6 Operational Directorate Taxonomy and Phylogeny, Royal Belgian
Institute of Natural Sciences, Vautierstraat 29, B-1000 Brussels,
Belgium.
Running title
Host-parasite interaction networks
Key words
Cichlidogyrus , Cichlidae, ectoparasites, flatworms,
functional-phylogenetic distances, host niche, Lake Tanganyika, Lake
Victoria, Monogenea, species interactions.
Type of article
Letter
Number of words in abstract
149
Number of words in text
5000
Number of references
147
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/10.5281/zenodo.4075171.
Abstract
Many species-rich ecological communities result from adaptive radiation
events. The effects of these explosive speciation events on community
assembly remain poorly understood. Here, we explore the well-documented
radiations of African cichlid fishes and interactions with their
flatworm gill parasites (Cichlidogyrus spp.) including 10529
reported infections and 477 different host-parasite combinations
collected through a survey of peer-reviewed literature. We assess the
evolutionary, ecological, and morphological parameters on
meta-communities partially affected by adaptive radiation evens using
network metrics, host repertoire measures, and network link prediction
(NLP). The hosts’ evolutionary history mostly determined host
repertoires. Ecological and evolutionary parameters predicted
host-parasite associations, but many interactions remain undetected
according to NLP. Parasite meta-communities under host adaptive
radiation are more specialised and stable while ecological opportunity
and ecological fitting have shaped interactions elsewhere. The
cichlid-Cichlidogyrus network is a suitable eco-evolutionary
study system but future studies should validate our findings in other
radiating host-parasite systems.
Graphical Abstract
Many species-rich ecological communities result from adaptive radiation
events. Here, 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
(heatmaps) (c). The hosts’ evolutionary history and environment
determine observed host repertoires and network structure (b). Cichlid
radiations in Eastern Africa have formed more specialised
meta-communities (c).
Introduction
Evolutionary processes are a major factor in how ecological communities
are formed (Toju et al. 2017) at both the ancient (Algar et
al. 2009) and recent (Fussmann et al. 2007) timescale. Many
species-rich communities are the result of adaptive radiations (Glor
2010), a form of explosive species formation. Adaptive radiation stem
from ecological opportunity arising from a great variety of newly
available ecological niches (Losos 2010). This mechanism has produced
several diverse and well-known species flocks including Darwin’s finches
(Petren et al. 2005), Caribbean anole lizards (Losos 2009), and
cichlid fishes (Salzburger et al. 2014). Despite this species
diversity, ecological research has focused mostly on the feeding ecology
of radiating lineages (e.g. Guerrero & Tye 2009; Takahashi &
Koblmüller 2011) with few studies investigating parasitic (e.g. Karvonen
& Seehausen 2012) or mutualistic interactions (e.g. Litsios et
al. 2012).
Metazoan parasites can be of particular interest in this context due to
their intimate associations that profoundly affect host fitness (Kutzer
& Armitage 2016) and shape biological communities (Gómez & Nichols
2013). For instance, host range, a key characteristic of parasite
ecology (Poulin et al. 2011), is influenced by environmental
factors as well as the hosts’ evolutionary history (Poulin et al.2011). Integrative measures account for host ecology as well as
evolutionary history (Clark & Clegg 2017), e.g. functional-phylogenetic
distance metrics (FPDist) (Cadotte et al. 2013). However, the
frequency of recorded host switches (see Agosta et al. 2010)
suggests that such metrics fail to fully grasp the niche limitations of
parasites. 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 ) (Agosta et al. 2010), i.e. host
species, and through the opportunity emerging from the rise and fall of
ecological barriers (D’Bastiani et al. 2020), e.g. after
anthropogenic introductions (Brooks et al. 2021). Therefore,realised host repertoires do not equate to the full repertoires
of host species that can potentially be infected (fundamentalhost repertoires ) (Braga et al. 2020). The oscillation of
host repertoires resulting from ecological fitting and opportunity has
been termed the Stockholm Paradigm (Brooks et al. 2019)
and is considered a major source 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 network analyses to characterise and visualise species
interactions (Pocock et al. 2016). Furthermore, increasing
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 (Fuet 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 (Blüthgen et al. 2008; Poulin et al.2011). Many of the metrics describing the structure of species networks,
such as nestedness, connectance, and specialisation, have been optimised
to account for interaction strength, i.e. the frequency of interactions
(see Blüthgen et al. 2008). Undetected links and interaction
strengths could 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 networks of multiple host lineages
evolved through adaptive radiation using network theory and NLP. As
model system, we selected one of the best-known examples for explosive
speciation: African cichlid fishes. The approximately 2000 species
residing in the East African Great Lakes are the result of multiple
adaptive radiation events (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-Lauferet al. 2021a). One parasite lineage infecting African cichlids,
monogenean flatworms belonging to Cichlidogyrus Paperna, 1960
sensu Wu et al. (2007) (including Scutogyrus Pariselle &
Euzet, 1995), is particularly species-rich. Currently, 143 species are
described that infect the gills of 139 cichlid and five non-cichlid
species (see Cruz-Laufer et al. 2021a). Monogenean 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 comparing
meta-communities of the African Great Lakes that are strongly shaped by
adaptive radiation events to those outside these lakes. First, we use
network metrics to characterise the structure of the observed networks
and meta-communities. Second, we assess host repertoires 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 for predicting
host-parasite interactions. 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 observed
host repertoires correlate with functional or phylogenetic host
diversity, (ii) what can network link prediction models reveal about
predictors of these interactions, (iii) how are
cichlid-Cichlidogyrus meta-communities structured when hosts
evolve through adaptive radiation?
Materials & methods