How can the functional response best be determined

Williams FM, and Juliano SA Further difficulties in the analysis of functional response experiments and a resolution. Download references. You can also search for this author in PubMed Google Scholar.

Reprints and Permissions. Trexler, J. How can the functional reponse best be determined?. Oecologia 76, — Download citation. Received : 17 January Issue Date : July Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Summary We evaluated three methods for the analysis of functional response data by asking whether a given method could discriminate among functional responses and whether it could accurately identify regions of positive density-dependent predation.

References Akre BG, Johnson DM Switching and sigmoid functional response curves by damselfly naiads with alternative prey available. Ecolution — Google Scholar Juliano SA, Williams FM A comparison of methods for estimating the functional response parameters of the random predator equation. Can Ent — Google Scholar Download references. McCulloch Authors Joel C. Trexler View author publications.

View author publications. Rights and permissions Reprints and Permissions. About this article Cite this article Trexler, J. For instance, pre-trial starvation of experimental animals is likely to increase consumption above normal rates to compensate Nandini and Sarma, , and the meta-analysis of functional responses by Li et al.

Alternatively, researchers can conduct simplified experiments, but should then understand the real metrics that these experiments yield: gut capacity, maximum feeding rates, etc.

While these types of data have value, they are less useful for application in population models because they do not reflect predation rates expected under realistic conditions.

Above I touched on two artifacts of functional response experiments that can lead to inaccurate estimates of attack rate and handling time: time spent in activities other than foraging, and the inclusion of only a single, focal prey species in the experiment.

Both of these artifacts can be problematic because of optimal foraging strategies of animals Abrams, , ; Stephens and Krebs, Here I explore three ways that optimal foraging may interfere with or complicate the measurement and scaling up of the functional response Abrams, Each provides a mechanistic understanding for why we might expect non-foraging activity during an experiment or why neglecting to have alternative prey for a non-specialist consumer would be a problem.

The first issue deals with the timing of experimental trials relative to the timing of natural foraging under field conditions. Many species adopt daily to monthly foraging patterns tied to solar, lunar, and tidal patterns in order to optimize food intake in the face of fluctuating availability. For example, many species, including marine mollusks Little, , marine iguanas Wikelski and Hau, , fish Burrows et al. These endogenous rhythms can remain in place for days to weeks, even after removing the environmental cue by moving the animals into static laboratory conditions e.

Consequently, ignoring these natural rhythms when designing short term feeding experiments has the potential to influence observed consumption in ways that obscure the true functional response of the study species. Similarly, abundant evidence demonstrates that animals adjust the timing of foraging activities in order to balance the competing risks of starvation and predation. For instance, blackbirds Turdus merula adjust their foraging throughout the day in different ways, depending on the time of year, to either increase mass gain in the early morning during winter to reduce starvation risk, or to increase mass gain later in the day during summer and autumn in order to reduce predation risk that increases with body mass Macleod et al.

The expectation that consumption rate should increase with prey density in functional response experiments is based on the assumption that it is always optimal for consumers to eat as much as they can. Yet the presence of constraints and tradeoffs that are context dependent complicates this assumption. Functional response experiments must consider consumption in light of optimal timing and amount of consumption to which the study organism has evolved. Mathematically building these tradeoffs and optimality considerations into the functional response framework would make the framework too context-dependent and would remove the generality of the model, but when trying to use the functional response in an applied, predictive way, these tradeoffs should be explicitly considered.

A second issue deals with optimal diet selection, a subset of optimal foraging theory that predicts that consumers should pass up low quality food when higher quality food is readily available Stephens et al.

The vast majority of functional response experiments are conducted using a single prey type, even for consumers that have a broad diet. The presence of alternative prey can drastically alter the functional response Hossie et al. Thus, for non-specialist consumers, whether the functional response measured in the laboratory on a single prey is transferrable at all to field conditions, depends on the abundance of different prey types and their relative quality as determined by energy content and handling time required.

This argument applies not only to different species of prey, but to different sizes of a single prey species as well, because prey profitability depends on size-specific energy content and handling time.

As predicted by optimal diet theory Emlen, , a consumer should only accept lower quality prey if the net energy gained by doing so exceeds the net energy gain from both finding and consuming the rare higher quality prey. Thus, a functional response measured using a lower quality prey in the lab will be meaningless in the field where higher quality prey are readily available and where consumers forage optimally, because only higher quality prey should be consumed.

Alternatively, if the functional response is measured in the laboratory using a high quality prey, transferring this to the field where prey are likely harder to find and where alternative lower quality prey are available, could shift an observed type II laboratory functional response to a type III response in the field, because of diet switching to lower quality prey when high quality prey are sufficiently rare. A third issue deals with the alternative foraging strategies of rate maximization vs.

The measurement of the functional response using standard experimental procedures assumes that the consumer is a rate maximizer—attempting to eat as much as possible during the allotted time. But if the consumer is instead a time minimizer i. Instead, foragers will seek a certain level of energy intake and will stop foraging once this level is reached.

Species may not be strict time minimizers, but may show behaviors consistent with aspects of time minimization e. When time minimization influences foraging strategies, we should expect that consumption may still increase with prey density up to an asymptote; however, this asymptote may be very different than expected for energy maximizers.

For time minimizers, the asymptote is set not by handling time or digestion efficiency, but by the fact that the energy intake quota has been reached. Thus, fitting functional response curves to these data for time minimizers would yield values for attack rate and handling time that are far off the mark, reflecting the time spent in activities other than foraging. The consumer functional response has been and will continue to be an important tool for studying and understanding consumer-resource interactions.

The arguments and discussions above point to three areas where future research should place greater emphasis in order to increase the accuracy and utility of the functional response in its important role as a link between empirical and theoretical approaches. First, future empirical measurements of the functional response should strive for greater realism by conducting experiments during appropriate temporal windows that coincide with the natural foraging patterns of the study organism, and by more closely mimicking the breadth of natural prey resources available to the consumer in the field.

Second, future work should collect the data necessary to ground-truth and hone model parameter estimates by measuring handling times directly and by observing the proportion of time during experiments that consumers spend actively foraging.

Third, future applications of laboratory-measured functional responses to field populations should make greater effort to identify factors that lead to discrepancies between predicted and observed scaled-up consumption by identifying implicit assumptions in the use of the functional response and where those assumptions are violated by the study system, both in the lab and in the field.

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