Predicting the strength of interference more quickly using behaviour‐based models

Abstract

Summary 1.Interference between foraging animals can be quantified directly only through intensive studies. A quicker alternative is to predict the strength of interference using behaviour-based models. We describe a field method to parameterize an interference model for shorebirds, Charadrii. 2. Kleptoparasitic attack distance is the main factor affecting the strength of interference but has rarely been measured. Attack distance is related to handling time, a frequently measured parameter, allowing the model to be parameterized for systems in which attack distance has not been measured. 3. The model accurately predicts the strength of interference between oystercatchers Haematopus ostralegus L. feeding on cockles Cerastoderma edule L. and the absence of interference between bar-tailed godwits Limosa lapponica L. feeding on lugworms Arenicola marina L. at low competitor densities. 4. We predict the strength of interference in black-tailed godwit Limosa limosa L. and oystercatcher systems in which it has not been measured previously. The strength of interference is almost entirely determined by attack distance; interference is stronger in systems with longer attacks. Interference is usually weaker in black-tailed godwits because handling time is generally shorter and this limits attack distance. 5. The interference model can be parameterized much more quickly than the alternative of measuring interference directly. Behaviour-based models have the potential to be a valuable tool for predicting the strength of interference.

Intake rates and the functional response in shorebirds (Charadriiformes) eating macro-invertebrates

“…Interference competition is widespread in shorebirds (Stillman et al, 2002) but its possible influence on intake rate could not be considered as bird density and/or the occurrence of aggressive interactions was usually unreported. However, most of the data were collected over low tide when birds would have been able to spread out, keeping interference to a minimum.…”

Interference competition in a planktivorous fish (Rutilus rutilus) at different prey densities and temperatures

“…As such an effect is difficult to exclude in experiments, little effort has been made to accurately assess the quantitative impact of interference on the feeding rates, growth, and survival rates of competing animals, despite evidence of their importance (Schoener 1983). While most studies facing this problem have focused on demonstrating the simultaneous effect of both types of competition (e.g., MacIsaac and Gilbert 1991;Rutten et al 2010), surprisingly, some have implicitly assumed that the effect of exploitation is negligible (e.g., Sutherland and Koene 1982;Ens and Goss-Custard 1984;Stillman et al 2002;Nakayama and Fuiman 2010;Ping et al 2011). However, it is clear that exploitative competition may lead to a gross overestimation of the effect of interference, since the latter phenomenon is prey density dependent (Sutherland and Koene 1982;Vahl et al 2005).…”

“…Most previous studies-either theoretical (Stillman et al 2000) or empirical (Sih 1981;Dolman 1995;Cresswell 1998;Triplet et al 1999)-support the notion that interference is stronger at lower prey densities (or reduced encounter rates). This may be the result of either a prolonged search time during which interference can happen (Ruxton et al 1992;Moody and Ruxton 1996) or the occurrence of an additional kind of interference that is profitable only when prey is sparse (e.g., kleptoparasitism in Stillman et al 2002;aggression in Sale 1972). Since none of the aforementioned studies attempted to exclude the effect of exploitation, the impact of prey density on the effects of interference is yet to be fully investigated.…”

Testing a stochastic version of the Beddington–DeAngelis functional response in foraging shore crabs

“…Interference models describing how the presence of competitors affects the feeding rate of animals have been used to examine the effects of interference on the patch and habitat choices and on population dynamics of animals (Holmgren 1995;Norris and Johnstone 1998;Ruxton et al 1992;Stillman et al 1997Stillman et al , 2002. Two approaches, one phenomenological and the other mechanistic, have been taken to model interference ( Van der Meer and Ens 1997).…”

“…Since, in contrast to phenomenological models, the behavioural rules of predators are clearly defined in mechanistic models, they provide an excellent way to explore the behavioural basis of interference. For example, a model of interference parameterized for oystercatchers (Haematopus ostralegus) and black-tailed godwits (Limosa limosa) showed that strength of interference was most sensitive to attack distance, followed by the searching speed of predators (Stillman et al 1997(Stillman et al , 2002.…”