Primates (2020) 61:189–200 https://doi.org/10.1007/s10329-019-00768-3 ORIGINAL ARTICLE Assessment of in situ nest decay rate for chimpanzees (Pan troglodytes ellioti Matschie, 1914) in Mbam‑Djerem National Park, Cameroon: implications for long‑term monitoring Serge Alexis Kamgang1,2,6 · Tuneu Corral Carme3 · Kadiri Serge Bobo4 · Ekwoge Enang Abwe5 · Mary Katherine Gonder5 · Brice Sinsin6 Received: 22 October 2018 / Accepted: 8 October 2019 / Published online: 28 October 2019 © The Author(s) 2019 Abstract Accurate assessment of great ape populations is a prerequisite for conservation planning. Indirect survey methods using nest and dung, and a set of conversion parameters related to nest decay rates, are increasingly used. Most surveys use the stand- ing crop nest count (SCNC) method, whereby nests are counted along transects and the estimated nest density is converted into chimpanzee density using an often non-local nest decay rate. The use of non-local decay rate is thought to introduce substantial bias to ape population estimates given that nest decay rates vary with location, season, rainfall, nest shape, and tree species used. SCNC method has previously been applied in Mbam-Djerem National Park (MDNP) in Cameroon, for chimpanzee surveys using a non-local nest decay rate. This current study aimed to measure a local nest decay rate for MDNP and implications for chimpanzee population estimates in the MDNP. The mean nest decay rate estimated using a logistic regression analysis was 127 [95% CI (100–160)] days. Moreover, the results suggested that rainfall strongly influenced the nest decay rate over the early stage of the lifetime of the nests. The study confirms that estimates of chimpanzee density and abundance using non-local decay rates should be treated with caution. Our research emphasized the importance of using local nest decay rates and other survey methods which do not depend on decay rates to obtain more accurate estimates of chimpanzee densities in order to inform conservation strategies of these great apes in MDNP. Keywords Mbam-Djerem National Park · Local nest decay rate · Chimpanzee density · Indirect survey · Conservation strategies Introduction (Guinness 2010) and sheltering most populations of Afri- can great apes (Fruth and Hohmann 1996; Williamson et al. The Congo Basin rainforest is the second largest wilder- 2013). This forest and the fauna inhabiting it are threatened ness area on the planet after the Amazon (De Wasseige by habitat loss, fragmentation, bushmeat hunting, climate et al. 2012), representing 70% of the African forest cover change, emerging infectious diseases, and other activi- ties such as logging and mining (Huijbregts et al. 2003; Walsh et al. 2003; Bermejo et al. 2006; Hart et al. 2008; Electronic supplementary material The online version of this Spehar et al. 2010; Wevers et al. 2011; IUCN 2014; Katsis article (https​://doi.org/10.1007/s1032​9-019-00768​-3) contains supplementary material, which is available to authorized users. 4 * Serge Alexis Kamgang Department of Forestry, Faculty of Agronomy sergekamgang@gmail.com and Agricultural Sciences, University of Dschang, PO Box 222, Dschang, Cameroon 1 Garoua Wildlife School, Face aéroport International de 5 Department of Biology, Drexel University, Philadelphia, Garoua, PO Box 271, Garoua, Cameroon PA 19104, USA 2 Ministry of Forestry and Wildlife, Yaoundé, Cameroun 6 Laboratory of Applied Ecology, Faculty of Agricultural 3 Department of Animal Biology, Plant Biology and Ecology, Science, University of Abomey-Calavi, 01, PO Box 526, Autonomous University of Barcelona, Bellaterra, Cotonou, Bénin 08193 Barcelona, Spain 13 Vol.:(0123456789) 190 Primates (2020) 61:189–200 et al. 2018; Hicks et al. 2010), which are exacerbated by and White 2005; Nzooh et al. 2015; Nzooh et al. 2015). poverty and pervasive corruption (Walsh et al. 2003; Hart Nest or dung surveys have been used to estimate abundance et al. 2008). As a consequence of all these factors, great ape for several mammal species such as bonobos Pan paniscus populations have experienced a precipitous decline within (Serckx et al. 2014), western lowland gorillas Gorilla gorilla the last 40 years (Fruth and Hohmann 1996; Walsh et al. (Walsh and White 2005; Haurez et al. 2014; Tsakem et al. 2003; Williamson et al. 2013). Thus, large-scale conserva- 2015), Bornean orangutans Pongo pygmaeus (Mathewson tion measures are needed urgently in order to avoid further et al. 2008; Spehar et al. 2010), African elephants Loxodonta decline of great ape populations as well as to mitigate the cyclotis (Fay 1991; Barnes et al. 1997; Nchanji and Plumptre various threats (Spehar et al. 2010; Cameron et al. 2016). 2001; Barnes and Dunn 2002; Jathanna et al. 2015), Euro- The common chimpanzee (Pan troglodytes, Blumenbach pean deer Capreolus capreolus, and kangaroos Macropus 1799) has the largest geographic distribution of all nonhu- rufus (Vernes 1999). man great ape species and is located discontinuously from The standing crop nest count (SCNC) method is typi- southern Senegal across the forested belt north of the Congo cally used to estimate chimpanzee densities from nest counts River to western Tanzania and western Uganda (Williamson along transects. In this method, nests are surveyed once and et al. 2013; IUCN 2014; Humle et al. 2016). Four subspecies nest density is converted to population density estimates are recognized: the western chimpanzee (P. t. verus, Schwarz using a production rate and a nest decay rate (Tutin and 1934), the Nigeria-Cameroon chimpanzee (P. t. ellioti, Fernandez 1984; Morgan et al. 2006). The nest creation rate Matschie 1914), the central chimpanzee (P. t. troglodytes, can be obtained by direct monitoring of habituated chim- Blumenbach 1799), and the eastern chimpanzee (P. t. sch- panzees (Tutin and Fernandez 1984), while estimating nest weinfurthii, Giglioli 1872) (Gonder et al. 2006; Gonder et al. decay rate in unhabituated populations requires more time 2009; Prado-Martinez et al. 2013). With less than 9000 wild and resources, and involves monitoring a sufficient num- individuals remaining, P. t. ellioti, the focal subspecies of ber of fresh nests from the time they are built to the time this study, has the smallest geographic range and the small- they disappear (Plumptre and Reynolds 1996). Most surveys est population size of all chimpanzee subspecies (Gonder use a non-locally-acquired nest decay and production rate, et al. 1997; Caldecott and Miles 2005; Morgan et al. 2011). although these two parameters are not reliably comparable All four chimpanzee subspecies are classified as Endangered across sites (Laing et al. 2003; Buij et al. 2003; Kühl 2008), by the IUCN Red List of threatened species (Oates et al. as nest decay rates might be affected by a number of local 2016). Given its more limited geographical distribution and factors such as climatic conditions, habitat type, and plant numbers, P. t. ellioti merits more urgent conservation action species used to build the nests (Laing et al. 2003; Maisels (Morgan et al. 2011). et al. 2009; Singleton 2009; Stokes et al. 2010). The SCNC An effective management of chimpanzees requires an method to estimate great ape densities is financially afford- accurate understanding of their population status includ- able for most protected areas, but few studies on nest pro- ing density, abundance, distribution, threats, and popula- duction and decay rates have been conducted (Plumptre and tion trends in order to prioritize strategies needed for their Reynolds 1996; Morgan et al. 2006). conservation (Kühl 2008; Kalan et al. 2016; Morgan et al. In comparison, the mark nest count method does not use 2011). Chimpanzees are elusive and most populations are the nest decay rate but it takes into account the length of assessed indirectly using nest counts (Plumptre 2000; Laing time between survey intervals (Mathewson et al. 2008; Spe- et al. 2003; Kühl 2008; Spehar et al. 2010; Head et al. 2013; har et al. 2010; Ndimbe et al. 2013). All nests are counted Hicks et al. 2014; Howe et al. 2017; Kamgang et al. 2018). and marked during the first survey and on subsequent sur- In all great ape species, each weaned individual builds each veys only new nests are counted and marked. However, the evening a nest made of leaves, branches, and other vegetal time interval between two consecutive surveys should be material in which to spend the night (Tutin and Fernandez shorter than the minimum time for the nest to disappear 1984). Nests provide comfort, thermoregulation, and protec- (Hashimoto 1995; Plumptre and Reynolds 1996). Several tion against predators and parasites (Kingdon and Largen surveys have been carried out in the MDNP to estimate 1997; Anderson 2000; Pruetz et al. 2008; Stewart et al. 2011; chimpanzee abundance (Maisels et al. 2000, 2009; Kamgang Koops et al. 2012; Samson and Hunt 2012). These nests can et al. 2018). However, all of these previous surveys used the remain intact from a few days to several months (Tutin and SCNC method with a non-locally acquired nest decay rate, Fernandez 1984; Mathewson et al. 2008) and have thus been which may not give a robust estimate of chimpanzee densi- used to ascertain the presence and estimate the population ties in the MDNP. of chimpanzees. Therefore, our study aimed to assess the local nest decay Over the past decades; circumstantial evidence (usually rate of chimpanzees in the core area of the MDNP, as well as animal dung and nests) has been increasingly used in wildlife to detect some of the environmental factors influencing nest surveys (Krebs 1989; Vernes 1999; Laing et al. 2003; Walsh decay rates. As no assessment of nest decay rates has been 13 Primates (2020) 61:189–200 191 conducted previously in this area, our research was essen- the park. Human activities such as poaching, illegal log- tial to achieve more precise estimates of P. t. ellioti density ging, and illegal grazing are among the main threats to the and to shape effective management practices for long-term wildlife in the MDNP landscape (Bobo and Weladji 2011; monitoring in the MDNP. WCS 2017). The study site was chosen based on informa- tion previously gathered on chimpanzee nest abundance and distribution in the core zone of the MDNP (Maisels et al. Methods 2000, 2009; Kamgang et al. 2018) (Fig. 1). Study area Data collection The forest–savannah transition zone in central Cameroon is Nest decay rate can be calculated by either the prospec- of high importance as it preserves interconnecting corridors tive or the retrospective method (Laing et al. 2003; Stokes between populations of the two chimpanzee subspecies (P. t. et al. 2010; Dutton 2012). The prospective method consists ellioti and P. t. troglodytes), which gives the region a unique of marking fresh nests at a given time and revisiting them importance (Mitchell et al. 2015). This region includes the at regular intervals until they disappear (Mathewson et al. MDNP (5° 72′ N, 12° 68′ E), which extends 4165 km2, with 2008). This method may lead to bias if seasonal fluctuations a core zone of 1662 km2 and is the largest protected area of exist in nest decay rates (Laing et al. 2003). In contrast, the forest–savannah mosaic habitat in Cameroon (Bobo et al. retrospective method, which we used in the current study, 2006; WCS 2017). About half of the park in the southern consists of marking fresh nests at different periods of time sector is covered by lowland tropical forest and the other and revisiting them only once in order to verify whether or half, in the northern sector, is covered by Sudano-Guinean not they have decayed. It is expected that during the revisit, tree and woodland savannah with a wide forest–savannah the number of nests remaining from the first visit will be mosaic in between, which promotes a very high level of fewer than those marked later. Using this technique, a period biodiversity (White 1983; WCS 2017). The climate in this of time for a visit is preselected and the mean decay rate of region has two seasons: a rainy season from mid-April to the nests already present can be estimated (Laing et al. 2003; mid-October, and a dry season from mid-October to mid- Mathewson et al. 2008). April (MINFOF 2007; Abwe et al. 2019). The relief is nearly flat, with a north-to-south altitudinal decline from 930 to Nest marking 650 m asl (Bobo and Weladji 2011). The park is drained by the Djerem River, which serves as a permanent water source We carried out 2-week surveys monthly between January for wildlife and more than 74 human communities around 2016 and March 2017. During this period, we patrolled areas Fig. 1  The Mbam-Djerem National Park showing the sur- rounding villages and the loca- tions where chimpanzee nests were marked in the core zone 13 192 Primates (2020) 61:189–200 of high activity for chimpanzee in the core zone to locate and visible (Van Schaik et al. 1995; Plumptre and Reynolds mark fresh chimpanzee nests (less than 24 h old). All nests 1996; Buij et al. 2003) (Supplementary File 3). were marked with codes referring to the survey number (S1, S2,…, S6), the nest group (G1, G2,…, Gn) and the number Data analysis of marked nests of the same group (N1, N2,…, Nn). We also marked other fresh nests as we walked transects dur- We used the software programme QGIS 3.2 Bonn to map ing biomonitoring activities in 2016 (Kamgang et al. 2018). the geographic location of all marked nests in the study area For each nest marked nest and nesting site, we recoded the (Fig. 1). We conducted a logistic regression in R to estimate following information: (1) date, (2) vegetation type (gallery the mean nest decay rate (Laing et al. 2003; Mathewson et al. forest, colonizing forest or savannah), (3) geographic coor- 2008; Nzooh et al. 2015). We considered only two variables dinates, (4) topography of the site (flat, gentle or steep), and in this analysis: the predictor variable “age” and the response (5) total rainfall during the first month of the nest’s lifetime. variable “absence/presence”. This model allowed us to plot the logistic regression curves of the nest age and to deter- Rainfall data mine the number of days needed for 50% of the nests to be considered decayed (Eq. 1). Rainfall data over the same time period as the study were ( ) 1 e𝛼+𝛽X+𝛼i obtained from the Wildlife Conservation Society office at P Y= = , (1) Xi 1 + e𝛼+𝛽X+𝛼i Mbakaou, at the northern edge of the MDNP (Abwe et al. 2019). For each nest marked, we considered the rainfall of where α is a constant and β the coefficient of time, and X the same month the nest was built. We then plotted the cli- representing the time (days). mate graph showing rainfall variation in 2017 (Fig. 2). We tested three logistic regression models in order to reduce the residual deviance following transformation of Revisiting marked nests the predictor variable: the first model (Model 1) used non- transformed data, the second model (Model 2) used the neg- We revisited all marked nests once (Supplementary File 1) ative-inverse transformation and the third model (Model 3) to attest whether or not they were still present. We marked considered the negative-reciprocal-root transformation. We nests that were present as (1) and those that were absent as did not use the logarithmic transformation in this analysis, (0) (Supplementary File 2). We considered a nest absent since sometimes it generates problems when fitting the upper when we were no longer able to identify its structure, that is, tail of the logistic regression curve, which may result in a the leaves had all decayed and only the framework remained biased estimation of the mean nest decay rate (Laing et al. Fig. 2  Climate graph of the 500 27 MDNP over the period of the study (2017) 26 400 25 Mean Temperature (°C) 300 24 Mean Rainfall (mm) 23 200 22 100 21 0 20 Rainfall Temperature 13 Primates (2020) 61:189–200 193 2003). We tested the suitability of the models by compar- Table 1  State of nests revisited (3 months after nest marking) in the ing the Akaike’s Information Criterion (AIC) values and Ganga region and other high-activity chimpanzee areas (HAA) the residual deviance. Furthermore, we tested the model Sectors Number of nests Number of nests Number accuracy by assessing the area under the curve (AUC). We revisited absent of nests derived the mean nest decay rate and the confidence interval present for each model by isolating the X factor from Eq. (1) using Ganga region 198 166 32 the coefficients α and β from each logistic regression (Eq. 2). Other HAA 111 46 65 ( ) Total 309 212 97 log Y1 − 1 + 𝛼 X= . (2) −𝛽 The results obtained in the first logistic regression analy- We also assessed whether or not the other recorded vari- sis showed a clear correlation between “absence/presence” ables influenced the nest decay time. Therefore, we added and “age” in the three models (P < 0.0001), with the prob- “precipitation”, “topography”, and their interactions to each ability of nests still being present decreasing significantly of the models tested previously in a second logistic regres- as age increased (Table 2, Fig. 3). The nest decay times (µX) sion analysis. In each case, we fitted a null model first and calculated were 141 [95% CI (109–186)] days in Model 1, we added the other variables sequentially. We did not con- 127 [95% CI (100–160)] days in Model 2 and 130 [95% CI sider “habitat type” or “altitude” in the models since a high (81–207)] days in Model 3 (Table 3). With a wider confi- proportion of nests were found in forest and the observed dence interval range, Model 3 was less defined compared altitude range was minimal (665–854 m asl). We assumed to Models 1 and 2. However, the three mean decay times that with the present sample size, the effect of altitude on obtained were similar. The same AUC was obtained in all the mean nest decay rate would not likely be accurately Models (0.962) and the AIC was 175.29 in Model 1, 127.25 detectable. We performed a third logistic regression analy- in Model 2, and 130.57 in Model 3. With the lowest AIC, sis considering only “age” and “precipitation” as predictor we considered Model 2 the best model fitting the logistic variables. regression curve. This model also showed the most reduced Finally, we converted the nest density calculated in the residual deviance (123.25) with the negative-inverse-trans- previous study (Kamgang et al. 2018) to chimpanzee density formation, followed by Model 3 with the negative-recipro- and abundance by using the local nest decay rate obtained cal-root-transformation (126.57), and finally Model 1 with in each model. As in Kamgang et al. (2018), the nest pro- no transformation of data (153.29). duction rate was one nest/day/weaned individual and the In the second logistic regression analysis, the effects of proportion of nest builders was one as well. We assessed “topography” and its interactions with other variables were chimpanzee density using Distance 7.2 software (Buckland not significant (P > 0.05) and hence, they were not consid- et al. 2001; Thomas et al. 2010). For this density assessment, ered in the final model (Supplementary File 4). The results we used data from the 2016 survey as well as the local nest obtained from the third logistic regression analysis without decay rate obtained from the current study. We then com- “topography” for Model 2 are presented in Table 4. With pared our results to those found by Kamgang et al. (2018) regards to the first logistic regression analysis, the variable to show how both the chimpanzee density and abundance “age” showed a strong effect on the decaying probability of varied when different non-local and locally acquired nest nests (P < 0.0001). The effect of “precipitation” was also sig- decay rates were used, emphasizing the importance of the nificant (P = 0.034), increasing the probability of nest decay present study. as the total rainfall in the first month increases (Fig. 4a). Under low monthly rainfall (0–120 mm) the mean value of “absence/presence” was 0.47, which decreased below 0.2 when the monthly rainfall was higher (> 120 mm) (Fig. 4b). Results However, the interaction “age*precipitation” was not signifi- cant (P > 0.05). The nest age also influenced the probability We marked and revisited a total of 309 fresh nests (Table 1) of nest decay, especially during the first month of the nest during this study. Among the 296 fresh nests marked in the (Fig. 5a, b). (Supplementary File 5). Ganga region, we were only able to revisit 198 due to time We assessed the chimpanzee density using a nest decay constraints and nest accessibility. We also marked and revis- rate of 127 days from the most suitable model (Model 2), ited 111 nests from other high-activity chimpanzee areas and obtained a value of 0.59 [95% CI (0.41–0.86)] chim- to assess the effects of environmental variables on the nest panzees/km2 with an abundance of 987 [95% CI (683–1427) decay rates. weaned chimpanzees. We then compared these results to those obtained from the 2016 survey (Table 5). 13 194 Primates (2020) 61:189–200 Table 2  Results obtained from fitting the three logistic regression models considering “absence/presence” as the response variable and “age” as the predictor variable Models Residual devi- Deviance df df change P AIC* AUC** ance change Model 1 Null 367.35 308 175.29 0.962 + Age 153.29 214.06 307 1 < 0.0001 Model 2 Null 367.35 308 127.25 0.962 + (−1/age) 123.25 244.06 307 1 < 0.0001 Model 3 Null 367.35 308 130.57 0.962 + (1/√age) 126.57 240.78 307 1 < 0.0001 *Akaike information criterion **Area under curve Fig. 3  The effects of nest age on the probability of nest decay. At observations, being 1 for present nests and 0 for decayed nests. At the the top, the logistic regression curves fitted under the three models: bottom, the ROC curves (d–f) under the respective model they belong a Model 1, b Model 2, and c Model 3. The dots show the recorded to, and their corresponding AUC value Discussion and decay rates, making the derived great ape densities questionable. In this study, we assessed the nest decay Techniques for estimating the abundance and population rate for chimpanzees in MDNP and tested the influence status of chimpanzee using nests are well known. Most of some factors on this parameter. The obtained value studies still rely on non-locally derived nest production allowed us to estimate with greater accuracy the density 13 Primates (2020) 61:189–200 195 Table 3  Estimated mean nest Models µX (days) 95% CI (Plumptre and Cox 2006; Ndimbe et al. 2013) and camera decay time (µX) with the 95% trap sampling (Howe et al. 2017; Agha et al. 2018), which do confidence interval (CI) for each Model 1 141 [109–185] model not depend on nest decay rates, are also important for achiev- Model 2 127 [100–160] ing a reliable density estimate (Mathewson et al. 2008). Model 3 130 [81–207] From our best fitted model (Model 2), there was no evi- In Model 1, X is non-trans- dence of a significant direct correlation between the prob- formed while in Model 2, X is ability of nest decay and “topography”. The latter is a poten- negative-inverse transformed tially overlooked variable which may influence tree cover and in Model 3, X is negative- and thus the availability of plants used for nesting and should reciprocal root-transformed be considered in future studies (Devos et al. 2008). Rainfall had an effect on the mean nest decay rate in the MDNP, of chimpanzees in the MDNP, which could to be useful for increasing the probability of nest decay in the first month of the long-term monitoring of populations across the park the nest’s existence. The effects of rainfall on nest and dung and adjacent areas. decay rates have been reported in several studies (Barnes The calculations of the probability of decay of chimpan- et al. 1997; Nchanji and Plumptre 2001; Walsh and White zee nests is mainly based on nest age, although other fac- 2005; Ndimbe 2013). With older nests, the probability of tors such as precipitation, topography, and plant material decay increases dramatically with little or no effect of rain- could be important (Laing et al. 2003; Walsh and White fall. As the monthly and annual precipitation fluctuates enor- 2005; Mathewson et al. 2008). Our mean nest decay rates mously between regions, it is important to take into account were similar to those found in Campo Ma’an National Park that this effect can vary strongly between sites, even when (south-western Cameroon, 420 km from MDNP), estimated they are located in close proximity to one another (Walsh at 130 days (Matthews and Matthews 2004), as well as in and White 2005). Lopé National Park (Gabon, 710 km from MDNP), estimated Chimpanzee density and abundance in this study were at 114 days (Tutin and Fernandez 1984). In contrast, very similar to previous estimates in the MDNP (Maisels et al. different decay rates have been found at other sites such as 2009; Kamgang et al. 2018). These previous studies high- 46 days at Budongo Forest (Uganda, 2150 km from MDNP) lighted the magnitude of the variation in estimates based (Plumptre and Reynolds 1996), 90 days at Nouabalé-Ndoki on the nest decay rate used, and how unreliable the den- National Park (Republic of Congo, 564 km from MDNP) sity and abundance estimated might be when non-local nest (Morgan et al. 2006), and 88 days at Ebo Forest, Cameroon decay rates are used to convert nest density into chimpanzee (250 km from MDNP) (Ndimbe et al. 2013). Such variation density. As many authors have reported, density estimates in nest decay rates despite proximity and similarity in habitat may be severely biased when using non-locally determined highlights the need for the acquisition of local nest decay nest decay rates (Laing et al. 2003; Wich et al. 2004; Stokes rates. Although it is recommended whenever possible that et al. 2010). When using a mean nest decay rate of 120 days, decay rates be used from the same area and same time period our results are similar to those of the previous studies in as the survey data (Plumptre and Reynolds 1996; Ndimbe MDNP. However, when comparing the extreme values (88 et al. 2013; Nzooh et al. 2015), we had no option but to use and 221 days) considered by Kamgang et al. (2018), the survey data collected from a different time than the present density and abundance of the chimpanzees were overesti- study. Our study aimed at assessing the nest decay rate for mated using 88 days, and underestimated using 221 days. MDNP, which could be used to assess chimpanzee density in Therefore, the assessment of locally determined nest decay the area instead of using a non-local nest decay rate. Further- rates is a crucial priority when applying the SCNC method more, testing other survey techniques such as mark nest count for chimpanzee surveys. Table 4  Results obtained from fitting the logistic regression model considering “absence/presence” as the response variable and “age” and “pre- cipitation” as predictor variables Model 2 Residual devi- Deviance df df change P AIC AUC​ ance change Negative-inverse transformed Null 367.35 308 + (− 1/Age) 123.25 244.10 307 1 < 0.0001 124.77 0.966 1 0.034 4.48 306 + Precipitation 118.77 13 196 Primates (2020) 61:189–200 Fig. 5  a The effects of the interaction “Age*Precipitation” (logistic regression curve of “Precipitation” fitted to nest data considering a nest age found between 0 and 250 days). b The effects of the interac- Fig. 4  a The effects of “precipitation” on the probability of nest tion “age*precipitation” (logistic regression curve of “precipitation” decay (logistic regression curve of “precipitation” fitted to nest data). fitted to nest data considering a nest age above 250 days) b The effects of “precipitation” on the probability of nest decay (“absence/presence” mean value and its 95% confidence interval con- sidering four ranges of the variable “precipitation” corresponding to the total rainfall during the first month of the nests: 0–120 mm, 120– were only able to evaluate a small region compared to the 240 mm, 240–360 mm, and > 360 mm) extent of the entire national park. The presence of the forest–savannah mosaic in MDNP increases the heterogeneity of both habitat characteristics and climatic conditions even more (Abwe et al. 2019) and, It is also important to highlight that the heterogeneity consequently, might also increase the geographical hetero- in the nest decay rate may not only depend on the fluc- geneity of the mean nest decay rate. At Ugalla, Tanzania, tuation of the climatic factors (such as rainfall), but other Stewart et al. (2011) and Hernandez-Aguilar et al. (2013) variables such as the nesting materials (tree species) and showed how environmental characteristics determined the nest height (Laing et al. 2003; Wich et al. 2004; Mathew- nesting site choice, highlighting the effects of habitat het- son et al. 2008). Also, factors like lignin content and the erogeneity on the nest decay rate variation. We recommend location of the nest in the tree may be considered as other more surveys to be conducted in the forest–savannah mosaic factors which might influence nest decay rate and should area since few fresh nests were found in this habitat dur- be the focus of further investigations. In this study, we ing our study. The use of the SCNC technique using locally 13 Primates (2020) 61:189–200 197 Table 5  Chimpanzee density Nest decay time Chimpanzees/km2 [CI] Chimpanzee abundance [CI] Studies and abundance at the core area (days) of MDNP and their respective 95% confidence interval (CI) for 88 0.83 [0.32–2.11] 1396 [535–3643] (Kamgang et al. 2018) each mean decay time 120 0.61 [0.23–1.59] 1026 [397–2650] 221 0.33 [0.12–0.86] 557 [216–1439] 127 0.59 [0.41–0.86] 987 [683–1427] Present study 130 0.58 [0.40–0.84] 964 [667–1394] 141 0.53 [0.37–0.77] 889 [615–1285] acquired nest production and decay rates to estimate nest and (Laing et al. 2003; Walsh and White 2005; Mathewson animal densities is valuable in reducing biases in the long et al. 2008). In order to improve the accuracy and preci- term. One strategy to avoid temporal heterogeneity biases of sion of nest decay rates and density estimates, a larger area the nest decay rate could be to estimate correlations between should be surveyed to obtain a more representative nest decay rate and factors influencing this parameter such as sample size for the whole national park and thus reduce habitat, rainfall, and nesting material. It might thus be pos- the source of error based on spatial fluctuations of the sible to estimate the decay rate in a given time interval based nest decay rate. We recommend that researchers carry out on data obtained from these covariates during the period in repeated surveys over time in order to minimize the prob- which the survey was performed (Barnes et al. 1997). An lem of temporal heterogeneity of this parameter as well as effective solution to detect population trends and changes in to detect short-term fluctuations of chimpanzee population population size is to carry out surveys repeatedly over long densities (Maisels et al. 2009; Morgan et al. 2011). Finally, periods of time (Morgan et al. 2006). In addition, Mathew- both the use of site-specific parameters as well as effi- son et al. (2008) suggested enhancing the detection of short- cient threat mitigation are essential tasks for an effective term population changes by monitoring the main threats to management and conservation of great ape populations the species at each site. Hence, an indirect estimation of the throughout their range. population variation could be obtained by measuring the magnitude of the fluctuations of these threats. We hope that Acknowledgements We thank the country director of Wildlife Conser- vation Society Biodiversity Conservation Program, Dr. Roger Fotso, by providing this local nest decay rate, our findings may help the WCS Mbam-Djerem National Park (MDNP) project manager, Mr. the MDNP management to more accurately assess chimpan- Bernard Fosso, and the conservator of the MDNP, Mr. Albert Mounga, zee population densities in order to shape their conservation for their collaboration and logistical support during fieldwork. Thanks strategies. Also, these results could alert other researchers also to Dr. Fiona Maisels for providing the inspiration for this study. Analyses were conducted as part of the Ph.D. dissertation of the first to the importance of assessing site-specific nest decay rates author, which was funded by WWF Russell E. Train Education for over the distribution range of great ape species, which will Nature and the Rufford Foundation for Nature Conservation. We also be crucial to making accurate comparisons of population thank Dr. Jorgelina Marino, Dr. Paul Johson, Dr. Zacharie Nzooh abundance across the landscape. Dongmo, and Dr. Julia Baum for their comments on initial drafts, and the editor and reviewers for their helpful comments. Authors contributions SAK, KSB, MKG, and BS designed the Conclusions research. SAK and EE Abwe marked the nests. SAK and CTC revis- ited the marked nests and performed data analysis. SAK and CT wrote In the present study, we estimated an in situ nest decay rate and revised the manuscript. All of the authors read and approved the final manuscript. of 127 [95% (100–160)] days, for an estimated abundance of about 987 [95% (683–1427)] weaned chimpanzees in Funding This research was funded by grants awarded to Serge Alexis MDNP. With this figure, the MDNP should be considered Kamgang from WWF Russell E. Train Education for Nature (Agree- as a site of exceptional priority for the conservation of ment #ST62) and the RSG Foundation (RSG 13184-2). The founders Nigeria-Cameroon chimpanzee, given the large area of had no role in study design, data collection and analysis, and 505 deci- sion to publish the findings. potential habitat and the long-term potential of the site (Morgan et al. 2011). Rainfall in a warm tropical region such as this plays an important role over the first period Compliance with ethical standards of a nest’s lifetime, significantly increasing the probabil- Conflict of interest The author(s) declares that they have no conflict ity of nest decay. Other factors such as type of habitat or of interest. tree species should be considered in future studies, since Our relationship with World Wide Fund for Nature (WWF) and Ruf- they may also have effects on the mean nest decay rate ford Small Grant (RSG) Foundation is simply that of a donor/grantee 13 198 Primates (2020) 61:189–200 relation. This does not hinder our commitment to the editorial policies Great Apes. PLoS One 11:e0154505. https​://doi.org/10.1371/ of Elsevier on publishing findings, data, and materials. journ​al.pone.01545​05 De Wasseige C, De Marcken P, Bayol N, Hiol-Hiol F, Mayaux P, Consent for publication Not applicable. Desclée B, Nasi R, Billand A, Defourny P, Atyi EA (2012) The forests of the Congo Basin: state of the forest 2010. 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