Experimental feeding validates nanofluidic array technology for DNA detection of ungulate prey in wolf scats

Author:

Di Bernardi Cecilia12ORCID,Wikenros Camilla2,Ciucci Paolo13,Boitani Luigi1,Sand Håkan2,Åkesson Mikael2ORCID

Affiliation:

1. Department of Biology and Biotechnologies “Charles Darwin” University of Rome La Sapienza Rome Italy

2. Department of Ecology, Grimsö Wildlife Research Station Swedish University of Agricultural Sciences Riddarhyttan Sweden

3. NBFC – National Biodiversity Future Center Palermo Italy

Abstract

AbstractThe study of carnivores' diet is a key component to enhance knowledge on the ecology of predators and their effect on prey populations. Although molecular approaches to detect prey DNA in carnivore scats are improving, the validation of their accuracy, a prerequisite for reliable applications within ecological frameworks, is still lagging behind the methodological advances. Indeed, variation in detection probability among prey species can occur, representing a potentially insidious source of bias in food‐habit studies of carnivores. Calibration of DNA‐based methods involves the optimization of specificity and sensitivity and, whereas priority is usually given to the former to avoid false positives, sensitivity is rarely investigated so that false negatives may be largely overlooked. We conducted feeding trials with captive wolves (Canis lupus) to validate a nanofluidic array technology recently developed for the detection of multiple prey species in scats. Using 371 scat samples from 12 wolves fed with a single‐prey diet, the sensitivity of our nanofluidic array method varied between 0.45 and 0.95 for the six main ungulate prey species. The method sensitivity was enhanced by using multiple markers per species and by a relatively low threshold of number of amplifying markers required to confirm a detection. Yet, at least two markers should be used to avoid false positives. By acknowledging sources of bias in sensitivity to reliably interpret the results of DNA‐based dietary methods, our study highlights the relevance of feeding experiments to optimally calibrate the relative thresholds to define a positive detection and investigate the occurrence and extent of biases in sensitivity.

Funder

Naturvårdsverket

Publisher

Wiley

Subject

Genetics,Ecology,Ecology, Evolution, Behavior and Systematics

Reference73 articles.

1. Bartón K.(2013).MuMIn: Multi‐model inference. Seehttps://cran.r‐project.org/web/packages/MuMIn/index.html.

2. Fitting Linear Mixed-Effects Models Usinglme4

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