UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Testing the potential of enviromental DNA methods for surveying Lake Tanganyika's highly diverse fish communities

Doble, Christopher James; (2020) Testing the potential of enviromental DNA methods for surveying Lake Tanganyika's highly diverse fish communities. Doctoral thesis (Ph.D), UCL (University College London). Green open access

[thumbnail of C.Doble_Thesis_final_accepted_version.pdf]
Preview
Text
C.Doble_Thesis_final_accepted_version.pdf - Accepted Version

Download (13MB) | Preview

Abstract

Environmental DNA (eDNA) metabarcoding has become established as a highly sensitive and effective method of surveying fish communities across a broad range of freshwater ecosystems. To date this method has largely been restricted to temperate ecosystems, with few applications in the tropics where global freshwater fish diversity is at its richest. This thesis develops an environmental DNA metabarcoding methodology for surveying one of the planet’s most species rich tropical lakes - East Africa’s Lake Tanganyika (LT). This global aquatic hotspot contains exceptionally diverse fish communities, and therefore represents an excellent system to test the effectiveness of eDNA metabarcoding for surveying species rich tropical fish communities, as well as closely related species within adaptive radiations. Chapter one provides an introduction to the field, identifying key knowledge gaps. Chapter two develops a near comprehensive fish list and reference database for 358 species found within LT’s basin across three mitochondrial gene regions. These data are subsequently used to design two new primer sets including a novel cichlid-specific primer (Cichlid_CR), as well as undertake in silico testing of published primer sets. Chapter three investigates the relative effectiveness of eDNA metabarcoding and traditional underwater visual survey methods for surveying the species richness of LT’s littoral fish communities. Building on the results of this work Chapter four addresses if increased sequencing depth, reference database expansion and bioinformatic filtering stringencies can improve species richness estimates for LT’s highly diverse cichlid communities, with further comparisons made against the visual survey dataset. Overall, despite initial challenges detecting closely related species within LT’s cichlid radiations, this thesis demonstrates through the use of a family-specific primer set the capacity of eDNA metabarcoding methods for surveying LT’s littoral fish communities and therefore highly diverse tropical ecosystems, even those containing many closely related species comprising adaptive radiations. These findings support the capacity of eDNA metabarcoding methods for revolutionising our ability to survey freshwater fish communities globally.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Testing the potential of enviromental DNA methods for surveying Lake Tanganyika's highly diverse fish communities
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
URI: https://discovery.ucl.ac.uk/id/eprint/10096356
Downloads since deposit
335Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item