7 Department of Botany and Zoology, Faculty of Science, Masaryk
University, Kotlářská 2, CZ-611 37, Brno, Czech Republic.
Running title
Host parasite interaction networks
Key words
Cichlidogyrus , Cichlidae, fish parasites, flatworms,
functional-phylogenetic distances, host niche, Monogenea, phylogenetic
specificity, species interactions, structural specificity.
Type of article
Letter
Number of words in abstract
150
Number of words in text
4995
Number of references
132
Number of figures
5
Number of tables
1
Corresponding author
Armando J. Cruz-Laufer, armando.cruzlaufer@uhasselt.be
Statement of authorship
AJCL conceptualised the study and conducted the literature survey. AJCL
performed all analyses and produced tables and graphs. AJCL produced
host phylogenies with input from SK. AJCL and MPMV wrote the manuscript
with input from TA, SK, AP, KS, and MVS.
Conflict of interest
The authors declare that they have no conflict of interest.
Data accessibility statement
Species interaction, host ecological, and community membership data as
well as DNA sequence alignments underlying this article are available in
Zenodo at www.zenodo.org, at https://dx.doi.org/XXXXXXXX.
Abstract
Hosts and parasites have often intimate associations. Therefore, the
evolution of their interactions is crucial for understanding
species-rich host-parasite communities. Yet relatively few studies
investigate eco-evolutionary feedbacks in these systems as large
datasets remain scarce. Here, we explore African cichlid fishes and
their flatworm gill parasites (Cichlidogyrus spp.) including 9901
reported infections and 473 different host-parasite combinations
collected through a survey of peer-reviewed literature. We apply network
metrics, estimate host repertoires, and use network link prediction
(NLP) algorithms to investigate meta-community structures and their
predictors including evolutionary, ecological, and morphological
parameters. Host repertoire was mostly determined by the hosts’
evolutionary history. Both ecological and evolutionary parameters
predicted host parasite associations but many interactions remain
undetected according to NLP. We conclude that ecological opportunity
paired with ecological fitting has shaped interactions. The
cichlid-Cichlidogyrus network is a suitable study system for
eco-evolutionary feedbacks but taxonomic research remains key to finding
undetected interactions.
Graphical Abstract
Hosts-parasite interactions are shaped by ecological and evolutionary
processes. We investigate interactions of African cichlids and their
flatworm parasites belonging to Cichlidogyrus (a) through network
analyses (b), host repertoire estimation, and network link prediction
(c). We show that the hosts’ evolutionary history and environment
determine observed host repertoires and network structure.
Cichlid-Cichlidogyrus interactions are, thus, shaped by host
phylogeny and ecological opportunity.
Introduction
Many species on the planet are parasites at least during a portion of
their lifetimes (Poulin 2014). Host-parasite interactions are often
intimate associations that can profoundly affect host fitness (Kutzer &
Armitage 2016) and, thus, shape biological communities (Gómez & Nichols
2013). However, host-parasite interactions, like other interactions, are
not fixed in time. Ancient (Algar et al. 2009) and recent
(Fussmann et al. 2007) evolutionary processes have produced
present-day communities (Toju et al. 2017). To investigate this
interplay of evolutionary history and community structure, integrative
analyses of ecological and evolutionary patterns are crucial (Segaret al. 2020). For instance, host range, a key characteristic of
parasite ecology (Poulin et al. 2011), is influenced not only by
environmental factors but also the evolutionary history of the hosts
(Poulin et al. 2011). More integrative measures, e.g.
functional-phylogenetic distance metrics (FPDist) (Cadotte et al.2013), can account for host ecology as well as evolutionary history
(Clark & Clegg 2017). But do these metrics fully grasp the niche
limitations of the parasites? The frequency of host switches recorded in
the past (see Agosta et al. 2010) suggests otherwise. Host
repertoires observed today have likely resulted from alternating phases
of host range expansions and isolation (oscillation hypothesis )
(Janz & Nylin 2008). Parasites expand their host range through their
capacity to access novel resources (ecological fitting ) (Agostaet al. 2010), i.e. host species, and through the opportunity
emerging from the rise and fall of ecological barriers (D’Bastianiet al. 2020), e.g. after anthropogenic introductions (Brookset al. 2021). Therefore, the realised host repertoire ,
which is approximated through FPDist, does not equate the full
repertoire of host species that can potentially be infected
(fundamental host repertoire ) (Braga et al. 2020).
The oscillation of host ranges resulting from ecological fitting and
opportunity has been termed the Stockholm Paradigm (Brookset al. 2019) and is considered one of the main sources of
parasite biodiversity (Agosta & Brooks 2020).
One of the aspects highlighted by the Stockholm paradigm is the
potential of predicting future host-parasite interactions in the context
of emerging parasitic diseases. Understanding the mechanisms behind
these diseases is increasingly relevant in a world where environmental
degradation promotes host switches between previously unconnected hosts
(Brooks et al. 2019). Host switches may present threats to human
health and food security (Fitzpatrick 2013; Jenkins et al. 2015;
Ekroth et al. 2019; Brooks et al. 2021). To understand
parasitic interactions (Bogich et al. 2013; Bordes et al.2017), ecological research has put forward network theory (Poulin 2010)
through which species are represented as discrete interacting units,
e.g. in plant-pollinator (Soares et al. 2017; Vizentin-Bugoniet al. 2018), predator-prey (Allesina & Pascual 2008), and
plant-mycorrhiza systems (Simard et al. 2012). Ecologists widely
employ this approach to characterise and visualise species interactions
(Pocock et al. 2016). Moreover, rising computational capacities
have promoted the use of network link prediction (NLP) algorithms to
model undetected interactions. These methods originating in social
network studies (Wang et al. 2015), have lately been optimised
for biological systems (Martínez et al. 2016) including
ecological networks (Dallas et al. 2017; Zhao et al. 2017;
Fu et al. 2019).
Few recent studies on the Stockholm paradigm have integrated
network analyses (but see D’Bastiani et al. 2020; Braga et
al. 2021). Instead, the focus has remained on inferring ancestral
host-parasite interactions (Braga et al. 2020, 2021) rather than
predicting undetected links. The distinction between undetected and
unrealised links remains a major hurdle for network studies as observed
interactions will often present an underestimation of the real
interaction diversity (Fu et al. 2019). Furthermore, previous
studies (Braga et al. 2020, 2021) treated interactions as
discrete states, e.g. as non-hosts, potential hosts, and real hosts,
despite the literature on network analyses substantiating that some
host-parasite interactions are more prevalent than others (Blüthgenet al. 2008; Poulin et al. 2011). Many of the metrics
describing the structure of species networks, like nestedness,
connectance, and specialisation, have been optimised to account for
interaction strength, i.e. the frequency of an observed interaction (see
Blüthgen et al. 2008). Undetected links and interactions strength
can be addressed through NLP as the algorithms account for both of these
issues (Dallas et al. 2017; Fu et al. 2019).
Here, we investigate host-parasite interactions in a species-rich
network using network theory and NLP. As model system, we selected one
of the best known examples for explosive speciation: African cichlid
fishes. Approximately 2000 species reside in the East African Great
Lakes alone, many of which are endemic (Salzburger et al. 2014).
Cichlid science has been at the forefront of evolutionary (e.g.
Salzburger 2018; Ronco et al. 2021) and behavioural (see
Koblmüller et al. 2019) research. Outside of feeding behaviour
(e.g. Cooper et al. 2010; Hulsey et al. 2019), and
fish-fish (e.g. Blažek et al. 2018; Marshall 2018) and human-fish
interactions (Irvine et al. 2019), studies on interactions of
cichlids with non-cichlid organisms have focused mostly on parasitic
interactions (Cruz-Laufer et al. 2021a). One parasite lineage
infecting African cichlids, the monogenean flatworms belonging toCichlidogyrus Paperna, 1960 sensu Wu et al. (2007)
(including Scutogyrus Pariselle & Euzet, 1995) is particularly
species-rich. Currently, 137 species are described that infect the gills
of 135 cichlid and five non-cichlid species (see Cruz-Laufer et
al. 2021a). For this reason, the monogenean gill parasites of cichlids
were proposed as model system for host-parasite interaction studies
(Pariselle et al. 2003; Vanhove et al. 2016) (Fig. 1).
We explore cichlid-Cichlidogyrus interactions at a global scale.
First, we use network metrics and community detection to characterise
the structure of the observed network and meta-communities. Second, we
assess the observed host range considering both functional and
phylogenetic host diversity (Poulin et al. 2011; Esser et
al. 2016) and discuss the limitations of this traditional approach to
host repertoires. Third, we assess the performance of two recently
proposed NLP models. We aim to address the following questions on the
ecology and evolution of parasites using the
cichlid-Cichlidogyrus network as a model system: (i) Do
cichlid-Cichlidogyrus meta-communities reflect the evolutionary
history of hosts and parasites, (ii) does the observed host repertoire
correlate with functional or phylogenetic host diversity, and (iii) what
can network link prediction models reveal about predictors of
host-parasite interactions?
Materials & methods