Optimal Foraging: Field Tests of Diet Choice and Habitat Switching 1

Transcription

1 AMER. ZOOL., 21: (1981) Optimal Foraging: Field Tests of Diet Choice and Habitat Switching 1 EARL E. WERNER Department of Zoology and Kellogg Biological Station, Michigan State University, Hickory Corners, Michigan AND GARY G. MITTELBACH Department of Zoology, Ohio State University, Columbus, Ohio SYNOPSIS. The application of optimal foraging theory to questions of predator behavior, and evidence bearing on the utility of this construct, are reviewed. Experimental tests of simple models predicting prey choice are examined with particular reference to the sizeselection of prey by fish. Laboratory estimates of model parameters are then used to predict prey choice in the field and data from several field tests are presented which corroborate these predictions. When parameters are habitat specific this permits predictions of net return from foraging in different habitats and consequently predictions of habitat use and switching. Field data gathered to test these predictions demonstrate that fish feed in the richer habitats and switch habitats when the profitability of one drops below that of another. Examples are provided showing how these models can then be used to relate behavioral and morphological differences between species and questions at higher levels such as the nature of species interactions and community structure. It is suggested that this may be one of the more useful applications of optimal foraging theory. Finally, some of the criticisms of the theory and important questions requiring further study are discussed. INTRODUCTION Evolutionary biology is largely devoted to the study of the manner in which the morphology, behavior and physiology of an organism permit it to meet the rigors of its environment. Evolutionary theorists generally view an organism's environment (physical or biotic) as posing a set of problems to which aspects of its morphology or behavior are solutions arrived at via the process of natural selection (Lewontin, 1978). Viewed in this way, it is evident that evolutionary biologists have implicitly treated natural selection as an optimization process in that plausible arguments are offered for the manner in which a structure or behavior best contends with the exigencies posed by the environment. Competing selective demands are viewed as tradeoffs and the elucidation of these tradeoffs is a major concern of the student of evolution. 1 From the Symposium on Theoretical Ecology presented at the Annual Meeting of the American Society of Zoologists, December 1980, at Seattle, Washington. 813 Recently, it has become popular to formulate these questions more quantitatively and utilize various forms of optimality theory and analogies to economic theory to arrive at predictions of the "best solution." The great interest in this approach derives from our ability now to make quantitative arguments and generate a priori hypotheses. In addition, such formalization makes explicit some of the assumptions that evolutionary biologists have always made in generating hypotheses on the adaptive nature of various structures or behaviors. In principle, this should permit more rigorous tests of various adaptive hypotheses if the theory is used in an appropriate fashion. Parallel with the recent increase in interest in this area, critics of the "adaptionist" approach have given closer scrutiny to the problems and pitfalls of the approach (e.g., Lewontin, 1979; Gould and Lewontin, 1979). While many of these criticisms are valid, they are generally of a cautionary nature. Thus single-minded adherence to the above view fails to ac-

2 814 E. E. WERNER AND G. G. MITTELBACH knowledge the importance of factors such as developmental and phylogenetic constraints in the evolution of an organism or to separate current utility from the original selective forces giving rise to a feature. In more extreme statements, theory in this area has been construed as untestable (Lewontin, 1979), but this view reflects more on recent careless use of the theory than properties inherent in it. In utilizing optimality models, biologists are not testing the proposition that nature optimizes, but instead specific hypotheses about selective forces, constraints and optimization criteria (e.g., Maynard Smith, 1978). We must view the use of optimality theory simply as a tactical tool, i.e., as a guide for organizing empirical evidence and suggesting further research, not as a key to the deeper laws of nature (Oster and Wilson, 1978). The theory will not supplant mechanistic descriptions of the genetics underlying the evolution of a trait and constraints bearing on that evolution. The power of the approach lies in the fact that models can be constructed on the basis of educated guesses concerning the essential features of a phenomenon, and the logical consequences of these models compared to empirical observations. If the correspondence is good we have reason to feel that some of the important variables have been identified; if not, we judge the direction and consistency of deviations and decide if these deviations identify other factors which must be added to the model as constraints, or we revise our basic assumptions about the process being studied. In either case, when assumptions are clear and explicit this iteration proceeds most rapidly. In this paper we consider some of the problems concerning prey choice and foraging behavior by an animal. This is an area in which we feel optimality theory can be used to formulate predictions which are eminently testable, indeed one of the major attractions of foraging theory is its accessabili'^ to tests. This area of ecological theory is nearly unique in that the models are inherently very simple and the parameters sufficiently explicit that it is obvious what has to be measured. OPTIMAL FORAGING THEORY Energy acquisition is clearly central to the fitness of organisms and most traits or behaviors associated with feeding can hardly be adaptively neutral. The optimal foraging approach assumes that those individuals which are most efficient in feeding will be favored by natural selection. The approach is to identify tradeoffs and constraints involved in the foraging behavior of an animal and construct cost/benefit models which incorporate these tradeoffs. Solving these models for the optimal diet or behavior then provides a concrete standard by which to judge the performance of the organism. The majority of optimal foraging models begin with the basic tradeoff between searching for and handling or pursuing prey (MacArthur and Pianka, 1966). This tradeoff is clear and explicit (Holling, 1965 although there are exceptions) unlike some areas of optimality theory where the existence of critical tradeoffs is often under question (e.g., in life history theory: Stearns, 1977). In testing optimal foraging theory we take an animal's search and handling capabilities as given, and ask only if it has the behavioral flexibility to respond to a changing environment in the manner predicted by the model. Thus we are not predicting the course of evolution, an area of much controversy with other uses of the theory. The great interest in optimal foraging theory since its formalization with the models of MacArthur and Pianka (1966) and Emlen (1966) can be attributed to a number of factors. Foremost of these is the potential the theory embraces for predicting the behavior of an animal given an array of options related to food or habitat use. Although there has been a long and venerable history of interest in predatorprey theory, this theory has been of a very different tradition. The early models of Lotka (1925), Volterra (1926), and Nicholson and Bailey (1935) addressed the dynamics of predator-prey systems emphasizing a single predator and prey. A different perspective came with the work of Holling (1959) and Ivlev (1961) which

3 OPTIMAL FORAGING 815 stressed the components of predation, e.g., the functional response, and how these components influenced the dynamics of interacting populations. Although the above predator-prey theory has contributed in a major way to our knowledge of factors important in the predation process (see Hassell, 1978, for a review), questions of prey choice were not easily handled and little progress had been made along these lines prior to the emergence of optimal foraging theory. The enormous literature on food habits of predators and comparisons with the distribution of available prey had led only to isolated inferences regarding factors affecting prey choice. The cost/benefit analysis introduced by foraging theory provided a general conceptual framework for dealing with the problem of predator choice and behavior. This has greatly stimulated work in all areas of predator behavior and many important contributions have emerged (Krebs, 1978; Maynard Smith, 1978). We have been interested in foraging theory as a tool, rather than an end in its own right. One of the areas where optimal foraging theory can potentially make a large contribution is in elucidating the niche relationships of interacting organisms. Ecologists have not widely appreciated or explored the theory's potential here (Werner, 1977). If studies in controlled environments indicate that the theory is predictive, then we can test predictions based on energetic considerations against actual resource utilization patterns in the field. Conformance to the predictions would support the role of exploitative competition in guild structure while divergence from these predictions is just as useful, suggesting other factors that need to be considered. Moreover, the foraging theory suggests a framework for exploring the contribution of functional morphology and behavior to resource partitioning and niche packing problems, providing direct evidence for the quantitative importance of features such as body size and bill size which have widely been used as indirect indices of guild structure (Werner, 1977). In the following sections we review some of our work testing foraging theory with fish. This work represents one of the few examples where the theory has been tested both in the field and laboratory and has also been used to explore species interactions. We further show how the iteration of theory and experiment can lead to useful inferences about factors influencing individual foraging behavior. A number of review articles on optimal foraging theory can be consulted to provide an overview of the field (e.g., Schoener, 1971; Pyke et al., 1977; Krebs, 1978; Cowie and Krebs, 1979). In addition a number of articles have been written examining some of the general philosophical problems associated with the optimality approach and evolutionary biology in general (Lewontin, 1979; Stearns, 1977; Maynard Smith, 1978; Oster and Wilson, 1978). Prey selection by the bluegill sunfish In our initial test of an optimal diet model we chose to work with fish for the following reasons. First, there exists abundant empirical evidence that fish exhibit strong size-selection when feeding, especially when feeding on open water plankton (Hall etai, 1970; Galbraith, 1967; Zaret, 1980). Second, fish are generally very adaptable to laboratory situations where the open water system can be mimicked easily in aquaria. We were interested in asking whether this size-selection was consistent with a model where selection was based on maximizing return per unit effort. We had evidence that the bluegill sunfish (Lepomis macrochirus) was very sizeselective (Hall et al., 1970) and so chose it and the cladoceran Daphnia magna for the experiments. We reasoned that time was an important cost for the bluegill which is exposed to high plankton densities for only short periods at dawn and dusk (e.g., Hall et al., 1979) and that biomass was a reasonable approximation for energy return since cladocera vary little in energy content (Cummins and Wuycheck, 1971). Thus we constructed a simple benefit/ cost model of the following form:

4 816 E. E. WERNER AND G. G. MITTELBACH B/T = (1) where B is the total biomass ingested in time, T, A, is the encounter rate with size i, Xj is the dry weight of prey size i, and k is the handling time per prey (which is constant since we are dealing with prey small relative to predator size see Werner, 1974). Prey were then ranked by individual profitability (i.e., x,/k) and the optimal diet is simply determined by adding prey to the diet from most to least profitable until equation (1) is maximized (Werner and Hall, 1974). To examine whether size-selection of prey was consistent with this model we brought bluegills into the laboratory, placed them in small pools, and fed them Daphnia populations of desired size-frequency distribution and density. These distributions were created by sorting the Daphnia by size class and accounting for the differences in relative visibility of different size prey. Relative visibility was determined by measuring reaction distances for each prey size and computing relative visual volumes. In this way we corrected the distributions placed in the pool for biases in visibility so that a given "effective" distribution was achieved. Handling time per prey was also measured in independent experiments. Fish were then starved for 24 hr to standardize hunger and permitted to feed for very short time intervals to minimize change in the density of prey available. We performed a number of these experiments over a range of prey densities (i.e., changes in search times) and the number of prey of each size eaten by the fish was determined from stomach contents (Werner and Hall, 1974). The simple foraging models such as equation (1) predict that as search costs increase (encounter rates decrease), the predator must include less profitable prey in the diet in order to maximize B/T. Three size classes of prey were used in the above experiments and at low prey densities the fish did consume all size classes in proportion to their predicted relative visibilities. Thus the stomach contents were biased to larger prey but no active selection of prey was evident. As prey densities were increased, the fish first selected the larger two sizes, and finally at high densities selected predominantly the largest size class of Daphnia (Fig. 1). Since all parameters of the model were estimable from these experiments we computed optimal diets over the range of prey densities (search times) used in the experiments. (Search times were estimated by subtraction, i.e., total experimental duration minus total time spent handling prey.) The size-selection exhibited by the fish conformed quite accurately to the predictions of the model (Fig. 2). In each experiment, the fish included prey of all sizes in the diet, i.e., prey selection was not discrete as the model would predict, but the overwhelming pattern was clear selection for those sizes predicted to constitute the optimal diet. Krebs et al. (1977) subsequently provided experiments testing the further prediction of this model that the diet is not influenced by changes in prey abundance outside of the optimal diet. Great tits (Parus major) remained highly selective for large prey when these prey constituted the optimal diet despite successive large increases in the abundance of small prey. O'Brien et al. (1976) have offered an alternative explanation for the results of the above experiments, demonstrating that similar patterns of prey size-selection would result if a bluegill simply ate the "apparently" largest prey in the visual field at all times (i.e., the prey subtending the largest angle on the retina). While this model and the optimal diet model predict similar patterns of prey selection in our experiments, this will not always be the case. In particular, the predictions of the two models will diverge when small suboptimal prey become very abundant in the environment. Several experiments now provide evidence against the apparent size hypothesis (Gardner, 1981; J.Jacobs, personal communication) and this hypothesis is also inconsistent with field studies which clearly show that size thresholds exist where smaller prey are virtually not eaten though extremely abundant in the envi-

5 OPTIMAL FORAGING (a) 12 1 f 1 b (b) te) III II III II I U FIG. 1. The mean number of each size class of Daphnia eaten per fish (size class I = 371 /i,g, II = 108 jug, III = 18 ^.g). The top histogram (a) is the mean of eight experiments at low density where the fish were given a uniform "effective" distribution of prey. The three histograms in panel (b) are experiments at various intermediate densities and (c) depicts experiments at high densities. In (b) and (c) the "effective" distribution was biased to larger prey as equal numbers of all classes were given to the fish. Superimposed on this histogram is a stippled area representing the expected number of items in the stomach if prey were eaten as encountered based on the actual numbers of class III eaten in the experiments. Thus deviation from this expectation shows positive selection. See Werner and Hall (1974) for further details. ronment (e.g., Galbraith, 1967). This question, however, needs much more study. Extensions to the field The above laboratory study with the bluegill was the first quantitative test of optimal foraging theory. The results were encouraging and prompted us to attempt an extension to field conditions. Clearly many important constraints which operate in the field (e.g., predator risk, the need to spend time on activities other than feeding) are neglected in the simple models in Search Time(s) FIG. 2. Experiments from Figure 1 grouped by selection pattern and plotted against the search time to encounter a common distribution of prey. A mean ± SE is plotted for the eight experiments at low density (a). The vertical dashed lines are the points where the theory predicts that selection should change; i.e., at search time <29 s only the largest prey should be selected, if >29 s but <295 s the two largest should be selected, and if >295 s no selection should occur. such as equation (1). Therefore it is not surprising that the most successful tests of foraging theory have been accomplished under rather rigidly controlled laboratory conditions (Werner and Hall, 1974; Krebs et al, 1977; Cowie and Krebs, 1979). Estimates of search costs especially are very difficult to obtain in the field so we performed a series of experiments to quantify these in the laboratory. In addition, this permitted us to improve on the initial test in the laboratory where search time was obtained by subtraction. A much stronger test is made if all parameters of the model are available independent of the test situation. Further, foraging costs were measured in terms of time alone in the laboratory with no consideration or accounting of energetics. Obviously energetic costs are a crucial concern and their inclusion would permit us additionally to extend the analysis to predators of different body size. Energetic costs can be added to equation (1) and the net energy intake (E n /T) from habitat j described as 2 ^ - i1 (2) where Eij = Ae u - 0,11,,, and A = the as-

6 818 E. E. WERNER AND G. G. MITTELBACH a. Ill o 20 mm 60 mm 100 mm FIG. 3. Prey encounter rates from laboratory experiments for three size classes of bluegills (20, 60, 100 mm) foraging in the vegetation as a function of prey size and density. Prey were damselfly naiads and ranged from 5 12 mm in length and prey/m 3 in density. similable fraction of the energetic content of prey, ey = energetic content of prey size i found in habitat type j (cal), H u = handling time of prey size i in habitat type j (sec) and C h = energetic costs of handling prey (cal/sec), C s = energetic cost of searching (cal/sec) and \ u = number of prey size i encountered per second of search in habitat type j. The inclusion of the energetic costs in the model necessitates that we specify body size of the forager, as metabolic costs will obviously be a function of body size. Additionally, in fish as in most organisms, searching ability and efficiency of handling prey will also be functions of body size. Therefore, in order to predict the optimal diet and test the theory in the field, it was necessary to determine the functional relationships between predator size and the parameters in expression (2). Handling times, Hj, are readily measured in the laboratory and extrapolation to the field presents few problems. The encounter rate (\i) with prey, however, has been the stumbling block in taking models such as this to field situations. Ecologists are generally able to sample the distribution of prey in the environment but somehow these prey distributions must be transformed into what an animal actually encounters, i.e., the distribution of prey actually available to a fish given a density and size-distribution in the environment. In an attempt to arrive at A'S which could be extrapolated to the field, we quantified prey encounter rates through a large series of laboratory feeding experiments again with the bluegill sunfish. These experiments were performed across various combinations of fish size, prey size and prey density in laboratory environments designed to simulate the structure and prey type found in each of three distinct aquatic habitats: the open water, bare sediments and vegetation. We knew from other experiments (Werner and Hall, 1976, 1979) that field tests would have to be accom-

7 "IV OPTIMAL FORAGING 819 LU CO.40'.20 Vegetal ion May Plankton N July w 1 L Jl in HI Pla n kton Aug. 03 O z LU O LU QC L_ ^^ Q_ o LU LU BluegiUs Jl I PREY LENGTH (mm) FIG. 4. The size-frequency distributions of prey in several habitats in Lawrence Lake, Michigan, the predicted optimal diet for 125 mm fish, and the actual diets of fish between 101 and 150 mm. Sample sizes range from 4-9 fish/date. plished on a habitat-specific basis as sunfish treat areas such as open water, sediments, and vegetation as discrete habitats, each requiring different foraging tactics. Realistic approximations of each of the three habitat types were constructed in large aquaria containing natural substrates and prey types (Daphnia for the open water; Chironomus (midge) larvae for the bare sediments; and Coenagrionidae (damselfly) nymphs for the vegetation). Results of these experiments showed prey encounter rates to be increasing functions of prey size, prey density and fish size in each habitat {e.g., Fig. 3), while prey handling times were a function of the relative size of predator and prey (Mittelbach, 1981a). Using multiple regression techniques we obtained equations predicting prey encounter rates and handling times in each habitat type as a function of the above variables (see Mittelbach, 1981a for details). The energetic costs of searching for and handling prey (C s and C h ) were estimated using the data of Wohlschlag and Juliano (1959). These investigators measured the oxygen consumption of bluegills as a function of body weight, swimming speed, and water temperature. C s and C h were calculated from Wohlschlag and Juliano's equations using the swimming speeds exhibited by fish searching for and handling prey in the laboratory experiments. The energy content of prey (e^ was determined by converting prey lengths to dry weights and then multiplying by the appropriate caloric equivalents (Cummins and Wuycheck, 1971; Mittelbach, 1981a). With this information, we can now use the foraging model to generate predictions of optimal diets in the field. The only in-

8 UJjJ 820 E. E. WERNER AND G. G. MITTELBACH , 34 mm 55 mm io- *- 30- c o a> u 20- Q. O 10- Per Length (mm) FIG. 5. The size-frequency distribution of Daphnia in the plankton of an experimental pond, the predicted optimal diet for three size classes of bluegills, and the actual diet of the bluegills feeding on Daphnia (n = 10 fish/size class). formation required from the field is the size-frequency distribution and abundance of prey. Estimates of all other parameters are available which are independent of the field situation. Field tests of the theory To test the above model in the field we sampled prey resources and fish from both a small Michigan lake and an artificial pond. Prey were sampled from the sediments, vegetation, and open water in Lawrence Lake, from May-August, Collections were made in the early morning as the bluegill is largely a diurnal feeder showing a major feeding peak at this time (Sarker, 1977; Wilsmann, 1979). Prey living in the sediments and vegetation were sampled by a SCUBA diver and open water prey were sampled by vertical net tows from the depth of the thermocline (Mittelbach, 1981a). We collected fish from the same area immediately following the prey sampling. Prey samples from each habitat were sized and enumerated and size-frequency distributions constructed. Stomach analyses of the fish were handled similarly. Specifying a fish size, then, we were able to take the size-frequency distributions of prey from Lawrence Lake and predict encounter rates (A,) from the laboratory experiments as well as handling and searching costs. These data were used to compute for each habitat an optimal diet for several size classes of bluegills. Figure 4 compares the distribution of prey sizes available in the plankton and vegetation on several dates to predicted optimal diets and the actual diets of large ( mm) bluegills which had >90% prey from that habitat by weight in their diet. In all cases the bluegills were highly size-selective in their feeding and the distribution of prey sizes eaten corresponded quite closely to that predicted by the optimal diet model.

9 OPTIMAL FORAGING 821 Moreover, the size-selection exhibited in the plankton was far greater than that which would result simply from the increased visibility of larger prey (Mittelbach, 1981a) and was also inconsistent with the apparent size model. Using similar methods we also examined prey-size selection by various size classes of bluegills when feeding on Daphnia in a small experimental pond (Werner et ai, in preparation). There was again very good correspondence between the diets of these different size fish and those predicted by the optimal foraging model (Fig. 5). Clearly, a relatively simple foraging model based upon body size relations and energetic returns can be quite successful in predicting bluegill diets in the field, especially in the simpler environments where a single or several similar species of prey dominate. Many other costs will have to be considered in ranking prey where movement, escape abilities, defenses such as spines, depth in sediments and the like are important differences between prey species. These will generally be important considerations and the strength of the optimal foraging framework is its flexibility to incorporate these sorts of factors. The dynamics of habitat use Patterns in habitat use among species are of fundamental importance in studies of species interactions and community structure. Moreover, species often exhibit habitat shifts either seasonally, ontogenetically, or in the presence of competing species. A theory enabling us to predict such shifts on the basis of resource changes within a habitat would obviously be a powerful tool in examining such species interactions and life history attributes. If the bluegill selects prey so as to maximize energetic return within habitats, one might also expect to find individuals choosing to forage in those habitats yielding the highest net energetic gain. Further, since prey abundances generally change across the summer in temperate lakes (Mittelbach, 19816), one would expect to see fish shift habitats in response to changes in relative habitat profitabilities. To examine this possibility in Lawrence Lake we computed the habitat specific optimal diets for different size classes of fish on each sample date and plotted the associated net energy return (E n /T). The first column in Figure 6 presents the seasonal pattern in predicted net return in each habitat. There were marked differences in habitat profitability; the vegetation habitat was by far the most profitable initially and then declined steadily in value across the summer. Plankton profitabilities were initially low, rose in late June, and remained above those of the vegetation habitat throughout July and August. The patterns were similar for all three size classes of fish and thus we would predict that the fish should exhibit exclusive use of the vegetation in May, shift to utilizing the plankton in late June, and continue to use the plankton exclusively through July and August. The actual seasonal habitat use of each size class of bluegills as determined by prey types in their diets is also presented in Figure 6. Bluegills >100 mm standard length closely matched predictions based on maximizing energetic return; these fish fed initially in the vegetation and then shifted to the open water plankton in July. The importance of this habitat shift in terms of energetic intake and potential growth is pronounced. Prior to shifting to zooplankton, large bluegills averaged <7 mg dry weight of prey in their stomachs. After shifting to zooplankton, gut contents increased to an average of mg dry weight (Fig. 6). The two smaller size classes did not shift habitats as predicted. With the exception of the last date, these fish continued to utilize the vegetation throughout the summer, despite calculations that they would have increased foraging intake rates markedly by switching to the open water in mid- June. Gut contents did indeed increase dramatically on the single date when these fish fed extensively on zooplankton (August 23). August 23, however, was unique in that it was the only sampling date where Daphnia were abundant in the littoral zone (within m of vegetation) and a comparison of Daphnia species eaten on this date indicated that small bluegills were

10 822 E. E. WERNER AND G. G. MITTELBACH PREDICTED ACTUAL.15 Large Large CO o.05 Z cr.10.osz o < o O H tn 7 5-\ \ 2 O < 2-22 MAY Med N Smal u rn 14 JUNE 19 JULY FIG. 6. The seasonal pattern of predicted habitat profitabilities (left) and actual habitat use (right) for bluegills in Lawrence Lake, Michigan. Actual habitat use was measured by the mean amount of prey (mg dry weight) eaten which were specific to each habitat. Dashed lines and solid circles represent the open water habitat, solid lines the vegetation, and dotted lines and open circles the sediments. Fish were grouped into three size classes: large ( mm), medium ( mm), and small (10-50 mm) and sample sizes ranged from 3 9 fish/ date. feeding on this nearshore Daphnia population (Mittelbach, 1981a). Thus, habitat use of small bluegills appeared limited to areas in or near vegetation. The smaller fish, of course, face the highest risk of being preyed upon by the dominant predator in this system, the largemouth bass (Micropterus salmoides) (Hall and Werner, 1977). Glass (1971) has shown that the largemouth bass is less effective in capturing prey in structured habitats such as weed beds. Therefore, the lack of fit between predicted habitat use (based upon maximizing energetic return) / 23 AUG and the actual habitat use of bluegill <100 mm in length would appear to be a consequence of size-related predation risk. We return to this question below. An additional test of our ability to predict habitat shifts is available from the pond experiment mentioned earlier. Habitat profitabilities were calculated across the season in the same manner as the Lawrence Lake study and initially, the plankton was decidedly the most profitable habitat (Fig. 7). Plankton profitabilities, however, declined precipitously over the following weeks whereas that of the sedi-

11 OPTIMAL FORAGING July 6 13 Augutt 6 14 September FIG. 7. The seasonal pattern of predicted habitat profitabilities (above) and actual habitat use (below) by bluegills in a pond experiment. Data are presented for small and large size classes which ranged from mm and mm respectively across the season. The solid lines represent the open water habitat and the dashed lines the sediments. Percent of the diet was calculated on the basis of the dry weight of prey in the diet which were unique to these two habitats. Sample sizes were 10 fish/date. ments rose significantly. The vegetation profitability remained below these two habitats for the entire period. All size classes of fish exhibited striking habitat shifts when the profitabilities of the plankton and sediments crossed in late July. The data for the largest and smallest classes are presented in Figure 7. Thus in the pond (with no predators present) all size classes obey the predictions of the model by foraging in those habitats which yield the highest energy return per unit effort foraging. The above examples show little lag in the response of the fish to changes in habitat profitabilities. This is probably due in part to the very large changes in habitat profitabilities over a short period of time. In general we would expect more of a lag in response due to the problems of sampling and estimating profitabilities of other habitats. Other experiments we have performed also suggest that learning contributes significantly to the ability of these fish to utilize different habitats and that this can lead to a lag in habitat switching (Werner et al., 1981). A possible illustration of this sort of lag is provided in Figure 8 where data from a different treatment of the pond experiment above indicate that profitability of the plankton and benthos crossed but remained similar for several weeks. Habitat switching by the fish in this case lagged considerably behind the point where profitabilities crossed. In general then we have had surprising success predicting habitat shifts in the absence of predators or by size classes which are too large to encounter much predation risk. The habitat use of the different size classes of bluegills in Lawrence Lake represents a classic case where field data provide some corroboration and some deviation from the predictions of a simple model. Intuition and evidence from other studies pointed to predation risk as an important constraint which might explain the deviation of the small classes in Lawrence Lake from the predictions of the model. We have recently conducted a pond experiment to test the hypothesis that predation risk is responsible for this deviation from predictions (Werner et al., in preparation). We find that in the presence of the largemouth bass that smaller bluegills spend significantly more time in areas of

12 824 E. E. WERNER AND G. G. MITTELBACH 0.3. S 02 -l o <v D o 60 S 40. ID Q July 6 13 August Large Zooplankton Benthos September Fic. 8. The seasonal pattern of predicted and actual habitat use by bluegills in another treatment of the pond experiment illustrated in Figure 7. Fish ranged from mm. Otherwise the same as Figure 7. complex vegetation structure, resulting in considerably reduced growth rates. We are currently modifying the foraging model to include predation risk as a constraint. The major difficulty here, of course, is that foraging profitability and predation risk are measured in very different units and ultimately must be related to some measure of fitness. We noted earlier that one of the most promising applications of the foraging theory was to studies of species interactions and niche structure. We have shown in other experiments that competition between species of sunfish can result in strong niche shifts (Werner and Hall, 1976, 1979). The above data suggest that we could predict these niche shifts by fish using the theory if interactions were of an exploitative sort. In order to extend our predictive power to the interspecific case, however, the parameters of the theory must be measured in terms not only of body size but also any morphological differences between species which influence their foraging efficiencies. Some of the initial elements of extending the foraging theory approach to interspecific interactions are laid out in Werner (1977). We utilized the foraging theory to assess the costs of using different food sizes for three species of centrarchids which differed systematically in their morphology. Handling and pursuit times for different prey were measured for the bluegill, green sunfish (L. cyanellus) and largemouth bass which form a graded series in body plan from a species adapted to handle small prey (short compressed body, small mouth) to one built to deal with large prey (fusiform body, large mouth). These data were used to generate continuous cost curves for these species as a function of prey and predator size. From these cost curves and the available size distribution of prey, a predicted mean food size and variance term for any size class of these species could be calculated (see Werner, 1977, for details). Constructing species utilization curves using the size structure of naturally coexisting populations, we then predicted that the bass and bluegill could coexist in the same habitat but that the green sunfish should exhibit niche complementarity. These predictions were corroborated by quantifying the habitat distribution of these fish in a natural lake. The habitat distributions of the bass and the bluegill were nearly identical and concentrated in deeper water, whereas the green sunfish was found only in shallow water resulting in nearly total segregation from the other two species. We feel that the importance of this study lies in the illustration of how theory on an individual behavior level can be mapped to community questions. This allows us to explore species interactions on a more mechanistic basis and quantitatively relate morphological features to competition and resource partitioning. We are currently incorporating morphological and body size differences between species into these feeding models and exploring the problem of interspecific competition. In this context we are also attempting to predict ontogenetic niche shifts and speculate on the consequences of these species interactions. These are areas where a predictive foraging theory would be a powerful tool.

13 OPTIMAL FORAGING 825 DISCUSSION Despite the burgeoning literature on optimal foraging theory since the mid-sixties relatively few careful tests of the theory have been accomplished (e.g., Werner and * Hall, 1974; Krebs et al., 1977). Moreover w the majority of these tests have been performed under highly artificial conditions in the laboratory. Only Belovsky (1978) and Goss-Custard (1977) in addition to ourselves have quantitatively tested the theory in the field. These studies found the theory quite useful in predicting the diet of moose, a wading bird and a fish respectively. With the exception of our work (Mittelbach, 1981a), however, no study has estimated the parameters of the theory (handling times, encounter rates, etc.) independent of the actual data collected for the test. This is an important step as it provides a stronger evaluation of theory and permits a more general application, allowing predictions to be made for different environments supporting different resource distributions. Thus in our case we were able to test the theory both in a natural lake and an artificial pond. We found the theory surprisingly useful not only in predicting prey size selection but habitat use and switches as well. Additionally, we have learned a great deal from the cases in which the fish did not behave as we predicted. If we obtain good correspondence to model predictions in the laboratory or under simplified conditions, the (inevitable) deviations from predictions when the model is applied to more complex situations suggest other factors or constraints which need to be considered. This is one of the most profitable ways to use these models. The simple costbenefit framework allows us to readily envision and postulate other factors which may be responsible for the deviations. Lewontin (1979) among others has criticized this approach of "ad hoc optimization" as rendering the program scientifically unsound and ultimately untestable. It is, of course, bad science to claim to have validated a model in the face of strong devia- _ ' tions by postulating that another factor is important. To hypothesize the role of that factor, however, and then devise experiments to test its importance and revise the model structure accordingly is an accepted modus operandi of any branch of science. In our own work on bluegill foraging, the addition?.nd test of the predation risk hypothesis illustrates one such iteration between model development and test. Unfortunately there are no hard and fast criteria to guide us in accepting or rejecting a conceptual framework when a large fraction of the variation in a phenomenon is accounted for but strong deviations still exist (see also Maynard Smith, 1978). Ultimately, it is the weight of evidence accumulated over time which determines the usefulness of any theory, and optimal foraging theory is no exception. What is called for is proper application of the theory and a reasoned perspective on its limitations {e.g., Oster and Wilson, 1978). Optimal foraging theory potentially could provide a very important bridge between ideas on individual behavior and niche structure in communities. One of the glaring oversights of most competition studies, for instance, has been the lack of attention to resources through which competitive effects are mediated. Foraging theory maps resource abundance and distribution to utilization functions of the competitors in question and, as such, holds promise for underpinning a dynamic microtheory of competitive mechanisms. Indeed habitat shifts in the presence of closely related forms have long been considered one of the strongest indications of the action of competition in natural communities. We have demonstrated such shifts experimentally with competing sunfish (Werner and Hall, 1976, 1979) and indicated above that such shifts can be predicted on the basis of resource levels in various habitats and the foraging abilities of the fish. Use of the theory along these lines could add an important dimension to the study of competition and community structure. It is also important in this regard to incorporate the influence of predation risk on foraging behavior in the models (see also Sih, 1980; Pearson, 1976; Rosenzweig, 1974). It is likely that size-specific tradeoffs between foraging profitability and preda-

14 826 E. E. WERNER AND G. G. MITTELBACH tion risk will be a common phenomenon among organisms whose populations are structured by body size. This area presents a very important challenge to both empirical and theoretical ecologists interested in foraging behavior and species interactions. We need to quantify the extent to which predation risk influences fitness through factors such as reduced growth, as well as by direct removal of individuals, and how prey assess the risk imposed by predators. We have noted that bluegills have the flexibility to alter habitat use in the absence of predators (Werner et ai, in preparation). This alone is not surprising, but the quantitative relations and consequences are virtually unexplored. The size-specific tradeoff in the bluegills has important implications. Spatial segregation among size classes is common (Hall and Werner, 1977; Keast, 1977) and has been interpreted as resource partitioning resulting from intraspecific competition (Keast, 1977). Our studies suggest that instead, this segregation is enforced by risk of predation and that in the absence of predators all size classes would generally be in the same habitats if maximizing foraging rate was the only criterion. The restriction of small size classes to the vegetation or less risky habitats results in a competitive refuge or habitat of exclusive resources for the larger classes. In fact, bluegills and many other species stocked in the absence of predators invariably develop stunted populations with large numbers of small individuals (Swingle and Smith, 1940; Wenger, 1972). It is likely that the lack of stunting in natural populations is as much a consequence of the predators' influences on size class segregation as it is on its direct removal of individuals from the population (Mittelbach, 1981a). The above scenario also points to the danger of interpreting patterns in spatial segregation as due to competition in the absence of other forms of information. Our studies with fish also call attention to several areas critical to the further development of foraging theory. Specifically the role learning and sampling play in the foraging behavior of animals is crucial. Laboratory experiments with the bluegill indicate that foraging efficiency increases sharply with experience when feeding on Daphnia or Chironomus (Werner et al., 1981). Moreover, pond studies show that there can be considerable individual specialization within a population on one resource or the other and also suggest that generalists obtain much less food than specialists even when different prey types are in close proximity to each other (Werner etal, 1981). We interpret this to mean that training bias or search image type phenomena are occurring such that consuming a mixed diet actually reduces a fish's feeding efficiency. Learning phenomena of this sort can have profound effects on our predictions of how an animal should forage. Hughes (1979) incorporated learning in an optimal foraging model as a function of absolute encounter rates with several prey types and has shown that specialization on a less profitable prey can occur if encounter rates with that prey are much higher. In this case, learning acts independently of the presence of other prey types. Hughes' model obviously cannot predict individual specialization of the type observed in the bluegill and is inappropriate for situations where training bias or search image phenomena can occur. Unfortunately, while learning can be very important in determining choice and habitat use, the potential diversity of learning mechanisms and their consequences can be so great that we may not be able to make broad generalizations from simple models. Rather, models incorporating learning effects may have to be specific to a particular situation. The above data are also pertinent to the problem of how organisms sample, or estimate the value of, prey or habitat types. In general optimal foraging models have assumed that the organism has perfect information concerning the environment. This, of course, is unrealistic and is an area that greatly needs careful study. It is clear that the fish are sampling alternate habitats and we need to know the factors which influence how and at what rate they sample these habitats. A related question is how organisms actually estimate return rates when foraging in different habitats

15 OPTIMAL FORAGING 827 or patches. Studies of the capabilities and limitations of animals along these lines will greatly aid us in developing second generation optimal foraging models. We have speculated on the basis of very preliminary evidence that sampling alternate habitats when inexperienced leads a fish to underestimate return rates and therefore switch habitats later than predicted (Werner et al., 1981). We also have noted better correspondence with larger fish to the predictions of optimal diet models. Several factors may be involved: first, as prey are added to the diet the E n /T curve exhibits a much more pronounced peak at the optimal diet for large fish, and second, large fish obtain many more items over the same feeding period (Mittelbach, 1981a). Both factors may be important in the ability of the fish to estimate return rates and approximate an optimal diet. The complexities of certain environments, and the stochastic nature of the feeding process suggest some of the problems animals will face as foragers (Oaten, 1977). We would expect that simple behavioral mechanisms have evolved which serve to approximate the solutions to optimal diet problems (Breck, 1978; Cowie and Krebs, 1979). Experiments on optimal foraging, both in the laboratory and in the field, have now demonstrated that animals can often reasonably approximate diets or habitat use which serve to maximize their foraging return. The experiments of Krebs and his co-workers (Krebs et al., 1977, 1978; Cowie and Krebs, 1979) have demonstrated that birds are able to solve very complex problems of optimal diet, patch choice, and sampling. However, the mechanisms by which they reach these solutions remain unknown (Cowie and Krebs, 1979) and need investigation. The interest optimal foraging theory has generated in recent years, both pro and con, has greatly stimulated work on the mechanisms of prey choice by predators. In this alone, the theorizing has been extremely useful. It is critical, however, that the theory now develop in concert with empirical work. This area, as many, runs the risk of compiling large amounts of trivial theory unguided by attention to what animals are actually capable of doing and therefore what the important problems are. In this state, polemics over the usefulness or "validity" of the theory degenerate into rather scholastic exercises. We must use the theory to build models and test their consequences, not to offer plausible explanations for observations we have made on the behavior or activity of animals. Further, we must pay more attention to what the theory allows us to do as a tool rather than its validity as a concept in evolutionary theory. Compared to many areas in ecology optimal foraging theory offers unusual potential for the interplay of theory and experiment and we should endeavor to provide a sound empirical guide for the development of that theory. ACKNOWLEDGMENTS We wish to thank James Gilliam and David Hart for their comments on the manuscript. This research has been generously supported by the National Science Foundation (DEB and DEB ). Contribution number 429 of the W. K. Kellogg Biological Station. REFERENCES Belovsky, G. E Diet optimization in a generalist herbivore: The moose. Theor. Pop. Biol. 14: Breck, J. E Sub-optimal foraging strategies for a patchy environment. Ph.D. Diss., Michigan State University. Cowie, R. J. and J. R. Krebs Optimal foraging in patchy environments. In R. M. Anderson, B. D. Turner, and L. R. Taylor (eds.), Population dynamics, pp Blackwell Scientific Publications, Oxford. Cummins, K. W. and J. C. Wuycheck Caloric equivalent for investigations in ecological energetics. Mitt. Int. Ver. Limnol. No. 18. Emlen, J. M The role of time and energy in food preference. Amer. Natur. 100: Galbraith, M. G., Jr Size-selective predation on Daphnia by rainbow trout and yellow perch. Trans. Amer. Fish Soc. 96:1-10. Gardner, M. B Mechanisms of size selectivity by planktivorous fish: A test of hypotheses. Ecology 62: Glass, N. R Computer analysis of predation energetics in the largemouth bass. In B. C. Patten (ed.), Systems analysis and simulation in ecology, pp Academic Press, New York. Goss-Custard, J. D Optimal foraging and the

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