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Determining plant-leaf miner-parasitoid interactions: a DNA barcoding approach.

Derocles SA, Evans DM, Nichols PC, Evans SA, Lunt DH - PLoS ONE (2015)

Bottom Line: We found that the 130 bp fragment is variable enough to identify all the species included in this study.Both COI fragments reveal that some leaf miner species could be composed of cryptic species.The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria.

View Article: PubMed Central - PubMed

Affiliation: School of Biological, Biomedical and Environmental Sciences, University of Hull, Hull, United Kingdom.

ABSTRACT
A major challenge in network ecology is to describe the full-range of species interactions in a community to create highly-resolved food-webs. We developed a molecular approach based on DNA full barcoding and mini-barcoding to describe difficult to observe plant-leaf miner-parasitoid interactions, consisting of animals commonly regarded as agricultural pests and their natural enemies. We tested the ability of universal primers to amplify the remaining DNA inside leaf miner mines after the emergence of the insect. We compared the results of a) morphological identification of adult specimens; b) identification based on the shape of the mines; c) the COI Mini-barcode (130 bp) and d) the COI full barcode (658 bp) fragments to accurately identify the leaf-miner species. We used the molecular approach to build and analyse a tri-partite ecological network of plant-leaf miner-parasitoid interactions. We were able to detect the DNA of leaf-mining insects within their feeding mines on a range of host plants using mini-barcoding primers: 6% for the leaves collected empty and 33% success after we observed the emergence of the leaf miner. We suggest that the low amplification success of leaf mines collected empty was mainly due to the time since the adult emerged and discuss methodological improvements. Nevertheless our approach provided new species-interaction data for the ecological network. We found that the 130 bp fragment is variable enough to identify all the species included in this study. Both COI fragments reveal that some leaf miner species could be composed of cryptic species. The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria.

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Quantitative food webs between plants, leaf mining insects and their parasitoids.The food webs were constructed from 407 infested leaves sampled at Stockbridge Technology Center (Cawood, United Kingdom) using a) a molecular approach b) a morphological identification. The series of bars represent plant abundance (bottom), leaf mining insect abundance (middle) and parasitoid abundance (top). The width of edge links between plants, leaf-miners and parasitoids illustrates the relative strength of each interaction. Dashed lines are used where we are unable to identify the insect host of a parasitoid. L1: Cameraria orhidella; L2: Stigmella samiatella; L3: Coleophora gryphipennella; L4: Stigmella splendidissimella; L5: Chrysoesthia drurella; L6: Ectoedemia albifasciella; D1: Chromatomyia nigra; D2: Phytomyza cirsii; D3: Pegomyia solennis; D4: Pegomyia bicolor; D5: Phytomyza spondylii; D6: Amauromyza flavifrons; D7: Agromyza frontella; P1: Aesculus hippocastanum; P2: Chenopodium album; P3: Compositae sp; P4: Epilobium sp; P5: Quercus robur; P6: Quercus sp; P7: Rosa canina; P8: Rubus fruticosus; P9: Rumex obtusifolius; P10: Rumex sp; P11: Stellaria graminea; P12: Trifolium sp.
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pone.0117872.g004: Quantitative food webs between plants, leaf mining insects and their parasitoids.The food webs were constructed from 407 infested leaves sampled at Stockbridge Technology Center (Cawood, United Kingdom) using a) a molecular approach b) a morphological identification. The series of bars represent plant abundance (bottom), leaf mining insect abundance (middle) and parasitoid abundance (top). The width of edge links between plants, leaf-miners and parasitoids illustrates the relative strength of each interaction. Dashed lines are used where we are unable to identify the insect host of a parasitoid. L1: Cameraria orhidella; L2: Stigmella samiatella; L3: Coleophora gryphipennella; L4: Stigmella splendidissimella; L5: Chrysoesthia drurella; L6: Ectoedemia albifasciella; D1: Chromatomyia nigra; D2: Phytomyza cirsii; D3: Pegomyia solennis; D4: Pegomyia bicolor; D5: Phytomyza spondylii; D6: Amauromyza flavifrons; D7: Agromyza frontella; P1: Aesculus hippocastanum; P2: Chenopodium album; P3: Compositae sp; P4: Epilobium sp; P5: Quercus robur; P6: Quercus sp; P7: Rosa canina; P8: Rubus fruticosus; P9: Rumex obtusifolius; P10: Rumex sp; P11: Stellaria graminea; P12: Trifolium sp.

Mentions: We built an ecological network consisting of plants – leaf mining insects – parasitoids using the molecular identification of insect specimens with the COI full barcode and the remaining DNA of leaf mining insects within the mines with the COI Minibarcode (Fig. 4A). When a parasitoid emerged from an infested leaf and we successfully detected and identified the leaf mining insect based on the remaining DNA of the mine, we were able to determine a tripartite interaction: we were able to find the leaf miner host for 15 of the 48 parasitoids emerged. In all other cases, we describe the bipartite interaction between 1) plants and leaf mining insects 2) plants and leaf miner parasitoids. We consider in the molecular network the four different Braconidae clusters found with COI ML tree as four different species due to the high interspecific variability (0.005–0.1004).


Determining plant-leaf miner-parasitoid interactions: a DNA barcoding approach.

Derocles SA, Evans DM, Nichols PC, Evans SA, Lunt DH - PLoS ONE (2015)

Quantitative food webs between plants, leaf mining insects and their parasitoids.The food webs were constructed from 407 infested leaves sampled at Stockbridge Technology Center (Cawood, United Kingdom) using a) a molecular approach b) a morphological identification. The series of bars represent plant abundance (bottom), leaf mining insect abundance (middle) and parasitoid abundance (top). The width of edge links between plants, leaf-miners and parasitoids illustrates the relative strength of each interaction. Dashed lines are used where we are unable to identify the insect host of a parasitoid. L1: Cameraria orhidella; L2: Stigmella samiatella; L3: Coleophora gryphipennella; L4: Stigmella splendidissimella; L5: Chrysoesthia drurella; L6: Ectoedemia albifasciella; D1: Chromatomyia nigra; D2: Phytomyza cirsii; D3: Pegomyia solennis; D4: Pegomyia bicolor; D5: Phytomyza spondylii; D6: Amauromyza flavifrons; D7: Agromyza frontella; P1: Aesculus hippocastanum; P2: Chenopodium album; P3: Compositae sp; P4: Epilobium sp; P5: Quercus robur; P6: Quercus sp; P7: Rosa canina; P8: Rubus fruticosus; P9: Rumex obtusifolius; P10: Rumex sp; P11: Stellaria graminea; P12: Trifolium sp.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4339730&req=5

pone.0117872.g004: Quantitative food webs between plants, leaf mining insects and their parasitoids.The food webs were constructed from 407 infested leaves sampled at Stockbridge Technology Center (Cawood, United Kingdom) using a) a molecular approach b) a morphological identification. The series of bars represent plant abundance (bottom), leaf mining insect abundance (middle) and parasitoid abundance (top). The width of edge links between plants, leaf-miners and parasitoids illustrates the relative strength of each interaction. Dashed lines are used where we are unable to identify the insect host of a parasitoid. L1: Cameraria orhidella; L2: Stigmella samiatella; L3: Coleophora gryphipennella; L4: Stigmella splendidissimella; L5: Chrysoesthia drurella; L6: Ectoedemia albifasciella; D1: Chromatomyia nigra; D2: Phytomyza cirsii; D3: Pegomyia solennis; D4: Pegomyia bicolor; D5: Phytomyza spondylii; D6: Amauromyza flavifrons; D7: Agromyza frontella; P1: Aesculus hippocastanum; P2: Chenopodium album; P3: Compositae sp; P4: Epilobium sp; P5: Quercus robur; P6: Quercus sp; P7: Rosa canina; P8: Rubus fruticosus; P9: Rumex obtusifolius; P10: Rumex sp; P11: Stellaria graminea; P12: Trifolium sp.
Mentions: We built an ecological network consisting of plants – leaf mining insects – parasitoids using the molecular identification of insect specimens with the COI full barcode and the remaining DNA of leaf mining insects within the mines with the COI Minibarcode (Fig. 4A). When a parasitoid emerged from an infested leaf and we successfully detected and identified the leaf mining insect based on the remaining DNA of the mine, we were able to determine a tripartite interaction: we were able to find the leaf miner host for 15 of the 48 parasitoids emerged. In all other cases, we describe the bipartite interaction between 1) plants and leaf mining insects 2) plants and leaf miner parasitoids. We consider in the molecular network the four different Braconidae clusters found with COI ML tree as four different species due to the high interspecific variability (0.005–0.1004).

Bottom Line: We found that the 130 bp fragment is variable enough to identify all the species included in this study.Both COI fragments reveal that some leaf miner species could be composed of cryptic species.The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria.

View Article: PubMed Central - PubMed

Affiliation: School of Biological, Biomedical and Environmental Sciences, University of Hull, Hull, United Kingdom.

ABSTRACT
A major challenge in network ecology is to describe the full-range of species interactions in a community to create highly-resolved food-webs. We developed a molecular approach based on DNA full barcoding and mini-barcoding to describe difficult to observe plant-leaf miner-parasitoid interactions, consisting of animals commonly regarded as agricultural pests and their natural enemies. We tested the ability of universal primers to amplify the remaining DNA inside leaf miner mines after the emergence of the insect. We compared the results of a) morphological identification of adult specimens; b) identification based on the shape of the mines; c) the COI Mini-barcode (130 bp) and d) the COI full barcode (658 bp) fragments to accurately identify the leaf-miner species. We used the molecular approach to build and analyse a tri-partite ecological network of plant-leaf miner-parasitoid interactions. We were able to detect the DNA of leaf-mining insects within their feeding mines on a range of host plants using mini-barcoding primers: 6% for the leaves collected empty and 33% success after we observed the emergence of the leaf miner. We suggest that the low amplification success of leaf mines collected empty was mainly due to the time since the adult emerged and discuss methodological improvements. Nevertheless our approach provided new species-interaction data for the ecological network. We found that the 130 bp fragment is variable enough to identify all the species included in this study. Both COI fragments reveal that some leaf miner species could be composed of cryptic species. The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria.

Show MeSH
Related in: MedlinePlus