Edited by: Jordi Figuerola, Estación Biológica de Doñana (EBD), Spain
Reviewed by: Alex Kenneth Piel, Liverpool John Moores University, United Kingdom; Beate Anna Apfelbeck, Wissenschaftszentrum Weihenstephan, Technische Universität München, Germany
This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution
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Savanna-mosaic habitats are thought to represent exceptional circumstances for chimpanzees (
Marginal habitats, typically peripheral in a spatial perspective of a species' biogeographic range (Kawecki,
Most research centered on species' habitat marginality is either theoretically- or experimentally-based work concerning small-bodied and short lived species (see Sexton et al.,
Savanna-mosaic habitats generally lie on the periphery of the chimpanzee's historical range, and as such, most research on savanna-mosaic chimpanzees was conducted under the assumption that these habitats are marginal to the chimpanzee ecological niche (McGrew et al.,
In absence of comparative demographic or life history characteristics, some circumstantial evidence appears to support this marginal categorization. For example, it is well established that chimpanzees in savanna habitats occur at lower densities than chimpanzees in more heavily forested habitats (e.g., Baldwin et al.,
Additionally, savanna habitats are notably drier than rainforest or forested habitats (Figure
Average annual temperature (°C) and annual rainfall (mm) for chimpanzee long-term research sites (
As such, our interpretation of the breadth of chimpanzee ecological and environmental tolerance shapes our perception regarding how adaptable and flexible a species like the chimpanzee can be in their behavior, physiology, or ecology, as well as to the range of conditions which can be considered suitable habitat. Additionally, implicit in the “marginal” denomination is the presumption that forested habitats are more suitable habitats for chimpanzees (Boesch,
Fongoli, Senegal, at the edge of the West African chimpanzee range, is considered to be at the extremes of a habitable ecological continuum for chimpanzees, and a coarse comparison of climatic data supports this assumption (Figure
We therefore compared physiological, climatic, and ecological data collected at Fongoli (Wessling et al.,
We intend to offer insight into the manner in which the assumed marginal savanna habitat imposes stresses on chimpanzees in comparison to presumably lusher habitats considered to promote higher chimpanzee population density (Boesch,
We compare our previously published dataset (Wessling et al.,
The Fongoli community (
In order to test our predictions, we first determined the extent to which the Taï environment varied seasonally relative to Fongoli. We next investigated whether or not Taï chimpanzees demonstrated seasonal physiological variation in three key biomarkers of heat (cortisol), hydration (creatinine) and nutritional (c-peptide) stress, and to relate this variation to seasonal variation of these biomarkers in Fongoli chimpanzees. Finally, we investigated potential environmental measures responsible for the biomarker variation observed at each site. In order to perform these tests, we acquired a wealth of environmental, behavioral, and physiological data collected at both sites, which we have detailed below. This non-invasive research was carried out in accordance with the recommendations of the American Society of Primatologists (ASP) Principles for the Ethical Treatment of Non-Human Primates, and the protocol was approved by the Max Planck Society.
Climatic and rainfall data were collected daily at the base camps of each community, located within each community's home range. Rainfall data were collected using a rain gauge, and measurements below the measureable limit of the gauge were set to the detection limit (0.1 mm). At Fongoli, a data logger (Onset Hobo Temp/RH Data Logger) was used to collect ambient temperature and relative humidity at 10-min intervals, in addition to a min-max thermohygrometer that was checked manually and reset daily on days when a researcher was at camp. At Taï, min-max thermohygrometers were checked and reset daily for each community. Daily temperature and humidity means were calculated as the average of the minimum and maximum temperature for the day, following the methods described in Wessling et al. (
For chimpanzees, a ripe-fruit specialist, we calculated three potentially relevant food availability indices (total food [FAItotal], total fruit [FAI
We used urinary c-peptide, creatinine, and cortisol levels to determine potential physiological responses to variation in nutrition, water consumption, and heat exposure respectively. At Fongoli, samples were collected in the morning below night nests and during focal follows of adult individuals, although sample collection was not restricted to focal individuals but were collected
For comparison with our dataset of Fongoli urine samples (
To control for the potential influence of behavioral correlates on biomarker variation, we collected behavioral data using full-day focal animal sampling (Altmann,
Samples collected from females at Taï who gave birth within 8.5 months' post-sample collection were classified as pregnant samples. Furthermore, since miscarriages may occur unnoticed by researchers, we examined all remaining female samples for exceptionally high pregnanediol levels indicative of pregnancy and detected four samples with high pregnanediol values. We therefore performed pregnancy tests upon these samples; all tests were negative and therefore these samples were included in our dataset as non-pregnant. No Fongoli females were pregnant during the study period.
We used the number of oestrus females seen over the course of a focal follow at Fongoli and the total number of oestrus females observed within each community by all observers at Taï as a control variable in all cortisol models. We used this variable as a rough proxy for perceived social stress (Muller et al.,
Chimpanzees live in groups characterized by fission-fusion social organization, and are well-known to vary their traveling parties according to ecological factors (Goodall,
Full details about sample analysis can be found in Wessling et al. (
Urinary cortisol levels were measured using liquid chromatography–tandem mass spectrometry using an adaptation of Hauser et al.'s (
To test our hypotheses, we conducted four sets of analyses (see Supplementary Material 1 for model sets and individual model construction) using the statistical program R (version 3.3.1; R Core Team,
Initially, we tested for differences between the sites in seasonal variation of 13 environmental variables (models 1.1–1.13) as well as the three physiological indicators (creatinine, c-peptide, cortisol; models 2.1–2.3). For the physiological models to meet the assumptions of normally distributed and homogenous error, c-peptide, and cortisol were log transformed whereas creatinine was square root transformed. To test for seasonal effects, we included into each model a seasonal term allowing for a single seasonal cycle and represented by both sine and cosine of Julian date (divided by 365.25 and then multiplied by 2π; Stolwijk et al.,
To account for potentially confounding effects on physiological status, we included several additional control predictors in the three biomarker models. None of the effects of these control predictors are expected to differ between sites, and therefore we did not include their interactions with the “site” term. Sample collection time was used to control for any potential diurnal effects on the indicators of interest. We also included individuals' dominance rank and sex to account for physiological differences among individuals. We used the proxy of number of oestrus females present in the focal party on the day prior to sample collection to control for potential social stress in the cortisol model. Additionally, we included the number of times the focal animal drank on the previous day, as well as the interaction between average party size and the method used to measure it as control predictors in the creatinine and c-peptide models, respectively (see Wessling et al. (
In order to test for the differences in each response between the two sites, we compared the fit of the full models (using a likelihood ratio test) with that of a respective null model lacking the term “site” (and therefore also the respective interactions), but being otherwise identical. As we were interested in determining whether the seasonal variation at the two sites differed in magnitude but not whether it differed in timing, we fitted a seasonal model with date adjusted such that the seasonal pattern was synchronous in the two sites (i.e., identical timing of peaks; models 2.1–2.3). Then to test the interaction between site and season we conducted a likelihood ratio test of the adjusted date seasonal model with a reduced model lacking only the interaction.
Second, as we were also interested in the differences between sites in the effect of both creatinine and c-peptide on cortisol variation, we fitted an identical model (model 3.1) to that described above for cortisol, however using the original unadjusted seasonal variation. To evaluate the effect of creatinine and c-peptide on cortisol, we compared the full model with models lacking each predictor one at a time (Barr et al.,
We used visual inspection of qq-plots and residuals plotted against fitted values to verify that each model met the assumptions of normally distributed and homogeneous residuals. These plots indicated three extreme outliers (residual values <-3) in the cortisol model originating from exceptionally low cortisol values in the Fongoli dataset. We therefore re-fitted the same model excluding these three samples, and found clear improvement in adherence to assumptions of normality and homogeneity of residuals and dropped these three points from the data. We evaluated model stability by excluding levels of the random effects one at a time and comparing the estimates derived from these datasets with those derived for the full dataset and found that all three models were sufficiently stable. Variance Inflation Factors (VIF) determined for a standard linear model excluding the random effects and interactions using the function vif of the R-package car (Fox and Weisberg,
Thirdly, to test for the effect of environmental variables on the three biomarkers, we replaced the generic seasonal term in models 2.1–2.3 with several potentially relevant predictors interacting with site (models 4.1–4.3). We fitted these models regardless of the outcome of the adjusted-seasonal models, as although seasonal variation may or may not differ between sites, the influence of potential environmental predictors should explain differences in timing in seasonal variation of the three biomarkers between the two sites.
To reduce redundancy among the environmental variables, we condensed several similar climatic variables (daily means, minima, maxima) of relative humidity, and temperature as well as mean heat index of all three communities into two uncorrelated factors using Factor Analysis (Supplementary Tables
VIFs of models comprising the environmental factors indicated problems with collinearity among predictors (maximum VIF: 50.35). Subsequently, we discovered a lack of overlap of the distributions of most environmental predictors (i.e., both climate factors and FAIs) between the sites, thereby greatly limiting the explanatory power of our models. To overcome this issue, we applied within-community centering for all environmental predictors except rain and monthly rain, whereby we subtracted the respective community mean per variable from each value of a given variable. Following the centering of environmental variables, VIFs suggested collinearity problems had been reasonably resolved (max VIF: 3.22 for “sex”).
We then compared the fit of each environmental model to a null model comprising all original predictors except “site” and the corresponding two-way interactions using a likelihood ratio test (Dobson,
Lastly, to further understand the effects of environmental variables on c-peptide and cortisol individually at each site, we re-fitted these environmental models on datasets from each site (models 5.1–5.4). These were not specific tests of our hypotheses and therefore are considered exploratory
At Taï, all climatic variables (temperature, rainfall, humidity, heat index) and food availability indices significantly varied seasonally within the year (models 1.1–1.13; Figure
Variation during one annual cycle (2013–2014) of environmental variables for three chimpanzee communities. These include
The magnitude of seasonal variation in creatinine values did not significantly differ between the two sites, nor did average creatinine values significantly differ between sites (model 2.1, controlling for the timing of seasonal; full-null model comparison: χ2 = 0.569, df = 3,
Results of models comparing seasonal variation in
|
||||
---|---|---|---|---|
(Intercept) | 0.961 ± 0.045 | |||
site |
0.021 ± 0.054 | 0.239 | 0.887 | |
site |
0.016 ± 0.047 | |||
site |
0.033 ± 0.053 | |||
sine (Julian date) | 0.141 ± 0.038 | |||
cosine (Julian date) | 0.109 ± 0.030 | |||
Taï South community |
−0.047 ± 0.055 | |||
Rank | 0.131 ± 0.097 | |||
Drinks |
0.025 ± 0.022 | |||
Sample collection time |
−0.011 ± 0.023 | |||
Sex |
0.004 ± 0.064 | |||
(Intercept) | 1.612 ± 0.250 | |||
site: sine (Julian date) |
< |
|||
site: cosine (Julian date) |
− |
|||
Site |
−0.792 ± 0.121 | |||
sine (Julian date) | 0.328 ± 0.073 | |||
cosine (Julian date) | −0.115 ± 0.064 | |||
Taï South community |
−0.013 ± 0.126 | |||
Pregnant |
0.016 ± 0.243 | |||
Rank | 1.108 ± 0.198 | |||
Party size |
0.169 ± 0.108 | |||
(Party size) method |
0.337 ± 0.241 | |||
Sample collection time |
0.069 ± 0.044 | |||
Sex |
−0.337 ± 0.155 | |||
Party size |
−0.015 ± 0.144 | |||
(Intercept) | 4.009 ± 0.103 | |||
site |
− |
|||
site |
− |
|||
Site |
−0.209 ± 0.122 | |||
sine (Julian date) | 0.514 ± 0.079 | |||
cosine (Julian date) | 0.162 ± 0.055 | |||
creatinine |
1.255 ± 0.054 | |||
c-peptide |
−0.013 ± 0.058 | |||
Taï South community |
0.029 ± 0.128 | |||
Estrus |
0.066 ± 0.035 | |||
Rank | −0.140 ± 0.241 | |||
Sample collection time |
−0.128 ± 0.043 | |||
Sex |
0.008 ± 0.153 | |||
Pregnant |
0.612 ± 0.222 | |||
site |
0.200 ± 0.074 | |||
site |
−0.119 ± 0.082 |
Adjusted and unadjusted seasonal variation of creatinine
In contrast to creatinine, Fongoli and Taï chimpanzees differed either significantly in the magnitude of seasonal variation or in overall levels of their c-peptide values (model 2.2; full-null model comparison: χ2 = 39.486, df = 3,
Lastly, we found that Fongoli and Taï significantly differed either overall or in the variation of their cortisol values (model 2.3; full-null model comparison: χ2 = 15.107, df = 3,
We next tested the independent effects of creatinine and c-peptide on cortisol variation at the two sites, while still controlling for seasonal variation at each site. We found that the contribution of these biomarkers to cortisol variation significantly differed between Fongoli and Taï chimpanzees (model 3.1, Figure
Effects of urinary c-peptide
Model results for the effects of urinary creatinine and c-peptide on cortisol levels.
|
||||
---|---|---|---|---|
(Intercept) | 4.009 ± 0.103 | |||
|
||||
site |
−0.119 ± 0.082 | 2.041 | 0.153 | |
Site |
−0.209 ± 0.122 | |||
sine (Julian date) | 0.514 ± 0.079 | |||
cosine (Julian date) | 0.162 ± 0.055 | |||
Creatinine |
1.255 ± 0.054 | |||
c-peptide |
−0.013 ± 0.058 | |||
Taï South community |
0.029 ± 0.128 | 0.050 | 0.823 | |
Estrus |
0.066 ± 0.035 | 3.422 | 0.064 | |
Rank | −0.140 ± 0.241 | 0.319 | 0.572 | |
Sample collection time |
−0.128 ± 0.043 | 7.601 | 0.006 | |
Sex |
0.008 ± 0.153 | 0.002 | 0.962 | |
Pregnant |
0.612 ± 0.222 | 6.753 | 0.009 | |
site |
−0.253 ± 0.124 | 4.123 | 0.042 | |
site |
−0.136 ± 0.102 | 1.756 | 0.185 |
The effect of four environmental predictors on creatinine values clearly differed between sites (model 4.1; χ2 = 20.472, df = 5,
Model results for inter-site differences of environmental variables on urinary biomarkers at Fongoli and Taï.
|
||||
---|---|---|---|---|
(Intercept) | 1.011 ± 0.046 | – | – | |
site |
0.022 ± 0.054 | 0.158 | 0.691 | |
site |
0.128 ± 0.069 | 3.347 | 0.067 | |
|
− |
|||
|
− |
|||
site |
0.000 ± 0.055 | – | – | |
Relative humidity |
−0.127 ± 0.026 | – | – | |
Temperature |
−0.035 ± 0.026 | – | – | |
Rainfall | 0.057 ± 0.027 | – | – | |
Monthly rainfall | 0.037 ± 0.028 | – | – | |
Rank | 0.077 ± 0.095 | 0.646 | 0.422 | |
Taï South community |
−0.036 ± 0.056 | 0.397 | 0.529 | |
Drinks |
0.014 ± 0.019 | 0.550 | 0.458 | |
Sample collection time |
0.016 ± 0.023 | 0.503 | 0.478 | |
Sex |
0.019 ± 0.064 | 0.087 | 0.768 | |
(Intercept) | 1.506 ± 0.273 | |||
|
− |
|||
|
− |
|||
|
||||
site |
−0.089 ± 0.080 | 1.245 | 0.265 | |
site |
−0.149 ± 0.148 | 0.996 | 0.318 | |
site |
−0.818 ± 0.137 | |||
FAItotal |
0.202 ± 0.101 | |||
FAItotal fruit |
0.206 ± 0.129 | |||
FAIripe fruit |
−0.176 ± 0.131 | |||
Rainfall | 0.076 ± 0.060 | |||
Monthly rainfall | −0.004 ± 0.061 | |||
Taï South community |
0.090 ± 0.131 | 0.465 | 0.495 | |
Rank | 0.100 ± 0.242 | 0.165 | 0.685 | |
Party size |
0.222 ± 0.122 | |||
Party size method |
0.521 ± 0.274 | |||
Sample collection time |
0.082 ± 0.047 | 2.690 | 0.101 | |
Sex |
−0.368 ± 0.156 | 4.973 | 0.026 | |
Pregnant |
1.295 ± 0.221 | 31.183 | 0.000 | |
Party size |
0.071 ± 0.158 | 0.200 | 0.655 | |
(Intercept) | 4.028 ± 0.105 | |||
|
||||
site |
0.089 ± 0.107 | 0.686 | 0.408 | |
site |
−0.053 ± 0.076 | 0.483 | 0.487 | |
|
− |
|||
site |
−0.161 ± 0.124 | |||
Relative humidity |
−0.357 ± 0.055 | |||
Temperature |
0.062 ± 0.038 | |||
Rainfall | 0.038 ± 0.052 | |||
Monthly rainfall | 0.095 ± 0.060 | |||
Taï South community |
0.001 ± 0.129 | 0.000 | 0.995 | |
Creatinine |
1.355 ± 0.038 | 140.771 | <0.001 | |
c–peptide |
−0.042 ± 0.041 | 1.037 | 0.309 | |
Estrus |
0.072 ± 0.035 | 4.125 | 0.042 | |
Rank | −0.056 ± 0.240 | 0.052 | 0.820 | |
Sample collection time |
−0.112 ± 0.039 | 7.498 | 0.006 | |
Sex |
−0.063 ± 0.153 | 0.164 | 0.685 | |
Pregnant |
0.470 ± 0.215 | 4.720 | 0.030 |
Effects of four environmental variables on urinary creatinine levels in three chimpanzee communities. Environmental variables displayed are: Factor 1 (relative humidity, centered to site;
The impact of three food availability indices as well as rainfall the day prior and monthly rainfall differed between the two sites (model 4.2; full-null model comparison: χ2 = 66.929, df = 6,
Effects of five environmental variables on urinary c-peptide levels in three chimpanzee communities. Environmental variables displayed are: total food availability (
When replacing the generic seasonal term with the four environmental variables (as in creatinine model), we found that Fongoli and Taï chimpanzees significantly differed in the contribution of these variables to cortisol variation (model 4.3; full-null model comparison: χ2 = 17.820, df = 5,
Effects of four environmental variables on urinary cortisol levels in three chimpanzee communities. Environmental variables displayed are: Factor 1 (relative humidity, centered to site;
Our results support the assertion that Fongoli as a savanna-mosaic habitat is more extreme and seasonal in its climate and ecology than a closed-canopy site independently of its effect upon chimpanzees. As for how this extreme and seasonal environment impacted chimpanzees, our results both supported and refuted our expectations. Fongoli chimpanzees appeared more seasonably stable in their c-peptide values than Taï chimpanzees, but showed more extreme variation in their cortisol values. Chimpanzees at both sites struggled equally with issues of dehydration, as indicated by seasonal creatinine variation and its impact upon cortisol values. As a result, categorizing savanna habitats as more challenging to chimpanzees than more heavily forested habitats, in the sense that they are singularly more physiologically costly and represent a niche to which they are not adapted, does not appear to be necessarily warranted as these habitats appear to exert constraints upon chimpanzees along different niche axes (Holt,
We found Fongoli to be significantly more extreme in both absolute levels as well as in the seasonal variation of all climatic markers. These results support previous comparisons among long term chimpanzee research sites that have concluded that savanna-woodland or open habitat environments are generally more extreme, with Senegalese habitats considered particularly exceptional in their extremity (McGrew et al.,
Possibly as a consequence of greater seasonality in food availability metrics, specifically overall food availability and total fruit availability in Taï, Taï chimpanzees appeared to be less energetically stable than Fongoli chimpanzees. Specifically, we found that Taï chimpanzees' c-peptide levels varied more strongly and were on average lower than Fongoli c-peptide levels, and that this variation was more heavily driven by fruit availability (specifically ripe fruit availability). These results contrast with the assumptions that savanna habitats offer fewer nutritional or energetic possibilities to the primates that inhabit them. Therefore, limitations stemming from lower arboreal density and diversity have been proposed to be restrictive to savanna chimpanzees, potentially necessitating alternative behavioral adaptations to accommodate reduction of opportunities (Kortlandt,
However, although Fongoli does have lower tree density in comparison to Taï, and the chimpanzees a reduced dietary breadth (Pruetz,
It therefore appears that Fongoli chimpanzees are able to cope with reduced food tree density and more seasonally extreme variation in preferred ripe fruit food items. Coping mechanisms range from dietary adjustment according to availability, depending on fallback or staple food items (Bogart and Pruetz,
However, as several variables contribute to energetic status in wild organisms, we must also offer concessions regarding these conclusions. First, we were unable to account for potential seasonal differences in energetic expenditure in our models. Therefore, it is possible that these three communities may not only differ in their energetic consumption but also that energetic expenditure effects inter-site differences in c-peptide levels. Taï chimpanzees regularly patrol territorial boundaries (Samuni et al.,
Contrary to c-peptide, Fongoli and Taï chimpanzees did not differ in the degree of seasonal variation of creatinine values, and at both sites creatinine showed a strong positive relationship with cortisol variation. To add to this, this relationship was significantly stronger for Taï than Fongoli chimpanzees, suggesting that not only do Taï chimps also show signs of hydration constraints, but that they do to a greater degree than chimpanzees occupying a much hotter and drier environment. While puzzling, these results may be indicative that either the climate at Taï is sufficiently dry to elicit dehydration challenges, that preformed water in their diet is an insufficient contributor to their hydration status, and/or that the behavioral strategies adopted by the Fongoli chimpanzees afford them partial evasion of potential stress otherwise expected by an even harsher environment.
Although among the wettest of all chimpanzee long term research sites, Taï chimpanzees exhibited virtually identical hydration challenges during the late dry season as Fongoli chimpanzees, indicating that annual rainfall as a measure of habitat wetness is likely not a reliable indicator of the potential dehydration challenges an individual may face in a particular environment. Although dryer sites like Fongoli, Mt. Assirik, and Ugalla are expected to be the most challenging for maintaining hydration, a site's “dryness” is not a dependable indicator of potential dehydration stress, as “wet” sites like Taï may also impose dehydration challenges. Rather, McGrew et al. (
Additionally, marked decreases in rainfall have been observed both regionally and locally at Taï over decades of observation (Paturel et al.,
At its core, our results suggest that chimpanzee habitats do not need to be relatively “dry” to pose a risk of dehydration to chimpanzees. Even in a moderately cooler and more stable site like Taï, chimpanzees experience dehydration, and therefore possible that dehydration challenges are experienced by chimpanzees throughout their range, at least in areas where rainfall is seasonal. Further investigations of creatinine and cortisol variation due to environmental changes across multiple chimpanzee localities will help elucidate this relationship. In our recent paper (Wessling et al.,
Ultimately, we found that cortisol levels of Fongoli chimpanzees tended to vary in a more extreme seasonal fashion than cortisol levels from Taï, indicating stronger seasonal constraints upon Fongoli chimpanzees. These results follow our prediction that the extreme habitat of Fongoli coupled with exceptional seasonality in climatic and ecological factors elicits stronger seasonal stress, specifically during the late dry season. Such conclusions are concordant with other range margin literature that has found that range margins are typically defined by abiotic or climate factors (see Sexton et al.,
Surprisingly, we found that differences in temperature variation appeared to have little effect on cortisol variation, but rather that relative humidity impacted chimpanzee cortisol at both sites. However, this effect was stronger in Fongoli than in Taï chimpanzees. Cortisol peaks toward the end of the dry season when both water is scarce (low humidity), temperatures are high, and humidity is low. It is likely that relative humidity more effectively captures the extremity of the end of the dry season than temperature, which remains high throughout the dry season. Relative humidity's inverse relationship with conditions of the dry season indicates that these conditions are significant, and further underlines the importance of adequate hydration levels for chimpanzees during the hottest period of the year.
In concert, these results supported our hypothesis that Fongoli, a site generally considered marginal, poses significantly higher seasonal costs to chimpanzees than less seasonal habitats. However, the extent of ecological differences in chimpanzee habitats, such as rainfall or food availability, yet a lack of difference in physiological response between these habitats suggests that behavioral flexibility is an important contribution to widening chimpanzee ecological tolerance, and perhaps even the chimpanzee niche. Such range expansion or ecological tolerance due to behavioral flexibility is a widely observed phenomenon found in many other taxa (e.g., Suarez et al.,
Here we have provided a first attempt at examining in situ effects of ecological and range marginality in a long-lived, large-bodied mammal. Our results have important implications for our understanding of biogeography, ecological tolerance, and more specifically, the limits of both for extant chimpanzees and extinct hominins. Although chimpanzees at both sites appeared to exhibit significant seasonal effects of dehydration, the more extreme environmental variation and general conditions at Fongoli prompted higher physiological seasonal costs (in the form of cortisol). Therefore, it appears that extreme savanna habitats serve as a thermoregulatory limit to the chimpanzee ecological niche in the hot and dry conditions of the dry season, and strain an individual's ability to maintain homeostasis during parts of the year. Such results support the use of savanna chimpanzees as models for understanding adaptation to marginal environments, such as that which has been proposed during the expansion of hominins into savanna habitat from forested habitat (Reed,
Nonetheless and contrary to expectation, Taï chimpanzees also appeared to face dehydration pressure, therefore refuting the assumption that savanna habitats are the only habitats in which water is a limiting factor (McGrew et al.,
Although stress response alone does not indicate long-term effects on an individual nor reduced habitat suitability, and fitness measures would better serve to address questions of marginality of chimpanzee habitat, we provide the first steps needed to approach such questions. For now, assumptions that the chimpanzee is strictly forest adapted (Boesch,
At a minimum, it appears therefore that savanna habitats still lie within the chimpanzee ecological niche. This should encourage researchers to view chimpanzee behavioral syndromes in these environments as generalizable to the species and within the range of species-typical behavior, and not as only originating in extraordinary circumstances. Therefore, given the range of habitats chimpanzees have been observed to occupy, the consideration of certain habitats to be ideal chimpanzee habitat should be revisited, or avoided entirely. Further comparisons across the chimpanzee ecological continuum therefore will allow us to understand the range of natural adaptation to their environment, especially with regard to the species' ability to adapt to an outstanding range of habitats. Such an initiative may be imperative in the face of climate change, habitat loss, and other threats to chimpanzee populations as a proper understanding of what constitutes suitable habitat for chimpanzees is a necessary prerequisite for adequate conservation effort and planning. Unquestionably, the categorization of savanna habitats as marginal or not impacts how these habitats are regarded for their conservation value to the species as a whole, thereby further underlining the complexity and importance of employing caution when evaluating chimpanzee habitat suitability across a range of habitats.
Additionally, these results highlight potential sensitivities of chimpanzee populations in savanna areas, for example seasonal periods of higher stress, such as the dry season. Erstwhile these results highlight sensitivities like water availability across habitats for chimpanzees that had previously been thought to be savanna-specific. As such, the consequences of threats like climate change may be far more severe than previously thought. Therefore, the effects of climate change will need to be taken into account when performing conservation evaluations like population viability analyses, as climate change may possibly amplify stochastic effects in vulnerable populations. Until now, all current attempts to model chimpanzee distribution or habitat suitability have been based upon static statistical models (with the exception of Lehmann et al.,
EW, TD, and HK: conceived and designed the study; EW: performed the research; EW and RM: analyzed the data; TD, RW, and JP: provided material; EW, TD, RM, RW, JP, and HK: drafted the manuscript. All authors approved the submission of this manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AP declared a past collaboration with one of the authors, EGW, to the handling Editor.
We would like to thank the République du Sénégal and the Direction des Eaux et Forêts et Chasses, as well as the Ivoirian Ministère de la Recherches Scientifiques, the Ministère de l'Environnement et des Eaux et Forêts, the Office Ivoirien des Parcs et Réserves, the directorship of the Taï National Park, and the Centre Suisse de Recherche Scientifique for permission to conduct this research. At Fongoli we thank Jacques Tamba Keita, Michel Sadiakho, Dondo Kante, and Stacy Lindshield and at Taï we thank Florent Goulei, Ble Fabrice, Apollinaire Gnahe Djirian, and Oulai Yehanon Frederic for their support in the field. For support in the lab we thank Roísín Murtaugh, Vera Schmeling, Verena Behringer, and Juliane Damm for their significant contributions, and Gaëlle Bocksberger for support with data processing and analysis. Sincere thanks are due to Liran Samuni, Karline Janmaat, Anna Preis, Alex Mielke, and Sylvain Lemoine who all contributed significant aspects and understanding which made such analyses possible. We thank Liran Samuni, Alex Piel, and one reviewer for helpful comments on an earlier draft of this manuscript.
The Supplementary Material for this article can be found online at:
Fongoli Savanna Chimpanzee Project
Taï Chimpanzee Project
Linear Mixed Model
Variance Inflation Factor.
1NOAA