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Landscape effects on diets of two canids in Northwestern Texas: A multinomial modeling approach

Journal of Mammalogy

By:
, , , ,
DOI: 10.1644/07-MAMM-A-291R1.1

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Abstract

Analyses of feces, stomach contents, and regurgitated pellets are common techniques for assessing diets of vertebrates and typically contain more than 1 food item per sampling unit. When analyzed, these individual food items have traditionally been treated as independent, which represents pseudoreplication. When food types are recorded as present or absent, these samples can be treated as multinomial vectors of food items, with each vector representing 1 realization of a possible diet. We suggest such data have a similar structure to capture histories for closed-capture, capturemarkrecapture data. To assess the effects of landscapes and presence of a potential competitor, we used closed-capture models implemented in program MARK into analyze diet data generated from feces of swift foxes (Vulpes velox) and coyotes (Canis latrans) in northwestern Texas. The best models of diet contained season and location for both swift foxes and coyotes, but year accounted for less variation, suggesting that landscape type is an important predictor of diets of both species. Models containing the effect of coyote reduction were not competitive (??QAICc 53.6685), consistent with the hypothesis that presence of coyotes did not influence diet of swift foxes. Our findings suggest that landscape type may have important influences on diets of both species. We believe that multinomial models represent an effective approach to assess hypotheses when diet studies have a data structure similar to ours. ?? 2010 American Society of Mammalogists.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Landscape effects on diets of two canids in Northwestern Texas: A multinomial modeling approach
Series title:
Journal of Mammalogy
DOI:
10.1644/07-MAMM-A-291R1.1
Volume
91
Issue:
1
Year Published:
2010
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
66
Last page:
78
Number of Pages:
13