A behavioral model of predator-prey functional responses

Holling, C. S.; Buckingham, Sandra

doi:10.1002/bs.3830210305

Cited by 27 publications

(14 citation statements)

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“…3.12), see also Nakamura (1974: Eq. 2; (L) Invertebrate model (Holling 1966; see also Metz and van Batenburg 1985a,b); (M) Vertebrate model (Holling 1965); (N) Holling and Buckingham (1976); (O) Rao and Kshiragar (1978); (P) Metz et al (1988; see also Metz and van Batenburg 1985a, b); (Q) Cushing (1968); (R) Tostowaryk (1972); (S) Random predator equation (Royama 1971, Rogers 1972); (T) Random parasite equation (Royama 1971, Rogers 1972); (U) Beddington (1975); (V) Hassell et al (1977); (W) Longstaff (1980); (X) Mills (1982); (Y) Crowley (1973); (Z) Oaten and Murdoch (1975); (AA) Real (1977); (BB) McNair (1980); (CC) Abrams (1982); (DD) Dunbrack and Giguere (1987); (EE) Abrams (1990a); (FF) Descriptive equation (Fujii et al 1986); (GG) Ungar and Noy-Meir (1988); (HH) Random patch model (Lundberg and Å strö m 1990; see also Lundberg and Danell 1990); (II) Juliano (1989); (JJ) Fryxell (1991; see also Wilmshurst et al 1995Wilmshurst et al , 1999Wilmshurst et al , 2000; (KK) Spalinger and Hobbs (1992; see also Laca et al 1994, Shipley et al 1994); (LL) Farnsworth and Illius (1996; see also Laca et al 1994, Shipley et al 1994); (MM) Hirakawa (...…”

Section: Previous Models: a Brief Reviewmentioning

confidence: 99%

“…The only family of models that treats digestion as a background process, which influences foraging activities but does not prevent them, is Holling's (1966) invertebrate model and its extensions (Holling 1965, Holling and Buckingham 1976, Curry and DeMichele 1977, Metz and van Batenburg 1985a. In the invertebrate model, the predation cycle is subdivided into several stages, and each stage depends on predator hunger level.…”

Section: Models Including Satiation But Not Handling Time-mentioning

confidence: 99%

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Predator Functional Responses: Discriminating between Handling and Digesting Prey

Jeschke¹,

Kopp²,

Tollrian³

2002

Ecological Monographs

262

475

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We present a handy mechanistic functional response model that realistically incorporates handling (i.e., attacking and eating) and digesting prey. We briefly review current functional response theory and thereby demonstrate that such a model has been lacking so far. In our model, we treat digestion as a background process that does not prevent further foraging activities (i.e., searching and handling). Instead, we let the hunger level determine the probability that the predator searches for new prey. Additionally, our model takes into account time wasted through unsuccessful attacks. Since a main assumption of our model is that the predator's hunger is in a steady state, we term it the steady-state satiation (SSS) equation.The SSS equation yields a new formula for the asymptotic maximum predation rate (i.e., asymptotic maximum number of prey eaten per unit time, for prey density approaching infinity). According to this formula, maximum predation rate is determined not by the sum of the time spent for handling and digesting prey, but solely by the larger of these two terms. As a consequence, predators can be categorized into two types: handling-limited predators (where maximum predation rate is limited by handling time) and digestion-limited predators (where maximum predation rate is limited by digestion time). We give examples of both predator types. Based on available data, we suggest that most predators are digestion limited.The SSS equation is a conceptual mechanistic model. Two possible applications of this model are that (1) it can be used to calculate the effects of changing predator or prey characteristics (e.g., defenses) on predation rate and (2) optimal foraging models based on the SSS equation are testable alternatives to other approaches. This may improve optimal foraging theory, since one of its major problems has been the lack of alternative models.

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Section: Previous Models: a Brief Reviewmentioning

confidence: 99%

Section: Models Including Satiation But Not Handling Time-mentioning

confidence: 99%

Predator Functional Responses: Discriminating between Handling and Digesting Prey

Jeschke¹,

Kopp²,

Tollrian³

2002

Ecological Monographs

262

475

View full text Add to dashboard Cite

show abstract

“…Before doing this, however, to illustrate the nature of the diversity of models that can be formulated, we note that Holling and Buckingham [15] developed a model with the same fundamental objective stated for this work-to gain an understanding of the basic dynamics underlying predator-prey interactions. A careful examination of this work reveals, however, that the predator motion is ''ballistic'' rather than diffusive.…”

Section: An Example Of a ''Realistic Prey-predator Population Dynamicmentioning

confidence: 98%

Anomalous invasion in a 2d model of chemotactic predation

Willemsen

2010

Physica A: Statistical Mechanics and its Applications

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“…While in many global biogeochemical models zooplankton is described by non-mechanistic formulations, such as Holling-type functions (Holling and Buckingham, 1976), in this study we apply a more realistic zooplankton model (Pahlow and Prowe,365 2010). Among the five zooplankton parameters, the maximum specific ingestion rate (g max ) and the capture coefficients of phytoplankton (φ phy ) and diazotrophs (φ dia ) are the most important, whereas the preference for detritus (φ det ) is generally less important.…”

Section: Zooplankton Parametersmentioning

confidence: 99%

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

Chien¹,

Pahlow²,

Schartau³

et al. 2020

Preprint

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We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO 3 -, PO 4 3 -, O 2 , and surface chlorophyll a concentrations. 5According to our metric the optimal model solutions comprise low rates of global N 2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the "best" model solutions we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO 3 inventory. Global O 2 varies by 10 a factor of two and NO 3 by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O 2 , which is also affected by the subsistence N quota of ordinary phytoplankton (Q N 0, phy ) and zooplankton maximum specific ingestion rate. Q N 0, phy is revealed as a major determinant of the oceanic NO 3 pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q N 0, phy , is a prerequisite for understanding the marine nitrogen inventory.The basic structure of most marine ecosystem models has been designed for resolving mass fluxes between nutrients, phy-25 toplankton, zooplankton and detritus, typically referred to as NPZD models. Mathematical formulations that describe growth and fate of marine phytoplankton and zooplankton biomass have been successfully applied over a range of scales, from local 0D-ecosystem models (e.g., Fasham et al., 1990;Edwards, 2001) to global 3D models (Sarmiento et al., 1993;Keller et al., 2012;Nickelsen et al., 2015). However, most of these NPZD models lack a sound mechanistic foundation, preventing them from explicitly accounting for the organisms' regulation of their internal physiological state. For example, N 2 fixation by algae 30 is often diagnosed from the availability of dissolved nutrients, so that it only occurs when the ratio of nitrate-to-phosphate concentrations falls below the Redfield ratio of 16:1 (Deutsch et al., 2007;Ilyina et al., 2013). As these assumptions neglect a number of environmental and ecological controls (e.g., grazing, often also temperature), they do not adequately describe the behaviour of plankton organisms and their sensitivity to changes in their environment. With the introduction of refined mechanistic (physiological) descriptions we here aim at alleviating this deficiency. In this study we introduce a new marin...

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A behavioral model of predator-prey functional responses

Cited by 27 publications

References 10 publications

Predator Functional Responses: Discriminating between Handling and Digesting Prey

Predator Functional Responses: Discriminating between Handling and Digesting Prey

Anomalous invasion in a 2d model of chemotactic predation

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

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