Incorporating costs of habitat use

11.5.2 Incorporating costs of habitat use

Abiotic costs

IFD theory assumes that predators pay no cost in choosing habitats, hence the epithet ‘free’. How- ever, in nature costs are real. For example, Tyler and Gilliam (1995) considered the abiotic cost of

Fish Foraging and Habitat Choice

257

Chapter 11

stream fishes that hold a position and that feed on energy gain, and was their response consistent prey that drift by. They argued that while faster- with maximizing fitness? flowing sites may deliver prey at a higher rate,

The difficulty in developing a theory of optimal these sites may be more costly in terms of energy habitat choice that incorporates both predation expended in holding a position and in terms of risk and foraging return is that these factors are

a reduced probability of successfully capturing expressed in very different units, predation risk as faster-drifting prey. Both of these costs have been the probability of death per unit time and foraging shown to be important in the choice of habitats by return as energy gained per unit time. Abrahams stream fishes (Fausch 1984; Hughes and Dill 1990; and Dill (1989) approached this problem by using Hill and Grossman 1993). Tyler and Gilliam (1995)

a fish’s behaviour to evaluate how much of an in- incorporated these costs into an IFD model and crease in energy gain is required to get a fish to ac- compared the predicted distributions of stream cept a given increase in mortality risk. Abrahams fishes under the IFD with costs to that of the and Dill (1989) used IFD theory to quantify the en- simple IFD, using blacknose dace (Rhinicthys ergetic equivalence of predation risk for guppies atratulus ) in a laboratory stream. They found that feeding in two laboratory habitats. In their experi- the IFD incorporating costs did a better job of ment, food was delivered at different rates in the predicting the fishes’ habitat use than did simple two habitats, and they found that in the absence of IFD theory.

predators guppies distributed themselves between habitats as predicted by the input-matching rule

of IFD theory. Adding a fish predator to one of the habitats caused some of the guppies to shift to feed-

Predation risk

The natural world is a dangerous place for predator ing in the safer habitat. The difference in per-capita and prey alike (see Krause et al., Chapter 13, this feeding rates between the safe and dangerous habi- volume). Risk of injury or death is another poten- tats therefore provided a measure of the energetic tial cost of habitat choice. Fish and other organ- equivalence of predation risk. This assumed that isms have been shown to respond to predation an individual’s predation risk does not change with risk in choosing habitats. For example, Mittelbach group size (Moody et al. 1996). In a second experi- (1981) and Werner et al. (1983a) found that large ment, Abrahams and Dill (1989) increased feeding bluegills, which were relatively immune to preda- rates in the risky habitat by the amount predicted tors, foraged in the habitats that yielded the to offset the influence of predation risk. They highest energy gain. However, smaller bluegill, found that the additional food resulted in a similar which were vulnerable to predation by largemouth number of guppies using both the risky and safe bass (Micropterus salmoides), were more limited habitats; female guppies, however, fit the pre- in their habitat use. Small bluegills fed in the pro- dicted response better than males. Grand and Dill tection of the littoral zone vegetation, even though (1996) adopted this same approach to examine the open-water habitat yielded a higher feeding the use of cover by stream-dwelling coho salmon rate and better growth (Werner et al. 1983b; see (Oncorhynchus kisutch). In nature, cover provides also Werner and Hall 1988). Thus, small bluegill the salmon with protection from predators but appeared to trade-higher energy gain for a lowered may also result in reduced food availability. Grand predation risk. These and other studies (Milinski and Dill (1996) used IFD theory to quantify the en- and Heller 1978) prompted theoreticians and em- ergetic equivalence of cover to the fish and then piricists to investigate the balance between forag- calculated how much additional food would have ing gain and predation risk, focusing on two main to be added to a risky patch in order to make it of questions: (i) what was the optimal habitat choice equal value to a safe patch. When they added this that would maximize fitness and (ii) how did fish additional amount of food, the fish returned to and other foragers respond to predation risk and the distribution they had before the risk became The natural world is a dangerous place for predator ing in the safer habitat. The difference in per-capita and prey alike (see Krause et al., Chapter 13, this feeding rates between the safe and dangerous habi- volume). Risk of injury or death is another poten- tats therefore provided a measure of the energetic tial cost of habitat choice. Fish and other organ- equivalence of predation risk. This assumed that isms have been shown to respond to predation an individual’s predation risk does not change with risk in choosing habitats. For example, Mittelbach group size (Moody et al. 1996). In a second experi- (1981) and Werner et al. (1983a) found that large ment, Abrahams and Dill (1989) increased feeding bluegills, which were relatively immune to preda- rates in the risky habitat by the amount predicted tors, foraged in the habitats that yielded the to offset the influence of predation risk. They highest energy gain. However, smaller bluegill, found that the additional food resulted in a similar which were vulnerable to predation by largemouth number of guppies using both the risky and safe bass (Micropterus salmoides), were more limited habitats; female guppies, however, fit the pre- in their habitat use. Small bluegills fed in the pro- dicted response better than males. Grand and Dill tection of the littoral zone vegetation, even though (1996) adopted this same approach to examine the open-water habitat yielded a higher feeding the use of cover by stream-dwelling coho salmon rate and better growth (Werner et al. 1983b; see (Oncorhynchus kisutch). In nature, cover provides also Werner and Hall 1988). Thus, small bluegill the salmon with protection from predators but appeared to trade-higher energy gain for a lowered may also result in reduced food availability. Grand predation risk. These and other studies (Milinski and Dill (1996) used IFD theory to quantify the en- and Heller 1978) prompted theoreticians and em- ergetic equivalence of cover to the fish and then piricists to investigate the balance between forag- calculated how much additional food would have ing gain and predation risk, focusing on two main to be added to a risky patch in order to make it of questions: (i) what was the optimal habitat choice equal value to a safe patch. When they added this that would maximize fitness and (ii) how did fish additional amount of food, the fish returned to and other foragers respond to predation risk and the distribution they had before the risk became

The above studies provide an example of an em- pirical approach to equating energy gain and preda- tion risk in determining habitat choice. However, in order to predict habitat use a priori, predation risk and foraging gain must be modelled in a com- mon currency associated with individual fitness. Gilliam (1982) developed the first of these models, using the methods of optimal control theory. Stephens and Krebs (1986) provide a description of the theory and see also Werner and Gilliam (1984). Gilliam (1982) found that in the simplest case, where fish are of pre-reproductive size, population size is constant and mortality rate within a habitat is constant, then the habitat choice that maxi- mizes a fish’s fitness is selection of the habitat that minimizes the ratio of mortality rate (m) to growth rate (g). That is, select the habitat which provides

a unit of growth at the lowest mortality cost. This result was quickly labelled the ‘m/g rule’ or Gilliam’s rule and it had a profound effect on the development of subsequent theory of habitat se- lection incorporating predation risk and foraging gain. In particular, Ludwig and Rowe (1990) and Rowe and Ludwig (1991) incorporated time con- straints into the predictions of optimal habitat choice, and Houston et al. (1993) developed a set of general models that incorporate the models of Gilliam (1982), Ludwig and Rowe (1990) and others as special cases. Mangel and Clark (1986, 1988) and Clark and Mangel (2000) show how ques- tions of optimal habitat choice can be approached using the methods of dynamic programming, and Houston and McNamara (1999) summarize much of the theoretical development and predictions of state-dependent models with regard to optimal habitat choice under predation risk.

Do fish select habitats so as to optimally balance foraging gain and predation risk using the ‘mini- mize m/g rule’ or some variant thereof? Clearly, a large number of studies show that both predation risk and foraging gain may influence the habitat

use of fishes, and in the past 15 years several hun- dred papers have been published on this topic (see reviews in Dill 1987; Lima and Dill 1990; Lima 1998a,b). While many of these studies show that fish respond to the foraging gain/predation risk trade-off in an adaptive manner, few studies have actually tested specific models of optimal habitat choice in fish. Gilliam and Fraser (1987) tested a modification of the m/g rule in the habitat choice of juvenile creek chubs (Semotilus atromaculatus). They showed that when fish have the choice be- tween feeding in two or more habitats that differ in energy gain and predation risk, and when the fish may also use a refuge that contains no food and therefore has no predation risk, the m/g rule collapses to the simpler rule, ‘when foraging, mini- mize the ratio of mortality rate to gross foraging rate (f)’. Gilliam and Fraser (1987) tested the ‘minimize m /f’ prediction by offering juvenile creek chubs a choice between two foraging areas that differed in experimentally manipulated resource densities and mortality risk from adult creek chubs. They found that the creek chubs choice of foraging habi- tats agreed well with the theoretical predictions (Fig. 11.3). In this study, the optimal habitat choice boiled down to a rather simple rule of thumb: prefer

habitat A over habitat B if m A /f A <m B /f B, or, rearrang- ing, prefer habitat A if m A /m B <f A /f B. Thus, foragers need only evaluate the relative mortality risks in two habitats compared with the relative feeding rates in order to make the correct habitat choice.

The natural world is of course much more com- plicated than the simple experimental environ- ment in which Gilliam and Fraser (1987) tested their creek chubs. In particular, one of the realities of nature is that the predation risk within a habitat may vary through time (Sih et al. 2000). For exam- ple, an individual’s risk within a habitat may decline when more foragers enter the habitat (a dilution of risk) due to increased vigilance by the group, predator satiation or predator confu- sion (Milinski and Heller 1978). A number of researchers have considered how changes in pre- dation risk with group size may impact habitat choice within the context of IFD theory (e.g. McNamara and Houston 1990; Moody et al. 1996);

Fish Foraging and Habitat Choice

259

Chapter 11

(a) Resource in

tive difference in competitive abilities among for- 1.0 1-predator

Predicted

agers (Moody et al. 1996; Grand and Dill 1999). site

switch

The more empirical aspects of predation are discussed by Juanes et al. (Chapter 12, this volume)

and of prey avoidance of risk through refuge by

0.5 Krause et al. (Chapter 13, this volume).