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Factors Influencing the Survival of Sympatric Gorilla (Gorilla gorilla gorilla) and Chimpanzee (Pan troglodytes troglodytes) Nests

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Accurate and precise surveys of primate abundance provide the basis for understanding species ecology and essential information for conservation assessments. Owing to the elusive nature of wild apes and the vast region of dense forest they inhabit, population estimates of central chimpanzees (Pan troglodytes troglodytes) and western lowland gorillas (Gorilla gorilla gorilla) have largely relied on surveys of their nests. Specific information about the nesting behavior of apes permits the estimation of the number of nests built (nest creation rate). Similarly, information on nest characteristics and environmental factors can be used to estimate the time it takes nests to decay (nest decay rate). Nest creation and decay rates are then used to convert nest density estimates to absolute ape densities. Population estimates that use site-specific estimates of nest creation and decay rates are more accurate and precise. However, it is common practice to generalize these conversion factors across sites because of the additional cost of studies required to gather the information to estimate them. Over a 9-mo study period, we detected and monitored the time to decay of gorilla nests (N = 514) and chimpanzee nests (N = 521) in northern Republic of Congo. We investigated the influence of nest characteristics and environmental factors on nest survivorship and estimated the mean time to nest decay (or equivalently survival) using MARK. Key factors influencing nest decay rate included ape species, forest type, nest height, mean rainfall, nest structure, nest type, and primary aspects of nest construction. Our findings highlight the synergistic effect of behavior and environment on great ape nest degradation, as well as providing practical insights for improving measures to monitor remaining populations of these endangered species.
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Factors Influencing the Survival of Sympatric
Gorilla (Gorilla gorilla gorilla)andChimpanzee
(Pan troglodytes troglodytes)Nests
David Morgan
1
&Crickette Sanz
2,3
&
Jean Robert Onononga
2
&Samantha Strindberg
4
Received: 28 March 2016 /Accepted: 7 October 2016 / Published online: 12 December 2016
#Springer Science+Business Media New York 2016
Abstract Accurate and precise surveys of primate abundance provide the basis for
understanding species ecology and essential information for conservation assessments.
Owing to the elusive nature of wild apes and the vast region of dense forest they
inhabit, population estimates of central chimpanzees (Pan troglodytes troglodytes)and
western lowland gorillas (Gorilla gorilla gorilla) have largely relied on surveys of their
nests. Specific information about the nesting behavior of apes permits the estimation of
the number of nests built (nest creation rate). Similarly, information on nest character-
istics and environmental factors can be used to estimate the time it takes nests to decay
(nest decay rate). Nest creation and decay rates are then used to convert nest density
estimates to absolute ape densities. Population estimates that use site-specific
estimates of nest creation and decay rates are more accurate and precise. However, it is
common practice to generalize these conversion factors across sites because of the
additional cost of studies required to gather the information to estimate them. Over a 9-
mo study period, we detected and monitored the time to decay of gorilla nests (N= 514)
and chimpanzee nests (N= 521) in northern Republic of Congo. We investigated
the influence of nest characteristics and environmental factors on nest survivorship and
estimated the mean time to nest decay (or equivalently survival) using MARK. Key
factors influencing nest decay rate included ape species, forest type, nest height, mean
rainfall, nest structure, nest type, and primary aspects of nest construction. Our findings
Int J Primatol (2016) 37:718737
DOI 10.1007/s10764-016-9934-9
Handling Editor: Joanna M. Setchell
*David Morgan
dmorgan@lpzoo.org
1
Fisher Center for the Study and Conservation of Apes, Lincoln Park Zoo, Chicago, IL 60614, USA
2
Wildlife Conservation Society, Congo Program, B.P. 14537, Brazzaville, Republic of Congo
3
Department of Anthropology, Washington University in Saint Louis, Saint Louis, MO 63130, USA
4
Global Conservation Program, Wildlife Conservation Society, Bronx, NY 10460, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... However, there are some disadvantages. Nest production rate [37][38][39] and nest decomposition or nest decay time [40,41] (i.e. conversion factors) are highly variable across species, space and time, but are required to scale down the number of counted nests to the number of apes [35], in order to permit estimations of great apes [42]. ...
... This is problematic, as nest decomposition may differ with differing climate conditions, leading to inaccurate population density estimates [56,63]. Nest-specific factors are known to drive nest decay; for example, nests built at the same time by members of the same group of apes exhibit different decomposition times [40]. However, rainfall is often reported as the most important variable affecting nest decomposition time, with lower rainfall resulting in longer decay times [38,40,41,64]. ...
... Nest-specific factors are known to drive nest decay; for example, nests built at the same time by members of the same group of apes exhibit different decomposition times [40]. However, rainfall is often reported as the most important variable affecting nest decomposition time, with lower rainfall resulting in longer decay times [38,40,41,64]. Therefore, a drier climate would be expected to increase the time for which great ape nests would remain visible in the forest [40]. ...
Article
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Since 1994, IUCN Red List assessments apply globally acknowledged standards to assess species distribution, abundance and trends. The extinction risk of a species has a major impact on conservation science and international funding mechanisms. Great ape species are listed as Endangered or Critically Endangered. Their populations are often assessed using their unique habit of constructing sleeping platforms, called nests. As nests rather than apes are counted, it is necessary to know the time it takes for nests to disappear to convert nest counts into ape numbers. However, nest decomposition is highly variable across sites and time and the factors involved are poorly understood. Here, we used 1,511 bonobo (Pan paniscus) nests and 15 years of climatic data (2003–2018) from the research site LuiKotale, Democratic Republic of the Congo, to investigate the effects of climate change and behavioural factors on nest decay time, using a Bayesian gamma survival model. We also tested the logistic regression method, a recommended time-efficient option for estimating nest decay time. Our climatic data showed a decreasing trend in precipitation across the 15 years of study. We found bonobo nests to have longer decay times in recent years. While the number of storms was the main factor driving nest decay time, nest construction type and tree species used were also important. We also found evidence for bonobo nesting behaviour being adapted to climatic conditions, namely strengthening the nest structure in response to unpredictable, harsh precipitation. By highlighting methodological caveats, we show that logistic regression is effective in estimating nest decay time under certain conditions. Our study reveals the impact of climate change on nest decay time in a tropical remote area. Failure to account for these changes would invalidate biomonitoring estimates of global significance, and subsequently jeopardize the conservation of great apes in the wild.
... Small-scale studies have looked at great ape densities in different forest types in the absence of logging and poaching. Detailed information on nesting patterns of both great apes allowed researchers to investigate the factors impacting nest decay (Morgan et al., 2016) and to develop a method to identify nest building species , which were subsequently applied in the landscape surveys of northern Congo . These studies found high gorilla densities in mixed species forest with open canopy (or forest dominated by terrestrial herbaceous vegetation: "Marantaceae forest") and nesting preference for chimpanzees in monodominant Gilbertiodendron forest Morgan et al., , 2019. ...
... This allowed us to investigate the impact of forest type in the absence of anthropogenic activities. We applied nest decay rates from previously published results from the Goualougo Triangle (Morgan et al., , 2016Sanz et al., 2007), which we assumed to be very similar to Mbeli Bai given the proximity of the sites and the similar survey periods. Similarly, we used a predictive model developed using the Goualougo Triangle nest data to assign the nests in our study to either chimpanzee or gorilla. ...
... We converted nest density into animal density using the conversion factors nest production and nest decay rate. We used a nest decay rate from the nearby Goualougo Triangle (146.4 ± 3 SE days; Morgan et al., 2016). We used published production rates (1.09 ± 0.05 SE nests per day; and applied them to both chimpanzees and gorillas. ...
Article
Full-text available
Small-scale comparisons help us to understand how habitat features and food availability impact primate abundance. This is particularly useful at sites without human impacts, as it allows for the investigation of the natural factors influencing nesting patterns and great ape abundance. We provide a small-scale study of sympatric great ape nests in an unlogged old-growth forest without poaching activities. We conducted a line transect survey (52 km total effort) around Mbeli Bai, a forest clearing in Nouabalé–Ndoki National Park, Congo, applying on-site nest decay rates and assigning nest builder using logistic regression. We found a high occurrence of monodominant Gilbertiodendron dewevrei at Mbeli Bai (34%) that correlates with low great ape densities at Mbeli Bai. Chimpanzees (Pan troglodytes troglodytes) showed a preference for nesting in trees in closed canopy monodominant forest. We found a low percentage of gorilla (Gorilla gorilla gorilla) nests in mixed species forest (35%) and a higher percentage in trees (64%) compared to other study sites. However, generalized additive models found higher gorilla nest encounter rates in mixed species forest with dense understory than in monodominant forest and open understory. We found no indication of higher gorilla densities close to Mbeli Bai than elsewhere, and line transect estimates were lower than the number of gorillas revealed from direct observations. There were substantial differences between our findings and those for nearby sites, demonstrating the utility of small-scale comparisons to further understand the factors determining chimpanzee and gorilla densities within and between sites and the limitations of nest surveys.
... We used a nest-building rate obtained through a long-term study (9). The model used to obtain spatially explicit nest decay rates came from a long-term study that accounted for habitat and nest characteristics, as well as variation in rainfall (10). Models themselves are also prone to biases due to mis-specification, as well as inaccurate or missing explanatory variables. ...
... This gave us a data set of more than 20,000 nests (7521 chimpanzee and 12,524 gorilla nests). During analyses of the survey data, we (i) modeled which factors-both environmental and human-were most likely to drive gorilla and chimpanzee distribution; (ii) used the resulting top-ranked models, which included the important environmental and human variables, to predict nest density surfaces (maps of nest density) for each taxon across their entire geographic range; (iii) applied a model of nest decay based on rainfall (10) to the nest density surfaces to obtain maps of gorilla and chimpanzee density and distribution across their range; and, on the basis of the animal density maps, (iv) estimated great ape abundance in each of the six countries in their range. ...
... Range-wide predictions of nest decay rate were derived from the species-specific averages of each of these variables, except for the Gilbertio variable (set to zero, as nests of this type decay more slowly and are uncommon) and rainfall (average rainfall was calculated using Global Climate Data for precipitation at a 2.5 arc min resolution). The model was assumed to asymptote for average rainfall values above 6 mm and below 3 mm per day to account for saturation at the upper extremes of daily average rainfall and other factors superseding rainfall at the lower extremes (10). The forested portion of WEA straddles the equator between 5.99°N and 5.76°S ranging across a longitude of 8.71°E to 20.49°E, where mean average daily rainfall is 4.7 mm (2.3 to 8.7 mm). ...
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We present a range-wide assessment of sympatric western lowland gorillas Gorilla gorilla gorilla and central chimpanzees Pan troglodytes troglodytes using the largest survey data set ever assembled for these taxa: 59 sites in five countries surveyed between 2003 and 2013, totaling 61,000 person-days of fieldwork. We used spatial modeling to investigate major drivers of great ape distribution and population trends. We predicted density across each taxon’s geographic range, allowing us to estimate overall abundance: 361,900 gorillas and 128,700 chimpanzees in Western Equatorial Africa—substantially higher than previous estimates. These two subspecies represent close to 99% of all gorillas and one-third of all chimpanzees. Annual population decline of gorillas was estimated at 2.7%, maintaining them as Critically Endangered on the International Union for Conservation of Nature and Natural Resources (IUCN) Red List. We quantified the threats to each taxon, of which the three greatest were poaching, disease, and habitat degradation. Gorillas and chimpanzees are found at higher densities where forest is intact, wildlife laws are enforced, human influence is low, and disease impacts have been low. Strategic use of the results of these analyses could conserve the majority of gorillas and chimpanzees. With around 80% of both subspecies occurring outside protected areas, their conservation requires reinforcement of anti-poaching efforts both inside and outside protected areas (particularly where habitat quality is high and human impact is low), diligent disease control measures (including training, advocacy, and research into Ebola virus disease), and the preservation of high-quality habitat through integrated land-use planning and implementation of best practices by the extractive and agricultural industries.
... To convert sign density to animal density, the sign density is divided by its estimated production rate and time to decay (Hedges, 2012;Laing et al., 2003). When correctly designed, these methods work well; however, these multipliers, introduce additional sources of error into density estimates when decay rates from non-representative sites or seasons are used (Morgan et al., 2016). Unfortunately, because of financial or time constraints, estimates from other sites or seasons are occasionally used in lieu of obtaining study-specific degradation rates. ...
... While we focused on forest elephants, these methods could improve indirect transect surveys for other difficult to observe species (e.g. deer, duikers, great apes; Koster and Hart, 1988;Rovero and Marshall, 2004) and could be applied to other types of degrading sign, such as ape nests (Stokes et al., 2010) especially when studies showing environmental influence on decay already exist (Morgan et al., 2016). ...
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Accurate and ecologically relevant wildlife population estimates are critical for species management. One of the most common survey methods for forest mammals – line transects for animal sign with distance sampling – has assumptions regarding conversion factors that, if violated, can induce substantial bias in abundance estimates. Specifically, for sign (e.g. nests, dung) surveys, a single number representing total time for decay is used as a multiplier to convert estimated sign density into animal density. This multiplier is likely inaccurate if not derived from a study reflecting the spatiotemporal variation in decay times. Using dung decay observations from three protected areas in Gabon, and a previous study in Nouabalé‐Ndoki National Park (Congo), we developed Weibull survival models to adaptively predict forest elephant (Loxodonta cyclotis) dung decay based on environmental variables from field collected and remotely sensed data. Seasonal decay models based on remotely sensed covariates explained 86% of the variation for the wet season and 79% for the dry season. These models included canopy cover, cloud cover, humidity, vegetation complexity and slope as factors influencing dung decay. With these models, we assessed sensitivity of elephant density estimates to spatiotemporal environmental heterogeneity, showing that our methods work best for large‐scale studies >50 km2. We simulated decay studies with and without these variables in four Gabonese national parks and reanalyzed two previous surveys of elephants in Minkébé National Park, Gabon. Disregarding spatial and temporal variation in decay rate biased population estimates up to 1.6 and 6.9 times. Our reassessment of surveys in Minkébé National Park showed an expected loss of 78% of forest elephants over ten years, but the elephant abundance was 222% higher than previously estimated. Our models incorporate field or remotely sensed variables to provide an ecological context essential for accurate population estimates while reducing need for expensive decay field studies. Population abundance for elusive species are estimated from sign counts and multipliers of sign production and decay rates. To combat the improper use of single decay rates often obtained from an unrepresentative site, we observed and modeled forest elephant dung decay rates and created adaptive models which incorporate spatiotemporal environmental variables. We showed that disregarding spatial and temporal variation in decay rate can bias population estimates by a factor of up to 1.6 and 6.9 times.
... For example, conditions of the local environment are a commonly acknowledged influence on probability of sign encounter, and heterogeneity is common in metrics of sign de-cay rates across locations (e.g., (Bessone et al., 2021;Kuehl et al., 2007;Walsh White, 2005). In both dung and nestcount surveying, sign decay is affected by climatic seasonality, especially in rainfall, as well as other contextual factors such as construction material or dung matrix, storm frequency, and sun exposure (Bessone et al., 2021;Kamgang et al., 2020;Kouakou et al., 2009;Laing et al., 2003;Morgan et al., 2016;Nchanji Plumptre, 2001;Plumptre, 2000). Because of these environmental influences, it is commonly recommended that local measures of decay rates must accompany surveying, as failure to do so may result in imprecise measurement and hinder validity of inter-site comparisons (e.g., (Bessone et al., 2021;Kühl, 2008;Laing et al., 2003;Mohneke Fruth, 2008). ...
Preprint
Wildlife population monitoring depends on accurate counts of individual animals or artefacts of behavior (e.g., nests or dung), but also must account for potential biases in the likelihood to encounter these animals or artefacts. In indirect surveying, which depends largely upon artefacts of behavior, likelihood to encounter indirect signs of a species is derived from both artefact production and decay. Although environmental context as well as behavior contribute to artefact abundance, variability in behaviors relevant to artefact abundance is rarely considered in population estimation. Here we demonstrate how ignoring behavioral variability contributes to overestimation of population size of a highly endangered great ape endemic only to the Democratic Republic of the Congo, the bonobo (Pan paniscus). Variability in decay of signs of bonobo presence (i.e., nests) is well documented and linked to environmental determinants. Conversely, a single metric of sign production (i.e., nest construction) is commonly used to estimate bonobo density, assumed to be representative of bonobo nest behavior across all contexts. We estimated nest construction rates from three bonobo groups within the Kokolopori Bonobo Reserve and found that nest construction rates in bonobos to be highly variable across populations as well as seasonal within populations. Failure to account for behavioral variability in nest construction leads to potentially severe degradation in accuracy of bonobo population estimates of abundance, accounting for a likely overestimation of bonobo numbers by 34%, and in the worst cases as high as 80% overestimation. Using bonobo nesting as an example, we demonstrate that failure to account for inter- and intra-population behavioral variation compromises our ability to monitor population change or reliably compare contributors to population decline or persistence. We argue that variation in sign production is but one of several potential ways that behavioral variability can affect conservation monitoring, should be measured across contexts whenever possible, and must be considered in population estimation confidence intervals. With increasing attention to behavioral variability as a potential tool for conservation, conservationists must also account for the impact that behavioral variability across time, space, individuals, and populations can play upon precision and accuracy of wildlife population estimation.
... For example, the decay rates of gorilla and chimpanzee nests depend on forest type, nest height, and structure, and above all, precipitation . The decay rates of signs should thus be estimated in the same survey area and season (Laing et al., 2003;Morgan et al., 2016), which may involve substantial effort and costs (Kuehl et al., 2007). Production rates of signs are less variable, hence estimates from similar or nearby sites can be used F I G U R E 1 This review is structured along four key questions that we believe need to be considered when choosing a monitoring method T A B L E 1 An overview of how observations by humans, camera traps, and passive acoustic sensors relate to the characteristics of interest for the four questions discussed in this article (e.g., Theuerkauf & Gula, 2010). ...
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Wildlife monitoring is essential for conservation science and data‐driven decision‐making. Tropical forests pose a particularly challenging environment for monitoring wildlife due to the dense vegetation, and diverse and cryptic species with relatively low abundances. The most commonly used monitoring methods in tropical forests are observations made by humans (visual or acoustic), camera traps, or passive acoustic sensors. These methods come with trade‐offs in terms of species coverage, accuracy and precision of population metrics, available technical expertise, and costs. Yet, there are no reviews that compare the characteristics of these methods in detail. Here, we comprehensively review the advantages and limitations of the three mentioned methods, by asking four key questions that are always important in relation to wildlife monitoring: (1) What are the target species?; (2) Which population metrics are desirable and attainable?; (3) What expertise, tools, and effort are required for species identification?; and (4) Which financial and human resources are required for data collection and processing? Given the diversity of monitoring objectives and circumstances, we do not aim to conclusively prescribe particular methods for all situations. Neither do we claim that any one method is superior to others. Rather, our review aims to support scientists and conservation practitioners in understanding the options and criteria that must be considered in choosing the appropriate method, given the objectives of their wildlife monitoring efforts and resources available. We focus on tropical forests because of their high conservation priority, although the information put forward is also relevant for other biomes. Wildlife monitoring is essential for conservation science and data‐driven decision‐making. We compare three commonly used monitoring methods by following four questions that are always important in relation to wildlife monitoring.
... As such, we used nest data as the only available ecologically sound and unbiased information on abundance distribution. Nest decay rate was shown to vary between gorilla and chimpanzee, and is also influenced by habitat types, nesting material and seasons (Morgan et al., 2016). It is evident that such variable nest decay rate may have influenced our observed nesting site distribution. ...
Thesis
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The ongoing global decline in mammal populations has led researchers and conservationists to question which factors drive their abundance and distribution. Specifically, there is an urgent need to understand how the ranging behaviour of mammals determines their response to human-induced environmental changes. Threats such as hunting and habitat fragmentation and degradation through agricultural expansion and logging have received considerable attention. However, a potential threat resulting from the non-consumptive use of natural systems by humans has often been overlooked. The ecological and anthropogenic factors influencing the abundance and distribution of mammal populations in tropical forests were evaluated using great apes (Gorilla gorilla gorilla and Pan troglodytes troglodytes) as focal species in order to improve understanding of the drivers of local extinction of species. To achieve this goal, data on diet, fruit phenology, botany, and nest abundance and distribution were collected in a design involving a sampling grid and line transects. Data were analysed using modelling techniques in R and ArcGIS. The preferred fruiting plants for both gorillas and chimpanzees were more abundant in chimpanzee-preferred nesting habitats, while their fallback fruits were more abundant in gorilla-preferred nesting habitats. The patterns of habitat use by both gorillas and chimpanzees varied seasonally. In the absence of human disturbance, the distribution of gorilla nests was predicted by the availability of their preferred nesting habitats, while the distribution of chimpanzee nests was predicted by elevation and their preferred nesting habitats. However, when considering the research camp and human settlements, the distribution of gorilla nests was predicted first by the distribution of human settlements and then by their preferred nesting habitats, while chimpanzee nests remained predicted by elevation and their preferred nesting habitats. The long-term monitoring of great ape nests in the research site revealed a decline in both gorilla and chimpanzee populations resulting from an increase in hunting activities in the site. Results suggest that in the absence of human disturbance, ecological factors (habitat preference, seasonal patterns of fruit availability, fruit preference, and spatial distribution of habitat types) may be responsible for seasonal changes in mammal population abundance and distribution. Animal species traits (body size, terrestrial/arboreal, level of specialization/generalization, and competitive inferiority/superiority) have a profound influence on the response of mammals to human activities. Due to their spatial flexibility and their reliance on more available fallback food sources when preferred fruits are scarce, gorillas may vacate areas disturbed by hunting and non-hunting human activities and related noise. Chimpanzees, on the other hand, persist in their preferred nesting habitats despite human disturbance due to their high level of specialisation in fruit consumption. Additionally, the competitive dominance of chimpanzees over gorillas facilitated by their grouping patterns may allow them to cope with human disturbance better than gorillas. Human impacts other than direct killing of animals may influence the abundance and distribution of great ape populations and may account for the long-term decrease in population size. As habitat and resource heterogeneity facilitate the local coexistence of gorillas and chimpanzees, preserving both preferred and fallback fruiting plant species is crucial. Patterns observed in great apes may be an indication that human disturbance is also negatively influencing other mammals. However, species may respond differently to human disturbance, depending on their interaction with other sympatric mammals, their level of dietary specialisation, and their interaction with their physical environment. Further research is required to assess how these biological traits affect mammal response to anthropogenic disturbance. Furthermore, future studies should investigate the threshold beyond which the non-consumptive use of natural systems by humans becomes detrimental to species and their habitats.
... ). Grâce à la méthode d'échantillonnage à distance, nous avons pu calculer l'abondance et la densité des populations des deux espèces avec le logiciel DISTANCE 7.0 qui prend en compte les taux de production et de décomposition des nids.Etant dans la difficulté de garder une équipe sur le terrain pendant suffisamment longtemps pour calculer les taux de décomposition des nids de gorilles de Grauer et de chimpanzés spécifiques à la zone d'étude, nous avons utilisé les taux de décomposition présentés dans le Tableau 4, calculés dans le Parc National de Nouabalé-Ndoki en République du Congo pour les gorilles des plaines de l'ouest (Gorilla gorilla gorilla) et les chimpanzés communs (Pan troglodytes troglodytes)(Morgan et al., 2016).Tableau 4. Valeurs des taux de production et de décomposition des nids utilisées pour estimer l'abondance des gorilles et des chimpanzés dans le PNKB.Des signes de 26 espèces de grands mammifères ont été observés dans le parc (Tableau 5).Tableau 5. Liste des espèces de grands mammifères observées dans le parc. ...
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