Opportunities and limitations: sampling bias, missing data, and databases
We produced the most extensive study on species interaction of cichlid fishes or any other lineage with adaptive radiations to date. Patterns of community structure were inferred through a series of network analytical methods ranging from more traditional to new approaches. Limitations could be addressed through the following measures:
Generally, the cichlid-Cichlidogyrus data serves as a study system eco-evolutionary studies because of a substantial amount of interaction, molecular, and morphological data for hosts and parasites. Addressing the limitations listed above might increase this potential. We were able to detect key mechanisms of ecology and evolution. First, the realised host repertoire is phylogenetically constrained as host range parameters are determined more by the host evolutionary history than by ecological parameters. However, recent host switches indicate that fundamental host repertoires might be more extensive than currently known. Second, network link prediction algorithms show that network structure is shaped by ecological opportunity induced by habitat sharing but host evolution, life style, and trophic level are also influential factors. Third, adaptive radiations of host lineages in Eastern Africa have created more specialised and potentially saturated meta-communities. Future studies should investigate whether our findings also apply in other host-parasite systems shaped by adaptive radiation. Therefore, we encourage researchers to reuse data provided here to diversify the portfolio of host-parasite interaction research in the future.
Acknowledgements
We would like to thank Walter A. Boeger for his extensive comments and thoughts on the manuscript. Data collection started within the BRAIN-be Pioneer Project BR/132/PI/TILAPIA (Belgian Federal Science Policy Office) under the supervision of Tine Huyse and Jos Snoeks and the Knowledge Management Centre project CiMonoWeb (Royal Museum for Central Africa) under the supervision of Tine Huyse with the kind help of Wouter Fannes. Part of the research leading to results presented in this publication was carried out with infrastructure funded by the European Marine Biological Research Centre (EMBRC) Belgium, Research Foundation – Flanders (FWO) project GOH3817N. AJCL (BOF19OWB02) and MPMV are funded by the Special Research Fund of Hasselt University (BOF20TT06). We thank the anonymous reviewers for suggesting improvements to the manuscript.
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List of table headers
Table 1. Evolutionary, ecological, and morphological parameters of hosts and parasites used for calculation of host habitat niche dendrogram and network link prediction (NLP) models. Host parameters were accessed in FishBase (Froese & Pauly 2000) and parasite parameters were reused from Cruz-Laufer et al. (2021b). To avoid overfitting NLP models, variable numbers per parameter were reduced through principial coordinate analyses (PCoA ) based on distance matrices of phylogenetic trees or dendrograms built through clustering methods (see number of PCoA axes used for NLP and their proportion of parameter variation in brackets).
List of figure captions
Figure 1. Ecological and evolutionary processes shape the structure of the cichlid-Cichlidogyrus network consisting of cichlid fishes, a model system for explosive speciation research, and the parasitic flatworms belonging to Cichlidogyrus infecting the gills of cichlid and few non-cichlid fishes. Species presented in the figure areCoptodon guineensis (Günther, 1862) and Cichlidogyrus gallus Pariselle & Euzet, 1995.
Figure 2. Cichlid-Cichlidogyrus species network. (A) Whole network with unweighted links and Lake Tanganyika (LT ), Lake Victoria regions (LV ), inferred species-rich communities (n > 10) highlighted in colours. Circles indicate host species and squares species of Cichlidogyrus . Meta-communities were detected using the Louvain cluster algorithm including the Lake Victoria (LV), ‘Coptodon zillii ’ (CZ), ‘Oreochromis niloticus’(ON), ‘Hemichromis ’ (He), and ‘Tilapia sparrmanii’ (TS) cluster. Many small unconnected clusters belong to LT . (B) Chord diagrams of the LT and LV clusters. (C) Four other species-rich meta-communities involving species of Cichlidogyrusand Scutogyrus with links weighted by number of observed infections communities. Unlike LT and LV , meta-communitiesCZ, ON, He, and TS are characterised by sampling bias towards few, economically relevant host species, e.g. Coptodon zillii , Oreochromis niloticus , Hemichromis fasciatus, andTilapia sparrmanii . Species names were omitted from (B) and (C) but are included in Appendix S4.
Figure 3. Changes of network metrics when only including natural host repertoires and geographical ranges of cichlid-Cichlidogyrusmeta-communities including Lake Victoria region (LV ), ´Oreochromis niloticus ’ (ON ), ‘Hemichromis ’ (He ), and ‘Coptodon zillii ’ (CZ ). Most values of the weighted nestedness based on overlap and decreasing fill (NODFw) (Almeida-Neto & Ulrich 2011), weighted connectance (Cw) (Bersier et al. 2002), specialisation asymmetry (SA) (Blüthgen et al. 2007), interaction evenness (Ei) (Bersier et al. 2002), and the standardised interaction diversity (H2’) (Blüthgenet al. 2006) that differed significantly from the null distributions (NM1, NM2) remained unchanged (see Appendix S1.2 for detailed discussion).
Figure 4. Functional-phylogenetic distances (FPDist) inferred from host repertoires of selected species of Cichlidogyrus calculated as mean pairwise distance (MPD) and mean nearest taxon distance (MNTD) weighted by abundancy of interactions (blue). FPDist matrices are a function of functional (FDist) and phylogenetic (PDist) distance matrices of the host species weighted by the parameter a . Shaded areas (grey) indicate 5% and 95% quantiles of 1000 null distributions resulting from taxon shuffling. If estimates fall outside the null distribution, they can be considered informative. Smaller values indicated higher functional-phylogenetic similarities of host repertoires. A decreasing trend for FPDist estimates indicates that host communities are more phylogenetically than ecologically similar. For plots of other species infecting at least two host species, see Appendix S6.
Figure 5. Network link prediction based on host [H] and parasite [P] data in the cichlid-Cichlidogyrus network, and Lake Tanganyika (LT ) and Lake Victoria regions (LV ) subnetworks including missingness map of input variables for whole networks (a), heat maps of host-parasite links (b), and bar plot of variable importances (c) predicted by the plug-and-play algorithm (Dallaset al. 2017). The missingness map illustrates significant gaps in the taxon coverage of phylogenetic data and host standard lengths. The heat maps shows that a large proportion of cichlid-Cichlidogyrusinteractions likely remain undetected (highlighted in colour) (for taxon labels, see Appendix S6) although most interactions of the studied organisms are most likely known for LT and LV . The variable importance graph indicates that the basins/basin types inhabited by the hosts are the most important predictor of cichlid-Cichlidogyrus interactions, but less so for LT andLV.Supporting information
Appendix S1. Other methods and results including phylogenetic reconstruction and structure of species-rich meta-communities in the cichlid-Cichlidogyrus system.
Appendix S2. GenBank accession numbers of DNA sequences used to render host phylogenetic distances.
Appendix S3. Host niche dendrograms resulting from different clustering algorithms.
Appendix S4. Chord diagrams of meta-communities presented in Fig. 2 with additional species labels. Host species names are abbreviated with the first three characters of the genus name and the first four charactersof the species epithet. Parasite species names are abbreviated with the first character and first six characters respectively.
Appendix S5. Functional phylogenetic distance (FPDist) plots of host repertoires of all species of Cichlidogyrus not included in Fig. 4.
Appendix S6. Heat maps of links predicted by the plug-and-playalgorithm with complete taxon labels. See Fig. 5c for simplified version.