Arena size modulates functional responses via behavioral mechanisms

Uiterwaal, Stella F.; Dell, Anthony I.; DeLong, John P.

doi:10.1093/beheco/ary188

Cited by 28 publications

(30 citation statements)

References 33 publications

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“…The strategy‐based differences we observe in encounter rates may necessitate in‐kind differences in metabolic rates, basal or otherwise, or may instead be mitigated by in‐kind differences in the proportion of time consumers spend foraging (Rizzuto et al ). Alternatively, it is conceivable that the non‐random space use and movement patterns exhibited by many consumers and resources result in misleading estimates of encounter and capture rates among strategies (Uiterwall et al ). It is also apparent that our phenomenological approximation of filter feeding detection regions do not capture the range of encounter mechanisms available to consumers defined as filter feeders (Jeschke et al ).…”

Section: Discussionmentioning

confidence: 99%

Biomass encounter rates limit the size scaling of feeding interactions

Barrios‐O'Neill

Kelly

Emmerson³

2019

Ecology Letters

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The rate that consumers encounter resources in space necessarily limits the strength of feeding interactions that shape ecosystems. To explore the link between encounters and feeding, we first compiled the largest available dataset of interactions in the marine benthos by extracting data from published studies and generating new data. These data indicate that the size‐scaling of feeding interactions varies among consumer groups using different strategies (passive or active) to encounter different resource types (mobile or static), with filter feeders exhibiting the weakest feeding interactions. Next, we used these data to develop an agent‐based model of resource biomass encounter rates, underpinned by consumer encounter strategy and resource biomass density. Our model demonstrates that passive strategies for encountering small, dispersed resources limits biomass encounter rates, necessarily limiting the strength of feeding interactions. Our model is based on generalisable assumptions, providing a framework to assess encounter‐based drivers of consumption and coexistence across systems.

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Section: Discussionmentioning

confidence: 99%

Biomass encounter rates limit the size scaling of feeding interactions

Barrios‐O'Neill

Kelly

Emmerson³

2019

Ecology Letters

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“…Several other issues in the design and analysis of functional response experiments have been addressed in the recent literature. These include effects of experimental arena size (Bergström and Englund 2002, Uiterwaal et al 2019), predator hunger level and experimental duration (Li et al 2018), and of the statistical fitting method (Rosenbaum and Rall 2018) on estimates of functional response parameters. While we did not explore any of these issues, we want to highlight the recent advance of fitting the functional response to a differential equation model of cumulative prey depletion (Rosenbaum and Rall 2018).…”

Section: Recommendations For the Design And Analysis Of Functional Response Measurementsmentioning

confidence: 99%

Fitting functional response surfaces to data: a best practice guide

2020

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Describing how resource consumption rates depend on resource density, conventionally termed "functional responses," is crucial to understanding the population dynamics of trophically interacting organisms. Yet, accurately determining the functional response for any given pair of predator and prey remains a challenge. Moreover, functional responses are potentially complex surfaces in multidimensional space, where resource density is only one of several factors determining consumption rates. We explored how three sources of error can be addressed in the design and statistical analysis of functional response experiments: ill-chosen spacing of prey densities, heteroscedastic variance in consumption rates, and non-independence of parameters of the function describing prey consumption in relation to prey density and additional environmental factors. We generated extensive, virtual data sets that simulated feeding experiments in which both prey density and environmental temperature were varied, and for which the true, deterministic functional response surface was known and realistic variance had been added. We compared eight different methods of functional response fitting, one of which stood out as best performing. We subsequently tested several conclusions from the simulation study against experimental data of zooplankton feeding on algae across a broad range of temperatures. We summarize our main findings in three best practice guidelines for the experimental estimation of functional response surfaces, of which the second is the most important: (1) space prey densities logarithmically, starting from very low densities; (2) log-transform prey consumption data prior to fitting; and (3) fit a multivariate functional response surface to all data (including all prey densities and other factors, in our case temperature) in a single step. We also observed that functional response surfaces were fitted more accurately and precisely than their component parameters. The latter occurred because parameter estimates were non-independent, which is an inevitable feature of fitting complex nonlinear functions to data: A given response surface can often be described with near-equal accuracy by multiple parameter combinations. We therefore conclude that fitted functional response models perform better at optimizing the fit of the overall response surface than at determining how component parameters, such as the attack rate or handling time, depend on environmental factors such as temperature.

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“…Many forms and sources of bias exist in the functional‐response literature. These include recently recognised sources related to an experiment's design and venue (Novak et al ., 2017; Li et al ., 2018; Preston et al ., 2018; Uiterwaal et al ., 2018; Uiterwaal and DeLong, 2018), as well as persistent model‐associated sources (Trexler et al ., 1988; Pascual and Kareiva, 1996; Marshal and Boutin, 1999; Jost and Arditi, 2000; Novak, 2010; Novak and Wootton, 2010; McCoy et al ., 2012; Morozov and Petrovskii, 2013; Barraquand, 2014; Hossie and Murray, 2016; Rosenbaum and Rall, 2018; Damgaard, 2020). Our analyses add to this list of issues, exposing the pervasive effect that small sample sizes have had on inferences of consumer dependence, but they are also not immune to them.…”

Section: Discussionmentioning

confidence: 99%

Systematic bias in studies of consumer functional responses

Novák

Stouffer

2020

Ecology Letters

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Functional responses are a cornerstone to our understanding of consumer–resource interactions, so how to best describe them using models has been actively debated. Here we focus on the consumer dependence of functional responses to evidence systematic bias in the statistical comparison of functional‐response models and the estimation of their parameters. Both forms of bias are universal to nonlinear models (irrespective of consumer dependence) and are rooted in a lack of sufficient replication. Using a large compilation of published datasets, we show that – due to the prevalence of low sample size studies – neither the overall frequency by which alternative models achieve top rank nor the frequency distribution of parameter point estimates should be treated as providing insight into the general form or central tendency of consumer interference. We call for renewed clarity in the varied purposes that motivate the study of functional responses, purposes that can compete with each other in dictating the design, analysis and interpretation of functional‐response experiments.

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Arena size modulates functional responses via behavioral mechanisms

Cited by 28 publications

References 33 publications

Biomass encounter rates limit the size scaling of feeding interactions

Biomass encounter rates limit the size scaling of feeding interactions

Fitting functional response surfaces to data: a best practice guide

Systematic bias in studies of consumer functional responses

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