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