Provisioning

Ronald C. Ydenberg

8.1 Prologue

A honeybee (Apis mellifera) colony contains thousands of foragers that collect large amounts ofnectar, pollen, propolis, and water and deliver them to the hive. The colony's activities and, ultimately, reproduction depend on these resources. Millions ofyears ofhoneybee evolution and thousands of years of domestication have selected for proficient resource provisioning.

Bees divide the labor ofresource acquisition and provisioning. Scout bees specialize in finding ephemeral resources and recruiting foragers to good locations. Foragers fuel up on the communal honey supply and leave the colony knowing where to go and what to expect. En route, they regulate their flight speed, micromanage their body temperature, and carefully collect a load for transport back to the hive. In the hive, a system of feedbacks involving behaviors, odors, and pheromones regulates the quantity and quality of future resource deliveries. Using this system, the colony can quickly refocus its activities on the commodities it needs most.

Many predators, including bears, honey badgers, honeyguides, honey buzzards, and hornets, covet the contents ofa hive, and the bees must defend it. Outside the hive, bee wolves and other predatory insects, as well as a suite of birds such as bee-eaters, make a forager's life hazardous. If she eludes all these dangers, she faces a routine of grueling work: after lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll only 20 days or so, her wings are tattered and her body pile worn. Soon all internal systems fail. When she dies, her comrades unceremoniously dump her body onto a pile of other spent workers outside the hive. Selection has not built workers to last, but to provision.

8.2 Introduction

This chapter considers provisioning: the collection and delivery of materials such as food, nesting material, or water. The quintessential feature of provisioning is that provisioning animals deliver material to a site where they either feed it to others or store it for later use (Ydenberg 1998). The earliest provisioning studies considered a parent bird working as hard as possible to deliver prey items to its altricial offspring. This chapter will show that provisioning raises important questions and issues that go far beyond the problems of a parent bird feeding its young.

The "parent bird" paradigm focuses attention on only a few key features of animal provisioning. Many other animal taxa provision, using a wide range of behaviors (table 8.1), and the realm of interesting provisioning phenomena includes aspects other than foraging theory's classic problems of prey choice and patch exploitation (Stephens and Krebs 1986; see chap. 1 in this volume). The extensive literature on diverse provisioning systems makes it clear that we must consider selective benefits beyond simple energy acquisition to understand the diversity of provisioning behavior.

Like the rest of behavioral ecology, provisioning models emphasize costs and benefits, and they ask how costs and benefits select for certain types of behavior. Fundamentally, these models assume, often implicitly, that selection (natural, sexual, or artificial) has acted on the structure and function of"deci-sion mechanisms" (Ydenberg 1998). Physiological processes and morphological structures inside the provisioner control these decision mechanisms. The models do not require cognitive functions such as memory, consciousness, or forethought, but they do not preclude them either. Decision mechanisms integrate information from sensory organs and internal indicators ofstate, such as hunger or weariness, to produce behavior. For example, seabirds such as thin-billed prions (see table 8.1) decide whether their next provisioning trip will be a short outing of a few days to the edge of the continental shelf or a long pelagic excursion (apparently evaluating their nestling's condition, their own condition, their recent provisioning history, and the availability of prey). Chapters 3, 4, and 5 consider some of the mechanisms animals use to integrate this information (see also Dukas 1998a).

For selection to act on provisioning behavior, decision mechanisms must affect the provisioner's reproduction or survival. This could happen in a variety

Table 8.1 Selected examples of provisioning tactics documented in free-living animals

Selection of prey for self-feeding

Sonerud (1989) describes how a kestrel (Falco tinnunculus) and a shrike (Lanius excubitor) direct small, medium, and large prey to self-feeding versus delivery. Foraging destination

In thin-billed prions (Pachyptila belcheri), parents deliver undigested meals after short trips, and lose condition themselves, evidently because they power the excursion from body reserves. After long trips, parent prions deliver prey (partially) concentrated into energy-rich stomach oil; parental condition improves (Duriez et al. 2000). Foraging mode

Bumblebees may fly or walk between flowers. Travel speed

On the outbound flight from hive to feeder, honeybees fly faster when the sucrose concentration at their destination is greater (von Frisch and Lindauer 1955). They fly more slowly on the return trip, and speed does not vary with concentration. Load size increases with concentration.

Body temperature

Honeybee workers have higher body temperatures and cool more slowly after landing on higher-concentration sucrose solutions (Schmaranzer and Stabenheiner 1988).

Prey processing into parts

Rands et al. (2000) describe models and observations of prey dismemberment for transport by a provisioning merlin (Falco columbarius).

Prey processing into partially digested material, or nutritious secretions

Carnivores may carry whole prey to the den or regurgitate partially digested prey. Of course, female mammals also lactate (Holekamp and Smale 1990).

Time devoted to provisioning

Spotted hyenas (Crocuta crocuta) vary attendance times at the den depending on prey availability (Hofer and East 1993).

Adjusting brood location

Lapland longspurs (Calcarius lapponicus) divide broods evenly into two units after nest departure, each tended by one parent (McLaughlin and Montgomerie 1989).

Body weight or constitution alteration and metabolic rate adjustment

The wet body mass of a worker honeybee drops 40%, and maximal thorax-specific oxygen consumption increases 10%, during the transition from hive bee to forager (Harrison 1986). Adjustment ofparticipation in brood rearing or helping

Adult pied kingfishers (Ceryle rudis) facing high demand recruit helpers (Reyer and Westerterp 1985).

Egg size, brooding, or delivery

Birds can supply materials to the nest in the egg itself, by brooding offspring, and by provisioning nestlings. These alternatives have different costs and benefits, and birds can adjust them accordingly (Hipfner et al. 2001).

Table 8.1 (continued)

Workload

Honeybees in large colonies work harder than those in small colonies (Wolf and Schmid-Hempel 1990; Eckert et al. 1994). Offspring gender ratio

In many mass-provisioning hymenopterans, provisioners adjust the gender ratio of the brood (Rosenheim et al. 1996) between small males and large females. Ovipositing females can also adjust the sequence and position of offspring sexes within the nest.

Trophic eggs

Many animal taxa provision young with trophic eggs (eggs used as food). In the poison arrow frog Dendrobatespumilio, mothers deliver trophic eggs to tadpoles secreted in phytotelmata (tiny pools of water up in trees; Brust 1993). In other poison arrow frog species, parents supply water to these pools to prevent them from drying.

of ways. Most often, investigators have considered direct effects of the amount of food provisioned on the quantity or quality of offspring. However, sexual selection could also act on decision mechanisms through their effects on the number or quality of mates attracted. For example, stickleback (Gasterosteus aculeatus) nests are built by males from material delivered to the assembly point and may advertise a male's qualities (Barber et al. 2001; see also Soler et al. 1996). Provisions placed in hoards can enhance survival when resources are scarce (see chap. 7), and in some species the size or quality of structures built from delivered material affects reproductive success (e.g., stone ramparts; Leader and Yom-Tov 1998).

In addition to evaluating benefits, researchers must carefully characterize the fitness costs of provisioning. Provisioning always involves work because provisioners must expend time and energy to collect and deliver materials. Whether provisioners deliver food, water, stones, or mud, the provisioner's metabolism generates the necessary power, and the provisioner must feed itself to provide the fuel for provisioning. Provisioning models pay careful attention to the relationships between self-feeding, metabolism, and delivery capacity, but they must also recognize the importance of factors other than energetics. Collecting materials or the extra food needed to fuel their delivery may expose the provisioner to danger or distract it from important tasks such as offspring care or the management of stored food.

This chapter outlines the structure of provisioning models and their relationship to traditional foraging models, investigates the rate of work and its relation to metabolism, considers how provisioners should respond to demand, and discusses provisioning in a life history context. It focuses throughout on the underlying ecological selective factors that shape the morphology, physiology, and life history of provisioning behavior.

8.3 Basic Models of Provisioning Behavior

This section outlines the history of provisioning models, focusing on how foraging models provided a framework for ideas about provisioning. It develops the most basic provisioning model and touches on the issues that connect provisioning and foraging models.

Central Place Foraging

Great tits (Parus major) are small songbirds living in European woodlands. Each spring a pair raises a brood of about eight nestlings in a tree cavity or nest box. While provisioning the nestlings, each parent spends almost all of its time searching through the trees in its territory for insect prey, especially caterpillars, which are fed to the brood. Each parent makes hundreds ofback-and-forth trips each day, delivering prey to fuel the growth of the nestlings. Better-fed broods grow faster and survive better.

Orians and Pearson (1979) invented the term "central place foraging" to describe this and similar situations in which animals make repeated foraging excursions from a central location. Their model introduced the basic concepts of central place foraging, developed the idea of "loading" prey, and distinguished "single-prey" and "multiple-prey" loaders, appreciating the different nature of the decisions that these foragers face. The simplicity, novelty, and applicability of this model inspired many field and experimental studies. Its simple framework can be applied to a variety of situations: box 8.1 considers as an example the effect of social interactions on central place foraging.

Central place foraging models consider the amount or type of prey that foragers should deliver to their central place. "Single-prey loaders" deliver a single prey item from a capture site on each trip, and the decision they face is the minimum size of prey acceptable for delivery. This decision implies a tradeoff, because low selectivity (capture any prey) means that the forager may spend too much time in transit with small prey, while high selectivity (capture only large prey) means that the forager may spend too much time at the capture site searching for suitable items. The selectivity giving the highest rate of energy gain depends on the size (energy content) distribution of prey, prey density, and travel time. Single-prey loader models predict that foragers should set a higher minimum prey size when prey are more abundant and when they must travel greater distances to capture sites.

Krebs and Avery's (1985) studies of bee-eaters (Meriops apiaster) provisioning their broods provide a field example. Bee-eater parents captured both small (mostly bees and wasps) and large (mostly dragonflies) prey, but delivered a lower percentage of small prey when returning from more distant capture sites.

BOX 8.1 Effects of Social Interactions at Resource Points on Provisioning Tactics

Social interactions at resource collection points often affect the tactics that provisioners use. Eastern chipmunks (Tamias striatus) defend territories and usually avoid one another. They compete aggressively at rich resource points, and even the mere proximity of a conspecific can reduce the rate at which they load seeds into their cheek pouches. Ydenberg et al. (1986) incorporated this interference effect into a central place foraging framework to explain Giraldeau and Kramer's (1982) observation that chipmunks collected smaller loads and spent more time exploiting experimentally provided seed piles as interference increased over repeated visits to the experimental patches (fig. 8.1.1; see Lair et al. 1994).

Figure 8.1.1. Interference among chipmunks slows loading, and so reduces load size, but increases patch residence time. The star indicates the predicted rate-maximizing load size at each level of interference.

Patch time

Figure 8.1.1. Interference among chipmunks slows loading, and so reduces load size, but increases patch residence time. The star indicates the predicted rate-maximizing load size at each level of interference.

Other creatures cooperate rather than compete in resource collection. Leaf-cutter ants, for example, travel along trails to particular bushes and trees, where they cut semi-discs from leaves, often stripping entire branches in the process. Trails to collection sites bustle with two-way traffic as ants transport leaf fragments to their large underground colonies, where they are processed into mulch. The ants grow fungus on the mulch and feed this fungus to the brood.

Foraging ants cut semi-discs from the leaf margin. Larger pieces are more profitable because cutting time increases linearly with the radius, while mass rises as the square of the radius. However, it takes more time to cut large pieces, so workers looking for cutting sites along the leaf margin may have to wait in a queue for the next available cutting site. So, while cutting large leaf fragments may increase the delivery rate for an individual, it can reduce the overall delivery rate. Students of social insects must frequently address similar conflicts between benefits at the individual and colony levels (e.g., Ydenberg and Schmid-Hempel 1994).

Burd et al. (2002) analyzed how this conflict affects delivery in the leaf-cutter ant Atta cephalotes. For an individual worker, the expression load mass/(outbound time + queuing time + cutting time + inbound time) gives the rate of delivery of leaf material. The size of the leaf fragment influences every term of this expression except outbound time. (Ant size influences all of the terms, because larger individuals travel and cut faster, and load mass affects larger individuals less.) Individual workers could theoretically diminish the effect of queuing by cutting smaller pieces, effectively reducing their own delivery rate to reduce the waiting time of their nestmates and so boost their delivery potential. Figure 8.1.2 displays Burd et al.'s measurements of leaf fragment sizes in relation to these predictions. Workers cut smaller leaf fragments than predicted by individual rate maximization, and the observed fragment sizes more closely matched the predictions of colony rate maximization. Ydenberg and Schmid-Hempel (1994), Kacelnik (1993), and Roces and Nunez (1993) provide more discussion of load size in leaf-cutter ants.

Ant iiie (femur length,mm)

Figure 8.1.2. Load masses of leaf fragments cut by leaf-cutter ants (Atta colombica) from the tree Tocoyena pittieri. The line labeled "individual maximum" shows predictions based on maximization of individual delivery rates in the absence of queuing. The lines labeled "whole colony rate" show predicted load masses if ants maximize delivery to the colony taking queuing into account, the magnitude of which is given by the parameter X. The "whole colony" lines lie below the "individual maximum" line and better match the data. (After Burd et al. 2002.)

(Box 8.1 continued)

In these two examples, interference and cooperation at resource collection sites both result in a tactical reduction of load size by provisioners. In other early studies, Martindale (1982) and Ydenberg and Krebs (1987) considered how territorial intruders affected provisioning tactics and found theoretical and empirical support for the idea that intruders cause a reduction in load size and patch residence time. Central place foraging models provide a simple framework for investigating the effects of social interactions on provisioning.

Bee-eaters feeding themselves or fledged young at these same sites (i.e., with no travel to the nest involved) ate many small prey, confirming that they must have been rejecting opportunities to deliver small prey in favor of waiting for larger items. Krebs and Avery used their field measurements to predict the critical travel times beyond which delivery of small items was no longer worthwhile and compared their predictions with their observational data (fig. 8.1).

"Multiple-prey loaders" face a different problem: they must decide how many prey items to collect before they return to the central place. Larger loads require increasingly long loading times, so multiple-prey models predict that foragers should collect large loads only when they must travel a long way from the central place. Kacelnik (1984) studied European starling (Sturnus vulgaris)

Figure 8.1. Measured and predicted composition (percentage of small prey) of prey collected for delivery by bee-eaters from capture sites distant from the nest in two different years. The dashed line shows the diet predicted by an energy gain-maximizing central place foraging model, which below the critical travel time should contain small and large prey in proportion to availability, and above it only large prey. The shaded bar shows the location of the best-fitting threshold, plus standard error, estimated from the data. (After Krebs and Avery 1985.)

Figure 8.1. Measured and predicted composition (percentage of small prey) of prey collected for delivery by bee-eaters from capture sites distant from the nest in two different years. The dashed line shows the diet predicted by an energy gain-maximizing central place foraging model, which below the critical travel time should contain small and large prey in proportion to availability, and above it only large prey. The shaded bar shows the location of the best-fitting threshold, plus standard error, estimated from the data. (After Krebs and Avery 1985.)

parents collecting mealworms according to an experimentally controlled schedule and at manipulated travel times. Individual starlings clearly upheld the basic prediction that larger loads are a consequence oflonger travel times (fig. 8.2).

Readers should understand that central place foraging is not synonymous with provisioning. The former uses the structure of repeated excursions from a central place to a site where some resource is collected. The essential feature of provisioning is the collection of a resource that does not fuel the provisioner's energy supply (e.g., nesting material or food for another). Many central place problems involve provisioning, but others, such as diving by air-breathing animals (Ydenberg 1988) or surface breathing by aquatic animals (Kramer 1988) clearly do not.

Currencies

What should central place foragers maximize? Kacelnik (1984) compared the load sizes that his European starlings collected with the predictions offour objective functions, or "currencies." The currency he called delivery is the total delivery of prey energy to the nest on each trip, divided by round-trip time. The currency called yield subtracts from the total delivery the amount ofener-gy spent by the parent on each trip, all divided by round-trip time; while that called family gain further subtracts the energy spent by the young during each trip. These three closely related measures are all rates and are all expressed in units ofwatts ( joules per second). The fourth currency is somewhat different: it takes the total delivery and divides by the energy expended by the parent. We call this currency efficiency (joules delivered to the nest per joule expended by the parent), and it has no units. Statistically, the family gain currency matched Kacelnik's observations best, but all four currencies made similar predictions, and he could not discriminate among them unambiguously.

Houston (1987) pointed out that all of these currencies combine the energy budgets of the parents and young in ways that do not accurately reflect who receives and who pays for the delivered energy. For example, yield subtracts the energy the parent expends from the energy delivered to the young, even though parents do not consume the prey they deliver to the nest. Field studies show that parents regularly consume prey items at the collection site, but always before beginning to collect a load for delivery (Brooke 1981; Kacelnik 1984; Krebs and Avery 1985). Central place foraging models simply ignore this self-feeding, and none of these studies accounted for it in making model predictions.

Figure 8.2. Number of prey (mealworms) collected for delivery to a nest by parent starlings from a feeding table atwhich the experimenter made prey available on a controlled schedule. The graphs show data (open circles) for two birds (Y and W) in relation to the predictions (solid line) of the four central place foraging currencies described in the text. Note that the data represented in the four panels for each bird are the same, but the prediction changes slightly. (After Kacelnik 1984.)

Figure 8.2. Number of prey (mealworms) collected for delivery to a nest by parent starlings from a feeding table atwhich the experimenter made prey available on a controlled schedule. The graphs show data (open circles) for two birds (Y and W) in relation to the predictions (solid line) of the four central place foraging currencies described in the text. Note that the data represented in the four panels for each bird are the same, but the prediction changes slightly. (After Kacelnik 1984.)

Provisioning Models

In a key step of the development of provisioning models from central place foraging models, modelers slowly recognized that they should account separately for the energy delivered to nestlings and the energy parents consume and expend (Ydenberg and Schmid-Hempel 1994). Only one central place foraging study published before Houston's (1987) paper recognized this key distinction. In a model of flight speed for parent birds delivering food to offspring, Norberg (1981) separated parent and offspring accounts by requiring that provisioners spend some time acquiring the food needed to cover the costs of the trip. We can measure delivery as the amount of energy or material delivered (e.g., to offspring) over some period without confusing this with theprovisioner's own energetics. So, a conceptually correct provisioning model must find the tactic that maximizes delivery, subject to the requirement that the provisioner (in this case, the parent bird) spend enough time to meet its own energy requirements. As Houston (1987, 255) says of the parent bird example, "the strategy that maximizes fitness is the strategy that maximizes the conversion of the parent's time and energy into energy for the young."

I call models with this explicit treatment of self-feeding "provisioning" models to distinguish them from central place foraging models. The differences are small but significant. Provisioning models keep the parent's energy budget separate from the energy delivered to the brood by measuring the parent's energy budget not in joules, but as the time the parent needs to find the food to balance its own books. This distinction means that we do not have to measure delivery in units of energy. We can consider the delivery of water to cool a wasp nest (e.g., Kasuya 1982), sticks to build a nest (e.g., McGinley 1984; Nores and Nores 1994), or any other material.

The Basic Provisioning Model

After delivering one prey item, a great tit must immediately turn around and fly back to find another. How fast should it fly to the foraging site? Faster flight, of course, reduces travel time, but it also increases the time that must be spent in collecting fuel for the trip. As Norberg (1981) noted in his original paper on the topic, the delivery-maximizing flight speed depends on the time that the provisioner must spend in feeding itself. The basic provisioning model analyzes this problem.

To find the solution, we assume that the provisioner can choose from a list of n behavioral tactics i = 1, 2, 3, ..., n. The tactics could be successively higher travel speeds, successively shorter patch residence times, successively smaller minimum prey sizes, or variations on any of the other tactics listed in table 8.1. When the provisioner uses delivery tactic i, it expends energy at rate c and delivers food at rate d;. By convention, we arrange theprovisioner's options in order of energy expenditure, so using option 1 costs the least per unit time, and using option n costs the most. The provisioning model finds the tactic (choice of i) that maximizes the total delivery over some time period

(usually a day), called D;. Typically the provisioner faces a trade-off because options that deliver food at higher rate also cost more to implement, and so require more self-feeding time.

Next, we divide a provisioner's time budget into time spent in delivery, self-feeding, and resting, so that total time T = td + ts + tr. The provisioner must allocate enough time to self-feeding, ts, to maintain a positive energy balance. During self-feeding the provisioner obtains energy at rate bs and expends energy at rate cs (obtaining a net self-feeding rate of bs — cs). When at rest, the provisioner expends energy at rate r.

With estimates of the basic cost and delivery parameters, we can easily calculate how much delivery time each option allows, and so compute the total daily delivery. The provisioner must maintain a positive energy balance, and so the energetic gain while self-feeding must equal the energetic expenditure on all activities. To begin, we assume that nothing limits the provisioner's total energy expenditure, which means that the provisioner doesn't need to spend time resting. (We consider this assumption further below.) With this simplification, self-feeding at rate bs for time ts recovers the day's energy expenditure, so that bs ■ ts = td ■ ci + ts ■ cs. Solving for tj yields the time available for delivery after accounting for the time that the provisioner must spend self-feeding:

To find the total daily delivery for option i, we multiply the time available for delivery [eq. (8.1)] by the rate of delivery (4):

Equation (8.2) summarizes the relationships between the net self-feeding rate (bs — cs) and the provisioning tactics available. A heightened net self-feeding rate increases the time available for delivery. However, it may at the same time allow a higher-workload tactic (higher c;) to increase the total delivery. Generally speaking, higher self-feeding rates permit the provisioner to sustain harder work, and the tactic that maximizes total daily delivery intensifies from lower-delivery to higher-delivery tactics (increasingly higher c;) as the self-feeding rate rises. Figure 8.3 gives a worked example.

The role of the self-feeding rate in these predictions helps us resolve a puzzle in foraging theory. Central place foraging models generally use performance criteria such as "maximize the net rate of energy gain," but studies have sometimes found that efficiency maximization gives a better fit to the data

Delivery Time

Figure 8.3. The dependence of total daily delivery on the self-feeding rate and the tactical options available, as described in equations (8.1) and (8.2). The three lines labeled i = 1, 2, 3 represent three successively higher-workload delivery tactics. For each tactic, open circles indicate the delivery time attainable if the provisioner adopts a low self-feeding rate; solid circles, an intermediate self-feeding rate; crosses, a high self-feeding rate. Along the line representing any tactical option, delivery time, and hence total delivery, increases with self-feeding rate, but at any self-feeding rate, working harder reduces the attainable delivery time. A shift to higherworkloads with increasing self-feeding rates maximizes the total daily delivery.

Delivery Time

Figure 8.3. The dependence of total daily delivery on the self-feeding rate and the tactical options available, as described in equations (8.1) and (8.2). The three lines labeled i = 1, 2, 3 represent three successively higher-workload delivery tactics. For each tactic, open circles indicate the delivery time attainable if the provisioner adopts a low self-feeding rate; solid circles, an intermediate self-feeding rate; crosses, a high self-feeding rate. Along the line representing any tactical option, delivery time, and hence total delivery, increases with self-feeding rate, but at any self-feeding rate, working harder reduces the attainable delivery time. A shift to higherworkloads with increasing self-feeding rates maximizes the total daily delivery.

(Ydenberg 1998). Provisioning models can explain this, because the predicted behavior depends on the self-feeding rate. The term di ¡C{ in equation (8.2) represents the efficiency of option i: at low self-feeding rates, the total delivery is determined largely by its value, and behavior (i.e., choice of i) should match that predicted by an efficiency (or efficiency-like) currency. As the self-feeding rate increases, it becomes possible to sustain a higher workload, and the measured behavior should approach the predictions ofthe three rate-maximizing currencies. McNamara and Houston (1997) give a general derivation and discussion of this important point. Thus, aprovisioning model can accommodate rate-maximizing and efficiency-maximizing behavior within a single framework.

Few studies have tested this critical prediction. (Figures 8.1 and 8.2 show measured behavior as well as predictions about behavior based on central place foraging currencies, but provisioning predictions require an estimate of the self-feeding rate, which we do not yet have.) Waite and Ydenberg (1994a, 1994b) measured the deliveries of Canada gray jays (Perisorus canadensis) hoarding raisins. Birds came to a feeder where they could have one raisin immediately and obtain two more if they waited an experimentally controlled time. (Waiting at the feeder for the larger load is a lower-workload tactic because waiting is an inexpensive activity relative to flying and hoarding.) Obviously, jays can do better with three-raisin loads when the waiting time is short.

Waite and Ydenberg (1994b) showed that birds shifted abruptly from three-raisin to one-raisin loads as they increased the experimental waiting time. More importantly, each individual shifted to lower-workload tactics during the winter, when the self-feeding rate presumably falls (Waite and Ydenberg 1994a). Figure 8.4A summarizes these results.

A direct experimental test would manipulate the self-feeding rate and predict the effect on provisioning behavior. Palestinian sunbirds (Nectarina osea) feed insects to their nestlings (Markman et al. 1999), but feed themselves largely on nectar. Few bird species show such a marked difference in parental and nestling foods (but see Davoren and Burger 1999), so Palestinian sunbirds provide an opportunity to manipulate the provisioner's self-feeding rate. Markman et al. (2002) randomly assigned sunbird territories to low, medium, or high self-feeding rate groups, which they manipulated by varying the sugar concentration in feeders placed in the territory. Changes in sugar concentration caused a variety ofbehavioral changes. Parents worked harder when high sugar concentrations produced high self-feeding rates: they visited the nest more (fig. 8.4B) and reared larger nestlings. Although not designed to test a provisioning model (Markman placed his work in a life history framework), these results agree with the expectations of the provisioning framework.

Markman et al. controlled the self-feeding rate in their experiment, but in nature, provisioners can often make decisions about their self-feeding rate. For example, parent bee-eaters feed themselves on prey caught at the same sites where they capture prey for their nestlings. As each potential prey item flies by, they must decide whether to ignore it, catch and eat it, or deliver it to their nestlings. This decision process affects the self-feeding rate, and hence the achievable delivery rate. In general, a change in the self-feeding options alters provisioning behavior, even if the provisioning options do not change (Houston and McNamara 1999).

This central feature of provisioning models has wide-ranging implications. For example, students ofavian breeding systems have assumed that the brood size of territorial birds increases with prey density because birds can find and deliver prey more easily. High prey densities could also mean that parents can achieve higher self-feeding rates, so that they can work harder at food delivery. We will need imaginative experimental work controlling both delivery and self-feeding opportunities to resolve this issue (e.g., Kay 2004). The possibility that different locations provide opportunities for self-feeding and food for delivery has interesting implications for provisioning; box 8.2 gives an example.

"O

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