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:
- Because of the sampling bias in cichlid-Cichlidogyrusinteractions studies towards economically relevant hosts (Cruz-Lauferet al. 2021a), the data likely give an incomplete picture as
confirmed by NLP (Fig. 5b). Null models can account for this issue,
but taxonomic research remains ultimately essential for closing
knowledge gaps. Data generated from such studies should be gathered in
online databases, e.g. the Global Interaction Database GLOBI (Poelenet al. 2014), to improve access to interaction data for
research communities worldwide (Molloy 2011; Upham et al.2021).
- The basin/basin-type parameter analysed here only summarises the
entire geographical range of the hosts. Future studies should also
account for geographical distribution as geocoordinates to infer local
interaction patterns as we expect climates to vary across basins and
species ranges.
- New models for NLP are being developed and employed in an increasing
number of fields (Martínez et al. 2016). We suggest that a
streamlined software package or library targeted at ecological
research could simplify implementation for ecologists.
- The NLP algorithms applied here differentiate between true (impossible
or ‘forbidden’ links) and false negatives (undetected links) (Dallaset al. 2017; Fu et al. 2019) among unobserved
interactions. Wildlife host-parasite infection data regularly include
prevalence data, i.e. ratios of uninfected host specimens. This
information could be incorporated into future models.
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.