2016
DOI: 10.1007/s10651-016-0341-3
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Formal modelling of predator preferences using molecular gut-content analysis

Abstract: 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 no… Show more

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Cited by 2 publications
(14 citation statements)
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“…An additional laboratory experiment that allowed spiders to digest either prey group for varying times and at varying temperatures would be necessary to fully test our assumptions about how DNA detectability affected our study (Greenstone et al., ). Nonetheless, the inferences made about spider selectivity use an expectation‐maximization algorithm to compute maximum‐likelihood estimates, which is still a robust statistical procedure without priors of DNA detection time (Roualdes et al., ).…”
Section: Discussionmentioning
confidence: 99%
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“…An additional laboratory experiment that allowed spiders to digest either prey group for varying times and at varying temperatures would be necessary to fully test our assumptions about how DNA detectability affected our study (Greenstone et al., ). Nonetheless, the inferences made about spider selectivity use an expectation‐maximization algorithm to compute maximum‐likelihood estimates, which is still a robust statistical procedure without priors of DNA detection time (Roualdes et al., ).…”
Section: Discussionmentioning
confidence: 99%
“…Population‐level prey selectivity was analysed with Roualdes’ c st using the R (Core Team R ) package spiders (Roualdes et al., ). First, the model uses the binary data from molecular gut‐content analysis of the predator population (presence or absence of prey DNA) and the count data from pitfall traps to create maximum‐likelihood estimates for the rate of prey capture.…”
Section: Methodsmentioning
confidence: 99%
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