Optimal foraging on the roof of the world: Himalayan langurs and the classical prey model.

Optimal foraging theory (OFT) operates on the assumption that behavior has been molded by natural selection, and utilizes mathematical models to predict animal feeding decisions (Stephens and Krebs 1986). Although OFT, first developed in the 1960s and 1970s (Charnov and Orians 1973; Emlen 1966; MacArthur and Pianka 1966; Schoener 1971) has engendered controversy (Gray 1987; Perry and Pianka 1997; Pierce and Ollason 1987), few can deny its impact on behavioral ecology. Largely descriptive work has been transformed into studies that convert measurable variables into quantitative, testable predictions concerning animal behavior. As a result, the breadth and applicability of its models continues to grow (Stephens et al. 2007). Foraging theory does not necessarily argue that animals are optimal; rather, it uses a mathematical tool (optimization) to denote how an animal should behave under specified conditions (Ydenberg et al. 2007). Often they do act as predicted (Nonacs 2001; Sih and Christensen 2001). In cases where OFT predictions are not supported, attention is directed towards novel lines of research and result in a more complete understanding of feeding behavior (Bulmer 1994; Orians 1980; Stephens and Krebs 1986). A seminal model in foraging theory is the classical prey model, variously called the attack, optimal diet, or contingency model, which predicts which foods in a set should be accepted by a forager under given conditions (Charnov 1976; MacArthur and Pianka 1966; Schoener 1971; Stephens and Krebs 1986). Food types are rank-ordered by energy or another currency divided by the handling time it takes to capture and consume them. The higher this value, the more profitable the food is considered. Food types, each of which has an associated mean energy content, handling time, and encounter rate, are then entered into the “prey algorithm” in the order of their profitability. As each new food type is entered, the algorithm gives the average rate of intake (En/T) if only this type and those of greater profitability were taken. The set of foods that results in the highest En/T is considered the optimal diet. In short, when an animal comes upon a potential food item, the forager should exploit that item if its profitability is above the threshold En/T value, but ignore it and continue searching if its profitability is below it. Many animals, including many primates, exploit food patches, or aggregations of food (e.g., leaves on trees), as opposed to individual food items. There are two ways in which the classical prey model can be modified to predict patch choice. The first is a direct analogy where a patch is treated exactly like a prey item, and this approach does not consider patch depression (Schoener 1974; Schoener 1987; Stephens and Krebs 1986, p. 34). Patch depression is a reduction in intake rate over time spent in a patch due to the depletion of food items, movement of prey, or other factors (Charnov et al. 1976). The second approach includes patch depression and solves concurrently for both patch choice and patch residence time (Stephens and Krebs 1986). The first approach (direct analogy) is explored here (Tables ​(Tables11 and ​and2),2), and the possible effects of depression on patch choice and departure will be investigated in later papers. TABLE 1 Assumptions of the classical prey model as applied to patch choice (after Stephens and Krebs, 1986; see also Post, 1984). TABLE 2 Predictions of the classical prey model as applied to patch choice (after Stephens and Krebs, 1986). Human behavioral ecologists have applied variants of the classical prey model to modern human hunter-gatherers and, to a lesser extent, the archaeological record (Kennett and Winterhalder 2006; Smith 1991; Winterhalder and Smith 1981). Hawkes and colleagues (1982), for example, used this approach to predict the caloric profitability threshold for food items to be included in the diet of Ache hunter gatherers (Hawkes et al. 1982; Kaplan and Hill 1992). Kurland and Beckerman (1985), in a similar vein, utilized the model to argue that selection favored reciprocity and information exchange in early hominid evolution due to its effects on reducing search costs. Interestingly, researchers of nonhuman primates have rarely applied OFT to their subjects, but this is not necessarily due to lack of interest. The data required to test these foraging models (e.g., intake rate) may be difficult to gather even in ideal field or captive situations, let alone with animals that are nocturnal, difficult to habituate, or living in high canopy. Although direct tests of OFT models are scarce (but see Altmann 1998; Grether et al. 1992), a number of primatologists have referenced foraging theory as an a posteriori tool to explain observed behavior (e.g., Gaulin 1979; Hamilton et al. 1978). Perhaps the most direct application of the classical prey model to nonhuman primates involves work on patch quality and selection in Japanese macaques (Macaca fuscata) (Nakagawa 1989; Nakagawa 1990) and orangutans (Pongo pygmaeus) (Baritell et al., 2009), and a review article on related topics covering the entire Order (Nakagawa 1996). Quantitative testing of model predictions, however, coupled with estimation of all variables and food type ranking, has yet to be undertaken with any nonhuman primate. Here we compare predictions of the classical prey model, modified for patch choice, with the behavior of Himalayan gray langurs (Semnopithecus entellus) living at a high altitude (3000-4000 m) site at Langtang National Park, Nepal. The gray langur is a colobine monkey possessing a large, multi-chambered stomach with symbiotic gut microorganisms which aid in the digestion of high-fiber foods (Bauchop and Martucci 1968; Kay and Davies 1994). Although colobines are popularly described as “leaf-eating monkeys,” gray langurs have an eclectic, generalist diet that varies seasonally (Koenig and Borries 2001), and this is particularly true of Himalayan populations (Curtin 1982; Sayers and Norconk 2008). This provides an ample opportunity to investigate predictions of the classical prey model as they pertain to behavioral shifts in response to changes in the abundance of foods. Field observations included continuous recording of feeding bouts and between-patch travel times, and laboratory work included standard nutritional analysis of langur foods. We apply a simple patch choice version of the classical prey model with corrections for search costs, and use three nutritional currencies (kcal, kcal with a flat correction for neutral detergent fiber fermentation, and crude protein). Since the classical prey model has not been fully applied to any nonhuman primate, we feel it is appropriate to begin with this simple, but potentially robust, model before moving to a more complex one with added constraints or nutritional variables (see Grether et al. 1992; Kaplan and Hill 1992).