Sooty mangabeys scavenge on nuts cracked by chimpanzees and red river hogs—An investigation of inter‐specific interactions around tropical nut trees

Abstract

Carrion scavenging is a well‐studied phenomenon, but virtually nothing is known about scavenging on plant material, especially on remnants of cracked nuts. Just like meat, the insides of hard‐shelled nuts are high in energetic value, and both foods are difficult to acquire. In the Taï forest, chimpanzees (Pan troglodytes) and red river hogs (Potamochoerus porcus) crack nuts by using tools or strong jaws, respectively. In this study, previously collected non‐invasive camera trap data were used to investigate scavenging by sooty mangabeys (Cercocebus atys), two species of Guinea fowl (Agelestres meleagrides; Guttera verreauxi), and squirrels (Scrunidae spp.) on the nut remnants cracked by chimpanzees and red river hogs. We investigated how scavengers located nut remnants, by analyzing their visiting behavior in relation to known nut‐cracking events. Furthermore, since mangabeys are infrequently preyed upon by chimpanzees, we investigated whether they perceive an increase in predation risk when approaching nut remnants. In total, 190 nut‐cracking events were observed in four different areas of Taï National Park, Ivory Coast. We could confirm that mangabeys scavenged on the nuts cracked by chimpanzees and hogs and that this enabled them to access food source that would not be accessible otherwise. We furthermore found that mangabeys, but not the other species, were more likely to visit nut‐cracking sites after nut‐cracking activities than before, and discuss the potential strategies that the monkeys could have used to locate nut remnants. In addition, mangabeys showed elevated levels of vigilance at the chimpanzee nut‐cracking sites compared with other foraging sites, suggesting that they perceived elevated danger at these sites. Scavenging on remnants of cracked nuts is a hitherto understudied type of foraging behavior that could be widespread in nature and increases the complexity of community ecology in tropical rainforests.

What animals do not do or fail to find: A novel observational approach for studying cognition in the wild

“…Different types of cognition can lead to a variety of knowledge that can help an animal to find, access, and guard food and mates. For example, to find food, animals may use foraging cognition, 33 that is, mechanisms that acquire, process, store, and act upon (a) sensory information about the cues emitted by foods, 69,91,119,183 (b) spatial information of the locations and efficient route of travel, 18,155,184 (c) temporal information of the timing of a visit, or return, 58,176,184 (d) ecological information of the characteristics of food sources and competitors (e.g., level of ephemerality, synchrony, fruit production, and depletion rates 19,70,71,74,186,187 ), and (e) social information about the decisions or knowledge of group members. 33,188,189 All these information types can either result in declarative or procedural knowledge.…”

“…When collecting behavioral data on wild animals, advanced technologies that enable camera trap or audio triangulation methods are increasingly applied. 99,[118][119][120] Such technological advances enable us to study behavior (through observations or experiments) in a highly noninvasive manner and to avoid the risks associated with habituation, such as disease transmission. 121 Such approaches make it possible to study wild animals without the need for long-term commitment to protecting the habituated animals from poachers.…”

“…The cognitive mechanism of interest to a researcher is only one of them. 8 Hence, to test for particular cognitive abilities, it helps to think of many alternative explanations, ideally before the start of data collec- [142][143][144] camera trapping, 98,119,145 satellite and aerial imaging, 146 and long-term field sites 21,140,141,147 that have decades of contextual data to draw from, provide the data that can help to rule out alternative explanations through statistical methods (Step 5).…”

“…For example, when designing a statistical model to test for planning abilities by predicting nest departure time, I could make use of a total of 46 field studies ranging from 1960 to 2013 to understand which variables should be included to predict primate sleeping site departure time. In addition, advances in data collection technologies such as high resolution, long‐term bio‐logging camera trapping, satellite and aerial imaging, and long‐term field sites that have decades of contextual data to draw from, provide the data that can help to rule out alternative explanations through statistical methods (Step 5).…”