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1 June 2014 The Impact of Sampling Method on Maximum Entropy Species Distribution Modeling for Bats
Paul R. Barnhart, Erin H. Gillam
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Abstract

As species distribution modeling (SDM) becomes more commonly incorporated into ecological studies, there is a need to address how the use of different sampling techniques for assessing the presence of a species can impact the final models produced. Over a four-year period, we sampled for the presence of bats throughout North Dakota via mist netting (physical capture) and ultrasonic acoustic monitoring. We used maximum-entropy modeling to develop habitat suitability maps for each study species using physical capture data, acoustic data and both detection techniques combined. We evaluated the amount of niche overlap between maps to determine how sampling technique impacted the final SDMs and which technique was best for modeling SDMs for each species. We found variation among species in the amount of overlap between SDMs, ranging from pronounced differences (33.9% overlap; Myotis septentrionalis) to highly similar models (80.4% overlap; Myotis lucifugus). Our findings show that acoustic detection results in better SDMs for Myotis spp. while physical capture was best for modeling Eptesicus fuscus and Lasionycteris noctivagans. Although both methods produce highly reliable SDMs, care must be taken when using maximum-entropy modeling for species in which presence data can be gathered in multiple ways. We emphasize that researchers should consider the ecology and behavioral characteristics of their focal species to address any biases associated with sampling technique.

© Museum and Institute of Zoology PAS
Paul R. Barnhart and Erin H. Gillam "The Impact of Sampling Method on Maximum Entropy Species Distribution Modeling for Bats," Acta Chiropterologica 16(1), 241-248, (1 June 2014). https://doi.org/10.3161/150811014X683435
Received: 5 February 2014; Accepted: 1 April 2014; Published: 1 June 2014
KEYWORDS
acoustic sampling
distribution
MaxEnt
mist nets
physical sampling
sampling bias
SDM
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