A multi-faceted approach to a"dark taxon"
The hyperdiverse and poorly known scuttle flies (Diptera: Phoridae)

Emily Hartop

Doctoral Thesis in Systematic Zoology at Stockholm University, Sweden 2021

A multi-faceted approach to a "dark taxon"

The hyperdiverse and poorly known scuttle flies (Diptera: Phoridae)
Emily Hartop
Academic dissertation for the Degree of Doctor of Philosophy in Systematic Zoology at
Stockholm University to be publicly defended on Tuesday 8 June 2021 at 13.00 online via
Zoom, public link is available at the department website.
Abstract

Most of the unknown animal biodiversity on earth is in groups of invertebrates that are hyperdiverse and abundant, yet
poorly known (“dark taxa”). The study of these organisms requires a multi-faceted approach and methodologies designed
to tackle large numbers of species and specimens. The scuttle flies (Diptera: Phoridae) are a classic example of a darktaxon and the focus of this thesis. Paper I is a molecular phylogeny of the phorid genus Megaselia based on one nuclear(28S rDNA) and three mitochondrial (ND1, COI and 16S) markers from 145 species of Nordic Megaselia. Molecular
data was analysed with Bayesian analysis, maximum likelihood, and parsimony methods. Based on these results, andsupporting morphological data, we divide Megaselia into 22 informal species groups, 20 of which fall into a monophyletic“core Megaselia”. We discuss implications for the future circumscription of Megaselia and associated genera. Paper II
presents a pipeline for rapid and cost-effective species discovery using the Oxford Nanopore mobile sequencing technology
MinION. This paper reveals the presence of ca. 650 species of Phoridae from a single Malaise trap placed in Kibale
National Park, Uganda. Based on our data, we estimate that the phorid fauna of the Afrotropical region could be as highas 100 000 species: this figure dwarfs previous diversity estimates. The implications for species discovery and description
are discussed, and a new species (Megaselia sepsioides sp. nov.) is described. Paper III outlines a large-scale integrative
approach to species discovery and delimitation in hyperdiverse groups, exemplified using a dataset of 18 000 phorid
flies from Sweden. COI minibarcodes (313 bp) were obtained for all specimens and classified into putative species using
different clustering methods (objective clustering, Poisson tree process, automatic barcode gap discovery and refined single
linkage). No clustering method was accurate enough to use for species delimitation without confirmation from additional
data. We found that the stability of a cluster to change across genetic-distance thresholds and the genetic variation withina cluster both accurately predict clusters where morphology is likely to be incongruent with barcode data. With molecularclustering integrated with morphological validation, we found that we could examine less than 5% of specimens and still
delimit all species fully and accurately. Paper IV addresses questions about the scuttle fly fauna of Sweden with data from32 000 scuttle flies from 37 sites and 4 time periods. We estimate that the total Swedish fauna contains 652-713 (basedon Chao 1 or CNE estimates, respectively) species of scuttle flies, 1.5 times the 372 species currently documented from
Sweden. Ordination techniques show that scuttle fly communities are organized in a gradient across Sweden, which iswell correlated with plant hardiness zones defined by the Swedish Horticultural Society. Hierarchical modelling of species
communities (HMSC) reveals that phorid community composition is largely determined by climatic and temporal variables,
but much of the variance remains unexplained by the models we explored. Comparison of our phorid data with that ofspecies more commonly utilised for biodiversity assessments revealed that phorids may allow more fine-scaled analysis as
they may exist in smaller ranges, and that they additionally may give unique patterns of distribution that are unlike thoseseen in other taxa.
Keywords: biodiversity, taxonomy, dark taxa.
Stockholm 2021
http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-192276
ISBN 978-91-7911-472-5
ISBN 978-91-7911-473-2

Department of Zoology
Stockholm University, 106 91 Stockholm

A MULTI-FACETED APPROACH TO A "DARK TAXON"

Emily Hartop

A multi-faceted approach to a"dark taxon"
The hyperdiverse and poorly known scuttle flies (Diptera: Phoridae)

Emily Hartop©Emily Hartop, Stockholm University 2021
ISBN print 978-91-7911-472-5
ISBN PDF 978-91-7911-473-2
Scuttle fly cover art by Megan Zehendner.
Printed in Sweden by Universitetsservice US-AB, Stockholm 2021

Dedicated to my parents
whose unwavering
support started with mychildhood “dead bugcollection” – here aresome words about abigger dead bugcollection!

ACKNOWLEDGEMENTS
Thank you to my family: Mom, Dad, Megan, Jason,
Shade-Contraption, Uncle Chris, Aunt Juliet, Charlotte,
Anthony, Z’Abigail, Darlene, and to the memory of my Nana
who saw the beginning of this journey but not the end – thankyou for being the best worldwide support system a person
could hope for.
To Brian Brown, thank you for getting me started on phorids
and being a wonderful friend and mentor. You are one of the
few true adventurers I know. Your passion and enthusiasm fornatural history are much needed reminders of why I became
an entomologist in the first place.
Thank you to Henry Disney for inspiring and encouraging
me to persevere with Megaselia. Our many conversations overtea have helped me stay away from the physics department
and come to terms with my life decision to study theimpossible!
To the entire team at Station Linné, thank you for yoursupport these past four years. A special thank you to Dave
Karlsson, without whom so many things would not have beenpossible. To Mathias and Catrin Jaschhof, I will miss vancampouts and Christmas Eve dinners…you have been suchimportant friends and colleagues during my time in Sweden.
To my amazing friends in Vickleby: Nina, Pelle, Phil, Cissi,
Mira, Marcus, Sofia, Vilhelm, and Stella. Thank you for being
the very best community of people I have ever known. So many
memories!
To Fredrik Sjöberg, it has been magical getting to know you. I
will continue working on my Swedish just to muddle throughyour beautiful books.
Thank you to fellow phorid specialists John Hash and Sibylle
Häggqvist for collaboration and consultation on all matterspertaining to the world’s most awesome flies.
Thank you to all in the Ronquist, Meier and Łukasik labs forhelping me navigate these last four years. A special thank you
to Allison Hsiang, you helped me solve several tough problems
when I was totally stuck!
Thank you to Tomas Roslin, Mattias Forshage, Piotr Łukasik,
for advice, comments, and conversation.
Thank you to my co-authors Darren Yeo, Mirkka Jones, andSven Olof Ulefors, working with you has allowed me to do somuch more than I could have done alone, and I have learned somuch. Super thanks to Leshon Lee, I am so glad you are
studying phorids, great things to come! A special thank you to
my co-author and friend Amrita Srivathsan. You amaze me!
Thank you for all your help. See you for a beer soon!
To my co-advisor, Kjell Arne Johanson, and my follow-up
group members, Chris Wheat and Kevin Holston for the
support and conversations these past four years.
To my advisor, Fredrik Ronquist – I am so grateful that you
suggested this venture! Thank you for your time these last four
years. I will continue to work on my R skills! Thank you to you
and Eva Ronquist for hosting me on my visits to Stockholm. We
still need to watch Holy Grail!
To Rudolf Meier, how on earth would I have ever made it
through the mar crudele of a PhD without you? I cannot thankyou enough. So much is ahead!
To Cat, you made being cooped up in an apartment writing
for months on end bearable. Your constant fluffy presence
really made a difference in morale.
Finally, to Frank. I could not have done this without yoursupport. WE did it! I love you.

A MULTI-FACETED APPROACH TO A “DARK TAXON”: THE
HYPERDIVERSE AND POORLY KNOWN SCUTTLE FLIES(DIPTERA: PHORIDAE)CONTENTSABSTRACT ...................................................................................................................1
SAMMANFATTNING PÅ SVENSKA ...............................................................................2
AUTHOR CONTRIBUTIONS .........................................................................................4
INTRODUCTION ..........................................................................................................5
SUMMARY OF PAPERS ..............................................................................................11
PAPER I: Scuttling towards monophyly: phylogeny of the megadiverse genus
Megaselia (Diptera: Phoridae).........................................................................11
PAPER II: Rapid, large-scale species discovery in hyperdiverse taxa using 1D
MinION sequencing ......................................................................................14
PAPER II: Large-scale Integrative Taxonomy (LIT): resolving the data
conundrum for dark taxa ................................................................................19
PAPER II: A first dive into the terrestrial deep-sea trenches of Sweden: species
richness, spatiotemporal distribution, and community composition of a darktaxon (Diptera: Phoridae) ...............................................................................25
DISCUSSION ..............................................................................................................30
CONCLUSION ................................................................................................... 37
REFERENCES .............................................................................................................38

ABSTRACT
Most of the unknown animal biodiversity on earth is in groups of invertebrates that are hyperdiverse
and abundant, yet poorly known (“dark taxa”). The study of these organisms requires a multi-faceted
approach and methodologies designed to tackle large numbers of species and specimens. The scuttle
flies (Diptera: Phoridae) are a classic example of a dark taxon and the focus of this thesis. Paper I is amolecular phylogeny of the phorid genus Megaselia based on one nuclear (28S rDNA) and threemitochondrial (ND1, COI and 16S) markers from 145 species of Nordic Megaselia. Molecular data wasanalysed with Bayesian analysis, maximum likelihood, and parsimony methods. Based on these results,
and supporting morphological data, we divide Megaselia into 22 informal species groups, 20 of whichfall into a monophyletic “core Megaselia”. We discuss implications for the future circumscription of
Megaselia and associated genera. Paper II presents a pipeline for rapid and cost-effective species
discovery using the Oxford Nanopore mobile sequencing technology MinION. This paper reveals thepresence of ca. 650 species of Phoridae from a single Malaise trap placed in Kibale National Park,
Uganda. Based on our data, we estimate that the Megaselia fauna of the Afrotropical region could be as
high as 100 000 species: this figure dwarfs previous diversity estimates. The implications for species
discovery and description are discussed, and a new species (Megaselia sepsioides sp. nov.) is described.
Paper III outlines a large-scale integrative approach to species discovery and delimitation in hyperdiverse
groups, exemplified using a dataset of 18 000 phorid flies from Sweden. COI minibarcodes (313 bp)
were obtained for all specimens and classified into putative species using different clustering methods(objective clustering, Poisson tree process, automatic barcode gap discovery and refined single linkage).
No clustering method was accurate enough to use for species delimitation without confirmation fromadditional data. We found that the stability of a cluster to change across genetic-distance thresholds
and the genetic variation within a cluster both accurately predict clusters where morphology is likely to
be incongruent with barcode data. With molecular clustering integrated with morphological validation,
we found that we could examine less than 5% of specimens and still delimit all species fully andaccurately. Paper IV addresses questions about the scuttle fly fauna of Sweden with data from 32 000
scuttle flies from 37 sites and 4 time periods. We estimate that the total Swedish fauna contains 652713 (based on Chao 1 or CNE estimates, respectively) species of scuttle flies, 1.5 times the 372 species
currently documented from Sweden. Ordination techniques show that scuttle fly communities areorganized in a gradient across Sweden, which is well correlated with plant hardiness zones defined by
the Swedish Horticultural Society. Hierarchical modelling of species communities (HMSC) reveals
that phorid community composition is largely determined by climatic and temporal variables, but muchof the variance remains unexplained by the models we explored. Comparison of our phorid data withthat of species more commonly utilised for biodiversity assessments revealed that phorids may allow
more fine-scaled analysis as they may exist in smaller ranges, and that they additionally may give uniquepatterns of distribution that are unlike those seen in other taxa.
1

SAMMANFATTNING PÅ SVENSKA
Den biologiska mångfalden på vår planet består till stor del av ryggradslösa djur, bland vilka ett antal
vanligt förekommande och synnerligen artrika grupper tillhör de allra sämst kända. Med ett fackuttryck
kallas de senare ”dark taxa”. Att studera sådana grupper kräver helt andra metoder än de som vanligen
används inom den taxonomiska forskningen. Man behöver hantera både det stora antalet arter och den
stora mängden exemplar i materialet man samlar in. Puckelflugorna (Diptera: Phoridae), som denna
avhandling fokuserar på, är ett typiskt exempel på ett ”dark taxon”. Puckelflugor är en grupp små flugor
som är talrika i de flesta miljöer. Deras biologi är mycket varierande: de kan vara parasiter på olika
insekter och andra leddjur, men också nedbrytare eller svampätare. Puckelflugorna är en av de artrikaste
och vanligaste insektsfamiljerna i Sverige, liksom i många anda delar av världen.
I den första uppsatsen i avhandlingen (uppsats I) analyserar vi släktskapsförhållandena i det mest artrika
släktet inom familjen, släktet Megaselia. Släktet omfattar mer än 1600 beskrivna arter. Det inbegriper
de flesta arter inom familjen och hör till ett av de mest artrika släktena i hela djurriket. Vår analys ärbaserad på en nukleär markör (28S rDNA; en gen från DNA i cellkärnan) och tre mitokondriellamarkörer (ND1, COI och 16S), som vi sekvenserat från 145 nordiska Megaselia-arter och några
närbesläktade referensgrupper, så kallade utgrupper. Data analyserades med tre olika typer av metoder:
Bayesiansk analys, maximum likelihood och parsimoni. Baserat på resultaten, och med stöd av
morfologiska data, delar vi in släktet Megaselia i 22 informella artgrupper, av vilka 20 bildar en
monofyletisk kärna (”core Megaselia”). Vi resonerar i uppsatsen kring vad resultaten skulle kunna betyda
för kommande systematiska bearbetningar av Megaselia och närstående släkten. Till exempel skulle denmonofyletiska kärnan vi identifierat kunna ligga till grund för en mer precis avgränsning av släktet.
Även om vår analys behöver kompletteras med betydligt fler arter och fler genetiska markörer, så utgör
den ett viktigt steg mot en bättre förståelse av de naturliga grupperingarna inom det enorma släktet
Megaselia.
I den andra uppsatsen (papper II) beskriver vi en snabb och kostnadseffektiv metod för att hitta nya,obeskrivna arter med hjälp av Oxford Nanopores mobila sekvenseringsteknik MinION. I artikeln visar
vi att ett material insamlat i en enda Malaisefälla i Kibale National Park i Uganda under åtta veckor
innehåller cirka 650 arter av puckelflugor. Baserat på detta uppskattar vi att Megaselia-faunan i den
afrotropiska regionen kan omfatta så många som 100 000 arter —en siffra som vida överstiger tidigare
uppskattningar. Även betydelsen av nyupptäckta arter och beskrivningar av dessa diskuteras och en ny
art (Megaselia sepsioides sp. nov.) beskrivs.
I den tredje uppsatsen (papper III) utvecklar vi en storskalig, integrerande metod för att snabbt hitta och
avgränsa arterna i stora material av megadiversa grupper (”dark taxa”). Metoden bygger på snabb ochbillig individbaserad sekvensering av små genetiska markörer från ett stort antal exemplar (som i papperII), kombinerat med detaljerade studier av några få utvalda exemplar för att snabbt kunna fastställa
2artgränserna. Vi prövar olika varianter av metoden på data från 18 000 puckelflugor insamlade i Sverige.
Vi tog först fram data för korta sekvenser av COI (313 bp; ofta kallade ”ministreckkoder” eller”minibarcodes” på engelska för att de är så användbara för att skilja arter) från alla exemplar. Enskilda
sekvensvarianter (”haplotyper”) grupperades sedan i presumtiva arter med hjälp av olika klustermetoder(”objective clustering”, ”Poisson tree process”, ”automatic barcode gap discovery” och ”refined single
linkage”), och vi testade om grupperna överensstämde med morfologiskt identifierade arter. Ingenenskild metod var tillräckligt utslagsgivande för att ensamt kunna användas för artavgränsning utan
kompletterande analyser av ytterligare data. Vi fann att både stor genetisk variationen inom ett klusteroch hur robust klustret är för ändringar av tröskelvärdet i klusteralgoritmen kunde förutsäga de kluster
som inte överensstämmer med morfologin med god precision. Genom att kombinera preliminär
klustring av streckkoder med morfologisk validering fann vi att vi behövde undersöka färre än 5% av
exemplaren i materialet för att kunna både hitta och avgränsa alla arter korrekt.
I den sista uppsatsen i avhandlingen (papper IV) analyserar vi data från 32 000 puckelflugor, insamladefrån 37 platser olika platser i Sverige och i 4 olika tidsperioder, för att öka kunskapen om faunans storlekoch sammansättning i Sverige. Analyserna av detta material indikerar att vårt land hyser mellan 652 och
713 arter puckelflugor (baserat på Chao 1 respektive CNE-estimat). Detta är mer än 1,5 gånger såmånga som de 372 arter som rapporterats från Sverige tidigare. Statistiska analyser visar att olika arter
puckelflugor är grupperade i en gradient, som korrelerar väl med de odlingszoner som Riksförbundet
Svensk Trädgård har definierat. Hierarkisk modellering av artsamhällen (HMSC) ger vid handen att
artsammansättningen puckelflugor till stor del bestäms av klimat- och tidsvariabler, men mycket av
variationen förblir oförklarad av de modeller vi analyserade. Våra analyser visar att puckelflugorna ger
en mer högupplöst bild av den svenska naturen än de grupper som oftast används i den här typen av
analyser. Detta eftersom de är betydligt mer artrika, och varje arts förekomst är mer begränsad rumsligt
och tidsmässigt. Våra analyser antyder också att puckelflugearternas utbredningsmönster skiljer sig på
viktiga sätt från dem vi ser hos mer välstuderade grupper.
Sammanfattningsvis bidrar vi i avhandlingen både till att lägga grunden till mer detaljerade systematiska
studier av puckelflugorna, samtidigt som vi tar viktiga steg i utvecklingen av en helt ny metodik som
kan få stor betydelse för att accelerera kartläggningen av mångfalden hos såväl puckelflugor som många
andra dåligt kända och samtidigt artrika grupper. Den sista uppsatsen visar också att studier av den här
typen av grupper har mycket att ge när det gäller förståelsen av hur våra ekosystem är sammansatta och
hur de fungerar, kunskap som är kritisk för att vi ska kunna bevara dem för framtiden.

3

AUTHOR’S CONTRIBUTIONS
The thesis is based on the following articles, which are referred to in the text by their Roman
numerals:I

Hartop E.*, Häggqvist S.*, Ulefors S.-O., Ronquist F. (2021) Scuttling towards monophyly:
phylogeny of the mega‐diverse genus Megaselia (Diptera: Phoridae). Systematic Entomology.
46.1:71-82. https://doi.org/10.1111/syen.12448

II

Srivathsan A., Hartop E.A., Puniamoorthy J., Lee W.T., Kutty S.N., Kurina O., Meier R. (2019)
Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing. BMC
Biology. 17:96. https://doi.org/10.1186/s12915-019-0706-9

III

Hartop, E., Srivathsan, A., Ronquist, F., Meier, R. (2021) Large-scale Integrative Taxonomy(LIT): resolving the data conundrum for dark taxa. bioRxiv. 2021.04.13.439467.
https://doi.org/10.1101/2021.04.13.439467

VI

Hartop, E., Lee, L., Srivathsan, A., Jones, M., Yeo, D., Meier, R. (2021) A first dive into theterrestrial deep-sea trenches of Sweden: species richness, spatiotemporal distribution, andcommunity composition of a dark taxon (Diptera: Phoridae). Manuscript.
*Joint first authorship

Candidate contributions to thesis articles*
I

II

III

IV

Conceived the study

3

3

1

1

Designed the study

1

3

1

1

Collected the data

3

1

1

1

Analysed the data

1

2

1

1

Manuscript preparation

1

1

1

1

* Contribution Explanation
3 Minor: contributed in some way, but contribution was limited.
2 Significant: provided a significant contribution to the work.
1 Substantial: took the lead role and performed the majority of the work.

4

INTRODUCTION
Små, svarta, svårbestända, charmlösa puckelflugor – Fredrik Sjöberg, Oslo, 2019

Much of the multicellular biodiversity on earth belongs to large groups of small invertebrates that areboth diverse and abundant. These groups are often referred to as “dark taxa”, a term originally coined
to refer to the growing numbers of sequences in public databases that were not linked to species names(Page 2011, 2015). In current usage, “dark taxa” more specifically refers to species rich groups for whicha large proportion of species are undescribed. Studying these groups presents myriad challenges, and we
often lack information that is considered basic knowledge for less diverse groups. For example, we often
do not know, or have limited knowledge of, the numbers, evolution, composition, delimitation, anddistribution of species in these hyperdiverse clades. This is due to the compounding factors of abundanceand diversity: when both specimen and species numbers are high, the processes of discovering anddelimiting species become extremely challenging. These are further complicated by often challenging
morphology and small size. It is no surprise that these groups are often largely made up of undescribed
species, and we know little about even the (comparatively) small numbers of those that have been
described.
It is important to prioritise the study of these groups, as they make up most of the terrestrial species
diversity and large amounts of biomass. They are likely crucial to the functioning of many ecologicalprocesses but are so understudied that we cannot begin to assess their impact. As anthropogenic change
alters our planet at an alarming rate, we must prioritise understanding those groups that arequantitatively indicated as critical as the future wellbeing of our society is dependent on the natural
resources and services they provide (Swiss Re Institute 2020).
Studying hyperdiverse groups requires a different approach than studying most taxa. The development
and implementation of tools and workflows that are not required for more “normal” groups is critical.
Methods must be able to organise and simplify the large numbers of species and specimens in dark taxa
to make the inaccessible accessible. Here, we present a series of papers that take a multi-faceted
approach to a dark taxon in Sweden. This thesis represents a progression of knowledge, from an
evolutionary backbone in paper I, to new and innovative methodologies for handling dark taxa in papers
II and III, to a first look at the richness, diversity, and spatiotemporal distributions of a dark taxon in
Sweden in paper IV.

5

Sweden has one of the best studied faunas (and floras) on earth. Sweden was home to Linnaeus, whopioneered the centuries of focused taxonomic study that would follow, aimed at discovering anddescribing biodiversity. Despite progress that was made, some groups of organisms remained poorly
known (dark taxa). In recent years, the Swedish Taxonomy Initiative (STI) has focused efforts on suchgroups, facilitating advancements of their study by funding projects like some of the work in this thesis.
Here, we use scuttle flies (Diptera: Phoridae) as an example of a dark taxon. This family is comprisedof over 4 000 described species but is dominated by the genus Megaselia, especially in temperate regions.
First described over 160 years ago, Megaselia contains approximately 1 700 formalised species. Despite
the description of so many species, we have not achieved anything close to a comprehensive
understanding of the genus, and Megaselia is estimated to contain tens, if not hundreds, of thousandsof species (Brown et al. 2018; Srivathsan et al. 2019). Unfortunately, we not only lack an agreed uponestimate of species numbers, we also lack an understanding of the evolutionary history and boundariesof this group, and we have no confident estimate as to the true numbers of species or their distributions
across time and space.
Megaselia is one of the most biologically diverse genera in the animal kingdom (Marshall 2012) and, aswith many dark taxa, it is a group that is certainly important to the function of ecosystems. The known
species of Megaselia are well documented as having multitudinous life histories across a wide range of
habitats (Disney 1979, 1990, 1994). Such a combination – of species richness and abundance coupledwith biological diversity – makes Megaselia a potential goldmine of ecological data. To make Megaselia(and the family Phoridae more generally) accessible for biodiversity assessments, however, we must beable to efficiently create and analyse large datasets on these groups. This requires a much better
understanding of the genus and family than we have at present.
The approach to scuttle flies herein is an attempt to answer a series of basic questions about this familyof flies, and to develop methods designed to facilitate this. These methods are not specific to scuttle
flies; they are designed to address challenges faced in all hyperdiverse/dark taxa. The problems are,chiefly, the diversity of species and the abundance of specimens. Each paper addresses a particularquestion about scuttle flies (or the genus Megaselia, in particular), and papers II-IV additionally use new
and innovative methods developed and optimised for use on hyperdiverse taxa.
In paper I, we address the need for an evolutionary framework for Megaselia. The subfamily
Metopininae, in which Megaselia resides, is poorly understood itself. Previous morphological analyses

6

of the subfamily have suggested that Megaselia is paraphyletic with respect to several other genera, but
a comprehensive analysis of the subfamily has yet to be completed (Disney, 1989).
Some classifications of Megaselia have been attempted in the past based on morphological characters.
A separation of the genus into two subgenera (Megaselia and Aphiochaeta) by Enderlein (1924) was
followed by further breakdowns into divisions and rows (Schmitz 1953, 1955, 1956, 1957, 1958).
Unfortunately, Schmitz died before completing his work on Megaselia and even with continued efforts
by others (Schmitz and Beyer 1965a, 1965b; Schmitz and Delage 1974, 1981), the study of somedivisions of Megaselia was never completed. The groups proposed by Enderlein and Schmitz have long
been used by Megaselia specialists as practical, but unnatural, divisions (Disney 1994). Despite attempts
at categorising Megaselia, it has remained a “dustbin” for species that cannot be put elsewhere, based ona loose characterisation that is made up largely of negative characters. An evolutionary framework has
long been needed to define and restrict Megaselia and, further, to begin to organise the species within
into manageable subunits.
In paper I we present the first attempt at a large molecular phylogeny for Megaselia that has ever been
completed. We used four molecular markers sequenced from 175 Nordic specimens that represented
145 species of Megaselia. The phylogeny reveals 22 well-supported species groups within the current
boundaries of the genus, including 20 that fall within a moderately well supported monophyletic “core”.
The two species groups that fall outside of the core of Megaselia are herein diagnosed, this should help
to focus future work on circumscribing Megaselia and related genera.
Prior to this study, the lack of an evolutionary framework for Megaselia has discouraged many fromtaking up the study of the genus. With no way to focus on smaller subunits or monophyletic clades,
most specialists have been unwilling to tackle Megaselia in its entirety (wise). With paper I we hope tofacilitate future studies on Megaselia, but we still need methods to efficiently discover and delimit species
within this framework. Paper II therefore focuses on a method to discover new species from large
molecular datasets.
In paper II, we address the problems of discovering species in hyperdiverse/dark taxa through an efficientand cost-effective molecular pipeline. Traditionally, species discovery is a slow process. In species richand specimen-abundant groups like Megaselia it can be extremely slow. To find new or rare species
within large insect samples that contain thousands, or even tens-of-thousands of insects, the samples
must be sorted. Preliminary sorting to higher taxonomic levels can be done by workers with basic
training (students or technicians), but once species-level sorting is required, the work must be completed7

by experts. This often involves time consuming techniques like painstaking dissections, specimen
mountings (e.g., slides), and detailed drawings. In groups like Megaselia, these processes can beformidably time consuming, making even the identification of a species as potentially new a drawn-out
process.
Thankfully, in recent years workflows that utilise rapid and cost-effective NGS barcoding to pre-sortlarge samples into putative species have been developed and optimised (Srivathsan et al. 2018; Wang etal. 2018a; Srivathsan et al. 2019; Yeo et al. 2020; Srivathsan et al. 2021). With the development of thistype of “reverse workflow”, barcoding can be completed on thousands of specimens in short periods oftime. Molecular barcodes can then be clustered using distance- or phylogeny-based algorithms, making
quick work out of sorting large numbers of specimens into putative species. After initial sorting, experts
can concentrate their efforts on species that are potentially new or interesting. In paper II, wedemonstrate such a pipeline on 7 059 phorid specimens from Kibale National Park, Uganda. Specimens
were collected over eight weeks from a single Malaise trap, and after sequencing were revealed to contain>650 species. This is more species of phorid than have previously been recorded in the entire
Afrotropical region. We estimate that continued processing from this single trap will reveal this site tobe home to upwards of 1 000 species of phorids and that the Afrotropical region may contain over
100 000 species of Megaselia, dwarfing previous diversity estimates. To demonstrate how this pipeline
streamlines well-informed, efficient, and inexpensive species discovery, we describe a new species(Megaselia sepsioides sp. nov.).
The study for paper II was completed using a portable sequencer, the Oxford Nanopore MinION. In
the study herein, we optimised this technology to a capacity of 3 500 barcodes per flowcell. Since thepublication of paper II in 2019, advancements have been made to MinION technology that have allowed
work to continue optimising this platform for large-scale barcoding. Now, MinION is truly the ideal
tool for low cost, high throughput sequencing for species discovery – up to 10 000 barcodes can besequenced on a single flowcell (Srivathsan et al. 2021). MinION is finally a perfect tool to bringbarcoding to everyone – the processes are streamlined, accessible bioinformatics tools have beendeveloped and made available, and the costs are now low enough to make this technology truly
affordable (Srivathsan et al. 2021).
In paper III we took inspiration from the discovery of Megaselia sepsioides in paper II to apply large-scalebarcoding to ca. 18 000 scuttle flies from across Sweden. Our goal was to develop a systematic andobjective approach to large molecular datasets that would streamline the species discovery and8delimitation processes while integrating multiple data sources. Integrative taxonomy has long been thegold standard in taxonomy, but systematic, efficient and formalised approaches are still underdeveloped(but see Puillandre et al. 2012b; Kekkonen and Hebert 2014). Instead, integrative taxonomy had mostoften relied on time consuming morphological sorting prior to the barcoding of representative
specimens (Butcher et al. 2012; Riedel et al. 2013a; Lücking et al. 2016) or alternatively, some authors
have equated molecular units to species without standardised methods of validation (Hebert et al. 2016;
Sharkey et al. 2021). Either method is undesirable. Morphological sorting and representative
sequencing is time consuming and is likely to miss some species, while delimiting based on barcodes
without set validation procedures is not advised even by those who develop such methods (Puillandre
et al. 2012a; Ratnasingham and Hebert 2013; Zhang et al. 2013).
Paper III therefore presents a solution to this integrative taxonomy conundrum by developing LIT:
Large-scale Integrative Taxonomy. LIT is an approach that utilises a first data source that is cheap andeasy to obtain to pre-sort large numbers of specimens into putative species. It then uses a second, more
expensive, data source to validate species units. In our study, we used NGS minibarcodes as the firstsource and morphology as the second to develop LIT using ca. 18 000 barcodes from Swedish phorids.
We first obtained NGS barcodes for all specimens and then used different molecular speciesdelimitation methods and thresholds to create barcode clusters. In a first stage, we randomly picked 100
clusters to test whether we could identify cluster-specific traits (e.g., number of haplotypes, maximum
pairwise distance) that would be able to predict whether a molecular cluster is likely to be incongruent
with morphology. Once we identified these predictors, we tested their effectiveness when applied to theremaining barcode clusters. We then developed explicit rules for picking specimens from within eachcluster that should be studied for validating the preliminary species hypotheses that were based on
barcode clusters. We compared different clustering methods and concluded that none was sufficient on
its own to circumscribe all species correctly. We finally formalise our system in an algorithm todemonstrate that it is systematic, objective, and effective.
In paper IV we wanted to use a large barcode dataset to address some fundamental questions about adark taxon. As we witness dramatic changes to our planet in the Anthropocene, it becomes increasingly
critical to monitor our biodiversity with quantitative priorities. This means addressing ecologicalquestions with data from organisms that represent large portions of the species diversity and biomass,in additional to representing a broad spectrum of life history strategies. Unfortunately, many of thegroups that would offer such abundant data are dark taxa that are poorly known and difficult to study.
9For this reason, studies often use better known, more charismatic taxa for assessments. Studies basedon organisms like birds and butterflies may or may not present accurate representations of biodiversity
more broadly. To assess how different phorid results might be, we utilised the same barcode set as inpaper III and sequenced a further ca. 14 000 barcodes for a total of ca. 32 000 barcodes. The additional
barcodes were from the same locations as in paper II but represented four time-periods (late spring,
midsummer, late summer and offseason). With this expanded dataset, we assessed the richness anddistribution of phorids across space and time in Sweden. We additionally wanted to assess if phorids,
as a representative dark taxon, showed patterns that would offer a unique perspective on biodiversity
when compared to organisms that are more commonly utilised in biodiversity assessments. Finally, we
wanted to test whether we needed to use species units for studying phorid distributions, or whether
genetic diversity (haplotypes) would yield comparable results.
With all papers in combination, we here present a multi-faceted approach to studying a dark taxon.
With the evolutionary framework presented in paper I combined with the richness and distribution dataobtained in paper IV, we have a much better understanding of the phorid fauna of Sweden. We haveadditionally developed and optimised large-scale molecular workflows as well as a systematic andobjective approach to morphological validation of the large-scale molecular datasets obtained with suchworkflows (papers I and II). All the tools and information in these four papers combined not only greatly
advance our knowledge of this enigmatic and fascinating group, but they present ways forward to movebeyond these studies to further taxonomic work (descriptions) and more in-depth ecological analyses.

10

PAPER I
Materials and Methods
We sequenced 145 species of Nordic Megaselia that represent a broad sampling across Megaselia groups
historically recognized by Schmitz (1956) and morphological working groups defined by Sven Olof
Ulefors (SOU). SOU developed these working groups through the examination of ca. 35 000 specimensfrom the Swedish Malaise Trap Project, an inventory of the Swedish insect fauna conducted at 55
localities across Sweden between 2003-2006 (Karlsson et al. 2020). Specimens from nine other genera
were selected as outgroups. Five were selected from within the Metopininae and four from the Phorinae.

Figure 1. Condensed consensus tree showing generic relationships and basal clades based on Bayesian(left) and maximum likelihood (right) analyses. Maximum parsimony values are indicated on the ML
tree in parentheses.

Our results are based on a combined analysis of four molecular markers: one nuclear – the D2 variable
expansion region of nuclear 28S rDNA (28S, 527 bp) – and three mitochondrial – the barcode regionof cytochrome oxidase I (COI, 658 bp), NADH1 dehydrogenase (ND1, 378 bp) and 543 bp of 16 S
ribosomal RNA (16S), for a total of 2.1 kb of sequence data. Phylogenetic analyses were conducted11

using Bayesian analysis with MrBayes 3.2.6(Ronquist et al. 2012), maximum likelihood
analysis with RAxML (Stamatakis 2014), andmaximum

parsimony

analysis

with

TNT

(Goloboff and Catalano 2016). Rogue taxon
analysis was conducted using RogueNaRok(Aberer et al. 2013).

Results
Our results confirm the monophyly of theMetopininae across all methods of analysis (Fig.
1). Furthermore, they reveal four major clades
within Megaselia: the spinigera and ruficornis groupof Megaselia, Myriophora elongata, and “coreMegaselia” (Fig. 1). The ruficornis group is strongly
supported across methods and is additionallymorphologically diagnosed by the presence ofdifferentiated intra-alar setae (Fig. 2a). The
spinigera group is supported by both Bayesian and
ML analyses and morphologically diagnosed bydifferentiated setae on the posterior margin of the
scutum (Fig. 2b). Core Megaselia contains 20
species groups (Fig. 3) and is diagnosed
morphologically by reduced setation on the
scutum, as seen in generic type species Megaselia

Figure 2. (a) Strongly differentiated intra-alarsetae (marked with arrows) as found in the
ruficornis group and Myriophora; (b) row of

costalis (Fig. 2c). The ability to diagnose species

differentiated setae on posterior margin ofwithin the “core” may, in future, mean that

scutum, between posterocentral setae, as in the

Megaselia will be restricted to this clade, making

spinigera group; (c) generic type species

the genus monophyletic at last.

Megaselia costalis with the reduced setation
proposed as a potentially diagnostic feature ofcore Megaselia.
12Core Megaselia contains most of the species diversity sampled and is divided into 20 strongly supported
species groups and eight species placed individually on the tree (Fig. 3). Relationships between thesegroups were poorly resolved in all analyses, and rogue taxon analysis did not make any major
improvements to the phylogeny. We give potentially useful morphological characters for all groups,
although most clades cannot be reliably diagnosed by morphology at this time.

Figure 3. Consensus tree for core Megaselia showing 20 well supported species groups. Colours of theclades correspond to setosity of the anepisternum. Blue: anepisternum bare, pink: mixed group withsome taxa with anepisternum bare and some setose, grey: anepisternum setose + differentiated bristle(s).

13

PAPER II
Materials and Methods
8,669 phorid flies were collected from a single site in Kibale National Park, Uganda (Kurina 2012) and
full-length COI barcodes (658 bp) were sequenced using tagged amplicons building on the reverse
workflow of Wang et al. (2018a). Sequencing was done with Oxford Nanopore’s MinION sequencer
using 1D library preparation. Two experiments were conducted. The first experiment ran for 48 hours
and weak products were then identified, re-pooled and run on a new flowcell. The second experiment,
endeavouring to lower costs and improve success rates, ran for 24 hours, then the flowcell was flushed
and weak products were re-pooled and run again on the same flowcell.
After base-calling and demultiplexing, we aligned reads using MAFFT v7 (Katoh and Standley 2013).
This was performed on a random subset of 100 reads from each demultiplexed file, a majority ruleconsensus was obtained, and all barcodes containing more than 1% ambiguous nucleotides werediscarded. MAFFT barcodes were then corrected with RACON (Vaser et al. 2017) and both MAFFT
and RACON barcodes were further corrected with an amino-acid correction pipeline (Srivathsan et al.
2018). We then consolidated these two sets of barcodes. A contamination check was performed using
BLAST and 6 251 specimens were also sequenced on the Illumina platform for comparison. Finally, amorphological check for congruence was performed on 100 randomly selected mOTUS.
Methods are diagrammed in Figure 4.
Results
We obtained 7 273 preliminary barcodes from the 8,699 extracted specimens. This total was the productof two MinION runs that included re-pooling and re-sequencing of weak products (Fig. 5).
After MAFFT and RACON correction steps, 7 221 barcodes remained. Amino acid (AA) correction
further reduced the barcode set to 7 178 AA-corrected MAFFT barcodes and 7 194 AA-corrected
RACON barcodes. The two sets were then consolidated into 7 155 barcodes that, after contamination
checks of the negative PCR controls and of the barcode set using BLAST, we obtained a final yield of
7 059 barcodes.

14Figure 4. MinION pipeline flowchart.

15

We obtained 6 251 mini barcodes (313 bp) via Illumina from the 7 059 specimens represented in the
final dataset. Our consolidated MinION barcodes were found to be 99.99% accurate when comparedto the Illumina results (accepting those as the true barcodes). Comparison of the mOTUs generated
from the two platforms had a match ratio of 0.951 when compared at the same percentage, but allclusters were congruent when compared at 1.9-3.7%.

Figure 5. Effect of re-pooling on barcode coverage.
We clustered barcodes at a priori thresholds between 2 and 4% minimum interspecific distance(Cbjective Clustering, part of TaxonDNA in Meier et al. 2006)) and obtained between 613 and 705
mOTUs (2%: 705, 3%: 663, 4%: 613). An alternative species delimitation based on Poisson Tree
Processes (Zhang et al. 2013) yielded a higher estimate of 747 mOTUs. We calculated the Chao 1
species richness estimate based on 3% mOTUs, this suggested the diversity exceeds 1 000 species at thesingle site sampled (Fig. 6).

Figure 6. Species richness estimation. Green: Chao1 Mean, Pink: S (Mean), Orange: Singleton Mean,
Purple: Doubleton Mean16

We conducted a morphological validation step, checking all specimens in 100 randomly selected
mOTUS. We found molecular-morphological congruence for 93% of mOTUs and >99% of specimens.
90% of the putative species were identified as belonging to the hyperdiverse genus Megaselia.

Figure 7. Lateral habitus and diagnostic features of Megaselia sepsioides sp. nov.
During the morphological validation process, we identified a distinctive new species that we described
as Megaselia sepsioides (Fig. 7). This was a practical demonstration of how this workflow can guide wellinformed species descriptions that include documentation of both molecular and morphologicalvariation (Fig. 8).

17

Figure 8. Documentation of morphological (left) and molecular (right) variation in Megaselia sepsioides
sp. nov.

18

PAPER III
Materials and Methods
Samples were collected at thirty-six sites across Sweden as part of the Swedish Insect Inventory Project(Fig. 9a) (Karlsson et al. 2020). Phorid flies were sorted from a summer sample selected from each siteand sequenced to obtain 313 bp minibarcodes. Barcodes were clustered at 3% minimum interspecificdistance threshold (Objective clustering (OC) in Meier et al. 2006) and haplotype networks were madefor each cluster using PopArt to aide in morphological validation (Leigh and Bryant 2015). Haplotype
networks were coloured according to the plant hardiness zones of the Swedish horticultural society (Fig.
9b) (Riksförbundet Svensk Trädgård 2018).

Figure 9. (a) Sites of the Swedish Insect Inventory Project, colour-coded by climatic zones identified
by the Swedish Horticultural Society, (b) Climatic zones (odlingszoner) of the Swedish Horticultural
Society (Riksförbundet Svensk Trädgård 2018), used with permission).

19

One-hundred clusters were randomly selected to conduct a thorough morphological validation
following previously established characters and character states (Hartop and Brown 2014). In this stage,
all main haplotypes (containing at least 20% of cluster specimens) were checked, and then haplotypes
across the cluster were checked until no haplotype >1 bp away from a checked cluster remained.
Additionally, at least one specimen from each geographic area was checked in this stage. Clusters wheremorphological results agreed with the initial molecular delimitation were considered validated, while
multi- or partial-species clusters failed validation. The set of 100 clusters were then evaluated todetermine if properties of clusters were predictive of cluster failure. We tested six explanatory variables:“haplo” (number of haplotypes in a cluster), “spec” (number of specimens in a cluster), “stability” (ameasure of cluster stability over thresholds of 1-3%), “max_p” (maximum pairwise distance within acluster), “zones” (number of geographic zones represented in a cluster), and “sites” (number of sites
represented within a cluster). We fitted a generalised linear model with quasibinomial errors to thevalidation results to assess which of the variables best predicted cluster congruence with morphology.

Figure 10. Match ratios for PTP, ABGD (all priors) and OC (all thresholds) versus morphology acrossmethods and settings.
Based on the results from the first 100 test clusters, we used maximum p-distance and stability to flagremaining clusters as potentially incongruent (PI) if they had over 1.5% intracluster distance or if they
had instability between 1-3%. The PI designated clusters were evaluated against a test set of non-PI
clusters to determine if variables “max_p” and “stability” accurately identified clusters that are20

incongruent. These two sets were evaluated using a reduced checking scheme that checked main andmost distant haplotypes, but eliminated the need to check more specimens across cluster variation or
geographic zones, as we had not found these procedures to be informative of cluster failure. The
remaining clusters (all non-PI) were evaluated using most distant haplotypes only.
To assess the robustness of our results to variations in the clustering algorithm, we evaluated the match
ratio (Ahrens et al. 2016) of our morphologically validated results with results from a range of clustering
methods and thresholds: Objective Clustering (OC) from 0.6-4% (Meier et al. 2006), Automated
Barcode Gap Discovery (ABGD) across the range of priors (0.001-0.0359) (Puillandre et al. 2012a),and Poisson Tree Process (PTP) (Zhang et al. 2013). We additionally compared the subset of our datathat matched Barcode Index Numbers (BINs) in the Barcode of Life Data Systems (BOLD) toindirectly assess results obtained with the Refined Single Linkage (RESL) algorithm that is not
published and could therefore not be evaluated directly.
We finally tested the robustness of our methods by pulling all available phorid barcodes from public
databases (GenBank and BOLD) and adding them to our dataset. We then evaluated how many
barcodes fit into our original Swedish clusters, and how the addition of these clusters shifted clusters
from non-PI to PI or resulted in fusion events.

Figure 11. The splitting and lumping of morphological clusters with ABGD (left) and OC (right)
across settings.

21

Results
A total of 19 570 phorid flies were sequenced and the final analysis focused on 17 443 barcodes thatgrouped into 315 putative species at 3% OC. Evaluation of the first 100 test clusters revealed that 7/100
contained multiple species and an additional two were of uncertain composition (“species complexes”).
A generalised linear model revealed that “stability” was the only factor that was significant in predicting
cluster failure after removing strongly covarying factors from the model. However, this variable was
highly collinear with variable “max_p”, so that it, too, would be predictive of cluster failure. We used
these two variables to identify clusters as PI in the next stage.
The next stage evaluated 43 PI clusters against 43 non-PI clusters to assess whether the identified cluster
properties correctly identified cluster failure. Of the 43 PI clusters, 26% (11/43) were found to containmultiple species, while none of the non-PI clusters did. The evaluation of the remaining 129 smaller,
non-PI clusters revealed no further incongruence.

Figure 12. An illustration of the congruence between morphology, PTP, ABGD, and OC methods
with (a) optimal settings (ABGD P=0.0077, OC 1.7%) and (b) conservative settings (ABGD P=0.0215,
OC 3.0%) and between morphology, PTP, ABGD, OC and RESL methods with (c) optimal settings(ABGD P=0.0077, OC 1.7%) and (d) conservative settings (ABGD P=0.0215, OC 3.0%).

22

Evaluation of different clustering methods and thresholds revealed that match ratios for OC andABGD with morphology were maximized at 0.897 (OC 1.6-17%, ABGD 0.0077) (Fig. 10). PTP had
a maximum match ratio of 0.841 (Fig. 10). We further evaluated congruence by looking at where
methods split and/or lumped species. OC starts lumping morphospecies at 0.6% and stops splitting
morphospecies at 2.8%, while ABGD lumps morphospecies across all priors, and stops splitting at
p=0.0215 (Fig. 11). PTP both splits and lumps morphospecies. Evaluation of congruence between
methods revealed that ABGD and OC were largely congruent, while PTP was an outlier andmorphology often revealed species not seen in any of the molecular delimitations (Fig. 12a, b). Similar
results were obtained with the subset of specimens evaluated with RESL (Fig. 12c, d).
The addition of barcodes from public databases largely did not affect the original Swedish clusters, as
out of the entire dataset of 84 656 barcodes, 58 572 did not match Swedish mOTUs. The clusters that
did match, however, affected 244 of the original 329 clusters. There were numerous shifts of clusters to
a PI designation and there were also fusion events involving 27 of the original clusters. The overall
percentage of PI clusters went from 21% in the original dataset to 43% in the expanded dataset.
Final LIT protocol is a set sequence of steps that (1) identifies potentially problematic clusters (2) selects
specimens for validation procedures and (3) follows through with species delimitation for multi-species
clusters (Fig. 13).

23

Figure 13. A flowchart of final LIT protocol.

24

PAPER IV
Materials and Methods
This project used the same sampling scheme as paper III (Fig. 9a) but included barcodes from anadditional ca. 14 000 specimens sampled from three other time-periods. The total dataset therefore
included ca. 32 000 specimens collected across late spring, midsummer, late summer, and offseason
samples. For most analyses, barcodes were clustered using objective clustering optimised to approximate
species at 1.7%, as in paper III results.
Species richness was analysed using Chao1 and raw species accumulation curves plotted with EstimateS(Colwell 2013) and the R package (R Development Core Team) iNEXT (Chao et al. 2014). iNEXT
was also used to plot rarefaction curves. Sites were again categorised according to the plant hardinesszones of the Swedish Horticultural Society (Fig. 9b), and these categorizations were used in non-metricdimensional scaling plots (NMDS) to visualise phorid communities across space and time. Comparative
analyses were done using different clustering thresholds and using categorisations based on mean annual
temperature and mean annual precipitation maps calculated from the normal period 1991-2020,
available from the Swedish
MeteorologicalandHydrological(smhi.se).

Institute
Analysisofsimilarities (ANOSIM) and
Similarity percentage analysis(SIMPER) tests were run with
PRIMER
Gorleyv7(Clarke2006)

toquantitative

data

and

provideonthedifferences between regions
and time periods and to assesstheFigure 14. Species accumulation curves including associatedChao1 estimates for (a) Sweden, (b) plant hardiness regions, and(c) time periods, all showing that the fauna is undersampled.
25

significancedifferences.

of

these

To analyse our data without the imposition of a priori map categorisations, we conducted hierarchal
modelling of species communities (HMSC) (Ovaskainen et al. 2017; Ovaskainen and Abrego). We ran
four models in total: two with presence-absence data and 2 with abundance data, and one each in bothcategories using either haplotype or 1.7% mOTUs. We included in these models as fixed effects a single
categorical variable representing the four time-periods (late spring, midsummer, late summer, andoffseason) (“Time.Period”) and five continuous variables: (1) Worldclim mean temperature in thewarmest quarter (“bio10”) (Fick and Hijmans 2017), (2) Percent forest/woodland cover (50m buffer)(“ForestWood”) and (3) Percent agricultural land cover (50m buffer) (“Agriculture”), both from theSwedish National Land Cover Database (http://www.swedishepa.se/State-of-the-environment/Mapsand-map- services/National-Land-Cover-Database/), and (4) number of trapping days per sample(“TrapDays”), and (5) a binary variable indicating whether all specimens in a sample were sequenced or
not (“FullSample”). We also included a spatially explicit random effect based on sample site coordinates(“Random: site”) and a temporally explicit random effect based on median sampling date (“Random:time”).

Figure 15. Diversity across sites, (a) Current species richness estimated from samples that were rarefied
to make them comparable across sites and (b) Chao1 estimates for each site.

26

Results
The final dataset consisted of
31

739

specimensthatclustered into 549 mOTUs at
OC 1.7%. Chao1 species
accumulation curves revealed
that all sites, regions andtime-periods

are

still

undersampled, with a further
100 species awaiting discovery(for a total of 652 estimatedspecies)

based

on

current

Figure 16. Plot of all samples (1.7% mOTUs, threshold of 100
specimens) colour-coded according to plant hardiness region.

sampling (Fig. 14). We rarefied current species richness to compare diversity across sites and found noclear pattern in overall richness, with high and low diversity sites found across the country (Fig. 15).

Figure 17. Samples plotted with mOTUs calculated at different thresholds with ellipses coloured
according to plant hardiness zones.

27

Spatial patterns were clearly correlated with the regions of the plant hardiness map visually (Fig. 16)
and were arranged in a near linear gradient across Sweden. We confirmed the relationship with a linear
regression model of our NMDS1 values plotted against ordered zones, this showed a significant positive
linear fit to our data based on either presence-absence data (adjusted R2=0.79) or abundance data
(adjusted R2=0.67). We additionally analysed this relationship over clustering thresholds from 0%(haplotypes) to 5% and found that patterns were nearly identical from 0-1.7%, after which there was aclear phase shift and then a blurring of patterns as species were lumped together (Fig. 17). We also
conducted a comparative analysis using the SMHI temperature and precipitation zones. Visually, thetemperature zones were a good fit to our data and closely mimicked the plants hardiness zones (that arelargely temperature dependent) and the precipitation map was a poor fit. This was confirmed with linear
regression models where the average annual temperature data had an adjusted R2 of 0.76 and 0.63 withpresence-absence and abundance data, respectively, and the precipitation data had an adjusted R2 of
0.37 and 0.30 with presence-absence and abundance data, respectively.
Seasonal samples were visually apparent linearly across the regional gradient, except for the offseason
where the sampling overlapped with other seasons, obscuring patterning (Fig. 18).
HMSC results confirmed the resultsthat we obtained using categorisations
based on a priori maps and additionally
showed that models based on either
haplotypes

or“species

equivalent”

mOTUs made little difference toresults.

Proportionsofexplained

variance were largely dependent on
temporal (seasonal) factors with >35%

Figure 18. Plot of all samples (threshold of 100
specimens) with regional ellipses greyed out and samples
colour coded according to time-period. With the
exception of the offseason, seasonal catches run linearlythrough regional ellipses.

of variance explained by variables“Random:time”“Time.Period”(Fig. 19). Presence-absence models
were additionally highly dependent on
climate (Fig. 19).

28

and

We analysed the positive andnegative

responsesofindividual haplotypes andmOTUs to our variables andfound that most taxa showedanegative

responsetooffseason trapping in bothpresence-absence
abundanceandmodels,indicating they are lesslikely to be present and lesslikely to occur in highnumbers, in the winter.
Taxon occurrences showed
a mixture of positive andFigure 19. Proportion of explained variance for the four models. Allfour models are largely temporally driven which includes variables“Random:time” and “Time.Period”. Presence-absence models arealso largely driven by the climatic variable “bio10”. Geography isvariable “Random: site”, Sampling effort is “FullSample” and“TrapDays”, and Habitat is variables “Agriculture” and“ForestWood”.

negative responses to thetemperature variable, reflecting the community gradient across Sweden we observed with our plantregion NMDS plots.
Residual correlations between taxa across sites were common for 1.7% mOTUs but less so across time.
Haplotype data had many fewer correlations, this may reflect the relative rarity of haplotypes across thedataset. Residual correlations were extremely low in the abundance models, indicating abundance
patterns were well modelled by the covariates of our models.

29

DISCUSSION
Here, we have taken a multi-faceted approach to a dark taxon by gathering data and developing tools
to facilitate a better understanding of a diverse and abundant group. This is critically needed in thistime of great anthropogenic change when we need to prioritise biodiversity assessments and groundthem in quantitative reasoning. Dark taxa represent some of the most species rich and abundant groupsof organisms. They represent a wide variety of life histories and a significant proportion of terrestrial
animal biomass. We must prioritise understanding these groups. To that end, in this thesis we havefirst created a phylogenetic framework to guide future studies on Megaselia (paper I), we have then
optimised methods for species discovery and delimitation from large-scale molecular datasets (papers II
and III), and finally, we have utilised these datasets to conduct a countrywide analysis of the richness
and spatiotemporal distribution of an enigmatic group.
In Paper I we discovered a potentially monophyletic core of Megaselia. This gives us hope that a natural
circumscription and diagnosis of the genus based on putative morphological apomorphies might be on
the horizon. The clades that fall outside the core of Megaselia will require further investigation, but our
preliminary morphological diagnoses may help to guide delimitations of the genera closely related to
Megaselia. Within the core of Megaselia, we will need to collect more data to give reliable morphologicaldiagnoses to the species groups we have identified. We have confirmed that previous divisions within
Megaselia were often unnatural and we have provided the framework for alternative divisions of thegenus based on phylogenetic data. Studies on the tentative species groups we have identified must
proceed utilising both morphological and molecular data to gain a better understanding of these groups
and the relationships between them.
The molecular framework for Megaselia from paper I is far from comprehensive. The 145 species it
includes are roughly one-quarter of the species-level molecular units found from Sweden for the dataset
used in paper IV. Therefore, we must be cautious as it covers only a small fraction of the diversity from
Sweden, a Palaearctic country that contains a small part of the world fauna. We will need many more
species, additional outgroup representatives, and sampling from many more regions before we getanywhere close to a comprehensive grasp of the evolutionary history of Megaselia and the clades within.

30

Papers II and III build on a previously established “reverse workflow” where large numbers of specimens
are NGS barcoded and sorted to putative species in preparation for work by specialists (Srivathsan etal. 2018, 2019, 2021; Wang et al. 2018b; Yeo et al. 2020). This process allows for the integration ofmultiple data sources into taxonomy in an accurate, efficient, and affordable way. Although integrative
taxonomy is accepted best practice (Dayrat 2005; Padial and Miralles 2010; Schlick-Steiner et al. 2010;
Pante et al. 2015; Vitecek et al. 2017), previous efforts to develop a system for large-scale integrative
taxonomy have been underdeveloped (but see Puillandre et al. 2012b; Kekkonen and Hebert 2014).
This is largely because barcoding was too expensive to sequence large numbers of specimens. Instead,specimens were often sorted to morphospecies and then representatives were picked for sequencing(Butcher et al. 2012; Riedel et al. 2013b; Lücking et al. 2016). Not only is such a process time
consuming, but it is prone to error, especially if morphospecies sorting is conducted by parataxonomists
and not by specialists (Krell 2004). Even when experts do the preliminary sorting, this approach cannot
detect cryptic species or easily process large numbers of specimens from groups that are exceedingly
difficult to determine. Fortunately, we have moved beyond the formal DNA extraction and Sanger
sequencing techniques that were too costly to allow the processing of all (or many) specimens in samples.
The ability to process large amounts of material offers many advantages over previous approaches. First,
specimens are immediately sorted to putative species, allowing the attention of experts to be focused on
taxa of interest. An expert working with the reverse workflow receives all specimens pre-sorted, withboth sexes and all life stages (if collected) already grouped together. The lab work does not require any
specialised skills but instead relies on technicians who can perform extraction and PCR at the rate ofca. 10 microplates (96 wells) per day. The time and expertise required for an NGS reverse workflow
barcoding approach to species sorting and discovery is a small fraction of that required for the variety
of morphological approaches commonly used today.
In paper II, we optimised a method of individual NGS barcoding on the Oxford Nanopore
Technologies (ONT) MinION using 1D sequencing. We demonstrated that with an improved
bioinformatics pipeline, new primer tags, and re-pooling of weak products it was possible to increase
the capacity of a standard MinION flowcell to ~3 500 specimens while reducing costs to <0.35 USD
per barcode. The ability to identify weak products in the first run and re-pool for a second run within a
day or so is crucial when dealing with thousands of specimens. This can be contrasted with sequencing
at centralised facilities, where re-pooling of select DNA extracts for new analysis might not be possible
for weeks after the initial run. The workflow presented in paper II made it feasible to use inexpensive
31

mobile technology for largescale barcoding. Since the publication of that paper in 2019, the MinION
has undergone a number of advancements and, with continued work with this technology, we are now
able to sequence up to 10 000 specimens per flowcell (Srivathsan et al. 2021). The optimisations of suchworkflows can provide inexpensive and efficient solutions to species discovery and delimitation in dark
taxa. This is greatly expanded upon in paper III where minibarcodes (313 bp) are used as the first data
source in the delimitation of 365 species. Although Yeo et al. (2020) have shown that minibarcodes
perform comparably to traditional 658 bp barcodes for the delimitation and identification of species,
smaller fragment lengths were necessitated by sequencing platforms like Illumina. Now that MinION
is a reasonable alternative, full length barcodes can easily be swapped out in workflows like the Largescale Integrative Taxonomy (LIT) proposed in paper III.
Working with portable sequencers is important as the world becomes increasingly focused on
democratising science. The MinION is a good low-cost option for those without access to centralised
molecular facilities. It requires only basic molecular lab equipment and computing power and cantherefore become a tool to bring molecular sequencing to countries that lack the resources for molecularsequencing, but also into grade schools and the homes of citizen/community scientists.
The last goal of paper II was the first use of a system that guides well-informed species descriptions. As
taxonomists have struggled to streamline species descriptions, various forms of “turbotaxonomy” havebeen suggested. Often, these systems involve the use of molecular barcodes in combination withphotographs and reduced morphological descriptions (Butcher et al. 2012; Riedel et al. 2013b). We
decided to take the approach of barcodes in combination with photographs, but without written
morphological descriptions. This requires that the photographs be of sufficient quality for a reader tobe able to observe in them any characters that normally would have been expressed in text. We do
include a written diagnosis to point out salient features. A similar approach, using barcodes andmorphology without morphological descriptions, has recently been applied to wasps in the family
Braconidae (Meierotto et al. 2019; Sharkey et al. 2021). A key difference between the two approaches
is that of validation. In our description of Megaselia sepsioides sp. nov., we not only document
morphological variation with high quality photographs, but we also document molecular variation witha haplotype network of all specimens we had available for description. We consider these haplotype
networks to be a crucial, formal addition to our version of a turbotaxonomic description. Documenting
all variation observed in a species must be done as clearly as possible to help inform future work. As
32

data on more species and specimens are added to our body of knowledge, we will be able to separate
species from intraspecific variation utilising all available data. The processes of discovery, delimitationand validation for M. sepsioides inspired a great expansion of these methods in paper III.
In paper III we made the processes of species discovery and delimitation explored in paper II systematicand objective by formalising procedures for moving from samples to large-scale barcode datasets tovalidated species units. We developed the Large-scale Integrative Taxonomy (LIT) system to transformthe study of dark taxa. We showed that we can accelerate biodiversity discovery while integrating
multiple data sources by obtaining a first data source for large numbers of specimens and thensystematically and objectively targeting a subset of specimens for the acquisition of a second, more
expensive data source. In our study, we first NGS barcoded ca. 18 000 phorid specimens and thenobtained morphological data for a small fraction of these (final protocol would require the checking of
915/18 000 specimens), but in the future a first data source could be automated image identification
and a second (or even third) data source could be nuclear markers or ecological data.
To reduce the number of specimens that required examination while guaranteeing that the validation
process was thorough, we first identified how to flag clusters that were most likely to be incongruent
with morphology. This allowed us to develop a final protocol where the minimum number of specimens(2 from the most distant haplotypes) can be checked from clusters that are not flagged as “potentiallyincongruent”, while clusters that are more likely to be problematic can be checked with 5-7 specimensfrom main and most distant haplotypes. The two variables that flag clusters for deeper examination are
logical, but efficient. Maximum p-distance over 1.5% identifies clusters that have high amounts ofvariation, while stability identifies clusters that have splits that would occur at thresholds below thoseused for clustering. In future, a potential alternative to stability values (that require a hierarchicalstructuring of clusters) might be the maximum branch length of a median joining network for a cluster.
The LIT protocol may be subject to complications with expanded sampling and geographic scope.
Although many of the public database barcodes did not fit into our original clusters, those that did
shifted many clusters to a PI designation and caused several fusion events. Although these data were
downloaded from public databases and we therefore have a limited ability to control for quality and no
ability to check morphology, they offer a look ahead at the potential issues the may be faced whenmoving beyond regional studies. It will be impossible to know exactly how to accommodate expansion
33

problems until they occur. Clustering at lower thresholds as interspecific distances decrease is one
option, but we will not know how well this will work until we have much larger datasets to work on,
and access to specimens for validation. Changing the second data source (from morphology to nuclear
markers) or expanding to a sequential workflow that uses a third data source for challenging areas areboth options. LIT may also turn out to be best applied to regional datasets, and not to a worldwide
fauna.
Our study in paper III clearly shows that no method or threshold of molecular clustering is accurateenough to be used without validation with a second data source. Even at optimal settings, there are anumber of morphospecies that are not detected by any clustering method. This is not surprising, as
algorithms are most likely to delimit improperly (although sometimes in agreement with each other) inareas where there are large, rapid radiations (Puillandre et al. 2012a; Ratnasingham and Hebert 2013;
Zhang et al. 2013). In such cases, integrative taxonomy will be a necessity to properly identify species.
Our study revealed that such complex areas may be a large proportion of some taxa. Of the 17 443
specimens in our final dataset, 41% (7 150) belonged to 3% OC clusters that contained multiple species.
If these specimens were identified based on barcode data only, most would be misidentified.
LIT is a precursor to species description, as conducted in paper II for M. sepsioides. In future, thespecimens from paper III will go through a similar workflow. The work to describe Megaselia sepsioides
was done in a day but in the future, when the many species from papers III (and IV) need to go throughthe same pipeline, the descriptive process (photography and the creation of haplotype networks) mustbe further optimised. This can be done by automating photography and the creation of haplotype
networks for several species per day. Even if just five species could be handled per day, the descriptionof all species analysed in paper III would take under a year. On the other hand, sorting, discovering, anddescribing as many specimens and species with traditional methods would take years, perhaps decades.
This is something of a moot point, however, because such work is rarely completed for hyperdiverse
groups like Megaselia. More often, groups like Megaselia, and phorids more generally, are left unsorted
in bulk samples after other (usually larger, more charismatic) taxa have been sorted out. Thus, the name“dark taxa”.
The investigation of some fundamental questions about Phoridae, as a representative dark taxon, is the
subject of Paper IV. Unlike better studied groups, for dark taxa we often do not have knowledge of34

fundamental questions regarding species richness and distributions. Our investigations into the phoridsof Sweden revealed an estimated 652-713 species (based on Chao 1 and CNE estimates as upper andlower limits) (CNE from Ronquist et al. 2020). This is well beyond the 374 species currently known
from the country and reveals that there is still much to explore, even in a country where the biodiversity
is as well-known as in Sweden. It is not surprising to have estimates of scuttle fly diversity that may beclose to twice the known fauna – a study in Los Angeles, CA documented up to 82 species living inbackyards, and a suburban garden in Cambridge, UK has been documented to contain nearly 100
species (Brown and Hartop 2016).
Future efforts to capture the remaining fauna can focus on filling in geographic or habitat gaps using
baseline material from the Swedish Malaise Trap Project (Karlsson et al. 2020) or newer material fromthe ca. 200 Malaise trap sites of the Insect Biome Atlas project (http://insectbiomeatlas.org).
Additionally, the material thus far examined has been exclusively from Malaise traps. Utilising othertrapping methods would undoubtedly reveal new species and raise faunal estimates. Even still, thediversity across Sweden seems unlikely to exceed the species diversity captured by the single trap in
paper II that gave an estimate of >1 000 species at that site based on far less sampling. The extrapolationof this information to an estimate of 100 000 species of Megaselia in the Afrotropical region willcertainly be revised as we gather more data and better understand species turnover. Paper IV reveals thatphorid communities exist in spatial and temporal gradients, and that nearly all the dissimilarity of phoridcommunities of Sweden is due to turnover rather than nesting of species. This makes the Afrotropicalestimate seem likely to be a reasonable one. Previous speculation had often centred around guesses of
10 000-20 000 species of Megaselia worldwide, but data from papers II and III suggest these may besignificant underestimates. This is especially true considering that BLAST results from paper II did nothave even one species-level match to the ca. 80 000 COI sequences in NCBI’s GenBank. Further
sampling and analysis will be needed to determine if there is truly no overlap between the phorids foundat a site in Uganda and the phorids currently sequenced from the rest of the world.
Another focus of paper IV was whether spatial and temporal studies could be conducted using haplotype
data. This may seem a strange focus after all the work in paper III to determine species units, but it isimportant to understand where dark taxa can be studied without taxonomic interpretation, and where
specialist attention will be critical to results. The revelation that haplotype data yielded comparable

35

results to “best species” approximation mOTUs in our community ordinations is promising news formolecular ecologists interested in patterns across time and space, and in response to change.
Our hierarchical species modelling (Ovaskainen et al. 2017; Ovaskainen and Abrego 2020) also revealed
similar results with haplotypes and species proxy mOTUs. HMSC showed that phorid communities
are explained by temporal (seasonal) variables, and that presence/absence is additionally driven byclimate (in a gradient across latitude and altitude). Some species showed positive responses towoodland/forest or agricultural habitats, while others did not. However, our models clearly captured
only some of the important factors, as residual plots of species associations reveal that a large part of thevariation is due to missing unknown variables. We hypothesise that microclimate associated with soil,
or variable within the soil, may turn out to be important to explain the structuring of phoridcommunities, and perhaps also many other dark taxa, as many exist just above, or sometimes within,
the soil. We will explore further variables in future modelling, and we also hope to incorporate
phylogenetic information to determine whether observed patterns have an evolutionary basis.
In additional to their tremendous species and ecological diversity and abundance, our results from paper
IV suggest that dark taxa may offer a fine-scale approach to biodiversity studies. Although sampling islimited thus far, there is potential for regional or even local endemism based on currently observed
distributions. Even at a larger scale, dark taxa may offer patterns of distribution that are different from
more commonly studied taxa like birds and butterflies. We downloaded bird and butterfly data fromstandard sampling schemes in Sweden and found that for both groups, approximately 2/3 of species arewidely distributed across the country. In contrast, based on current sampling, nearly 2/3 of phorids
species are restricted to the northern or southern part of the country. Admittedly, our phorid results are
based on an undersampled fauna and the sampling schemes for birds and butterflies are quite different,
utilising many more sites/transects over shorter periods of time. Until we have the phorids of Sweden
much more completely sampled and a better comparison can be made, we will not know for sure howunique their patterns of distribution may be.

36CONCLUSION
Dark taxa require a unique approach, but they may yield results that are impossible to obtain with taxa
that are easier to study. Due to overwhelming species and specimen numbers combined with challenging
taxonomy, a multi-faceted approach is essential. Here, we combined several approaches to better
understand the fly family Phoridae by first constructing a framework on which to base future studies(paper I), then improving methods for large-scale species discovery and delimitation (papers II and III),and finally expanding knowledge of how phorid communities change across time and space (paper IV).
These are all significant advances in our knowledge of Phoridae, and especially of the genus Megaselia,
which makes up a majority of specimens and species in temperate samples. Widespread adoption andfurther refinement of such methods to study other dark taxa will rapidly advance our understanding of
the world’s biodiversity. This will be facilitated by developments in image recognition, robotics, andsequencing technologies, all of which will make the study of dark taxa increasingly inexpensive andefficient.
With ever increasing societal concern over the implications of biodiversity loss for the long-term survivalof mankind, this is urgent work and there is still much to be done. We must be able to accurately
document our biodiversity as it undergoes rapid losses and transformations due to climate change andhabitat loss. To do this, we must focus on taxa that contribute great species richness, significant biomass,
and great biological diversity. Doing so would result in a truly quantitative approach to biodiversity
studies that is much needed to accurately assess our natural systems and is especially critical in this timeof great anthropogenic change.

37

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