2011
DOI: 10.1111/j.1365-2664.2010.01947.x
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Which prey sustains cold-adapted invertebrate generalist predators in arable land? Examining prey choices by molecular gut-content analysis

Abstract: Summary1. Generalist predators such as carabid and cantharid beetles form an important component within natural enemy communities in arable land. Usually, predator-prey interactions are examined during the vegetation period, largely ignoring food web interactions occurring in the cold season. This is, however, when the larval stages of many polyphagous beetles develop whose survival critically depends on the availability of suitable prey. 2. In this study, we examined intra-and extraguild feeding links in larv… Show more

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Cited by 74 publications
(44 citation statements)
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References 50 publications
(63 reference statements)
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“…The strong performance of barcoding resulted from its ability to identify degraded tissue remains with few or no discernable morphological characteristics [22,23]; the physical characteristics required for morphological identifications were degraded or absent from many stomach-content items in our study. Our results support the conclusions of many studies indicating that barcoding is highly effective for determining predator diet from stomach contents or feces [24][25][26][27][28][29][30]. For a small number of stomach-content items, we found that barcoding could only identify prey to a taxon above the species level but still outperformed morphology.…”
Section: Discussionsupporting
confidence: 89%
See 1 more Smart Citation
“…The strong performance of barcoding resulted from its ability to identify degraded tissue remains with few or no discernable morphological characteristics [22,23]; the physical characteristics required for morphological identifications were degraded or absent from many stomach-content items in our study. Our results support the conclusions of many studies indicating that barcoding is highly effective for determining predator diet from stomach contents or feces [24][25][26][27][28][29][30]. For a small number of stomach-content items, we found that barcoding could only identify prey to a taxon above the species level but still outperformed morphology.…”
Section: Discussionsupporting
confidence: 89%
“…The efficacy of barcoding to establish feeding interactions comes in large part from its proven utility in identifying animal tissues when little or no morphological information is available [22,23]. Many studies have now confirmed the value of barcoding in identifying prey in the stomach contents or feces of certain predators, such as bats [24], beetles [25], marine invertebrates [26,27], seabirds [28], sharks [29], and other marine fishes [28,30]. Additionally, some researchers have used taxon-specific approaches that rely on the DNA barcode region to identify feeding interactions of interest in bats [31] and insects [32], but very few studies have used barcoding at the scale of whole food webs.…”
Section: Introductionmentioning
confidence: 99%
“…The current screening results on centipede predators suggest high consumption rates of decomposer prey, particularly collembolans while intra-guild prey may only be accepted occasionally. This fits to findings on the diet of other soildwelling generalist predators such as predatory beetle larvae (Eitzinger and Traugott, 2011). Still, a larger set of individuals would need to be tested to better characterize the lithobiids' feeding preferences.…”
Section: Introductionmentioning
confidence: 50%
“…Our initial concern was that this positioning was an artefact of the small size of the spiders, identifying them by default as prey items for the learning. Recently, however, Davey et al (2013) tested this learnt trophic link explicitly using molecular methods and found that this apparently illogical hypothetical link was indeed supported by the molecular data: carabids, such as P. melanarius, prey extensively upon spiders; a finding corroborated by Eitzinger and Traugott (2011). Bohan et al (2013) raised the question of generality in ecological data and 'growing' larger datasets for learning wider networks.…”
Section: Discussionmentioning
confidence: 89%