Formal modelling of predator preferences using molecular gut-content analysis
The literature on modelling a predator’s prey selection describes many intuitive indices, few of which have both reasonable statistical justification and tractable asymptotic properties. Here, we provide a simple model that meets both of these criteria, while extending previous work to include an array of data from multiple species and time points. Further, we apply the expectation–maximisation algorithm to compute estimates if exact counts of the number of prey species eaten in a particular time period are not observed. We conduct a simulation study to demonstrate the accuracy of our method, and illustrate the utility of the approach for field analysis of predation using a real data set, collected on wolf spiders using molecular gut-content analysis.
KeywordsElectivity Expectation–maximisation Food web analysis Generalist predators Predator–prey interactions
The information reported in this paper (No. 15-08-008) is part of a project of the Kentucky Agricultural Experiment Station and is published with the approval of the Director. Support for this research was provided by the University of Kentucky Agricultural Experiment Station State Project KY008055 and the National Science Foundation Graduate Research Fellowship Program.
- Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
- Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodological) 39(1):1–38Google Scholar
- Ivlev VS (1964) Experimental ecology of the feeding of fishes. Yale University Press, New HavenGoogle Scholar
- Manly B, McDonald L, Thomas D, McDonald T, Erickson W (2002) Resource selection by animals: statistical analysis and design for field studies. Kluwer, NordrechtGoogle Scholar
- McLachlan G, Krishnan T (2007) The EM algorithm and extensions, vol 382. Wiley, HobokenGoogle Scholar
- Roualdes EA, Bonner S (2014) spiders: fits predator preferences model. R package version 1Google Scholar
- Serfling RJ (2001) Approximation theorems of mathematical statistics. Wiley, HobokenGoogle Scholar
- Uetz GW, Halaj J, Cady AB (1999) Guild structure of spiders in major crops. J Arachnol 27(1):270–280Google Scholar