area, since at least three times the number of samples as expected individuals has been recommended to achieve a narrow confidence interval for population size estimates using mark-recapture methods [33, 43, 44]. Because additional information on chimpanzee presence was gained over the course of the study period, target sample sizes were adjusted as necessary. To help achieve this sampling goal and to ensure adequate resampling across fission–fusion chimpanzee communities, we attempted to search forests a minimum of once every 3 months, except where local research permissions were granted only for a limited time period.

We collected samples under nests and opportunistically along chimpanzee trails and at feeding sites. For each sample collected, a GPS waypoint was recorded with a Garmin GPSMap® 60CSx. We recorded samples with unique identification numbers corresponding to GPS waypoints, and with party association data when applicable. Samples were recorded as belonging to a party when two or more same-age samples were collected within 30 m of each other. Distances were determined using GPS data and, when necessary, a laser rangefinder to ensure accuracy. We avoided collecting two samples under the same nest or in close proximity on trails, due to the likelihood of collecting redundant samples from the same individual and the possibility that closely deposited samples may have cross-contaminated each other. Samples were collected and stored according to the two-step ethanol-silica method described in Nsubuga et al. [64].

Data collection was carried out with the permission of the Uganda National Council for Science and Technology, the Uganda Wildlife Authority, and the National Forestry Authority of Uganda. Additional permissions were granted by local landowners where applicable, as described above. Because fecal sample collection was entirely noninvasive and required no contact with the chimpanzees, ethical consent was not necessary for this project.

DNA extraction and amplification

Samples were stored in the field for up to 6 months prior to arrival at Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, where they were then stored at 4°C prior to extraction. DNA was extracted using either the GeneMATRIX Stool DNA Purification Kit (Roboklon) according to manufacturer’s instructions or the QIAmp Stool kit (QIAGEN) with minor procedural adjustments [64].

We used autosomal microsatellite loci to determine individual chimpanzee genotypes. To do so, each DNA extract was first evaluated by simultaneously amplifying three autosomal microsatellite loci, along with an X-Y homologous segment of the amelogenin gene, used for sex determination [65], in a one-step multiplex polymerase chain reaction (PCR) (Table 1). For each reaction, we used 0.5 μL 2× Type-It Multiplex PCR Master Mix (QIAGEN) and 2 μL template DNA with the following optimized concentrations of each forward labeled and nested reverse primer [66, 67]: 0.03 mM amelogenin, 0.15 mM D18s536, 0.32 mM D12s66, and 0.30 mM D1s1622 in a total 10-μL reaction volume. Each PCR consisted of DNA extracts, as well one to two negative controls from each extraction, in four independent reactions. In addition, to monitor for consistency and possible contamination as is prudent when working with low concentration DNA derived from noninvasive samples, each PCR included one positive control from a chimpanzee extract with a known genotype and seven negative controls, which consisted of purified H2O instead of DNA. A PTC-225 Thermal Cycler (MJ Research) was used for PCR thermocycling as follows: denaturation for 5 min at 95°C; 45 cycles of 30 s at 95°C, 90 s at 58°C, and 30 s at 72°C; and a final extension for 30 min at 72°C, followed by incubation at 10°C. Each PCR product was then diluted 1:30 with purified H2O, and 27.4 µL of a 1:135 dilution of ROX labeled GENESCAN 400HD (Applied Biosystems) and H2O was added to size alleles relative to an internal standard. PCR products from all four loci were then electrophoresed using an ABI PRISM 3100 Genetic Analyser. We used GeneMapper version 3.7 (Applied Biosystems) to analyze the data.
Table 1

Autosomal and Y-chromosome microsatellite loci used in this study

Microsatellite locus

No. alleles

No. individuals typed

Autosomal

 D1s1622*

8

180

 D12s66*

11

181

 D18s536*

7

174

 D1s1656

14

189

 D2s1326

10

182

 D3s2459

9

193

 D3s3038

9

183

 D4s1627

9

179

 D5s1457

8

195

 D5s1470

9

188

 D7s817

9

188

 D7s2204

8

179

 D10s676

7

188

 D11s2002

7

188

Y-chromosome

 DYs439

2

74

 DYs469

3

76

 DYs510

2

74

 DYs517

2

76

 DYs520

4

76

 DYs588

2

76

 DYs612

5

76

 DYs630

3

76

 (DYs392)

1

N/A

 (DYs502)

1

N/A

 (DYs533)

1

N/A

 (DYs562)

1

N/A

 (DYs632)

1

N/A

Asterisks indicate loci included in the single-step test multiplex. Y-chromosome loci indicated in parentheses were tested but were not variable and thus were not used further.

DNA extracts that reliably amplified at a minimum of 3 of the 4 loci in at least 3 independent amplifications were then genotyped in triplicate at an additional 11 autosomal microsatellite loci (Table 1). Extracts that failed to meet these criteria were not amplified further. The additional 11 loci were amplified in a two-step multiplex PCR procedure as described in detail in Arandjelovic et al. [66].

At each locus, heterozygous genotypes were confirmed by observation in at least two independent reactions [66, 68]. Homozygous genotypes were confirmed when observed in a minimum of three independent reactions. Individual loci that failed to meet these criteria were instead coded with asterisks and were excluded from analyses. To further ensure that apparent homozygotes were not the result of allelic dropout, we calculated allelic dropout rates by locus after recording all alleles and confirmed that a maximum of two replicates was required at any locus to confirm homozygosity with 99% certainty (Additional file 1) [68, 69]. Thus, we exceeded this threshold and ensured minimal allelic dropout by confirming homozygotes only when alleles were observed consistently in three reactions.

Determination of Y-chromosome haplotypes

To determine Y-chromosome haplotypes, we first used a two-step multiplex PCR to assess the variability of 13 human-derived Y-chromosome microsatellite loci in a test set of 29 male individuals (Table 1) [57, 70]. Eight loci were polymorphic, with at least two alleles present. Thus, the remaining 47 males were typed at only these eight variable loci, which is similar to the number of variable Y-chromosome microsatellite loci found in various other studies of chimpanzees [38, 57, 71, 72], bonobos [73], western lowland gorillas [74, 75], and humans [7678].

Discriminating chimpanzee genotypes

Individual chimpanzee genotypes were distinguished using an identity analysis in CERVUS 3.0.7 software [79]. Using the allele frequencies of the study population, we determined the minimum number of loci necessary to achieve a PIDsib < 0.001, which would allow us sufficient power to distinguish among genotypes and determine with statistical confidence that two matching genotypes from different samples originate from the same chimpanzee rather than from full siblings. Matching genotypes were assigned a consensus name and composite genotype data. Up to four mismatches were permitted to flag potential matches despite genotyping errors. Any mismatch was therefore either resolved as a true match with corrected errors or as a true mismatch comprising distinct genotypes. For rare instances in which genotypes matched with PIDsib > 0.001, the less complete of the two genotypes was eliminated from further analysis.

Assignment of putative communities and Y-chromosome haplotype distributions

Putative chimpanzee communities were defined according to the spatial clustering of co-sampled genotypes. In other words, genotypes found in association with other genotypes, e.g., as part of the same nest group, were assumed to belong to members of the same community. Further, additional lone samples from those individuals, such as samples found singly on chimpanzee trails, were inferred to lie within the home range of that individual’s community [38]. Using spatial data from these genotype clusters, we constructed 100% minimum convex polygons using the Minimum Convex Polygon Plugin for QGIS version 2.4.0 software [80] to represent the minimum home ranges of communities based on genotypes found in association. Additional genotypes found within these polygons were also assumed to originate from members of the same community, since extensive spatial overlap among territories is generally not expected [8183]. Y-chromosome haplotype distributions were analyzed using a median joining network constructed in Network 4.6.1.3 Software (Fluxus Technology Ltd), and were mapped according to putative community distributions to determine whether spatial clustering of Y-chromosome haplotypes occurred in agreement with putative community distributions.

Abundance estimation

We estimated total and community-specific population sizes using capture with replacement (capwire) models [44]. We used a likelihood ratio test to evaluate whether the “even capture” model (ECM), which assumes all individuals have an equal likelihood of capture, or the “two innate rates” model (TIRM), which allows for individual heterogeneity, provided a better fit to each data set. We expected capture probabilities to vary among individuals due to spatially and temporally variable search effort and possibly other factors, so we selected the TIRM when the P-value for the test was <0.10. Where the TIRM was selected we tested whether partitioning the data into three groups further improved the fit. The test statistic used was the ratio of multinomial log likelihoods for a two-class vs. a three-class multinomial distribution of the capture counts [84, 85], and was evaluated at an alpha level of 0.05. Confidence intervals were estimated by parametric bootstrap [44].

We also estimated chimpanzee density and population size using SECR models for area searches [52]. Search area polygons were defined as the perimeter of aggregations of adjacent, searched grid cells, or as individual cells if no adjacent cells were searched. We defined a contiguous region of integration as a 3-km buffer around these polygons, and verified that using a larger region did not affect estimates of model parameters. We defined two different integration meshes or “habitat masks” within this region in order to estimate densities both across the fragmented landscape and within the forest fragments. One mask treated the entire region of integration as suitable habitat where individuals’ activity centers could occur; for the other, we used spatial data describing forest cover [86] to exclude deforested areas from the mask. Multiple detections of the same individual were modeled as counts during a single sample [87]. Temporal variation in search effort was modeled as the average number of visits to the grid cells included in each search area polygon [88]. We assumed detectability declined with distance according to a half normal detection function, and that home range center locations were Poisson-distributed. We estimated detection parameters by maximizing the conditional likelihood for area searches, and density as a derived parameter from the fitted model [50, 52, 53]. We estimated population size by extrapolating the estimated density within forest fragments across forested habitat within the region of integration [89] (Figure 3).
Figure 3

Genotyped sample collection locations across the study area. Not all samples are visible due to map scaling. The black line indicates the region of integration used in the SECR model. Samples outside the region of integration were collected in Siiba Forest Reserve and were excluded from analysis.

All models assumed that (1) the population was demographically closed during sampling, (2) detections were independent events, and (3) individuals were correctly identified. Capwire models further assumed (4) geographic closure, and (5) that all individuals in the population of interest were at risk of detection. SECR estimates did not rely on assumptions 4 or 5 above, but assumed (6) that animals occupied approximately circular home ranges, the central location of which was fixed during sampling [51].

Analyses were performed in R version 3.1.2 [90] employing functions implemented in the “capwire” [84], “secr” [91], and dependent R packages.

Results

Genetic sampling and discriminating individual chimpanzees

We collected a total of 865 fecal samples over 633 km2 during the study period (Figure 3). Of these, 662 (76%) amplified reliably at a minimum of three of four test loci and were thus genotyped at an additional 11 loci. Based on our allele frequencies, we calculated that comparison at a minimum of nine loci was necessary to obtain a PIDsib < 0.001 and thus confidently determine that identical genotypes originated from the same individual rather than two different individuals, including for example full siblings. Of the 662 genotypes, 459 matched exactly to one or more other genotypes and were merged to create consensus genotypes. An additional five genotypes were removed from analysis because they matched other genotypes with a PIDsib > 0.001. The final genotype list consisted of 128 individuals identified in multiple samples (range 2–12) and 68 individuals genotyped once. For the analyses presented here, we removed 16 genotypes representing 14 individuals from a chimpanzee community in Siiba Forest Reserve, a continuous forest located to the south of the Budongo Forest (Figure 3). Since these genotypes originated from few samples in an under-searched area of continuous forest habitat, they were not informative or representative of the study population. The remaining genotypes represented 182 individuals, of which 111 (61%) were identified as female and 71 (39%) as male (Additional file 2). Consensus genotypes for these individuals were 95% complete, with 134 individuals typed at all 14 loci. Nine individuals were genotyped at fewer than nine loci, but their genotypes did not match any others and thus were retained in the data set. The mean number of captures per genotyped individual was 3.5.

Putative chimpanzee communities and Y-chromosome haplotype distributions

By grouping genotypes from samples found together we found ten spatial clusters that were geographically distinct from one another, thus suggesting the presence of at least nine potential communities in the study area, along with one additional cluster, Kiraira. Community-specific population sizes estimated using capwire ranged from 5 to 48, and totaled 244 (Table 2). Data were insufficient to evaluate the fit of different models to data from Kiraira, and the upper confidence limit under the ECM was equal to the maximum population size we provided when fitting the model, indicating estimation problems. Figure 4 displays the distribution of putative communities.
Table 2

Community-specific capwire estimates

Group

n

N (groups)

95% CI

Monitoring estimate

Bulindi

17

19 (2)

17–21

19

Kasokwa

8

8 (1)

8–9

15

Kasongoire

28

38 (3)

31–56

34

Katanga

26

48 (1)

31–83

 

Kiraira

5

5 (1)

5–200

 

Kiryangobe

13

15 (2)

13–20

 

Kityedo

16

18 (2)

16–21

 

Kyamuchumba

11

13 (1)

11–19

 

Mukihani

25

46 (2)

36–70

 

Wagaisa

33

34 (2)

33–38

 

Group-specific total

182

244

  

Overall total

182

256 (3)

246–321

 

Numbers of unique individuals genotyped (n) and population sizes (N) are shown with 95% confidence intervals (CI) for each putative chimpanzee community in the study area. The numbers of groups of chimpanzees with different probabilities of detection included in the estimate model appear in parentheses following the abundance estimate. Monitoring estimates refer to the number of chimpanzees reported during the study period for communities monitored for research or conservation (provided via pers. comm. as follows: Bulindi, Matthew McLennan; Kasokwa, Janette Wallis; Kasongoire, Geoffrey Muhanguzi). The sum of group-specific estimates, and the estimate of total population size obtained by pooling data from all communities for analysis, appear at the bottom.

Figure 4

Putative chimpanzee communities (a) and associated Y-chromosome haplotypes (b). a Minimum convex polygons (MCPs) for genotyped samples found in association. Names of putative chimpanzee communities correspond to nearest villages and are listed below the MCP, with Y-chromosome haplotypes found in that putative community listed in parentheses. Underlined names indicate researched communities with preexisting data on approximate community sizes and home range extents. Each community is represented by a unique color. b Median joining network for the 14 Y-chromosome haplotypes. The relative similarity of haplotypes is represented by the lengths of branches, and the relative frequency of occurrence of each haplotype is indicated by the sizes of circles. Colors in haplotype circles correspond to putative communities in (a) exhibiting that haplotype.

From 76 total males (including those from Siiba) we found 14 Y-chromosome haplotypes, and these were 99% complete. Ten of these haplotypes were observed respectively only in single putative communities, thereby supporting community association data from genotype clusters. However, four haplotypes were shared among more than one putative community (Haplotypes B, G, I, and M; Figure 4). Overall, haplotypes shared a high degree of similarity as shown by their proximity in a median joining network (Figure 4).

Abundance estimation

A likelihood ratio test supported the capwire TIRM model over the ECM model when fit to the full data set (ratio 132.4, P < 0.01). Partitioning into three groups was also supported (P < 0.01). We obtained a population size estimate of 256 (95% confidence interval (CI) 246–321]. The SECR estimate of average density across the fragmented landscape was 0.404 chimpanzees per km2 (SE = 0.033, 95% CI 0.34–0.47). The SECR density within forest fragments was 2.13 chimpanzees per km2, (SE = 0.17, 95% CI 1.8–2.5). The associated estimate of population size was 319 (SE = 17.6, 95% CI 288–357). The precision of the population size estimates, calculated as the CI width divided by the estimate, was 29% and 22% for the capwire and SECR estimates, respectively. The coefficient of variation (CV) of the SECR population estimate, measured as SE divided by the estimate, was 0.055.

Discussion

Abundance estimation

We employed two established estimators to determine the abundance of chimpanzees in a human-dominated landscape composed of small fragmented forests amid agricultural land. While a previous census estimated a population of ~70 chimpanzees in the study region [54], we obtained population size estimates of 256 and 319, more than tripling this previous estimate. These substantially higher estimates likely reflect the advantages of this approach over indirect abundance estimates, which can lack accuracy if little is known regarding habitat suitability and species distribution [22, 23]. Indeed, our estimates more closely resemble those of McLennan [55], who extrapolated chimpanzee density in the studied Bulindi community to similarly suitable habitat across the corridor region. One could alternatively explain the higher estimates as evidence of substantial population growth since the time of the previous census. However, given the slow interbirth interval of chimpanzees and the high rate of habitat loss throughout the region over the intervening years between surveys, this explanation seems highly improbable.

In addition to the improved accuracy of our estimates, our high recapture rate for chimpanzee genotypes across the study area also resulted in a relatively high degree of precision. Though adequate sampling is necessary to achieve precise estimates using mark-recapture methods [43, 44], this has proved challenging in numerous prior studies of great apes [38, 40, 92]. Our relatively high rate of resampling was aided by habitat heterogeneity, which led to a clustering of samples in confined areas of suitable forested habitat despite the large size of the total study area. We also directed our search efforts based on reports from local residents who live near the chimpanzees, which further benefited our sampling success rate.

Despite their relative precision, we found differences in the population size estimates provided by the capwire and SECR estimators, which may be an artefact of the differences in the specific quantities estimated by the models and their applications to a population with a heterogeneous distribution over a large spatial area. Capwire assumes all individuals were at risk of being detected. However, this may not have been the case, given the presence of spatial gaps in sampling and chimpanzees’ fission–fusion social structure, which could have caused us to resample parties of similar composition while failing to detect some community members, particularly where search effort was low. This may have caused underestimation of overall and group-specific population sizes when using capwire. To examine this possibility, we can assess the relative accuracy of our group-specific capwire estimates by comparing them with community size estimates based on observational data from communities being monitored for research or conservation. Of three such communities, two (Kasongoire and Bulindi) resulted in monitoring estimates that fall within the 95% confidence interval of our capwire estimates (Table 2). For the third community, Kasokwa, the TIRM estimate we obtained was substantially lower than the monitoring estimate. Spatial search effort in this region was relatively light, which may have resulted in identification of fewer genotypes from chimpanzees there and a corresponding underestimate as compared to Kasongoire, for which available search effort data reflect a broader spatial area search (Figure 2). Therefore, where search effort was greater and more broadly distributed, the TIRM estimate appears to be highly accurate, while in undersearched areas the TIRM estimate may fall short.

In contrast, by modeling detection probability as a function of distance between animals’ activity centers and areas searched, SECR models allow for the presence of additional individuals whose probability of detection is negligible because they spend most or all of their time outside the areas searched. However, this also means that the SECR model could have slightly overestimated population size if forest fragments far from the areas searched were, in fact, not occupied. We also note that the SECR region of integration included small portions of contiguous forest in the Katanga area (near Siiba Forest Reserve; Figure 3), such that our SECR model slightly overestimates the number of animals that rely exclusively on small forest fragments (between forest reserves). Despite the differences between estimators, the capwire and SECR estimates were qualitatively similar, with overlapping confidence intervals. Perhaps most importantly, the 182 distinct genotypes alone confirm a minimum corridor population size far exceeding that estimated in the previous nationwide census of chimpanzees in Uganda.

Additionally, the estimates presented here can be considered conservative if applied to the entire study area. The search area did not include some southern sections of the corridor, and we refrained from extrapolating density estimates to these areas since little is known regarding the current distribution of chimpanzees there (Figure 3). Chimpanzees have, however, been reported to inhabit forest fragments to the south and east of Wambabya Forest Reserve near the villages of Bugambe, Munteme, and Buhimba in Hoima District [54, 93, 94]. Additionally, Wambabya Forest has an estimated chimpanzee population of 136 individuals [54]. Our searches of the northern part of this forest yielded no evidence of chimpanzees, though relatively few searches could be allocated to this region. One additional chimpanzee community may also inhabit Rwensama Forest Reserve, just south of the Budongo Forest, but little is known regarding the size or range of this putative community. Future censuses in these areas may help clarify chimpanzee population size and distribution in Rwensama Forest Reserve, Wambabya Forest Reserve, and neighboring fragments of riparian forest.

Our estimates may also be conservative given that genetic censuses of great ape population size may tend to under-sample infants and juveniles due to the difficulty of finding their fecal samples. Based on a review of published demographic data from habituated chimpanzee communities, an average of 39% of a chimpanzee community is typically composed of infants and juveniles. If none of these individuals are sampled and are effectively at zero risk of detection, then the total size of a community or population will be underestimated. However, given our efforts to exhaustively search areas with evidence of chimpanzee presence, as well as our data indicating the small bolus size of some samples, we have reason to believe some infants and juveniles were sampled in our study population. If so, their detection risk would be elevated and our estimates should have adjusted accordingly to accommodate them.

Despite the advantages of these abundance estimators, potential model assumption violations should still be noted. Given the timescale of this study (15 months of sample collection over a total period of two total years), it is possible that we violated the assumption of demographic closure. However, given the slow life history traits of chimpanzees, whose average interbirth interval is more than 5 years [95], this is unlikely since relatively few deaths, births, or migrations into or outside of the corridor area would be expected to occur during this time. In addition, Arandjelovic et al. [33] found similar TIRM estimates when one longer-term (3 years) and two shorter-term (<1 year) sampling periods were compared for the same population of western lowland gorillas, suggesting the sampling period used in this study should not have strongly impacted abundance estimates. Community transfers would violate the assumption of fixed activity centers, but given the relative infrequency of female transfers in eastern chimpanzees [81, 96, 97], few instances are expected during the study period.

Chimpanzee density in the corridor region

We used SECR models to estimate chimpanzee density both across the entire fragmented study area and within the forest fragments, obtaining estimates of 0.40 and 2.13 per km2, respectively. Estimated densities for chimpanzees in the Budongo and Bugoma Forests are approximately 1.3 and 2 chimpanzees per km2, respectively [54, 98]. Therefore, it appears that while the overall density of chimpanzees in the corridor region is relatively low, the density within forest habitat is much higher and may exceed that in continuous forest nearby. Chancellor et al. [39] found similarly high chimpanzee density (~2.1 individuals/km2) for eastern chimpanzees in a forest fragment of western Rwanda despite lower densities in montane rainforest nearby. Such findings may (1) indicate a crowding effect, whereby chimpanzee density is particularly high in small remaining areas of suitable habitat, (2) reflect the expected distribution of chimpanzees in a mosaic habitat with clumped resources, or (3) result from a combination of these factors. Previous estimates, however, have employed various non-genetic survey methods, thereby limiting our ability to draw conclusions by comparing densities across fragmented and continuous forests.

Putative communities and Y-chromosome haplotypes

The spatial clustering of genotypes suggests the presence of at least nine different chimpanzee communities in the study area, in a non-overlapping distribution similar to that seen elsewhere among studied chimpanzees [81, 83]. Overall, Y-chromosome haplotypes show a structuring across putative communities, but 4 of 14 haplotypes are shared among more than one putative community. This overlap could indicate (1) remnants of older diversity from precursor groups in the region that eventually fissioned into different chimpanzee communities, (2) transfer events in which parous females with sons emigrated to new communities, thereby bringing with them new Y-chromosome haplotypes, (3) instances of extra-group copulations resulting in male offspring of different communities sharing the same Y-chromosome haplotype, or (4) mutations at microsatellite loci that caused closely related Y-chromosome haplotypes to converge into a single haplotype as defined using our markers. The reasons for its occurrence in this study cannot yet be determined but may result from one or a combination of these factors. A less plausible explanation is that shared Y-chromosome haplotypes indicate adult male dispersal. However, given that eastern chimpanzee males display a high degree of territoriality and intercommunity aggression [99, 100], this explanation seems unlikely, even in a degraded habitat. One additional possibility is that putative communities sharing a single haplotype are actually a single community. However, this explanation also seems unlikely given the high average recapture rate in this study, which often led to individuals being sampled among different party associations, as well as the large distances between some sampling clusters sharing a haplotype. For example, if we consider the maximum distance between sampling points for two males sharing the same haplotype (~34 km), and conservatively assume those points demarcate the outer edges of a single community home range, their circular home range would measure more than 900 km2 in size. The sharing of Y-chromosome haplotypes among multiple chimpanzee communities has also been seen elsewhere [71, 72, 101]. Future studies may better clarify the distribution of male philopatric chimpanzee communities across this region. Nonetheless, our results indicate likely conservatism in male philopatric territorial community structure despite substantial habitat degradation, a pattern that appears to hold for chimpanzees across numerous habitat types [102]. These findings support the behavioral data collected for chimpanzee communities in the region such as Bulindi, where fission–fusion community structure within defined territories appears intact despite widespread anthropogenic habitat destruction [55].

Conservation implications

The results of this study suggest chimpanzees are both numerous and widespread in the human-dominated landscape between the Budongo and Bugoma Forests. This is perhaps surprising, given the paucity of forest habitat and the high human population density of 157 residents per km2 in this region [103]. However, chimpanzees in this area are known to utilize home ranges encompassing numerous forest fragments while feeding on a combination of natural and cultivated food resources [60, 104]. These forest fragments, which are largely riparian, are additionally known to harbor relatively high fruit tree density [8]. Indeed, riparian forest fragments in Central Africa have been noted for having high conservation value for chimpanzees and other species [7, 105]. In addition, chimpanzee survival under anthropogenic pressure is likely aided by their behavioral flexibility [106, 107]. Though their behavioral strategies in such habitats remain little understood, they include incorporating new (often human-cultivated) foods into their diets and adopting more aggressive or cryptic behaviors to mitigate human threats [21, 104, 108110]. In western Uganda, their persistence is also attributable to relatively low hunting pressure, since Ugandans traditionally have not hunted chimpanzees for meat as in some other countries. However, customs are changing and chimpanzees are sometimes hunted for meat or killed as pests in Uganda, thereby making anthropogenic activities a threat to chimpanzee survival there [55, 111].

Despite anthropogenic pressures, these findings underscore the importance of greater investment in chimpanzee conservation in this region. A targeted solution such as translocating individual chimpanzee communities, as has been discussed [60, 112, 113], appears impractical given the large and broadly distributed population documented in our study. In contrast, our results suggest the potential may be high for a corridor enhancement project to benefit chimpanzees in this region [56], given that an increase in functional connectivity to the chimpanzee populations in the Budongo and Bugoma Forests would collectively impact 30% of Uganda’s total chimpanzee population (including the chimpanzee populations of both forests and the region between them). Any such project must be considered carefully, however. The need for firewood, building materials, and agricultural land are often cited as reasons for deforestation of the region’s unprotected forests [114]. Humans and chimpanzees also have a history of conflict interactions, given their close co-residence in this area [60, 112]. Stakeholder needs such as these must be taken into consideration to ensure the effectiveness of any conservation initiative. However, riparian forests play a key role in protecting rivers and the agricultural needs they support, so their conservation may increasingly be recognized as vital to the futures of both humans and other species locally. Additionally, habitat corridors may protect wildlife against the detrimental effects of climate change, thereby enhancing their value even further [115].

Our findings point to the value of conservation planning for unprotected areas with great potential to enhance gene flow and population viability among endangered wildlife populations. In this region as with many others like it, however, conservation action is urgently required. At least 450 km2 of forest is estimated to have been lost between the Budongo and Bugoma Forests from 2000 to 2010 [56]. Given the human population growth rate, this trend is likely only to change if concerted efforts are made to slow the rate of deforestation in the region. Though chimpanzees have proved surprisingly resilient to date in this habitat, their ability to withstand continued habitat losses, along with other threats to their survival, is highly uncertain.

Conclusions

Using genetic censusing, we found a surprisingly large population of chimpanzees inhabiting largely unprotected forest fragments in western Uganda. The large size and widespread distribution of this population suggests it serves as a vital link between larger populations in the neighboring Budongo and Bugoma Forests. These results demonstrate the potential for forest fragments to serve as wildlife corridors, and for animal populations to be widely distributed in degraded habitats. Despite this potential, however, the habitat is rapidly being altered, and its capacity to support chimpanzees and other species may not persist unless the rate of habitat change is slowed considerably.

Availability of supporting data

The data sets supporting the results of this article are included within the article and its additional files.

Abbreviations

SECR: 

spatially explicit capture–recapture

PCR: 

polymerase chain reaction

ECM: 

even capture model

TIRM: 

two innate rates model

CI: 

confidence interval

CV: 

coefficient of variation

DAAD: 

Deutscher Akademischer Austausch Dienst (German Academic Exchange Service)

MCP: 

minimum convex polygon

Declarations

Authors’ contributions

MSM, LV, MA, and CBS designed the study. MSM and JDL conducted the research. MSM, JDL, and EJH analyzed the data. MSM, LV, MA, EJH, and CBS wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank the Uganda National Council for Science and Technology, the Uganda Wildlife Authority, and the National Forestry Authority for permission to conduct this research. This study was funded by the American Society of Primatologists, the German Academic Exchange Service (DAAD), the Max Planck Society, the University of Southern California Jane Goodall Research Center and Dornsife College of Letters, Arts, and Sciences, the Nacey Maggioncalda Foundation, and Primate Conservation, Inc. For assistance during field work, we thank Henry Irumba, Matthew McLennan, Nicholas Rugadya, Tom Sabiiti, Moses Ssemahunge, and Emily Stewart. We also thank Janette Wallis and the Kasokwa Forest Project for support with data collection in the Kasokwa Forest. For assistance with laboratory work, we thank Anette Abraham, Amy Heilman, and Veronika Staedele. For helpful advice and during data collection and analysis, we thank Kevin Langergraber and Deborah Moore. For helpful comments on an earlier version of this manuscript, we thank Verena Behringer and Matthew McLennan.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biological Sciences, Dana and David Dornsife College of Letters, Arts, and Sciences, University of Southern California
(2)
Max Planck Institute for Evolutionary Anthropology
(3)
Centre for Research into Ecological and Environmental Modelling, The Observatory, Buchanan Gardens, University of St Andrews

References

  1. Wiens J (1996) Wildlife in patchy environments: metapopulations, mosaics, and management. In: McCullough DR (ed) Metapopulations and wildlife conservation. Island Press, Washington, D.C., pp 53–84Google Scholar
  2. Haddad NM, Brudvig LA, Clobert J, Davies KF, Gonzalez A, Holt RD et al (2015) Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci Adv 1:e1500052Google Scholar
  3. Gerlach G, Musolf K (2000) Fragmentation of landscape as a cause for genetic subdivision in bank voles. Conserv Biol 14:1066–1074Google Scholar
  4. Keller I, Largiader CR (2003) Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc R Soc B Biol Sci 270:417–423Google Scholar
  5. Stratford JA, Stouffer PC (1999) Local extinctions of terrestrial insectivorous birds in a fragmented landscape near Manaus, Brazil. Conserv Biol 13:1416–1423Google Scholar
  6. Sheil D, Meijaard E (2010) Purity and prejudice: deluding ourselves about biodiversity conservation. Biotropica 42:566–568Google Scholar
  7. Gautier-Hion A, Brugiere D (2005) Significance of riparian forests for the conservation of Central African primates. Int J Primatol 26:515–523Google Scholar
  8. McLennan MR, Plumptre AJ (2012) Protected apes, unprotected forest: composition, structure and diversity of riverine forest fragments and their conservation value in Uganda. Trop Conserv Sci 5:79–103Google Scholar
  9. Bergl RA, Warren Y, Nicholas A, Dunn A, Imong I, Sunderland-Groves JL et al (2012) Remote sensing analysis reveals habitat, dispersal corridors and expanded distribution for the Critically Endangered Cross River gorilla Gorilla gorilla diehli. Oryx 46:278–289Google Scholar
  10. McShea WJ, Stewart C, Peterson L, Erb P, Stuebing R, Giman B (2009) The importance of secondary forest blocks for terrestrial mammals within an Acacia/secondary forest matrix in Sarawak, Malaysia. Biol Conserv 142:3108–3119Google Scholar
  11. Ranta P, Blom T, Niemela J, Joensuu E, Siitonen M (1998) The fragmented Atlantic rain forest of Brazil: size, shape and distribution of forest fragments. Biodivers Conserv 7:385–403Google Scholar
  12. Brncic TM, Amarasekaran B, McKenna A (2010) Sierra Leone national chimpanzee census. Tacugama Chimpanzee Sanctuary, FreetownGoogle Scholar
  13. Gaveau D, Wandono H, Setiabudi F (2007) Three decades of deforestation in southwest Sumatra: have protected areas halted forest loss and logging, and promoted re-growth? Biol Conserv 134:495–504Google Scholar
  14. Plumptre AJ, Rose R, Nangendo G, Williamson EA, Didier K, Hart J et al (2010) Eastern chimpanzee (Pan troglodytes schweinfurthii): status survey and conservation action plan 2010–2020. IUCN/SSC Primate Specialist Group, GlandGoogle Scholar
  15. Moyer D, Plumptre AJ, Kamenya S, Athumani S, Sikombe S (2006) Surveys east of Mahale Mountains National Park. In: Moyer D, Plumptre AJ, Pintea L, Hernandez-Aguilar A, Moore J, Stewart FA, Davenport TRB, Piel A, Kamenya S, Mugabe H, Mpunga N, Mwangoka M (eds) Surveys of chimpanzees and other biodiversity in Western Tanzania. Wildlife Conservation Society, Jane Goodall Institute, University of California San Diego, pp 20–23Google Scholar
  16. FAO (2010) Global forest resources assessment 2010: country report. Uganda, Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  17. MWLE (2002) The national forest plan. Ministry of Water, Lands, and Environment, KampalaGoogle Scholar
  18. Kormos R, Boesch C, Bakarr M, Butynski TM (2003) Status survey and conservation action plan: West African chimpanzees. IUCN/SSC Primate Specialist Group, GlandGoogle Scholar
  19. Oates JF, Tutin CEG, Humle T, Wilson ML, Baillie J, Balmforth Z et al (2008) Pan troglodytes. The IUCN Red List of Threatened Species. Version 2015.1Google Scholar
  20. Gruen L, Fultz A, Pruetz J (2013) Ethical issues in African great ape field studies. ILAR J 54:24–32PubMedGoogle Scholar
  21. McLennan MR, Hill CM (2010) Chimpanzee responses to researchers in a disturbed forest-farm mosaic at Bulindi, western Uganda. Am J Primatol 72:907–918PubMedGoogle Scholar
  22. Boyko RH, Marshall AJ (2010) Using simulation models to evaluate ape nest survey techniques. PLoS One 5:e10754PubMed CentralPubMedGoogle Scholar
  23. Plumptre AJ, Reynolds V (1996) Censusing chimpanzees in the Budongo Forest, Uganda. Int J Primatol 17:85–99Google Scholar
  24. Devos C, Sanz C, Morgan D, Onononga JR, Laporte N, Huynen M-C (2008) Comparing ape densities and habitats in northern Congo: surveys of sympatric gorillas and chimpanzees in the Odzala and Ndoki regions. Am J Primatol 70:439–451PubMedGoogle Scholar
  25. Buckland ST, Plumptre AJ, Thomas L, Rexstad EA (2010) Design and analysis of line transect surveys for primates. Int J Primatol 31:833–847Google Scholar
  26. Kouakou CY, Boesch C, Kuehl H (2009) Estimating chimpanzee population size with nest counts: validating methods in Taï National Park. Am J Primatol 71:447–457PubMedGoogle Scholar
  27. Head JS, Boesch C, Robbins MM, Rabanal LI, Makaga L, Kühl HS (2013) Effective sociodemographic population assessment of elusive species in ecology and conservation management. Ecol Evol 3:2903–2916PubMed CentralPubMedGoogle Scholar
  28. Heinicke S, Kalan AK, Wagner OJJ, Mundry R, Lukashevich H, Kühl HS (2015) Assessing the performance of a semi-automated acoustic monitoring system for primates. Methods Ecol Evol 6:753–763Google Scholar
  29. Kalan AK, Mundry R, Wagner OJJ, Heinicke S, Boesch C, Kühl HS (2015) Towards the automated detection and occupancy estimation of primates using passive acoustic monitoring. Ecol Indic 54:217–226Google Scholar
  30. Kühl HS, Burghardt T (2013) Animal biometrics: quantifying and detecting phenotypic appearance. Trends Ecol Evol 28:432–441PubMedGoogle Scholar
  31. Howe EJ, Obbard ME, Kyle CJ (2013) Combining data from 43 standardized surveys to estimate densities of female American black bears by spatially explicit capture–recapture. Popul Ecol 55:595–607Google Scholar
  32. Guschanski K, Vigilant L, McNeilage A, Gray M, Kagoda E, Robbins MM (2009) Counting elusive animals: comparing field and genetic census of the entire mountain gorilla population of Bwindi Impenetrable National Park, Uganda. Biol Conserv 142:290–300Google Scholar
  33. Arandjelovic M, Head J, Kühl H, Boesch C, Robbins MM, Maisels F et al (2010) Effective non-invasive genetic monitoring of multiple wild western gorilla groups. Biol Conserv 143:1780–1791Google Scholar
  34. Arandjelovic M, Bergl RA, Ikfuingei R, Jameson C, Parker M, Vigilant L (2015) Detection dog efficacy for collecting faecal samples from the critically endangered Cross River gorilla (Gorilla gorilla diehli) for genetic censusing. R Soc Open Sci 2:140423PubMed CentralPubMedGoogle Scholar
  35. Junker J, van Aarde RJ, Ferreira SM (2008) Temporal trends in elephant Loxodonta africana numbers and densities in northern Botswana: is the population really increasing? Oryx 42:58–65Google Scholar
  36. Arrendal J, Vilà C, Björklund M (2007) Reliability of noninvasive genetic census of otters compared to field censuses. Conserv Genet 8:1097–1107Google Scholar
  37. Zhan X, Li M, Zhang Z, Goossens B, Chen Y, Wang H et al (2006) Molecular censusing doubles giant panda population estimate in a key nature reserve. Curr Biol 16:R451–R452PubMedGoogle Scholar
  38. Arandjelovic M, Head J, Rabanal LI, Schubert G, Mettke E, Boesch C et al (2011) Non-invasive genetic monitoring of wild central chimpanzees. PLoS One 6:e14761PubMed CentralPubMedGoogle Scholar
  39. Chancellor RL, Langergraber K, Ramirez S, Rundus AS, Vigilant L (2012) Genetic sampling of unhabituated chimpanzees (Pan troglodytes schweinfurthii) in Gishwati Forest Reserve, an isolated forest fragment in western Rwanda. Int J Primatol 33:479–488Google Scholar
  40. Moore DL, Vigilant L (2013) A population estimate of chimpanzees (Pan troglodytes schweinfurthii) in the Ugalla region using standard and spatially explicit genetic capture–recapture methods. Am J Primatol 76:335–346PubMedGoogle Scholar
  41. Taberlet P, Waits LP, Luikart G (1999) Noninvasive genetic sampling: look before you leap. Trends Ecol Evol 14:323–327PubMedGoogle Scholar
  42. Lukacs PM, Burnham KP (2005) Review of capture–recapture methods applicable to noninvasive genetic sampling. Mol Ecol 14:3909–3919PubMedGoogle Scholar
  43. Petit E, Valiere N (2006) Estimating population size with noninvasive capture-mark–recapture data. Conserv Biol 20:1062–1073PubMedGoogle Scholar
  44. Miller CR, Joyce P, Waits LP (2005) A new method for estimating the size of small populations from genetic mark-recapture data. Mol Ecol 14:1991–2005PubMedGoogle Scholar
  45. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMed CentralPubMedGoogle Scholar
  46. Obbard ME, Howe EJ, Kyle CJ (2010) Empirical comparison of density estimators for large carnivores. J Appl Ecol 47:76–84Google Scholar
  47. Soisalo MK, Cavalcanti SMC (2006) Estimating the density of a jaguar population in the Brazilian Pantanal using camera-traps and capture–recapture sampling in combination with GPS radio-telemetry. Biol Conserv 129:487–496Google Scholar
  48. Butynski TM (1990) Comparative ecology of blue monkeys (Cercopithecus mitis) in high- and low-density subpopulations. Ecol Monogr 60:1–26Google Scholar
  49. Imong I, Robbins MM, Mundry R, Bergl R, Kühl HS (2014) Distinguishing ecological constraints from human activity in species range fragmentation: the case of Cross River gorillas. Anim Conserv 17:323–331Google Scholar
  50. Borchers DL, Efford MG (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64:377–385PubMedGoogle Scholar
  51. Efford MG (2004) Density estimation in live-trapping studies. Oikos 106:598–610Google Scholar
  52. Efford MG (2011) Estimation of population density by spatially explicit capture–recapture analysis of data from area searches. Ecology 92:2202–2207PubMedGoogle Scholar
  53. Efford MG, Borchers DL, Byrom AE (2009) Density estimation by spatially explicit capture–recapture: likelihood-based methods. In: Thompson DL, Cooch EG, Conroy MJ (eds) Modeling demographic processes in marked populations. Springer, New York, pp 255–269Google Scholar
  54. Plumptre AJ, Cox D, Mugume S (2003) The status of chimpanzees in Uganda. Wildlife Conservation Society, KampalaGoogle Scholar
  55. McLennan MR (2008) Beleaguered chimpanzees in the agricultural district of Hoima, western Uganda. Primate Conserv 23:45–54Google Scholar
  56. Nangendo G, Plumptre AJ, Akwetaireho S (2010) Identifying potential corridors for conservation in the Murchison-Semliki Landscape. Wildlife Conservation Society, KampalaGoogle Scholar
  57. Langergraber KE, Siedel H, Mitani JC, Wrangham RW, Reynolds V, Hunt K et al (2007) The genetic signature of sex-biased migration in patrilocal chimpanzees and humans. PLoS One 2:e973PubMed CentralPubMedGoogle Scholar
  58. Eggeling WJ (1947) Observations on the ecology of the Budongo Rain Forest, Uganda. J Ecol 34:20–87Google Scholar
  59. Langdale-Brown I, Osmaston HA, Wilson JG (1964) The vegetation of Uganda and its bearing on land use. Government Printer, EntebbeGoogle Scholar
  60. Reynolds V (2005) Chimpanzees of the Budongo Forest. Oxford University Press, New YorkGoogle Scholar
  61. Paterson JD (1991) The ecology and history of Uganda’s Budongo Forest. For Conserv Hist 35:179–187Google Scholar
  62. Mwavu EN, Witkowski ETF (2008) Land-use and cover changes (1988–2002) around Budongo Forest Reserve, NW Uganda: implications for forest and woodland sustainability. Land Degrad Dev 19:606–622Google Scholar
  63. Okecha A, Newton-Fisher NE (2006) The diet of olive baboons (Papio anubis) in the Budongo Forest Reserve, Uganda. In: Newton-Fisher NE, Notman H, Paterson JD, Reynolds V (eds) Primates of western Uganda. Springer, New York, pp 61–74Google Scholar
  64. Nsubuga A, Robbins MM, Roeder AD, Morin PA, Boesch C, Vigilant L (2004) Factors affecting the amount of genomic DNA extracted from ape faeces and the identification of an improved sample storage method. Mol Ecol 13:2089–2094PubMedGoogle Scholar
  65. Bradley BJ, Chambers KE, Vigilant L (2001) Accurate DNA-based sex identification of apes using non-invasive samples. Conserv Genet 2:179–181Google Scholar
  66. Arandjelovic M, Guschanski K, Schubert G, Harris TR, Thalmann O, Siedel H et al (2009) Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples. Mol Ecol Res 9:28–36Google Scholar
  67. Römpler H, Dear PH, Krause J, Meyer M, Rohland N, Schöneberg T et al (2006) Multiplex amplification of ancient DNA. Nat Protoc 1:720–728PubMedGoogle Scholar
  68. Morin PA, Chambers KE, Boesch C, Vigilant L (2001) Quantitative polymerase chain reaction analysis of DNA from noninvasive samples for accurate microsatellite genotyping of wild chimpanzees (Pan troglodytes verus). Mol Ecol 10:1835–1844PubMedGoogle Scholar
  69. Broquet T, Petit E (2004) Quantifying genotyping errors in noninvasive population genetics. Mol Ecol 13:3601–3608PubMedGoogle Scholar
  70. Erler A, Stoneking M, Kayser M (2004) Development of Y-chromosomal microsatellite markers for nonhuman primates. Mol Ecol 13:2921–2930PubMedGoogle Scholar
  71. Langergraber KE, Rowney C, Schubert G, Crockford C, Hobaiter C, Wittig R et al (2014) How old are chimpanzee communities? Time to the most recent common ancestor of the Y-chromosome in highly patrilocal societies. J Hum Evol 69:1–7PubMedGoogle Scholar
  72. Moore DL, Vigilant L (2014) Genetic diversity at the edge: comparative assessment of Y-chromosome and autosomal diversity in eastern chimpanzees (Pan troglodytes schweinfurthii) of Ugalla, Tanzania. Conserv Genet 15:495–507Google Scholar
  73. Eriksson J, Siedel H, Lukas D, Kayser M, Erler A, Hashimoto C et al (2006) Y-chromosome analysis confirms highly sex-biased dispersal and suggests a low male effective population size in bonobos (Pan paniscus). Mol Ecol 15:939–949PubMedGoogle Scholar
  74. Douadi MI, Gatti S, Levrero F, Duhamel G, Bermejo M, Vallet D et al (2007) Sex-biased dispersal in western lowland gorillas (Gorilla gorilla gorilla). Mol Ecol 16:2247–2259PubMedGoogle Scholar
  75. Inoue E, Akomo-Okoue EF, Ando C, Iwata Y, Judai M, Fujita S et al (2013) Male genetic structure and paternity in western lowland gorillas (Gorilla gorilla gorilla). Am J Phys Anthropol 151:583–588PubMedGoogle Scholar
  76. Oota H, Settheetham-Ishida W, Tiwawech D, Ishida T, Stoneking M (2001) Human mtDNA and Y-chromosome variation is correlated with matrilocal versus patrilocal residence. Nat Genet 29:20–21PubMedGoogle Scholar
  77. Kayser M, Brauer S, Weiss G, Schiefenhovel W, Underhill P, Shen P et al (2007) Reduced Y-chromosome, but not mitochondrial DNA, diversity in human populations from West New Guinea. Am J Hum Genet 72:281–302Google Scholar
  78. Kumar V, Langstieh BT, Madhavi KV, Naidu VM, Singh HP, Biswas S et al (2006) Global patterns in human mitochondrial DNA and Y-chromosome variation caused by spatial instability of the local cultural processes. PLoS Genet 2:e53PubMed CentralPubMedGoogle Scholar
  79. Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment. Mol Ecol 16:1099–1106PubMedGoogle Scholar
  80. Quantum GIS (2014) Quantum GIS Geographic Information System. Open Source Geospatial Foundation ProjectGoogle Scholar
  81. Goodall J (1986) The chimpanzees of Gombe: patterns of behaviour. Harvard University Press, CambridgeGoogle Scholar
  82. Nishida T (1979) The social structure of chimpanzees in the Mahale Mountains. In: Hamburg DA, McCown ER (eds) The great apes. Benjamin/Cummings, Menlo ParkGoogle Scholar
  83. Herbinger I, Boesch C, Rothe H (2001) Territory characteristics among three neighboring chimpanzee communities in the Taï National Park, Côte d’Ivoire. Int J Primatol 22:143–167Google Scholar
  84. Pennell MW, Stansbury CR, Waits LP, Miller CR (2012) Capwire: a R package for estimating population census size from non-invasive genetic sampling. Mol Ecol Res 13:154–157Google Scholar
  85. Stansbury CR (2012) Monitoring gray wolves (Canis lupus) using noninvasive genetic sampling at rendezvous sites. Master’s thesis. University of Idaho, MoscowGoogle Scholar
  86. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853PubMedGoogle Scholar
  87. Efford MG, Dawson DK, Borchers DL (2009) Population density estimated from locations of individuals on a passive detector array. Ecology 90:2676–2682PubMedGoogle Scholar
  88. Efford MG, Borchers DL, Mowat G (2013) Varying effort in capture–recapture studies. Methods Ecol Evol 4:629–636Google Scholar
  89. Efford MG, Fewster RM (2013) Estimating population size by spatially explicit capture–recapture. Oikos 122:918–928Google Scholar
  90. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical, Computing, ViennaGoogle Scholar
  91. Efford MG (2015) SECR: spatially explicit capture–recapture models. R package version 2.9.3Google Scholar
  92. Roy J, Vigilant L, Gray M, Wright E, Kato R, Kabano P et al (2014) Challenges in the use of genetic mark-recapture to estimate the population size of Bwindi mountain gorillas (Gorilla beringei beringei). Biol Conserv 180:249–261Google Scholar
  93. JGI/UWA (2002) Investigation into the human–chimpanzee conflict in Hoima District. Jane Goodall Institute (JGI), Uganda Wildlife Authority (UWA), EntebbeGoogle Scholar
  94. Plumptre AJ, Akwetaireho S, Leal M, Mutungire N, Kyamanywa J, Tumuhamye D et al (2011) Biodiversity surveys of the corridor forests east of Bugoma Forest Reserve up to Budongo Forest Reserve. Wildlife Conservation Society, KampalaGoogle Scholar
  95. Emery Thompson M, Jones JH, Pusey AE, Brewer-Marsden S, Goodall J et al (2007) Aging and fertility patterns in wild chimpanzees provide insights into the evolution of menopause. Curr Biol 17:2150–2156PubMedGoogle Scholar
  96. Langergraber KE, Rowney C, Crockford C, Wittig R, Zuberbuhler K, Vigilant L (2014) Genetic analyses suggest no immigration of adult females and their offspring into the Sonso community of chimpanzees in the Budongo Forest Reserve, Uganda. Am J Primatol 76:640–648PubMedGoogle Scholar
  97. Nishida T, Corp N, Hamai M, Hasegawa T, Hiraiwa-Hasegawa M, Hosaka K et al (2003) Demography, female life history, and reproductive profiles among the chimpanzees of Mahale. Am J Primatol 59:99–121PubMedGoogle Scholar
  98. Plumptre AJ, Cox D (2006) Counting primates for conservation: primate surveys in Uganda. Primates 47:65–73PubMedGoogle Scholar
  99. Mitani JC, Watts DP, Amsler SJ (2010) Lethal intergroup aggression leads to territorial expansion in wild chimpanzees. Curr Biol 20:R507–R508PubMedGoogle Scholar
  100. Wilson ML, Boesch C, Fruth B, Furuichi T, Gilby IC, Hashimoto C et al (2014) Lethal aggression in Pan is better explained by adaptive strategies than human impacts. Nature 513:414–417PubMedGoogle Scholar
  101. Schubert G, Stoneking CJ, Arandjelovic M, Boesch C, Eckhardt N, Hohmann G et al (2011) Male-mediated gene flow in patrilocal primates. PLoS One 6:e21514PubMed CentralPubMedGoogle Scholar
  102. Moore DL, Langergraber KE, Vigilant L (2015) Genetic analyses suggest male philopatry and territoriality in savanna-woodland chimpanzees (Pan troglodytes schweinfurthii) of Ugalla, Tanzania. Int J Primatol 36:377–397Google Scholar
  103. UBOS (2014) NPHC 2014 provisional results report. Uganda Bureau of Statistics, KampalaGoogle Scholar
  104. McLennan MR (2013) Diet and feeding ecology of chimpanzees (Pan troglodytes) in Bulindi, Uganda: foraging strategies at the forest–farm interface. Int J Primatol 34:585–614Google Scholar
  105. Fleury-Brugiere M-C, Brugiere D (2010) High population density of Pan troglodytes verus in the Haut Niger National Park, Republic of Guinea: implications for local and regional conservation. Int J Primatol 31:383–392Google Scholar
  106. Hockings KJ, McLennan MR, Carvalho S, Ancrenaz M, Bobe R, Byrne RW et al (2015) Apes in the Anthropocene: flexibility and survival. Trends Ecol Evol 30:215–222PubMedGoogle Scholar
  107. Junker J, Blake S, Boesch C, Campbell G, Toit LD, Duvall C et al (2012) Recent decline in suitable environmental conditions for African great apes. Divers Dist 18:1077–1091Google Scholar
  108. Hicks TC, Roessingh P, Menken SBJ (2013) Impact of humans on long-distance communication behaviour of eastern chimpanzees (Pan troglodytes schweinfurthii) in the northern Democratic Republic of the Congo. Folia Primatol 84:135–156PubMedGoogle Scholar
  109. Hockings KJ, Anderson JR, Matsuzawa T (2012) Socioecological adaptations by chimpanzees, Pan troglodytes verus, inhabiting an anthropogenically impacted habitat. Anim Behav 83:801–810Google Scholar
  110. Tagg N, Willie J, Petre C-A, Haggis O (2013) Ground night nesting in chimpanzees: new insights from central chimpanzees (Pan troglodytes troglodytes) in South-East Cameroon. Folia Primatol 84:362–383PubMedGoogle Scholar
  111. McLennan MR, Hyeroba D, Asiimwe C, Reynolds V, Wallis J (2012) Chimpanzees in mantraps: lethal crop protection and conservation in Uganda. Oryx 46:598–603Google Scholar
  112. McLennan MR, Hill CM (2012) Troublesome neighbors: changing attitudes towards chimpanzees (Pan troglodytes) in a human-dominated landscape in Uganda. J Nat Conserv 20:219–227Google Scholar
  113. Reynolds V, Wallis J, Kyamanywa R (2003) Fragments, sugar, and chimpanzees in Masindi District, western Uganda. In: Marsh L (ed) Primates in fragments. Kluwer, New York, pp 309–320Google Scholar
  114. Akwetaireho S, Akugizibwe T, Plumptre A (2011) Socioeconomic values of corridor forests in the Albertine Rift forests of the Murchison-Semliki Landscape. Wildlife Conservation Society, KampalaGoogle Scholar
  115. Smith JB (1997) Setting priorities for adapting to climate change. Glob Environ Chang 7:251–264Google Scholar

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