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Mammal dung–dung beetle trophic networks: an improved method based on gut-content DNA

Pedersen, Karen M. ; Beeren, Christoph von ; Oggioni, Arianna ; Blüthgen, Nico (2024)
Mammal dung–dung beetle trophic networks: an improved method based on gut-content DNA.
In: PeerJ, 12
doi: 10.7717/peerj.16627
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

BACKGROUND

Dung beetles provide many important ecosystem services, including dung decomposition, pathogen control, soil aeration, and secondary seed dispersal. Yet, the biology of most dung beetles remains unknown. Natural diets are poorly studied, partly because previous research has focused on choice or attraction experiments using few, easily accessible dung types from zoo animals, farm animals, or humans. This way, many links within natural food webs have certainly been missed. In this work, we aimed to establish a protocol to analyze the natural diets of dung beetles using DNA gut barcoding.

METHODS

First, the feasibility of gut-content DNA extraction and amplification of 12s rDNA from six different mammal dung types was tested in the laboratory. We then applied the method to beetles caught in pitfall traps in Ecuador and Germany by using 12s rDNA primers. For a subset of the dung beetles caught in the Ecuador sampling, we also used 16s rDNA primers to see if these would improve the number of species we could identify. We predicted the likelihood of amplifying DNA using gut fullness, DNA concentration, PCR primer, collection method, and beetle species as predictor variables in a dominance analysis. Based on the gut barcodes, we generated a dung beetle-mammal network for both field sites (Ecuador and Germany) and analyzed the levels of network specificity.

RESULTS

We successfully amplified mammal DNA from dung beetle gut contents for 128 specimens, which included such prominent species as (jaguar) and (puma). The overall success rate of DNA amplification was 53%. The best predictors for amplification success were gut fullness and DNA concentration, suggesting the success rate can be increased by focusing on beetles with a full gut. The mammal dung-dung beetle networks differed from purely random network models and showed a moderate degree of network specialization (': Ecuador = 0.49; Germany = 0.41).

CONCLUSION

We here present a reliable method of extracting and amplifying gut-content DNA from dung beetles. Identifying mammal dung DNA reference libraries, we created mammal dung-dung beetle trophic networks. This has benefits over previous methods because we inventoried the natural mammal dung resources of dung beetles instead of using artificial mammal baits. Our results revealed higher levels of specialization than expected and more rodent DNA than expected in Germany, suggesting that the presented method provides more detailed insights into mammal dung-dung beetle networks. In addition, the method could have applications for mammal monitoring in many ecosystems.

Typ des Eintrags: Artikel
Erschienen: 2024
Autor(en): Pedersen, Karen M. ; Beeren, Christoph von ; Oggioni, Arianna ; Blüthgen, Nico
Art des Eintrags: Bibliographie
Titel: Mammal dung–dung beetle trophic networks: an improved method based on gut-content DNA
Sprache: Englisch
Publikationsjahr: 2024
Titel der Zeitschrift, Zeitung oder Schriftenreihe: PeerJ
Jahrgang/Volume einer Zeitschrift: 12
DOI: 10.7717/peerj.16627
Kurzbeschreibung (Abstract):

BACKGROUND

Dung beetles provide many important ecosystem services, including dung decomposition, pathogen control, soil aeration, and secondary seed dispersal. Yet, the biology of most dung beetles remains unknown. Natural diets are poorly studied, partly because previous research has focused on choice or attraction experiments using few, easily accessible dung types from zoo animals, farm animals, or humans. This way, many links within natural food webs have certainly been missed. In this work, we aimed to establish a protocol to analyze the natural diets of dung beetles using DNA gut barcoding.

METHODS

First, the feasibility of gut-content DNA extraction and amplification of 12s rDNA from six different mammal dung types was tested in the laboratory. We then applied the method to beetles caught in pitfall traps in Ecuador and Germany by using 12s rDNA primers. For a subset of the dung beetles caught in the Ecuador sampling, we also used 16s rDNA primers to see if these would improve the number of species we could identify. We predicted the likelihood of amplifying DNA using gut fullness, DNA concentration, PCR primer, collection method, and beetle species as predictor variables in a dominance analysis. Based on the gut barcodes, we generated a dung beetle-mammal network for both field sites (Ecuador and Germany) and analyzed the levels of network specificity.

RESULTS

We successfully amplified mammal DNA from dung beetle gut contents for 128 specimens, which included such prominent species as (jaguar) and (puma). The overall success rate of DNA amplification was 53%. The best predictors for amplification success were gut fullness and DNA concentration, suggesting the success rate can be increased by focusing on beetles with a full gut. The mammal dung-dung beetle networks differed from purely random network models and showed a moderate degree of network specialization (': Ecuador = 0.49; Germany = 0.41).

CONCLUSION

We here present a reliable method of extracting and amplifying gut-content DNA from dung beetles. Identifying mammal dung DNA reference libraries, we created mammal dung-dung beetle trophic networks. This has benefits over previous methods because we inventoried the natural mammal dung resources of dung beetles instead of using artificial mammal baits. Our results revealed higher levels of specialization than expected and more rodent DNA than expected in Germany, suggesting that the presented method provides more detailed insights into mammal dung-dung beetle networks. In addition, the method could have applications for mammal monitoring in many ecosystems.

ID-Nummer: pmid:38500531
Fachbereich(e)/-gebiet(e): 10 Fachbereich Biologie
10 Fachbereich Biologie > Ecological Networks
Hinterlegungsdatum: 26 Mär 2024 07:00
Letzte Änderung: 26 Mär 2024 07:40
PPN: 516554662
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