Limitations and opportunities: Sampling bias, missing data, and
databases
Through a literature survey, we produced the most extensive study on
species interaction of cichlid fishes or any other lineage of adaptive
radiations to date. We also inferred patterns in the community structure
through a series of network analytical methods ranging from more
traditional to new approaches. However, this study has also limitations.
We suggest addressing these limitations through the following measures:
- Because of the sampling bias in cichlid-Cichlidogyrusinteractions studies towards economically relevant hosts (Cruz-Lauferet al. 2021a), the present data likely give an incomplete
picture as confirmed by the plug-and-play algorithm (Fig. 5b).
Null models can account for this issue but, ultimately, taxonomic
research remains essential for closing knowledge gaps on
cichlid-Cichlidogyrus interactions. Data generated from such
studies should be gathered in online databases, e.g. the Global
Interaction Database GLOBI (Poelen et al. 2014), to increase
accessibility of this study system for research communities worldwide
(Molloy 2011; Upham et al. 2021).
- The host-parasite interaction analysed here are pooled from different
geographic regions and climatic conditions. Future studies should also
account for geographical distribution as geographic coordinates to
infer local interaction patterns as we expect environmental parameters
to vary considerably across species ranges.
- New models for NLP are continuously 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 model 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. However, wildlife host-parasite infection data often
include additional information in the form of prevalence data, i.e.
ratios of uninfected host specimens. This information could be
included in future models.
Addressing these limitations will increase the potential of cichlids and
their gill parasites belonging to Cichlidogyrus as an upcoming
model system for eco-evolutionary studies in host-parasite systems. 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 repertoire might be more extensive than the present
data suggest. Second, network link prediction algorithms show that
network structure is shaped by ecological opportunity induced by habitat
sharing but host and parasite evolution and host trophic level are also
influential factors. Our results demonstrate that
cichlid-Cichlidogyrus data can be utilised for a range of network
analyses because of a substantial amount of interaction data, and
molecular and morphological information for hosts and parasites. We
encourage researchers to reuse the 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 is funded by Hasselt University
(BOF19OWB02) and MPMV receives support from the Special Research Fund of
Hasselt University (BOF20TT06).
References
Agosta, S.J. & Brooks, D.R. (2020). Chapter 10: The Stockholm Paradigm.
In: The major metaphors of evolutionary: Darwinism then and now ,
Evolutionary biology – new perspectives on its development, vol. 2
(eds. Agosta, S.J. & Brooks, D.R.). Springer, Cham, Germany, pp.
219–242.
Agosta, S.J., Janz, N. & Brooks, D.R. (2010). How specialists can be
generalists: resolving the and “parasite paradox” and implications for
emerging infectious disease. Zoologia , 27, 151–162.
Algar, A.C., Kerr, J.T. & Currie, D.J. (2009). Evolutionary constraints
on regional faunas: whom, but not how many. Ecol. Lett. , 12,
57–65.
Allesina, S. & Pascual, M. (2008). Network structure, predator–prey
modules, and stability in large food webs. Theor. Ecol. , 1,
55–64.
Almeida-Neto, M. & Ulrich, W. (2011). A straightforward computational
approach for measuring nestedness using quantitative matrices.Environ. Model. Softw. , 26, 173–178.
de Araújo, W.S., de Freitas, É.V.D., Silveira, L.T. & Daud, R.D.
(2020). Network structure of interactions between phytophagous mites and
their host-plants in natural ecosystems in Brazil. Syst. Appl.
Acarol. , 25, 821–832.
de Araújo, W.S. & Maia, V.C. (2021). Topological structure of a
tritrophic network composed of host plants, gall‐inducing insects and
parasitoids in a restinga area in Brazil. Entomol. Sci. , 24,
201–216.
de Azevedo, T.M.P., Martins, M.L., Bozzo, F.R. & de Moraes, F.R.
(2006). Haematological and gill responses in parasitized tilapia from
valley of Tijucas River, SC, Brazil. Sci. Agric. , 63, 115–120.
Bellay, S., de Oliveira, E.F., Almeida-Neto, M., Abdallah, V.D., de
Azevedo, R.K., Takemoto, R.M., et al. (2015). The patterns of
organisation and structure of interactions in a fish-parasite network of
a neotropical river. Int. J. Parasitol. , 45, 549–557.
Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. (2002).
Quantitative descriptors of food-web matrices. Ecology , 83,
2394–2407.
Birgi, E. & Euzet, L. (1983). Monogènes parasites des poissons des eaux
douces du Cameroun. Présence des genres Cichlidogyrus etDactylogyrus chez Aphyosemion (Cyprinodontidae).Bull. la Soc. Zool. Fr. , 108, 101–106.
Birgi, E. & Lambert, A. (1986). Présence chez un Nandidae (Téléostéen),
Polycentropsis abbreviata Boulenger, 1901, du genre Cichlidogyrus
(Monogenea, Monopisthocotylea, Ancyrocephalidae). Ann. Parasitol.
Hum. Comp. , 61, 521–528.
Blažek, R., Polačik, M., Smith, C., Honza, M., Meyer, A. & Reichard, M.
(2018). Success of cuckoo catfish brood parasitism reflects
coevolutionary history and individual experience of their cichlid hosts.Sci. Adv. , 4, eaar4380.
Blondel, V.D., Guillaume, J.L., Lambiotte, R. & Lefebvre, E. (2008).
Fast unfolding of communities in large networks. J. Stat. Mech.
Theory Exp. , 2008.
Blüthgen, N., Fründ, J., Vazquez, D.P. & Menzel, F. (2008). What do
interaction network metrics tell us about specialization and biological
traits? Ecology , 89, 3387–3399.
Blüthgen, N., Menzel, F. & Blüthgen, N. (2006). Measuring
specialization in species interaction networks. BMC Ecol. , 6, 9.
Blüthgen, N., Menzel, F., Hovestadt, T., Fiala, B. & Blüthgen, N.
(2007). Specialization, constraints, and conflicting interests in
mutualistic networks. Curr. Biol. , 17, 341–346.
Boettiger, C., Lang, D.T. & Wainwright, P.C. (2012). rfishbase:
exploring, manipulating and visualizing FishBase data from R. J.
Fish Biol. , 81, 2030–2039.
Bogich, T.L., Funk, S., Malcolm, T.R., Chhun, N., Epstein, J.H., Chmura,
A.A., et al. (2013). Using network theory to identify the causes
of disease outbreaks of unknown origin. J. R. Soc. Interface , 10.
Bordes, F., Caron, A., Blasdell, K., de Garine-Wichatitsky, M. &
Morand, S. (2017). Forecasting potential emergence of zoonotic diseases
in South-East Asia: network analysis identifies key rodent hosts.J. Appl. Ecol. , 54, 691–700.
Braga, M.P., Araújo, S.B.L. & Boeger, W.A. (2014). Patterns of
interaction between Neotropical freshwater fishes and their gill
Monogenoidea (Platyhelminthes). Parasitol. Res. , 113, 481–490.
Braga, M.P., Janz, N., Nylin, S., Ronquist, F. & Landis, M.J. (2021).
Phylogenetic reconstruction of ancestral ecological networks through
time for pierid butterflies and their host plants. Ecol. Lett.
Braga, M.P., Landis, M.J., Nylin, S., Janz, N. & Ronquist, F. (2020).
Bayesian inference of ancestral host-parasite interactions under a
phylogenetic model of host repertoire evolution. Syst. Biol. , 69,
1149–1162.
Brooks, D.R., Hoberg, E.P. & Boeger, W.A. (2019). The Stockholm
Paradigm: climate change and emerging disease . University of Chicago
Press, Chicago, USA.
Brooks, D.R., Hoberg, E.P., Boeger, W.A. & Trivellone, V. (2021).
Emerging infectious disease: An underappreciated area of strategic
concern for food security. Transbound. Emerg. Dis.
Burbrink, F.T., Lorch, J.M. & Lips, K.R. (2017). Host susceptibility to
snake fungal disease is highly dispersed across phylogenetic and
functional trait space. Sci. Adv. , 3, e1701387.
Cadotte, M., Albert, C.H. & Walker, S.C. (2013). The ecology of
differences: Assessing community assembly with trait and evolutionary
distances. Ecol. Lett. , 16, 1234–1244.
Campbell, V., Legendre, P. & Lapointe, F.J. (2011). The performance of
the Congruence Among Distance Matrices (CADM) test in phylogenetic
analysis. BMC Evol. Biol. , 11.
Candes, E.J. & Plan, Y. (2010). Matrix completion with noise.Proc. IEEE , 98, 925–936.
Clark, N.J. & Clegg, S.M. (2017). Integrating phylogenetic and
ecological distances reveals new insights into parasite host
specificity. Mol. Ecol. , 26, 3074–3086.
Cooper, W.J., Parsons, K., McIntyre, A., Kern, B., McGee-Moore, A. &
Albertson, R.C. (2010). Bentho-pelagic divergence of cichlid feeding
architecture was prodigious and consistent during multiple adaptive
radiations within African Rift-Lakes. PLoS One , 5, e9551.
Cruz-Laufer, A.J., Artois, T., Smeets, K., Pariselle, A. & Vanhove,
M.P.M. (2021a). The cichlid–Cichlidogyrus network: a blueprint
for a model system of parasite evolution. Hydrobiologia , 848,
3847–3863.
Cruz-Laufer, A.J., Pariselle, A., Jorissen, M.W.P., Muterezi Bukinga,
F., Al Assadi, A., Van Steenberge, M., et al. (2021b). Somewhere
I belong: phylogenetic comparative methods and machine learning to
investigate the evolution of a species-rich lineage of parasites
[preprint]. bioRxiv , 10.1101/2021.03.22.435939.
Csardi, G. & Nepusz, T. (2006). The igraph software package for complex
network research. InterJournal, Complex Syst. 1695 .
D’Bastiani, E., Campiaõ, K.M., Boeger, W.A. & Araújo, S.B.L. (2020).
The role of ecological opportunity in shaping host-parasite networks.Parasitology , 147, 1452–1460.
Dallas, T., Park, A.W. & Drake, J.M. (2017). Predicting cryptic links
in host-parasite networks. PLoS Comput. Biol. , 13, e1005557.
Dallas, T. & Presley, S.J. (2014). Relative importance of host
environment, transmission potential and host phylogeny to the structure
of parasite metacommunities. Oikos , 123, 866–874.
Desdevises, Y., Morand, S. & Legendre, P. (2002). Evolution and
determinants of host specificity in the genus Lamellodiscus(Monogenea). Biol. J. Linn. Soc. , 77, 431–443.
Dormann, C.F. (2011). How to be a specialist? Quantifying specialisation
in pollination networks. Netw. Biol. , 1, 1–20.
Dormann, C.F., Fründ, J., Blüthgen, N. & Gruber, B. (2009). Indices,
graphs and null models: analyzing bipartite ecological networks.Open Ecol. J. , 2, 7–24.
Dormann, C.F., Gruber, B. & Fründ, J. (2008). Introducing the bipartite
package: analysing ecological networks. R News , 8, 8–11.
Duncan, B.L. (1973). Cichlidogyrus sclerosus Paperna and Thurston
from cultured Tilapia mossambica . Philipp. J. Biol. , 2,
154–158.
Duponchelle, F., Paradis, E., Ribbink, A.J. & Turner, G.F. (2008).
Parallel life history evolution in mouthbrooding cichlids from the
African Great Lakes. Proc. Natl. Acad. Sci. U. S. A. , 105,
15475–15480.
Ekroth, A.K.E., Rafaluk-Mohr, C. & King, K.C. (2019). Host genetic
diversity limits parasite success beyond agricultural systems: a
meta-analysis. Proc. R. Soc. B Biol. Sci. , 286.
Esser, H.J., Herre, E.A., Blüthgen, N., Loaiza, J.R., Bermúdez, S.E. &
Jansen, P.A. (2016). Host specificity in a diverse Neotropical tick
community: an assessment using quantitative network analysis and host
phylogeny. Parasit. Vectors , 9, 372.
Eyngor, M., Zamostiano, R., Tsofack, J.E.K., Berkowitz, A., Bercovier,
H., Tinman, S., et al. (2014). Identification of a novel RNA
virus lethal to tilapia. J. Clin. Microbiol. , 52, 4137–4146.
Fathi, M., Dickson, C., Dickson, M., Leschen, W., Baily, J., Muir, F.,et al. (2017). Identification of Tilapia Lake Virus in Egypt in
Nile tilapia affected by ‘summer mortality’ syndrome.Aquaculture , 473, 430–432.
Fitzpatrick, J.L. (2013). Global food security: the impact of veterinary
parasites and parasitologists. Vet. Parasitol. , 195, 233–248.
Froese, R. & Pauly, D. (Eds.). (2000). FishBase 2000: Concepts,
designs and data sources . ICLARM, Los Baños, Laguna, Philippines.
Fründ, J., Mccann, K.S. & Williams, N.M. (2016). Sampling bias is a
challenge for quantifying specialization and network structure: Lessons
from a quantitative niche model. Oikos , 125, 502–513.
Fu, X., Seo, E., Clarke, J. & Hutchinson, R.A. (2019). Link prediction
under imperfect detection: collaborative filtering for ecological
networks. IEEE Trans. Knowl. Data Eng. , 33, 3117–3128.
Fussmann, G.F., Loreau, M. & Abrams, P.A. (2007). Eco-evolutionary
dynamics of communities and ecosystems. Funct. Ecol. , 21,
465–477.
Gobbin, T.P., Vanhove, M.P.M., Seehausen, O., Maan, M.E. & Pariselle,
A. (2021). Four new species of Cichlidogyrus (Platyhelminthes,
Monogenea, Dactylogyridae) from Lake Victoria haplochromine cichlid
fishes, with the redescription of C. bifurcatus and C.
longipenis [preprint]. bioRxiv , 2021.01.29.428376.
Gómez, A. & Nichols, E. (2013). Neglected wild life: parasitic
biodiversity as a conservation target. Int. J. Parasitol.
Parasites Wildl. , 2, 222–227.
Gower, J.C. (1971). A general coefficient of similarity and some of its
properties. Biometrics , 27, 857.
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. (2014). Circlize
implements and enhances circular visualization in R.Bioinformatics , 30, 2811–2812.
Honaker, J., King, G. & Blackwell, M. (2011). Amelia II: a program for
missing data. J. Stat. Softw. , 45, 1–45.
Hoyal Cuthill, J. & Charleston, M. (2012). Phylogenetic codivergence
supports coevolution of mimetic Heliconius butterflies.PLoS One , 7, e36464.
Hulsey, C.D., Alfaro, M.E., Zheng, J., Meyer, A. & Holzman, R. (2019).
Pleiotropic jaw morphology links the evolution of mechanical modularity
and functional feeding convergence in Lake Malawi cichlids. Proc.
R. Soc. B Biol. Sci. , 286.
Irvine, K., Etiegni, C.A. & Weyl, O.L.F. (2019). Prognosis for
long-term sustainable fisheries in the African Great Lakes. Fish.
Manag. Ecol. , 26, 413–425.
Jacquemyn, H., Merckx, V., Brys, R., Tyteca, D., Cammue, B.P.A., Honnay,
O., et al. (2011). Analysis of network architecture reveals
phylogenetic constraints on mycorrhizal specificity in the genusOrchis (Orchidaceae). New Phytol. , 192, 518–528.
Janz, N. & Nylin, S. (2008). The oscillation hypothesis of host-plant
range and speciation. In: Specialization, speciation, and
radiation: the evolutionary biology of herbivorous insects . University
of California Press, pp. 203–215.
Jarkovský, J.J., Morand, S., Šimková, A. & Gelnar, M. (2004).
Reproductive barriers between congeneric monogenean parasites
(Dactylogyrus: Monogenea): attachment apparatus morphology or copulatory
organ incompatibility? Parasitol. Res. , 92, 95–105.
Jenkins, E.J., Simon, A., Bachand, N. & Stephen, C. (2015). Wildlife
parasites in a One Health world. Trends Parasitol. , 31, 174–180.
Jiménez-Garcia, M.I., Vidal-Martínez, V.M., Lopez-Jiménez, S.,
Jiménez-García, M.I., Vidal-Martínez, V.M. & López-Jiménez, S. (2001).
Monogeneans in introduced and native cichlids in México: Evidence for
transfer. J. Parasitol. , 87, 907.
Jorissen, M.W.P., Huyse, T., Pariselle, A., Wamuini Lunkayilakio, S.,
Muterezi Bukinga, F., Chocha Manda, A., et al. (2020). Historical
museum collections help detect parasite species jumps after tilapia
introductions in the Congo Basin. Biol. Invasions , 11, 1123.
Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H.,
Ackerly, D.D., et al. (2010). Picante: R tools for integrating
phylogenies and ecology. Bioinformatics , 26, 1463–1464.
Kmentová, N., Gelnar, M., Mendlová, M., Van Steenberge, M., Koblmüller,
S. & Vanhove, M.P.M. (2016). Reduced host-specificity in a parasite
infecting non-littoral Lake Tanganyika cichlids evidenced by
intraspecific morphological and genetic diversity. Sci. Rep. , 6,
39605.
Koblmüller, S., Albertson, R.C., Genner, M.J., Sefc, K.M. & Takahashi,
T. (2019). Preface: advances in cichlid research III: behavior, ecology,
and evolutionary biology. Hydrobiologia , 832, 1–8.
Kutzer, M.A.M. & Armitage, S.A.O. (2016). Maximising fitness in the
face of parasites: a review of host tolerance. Zoology , 119,
281–289.
Legendre, P. & Lapointe, F.-J. (2004). Assessing congruence among
distance matrices: single-malt scotch whiskies revisited. Aust. N.
Z. J. Stat. , 46, 615–629.
Lima Jr, D.P., Giacomini, H.C., Takemoto, R.M., Agostinho, A.A. & Bini,
L.M. (2012). Patterns of interactions of a large fish-parasite network
in a tropical floodplain. J. Anim. Ecol. , 81, 905–913.
Locke, S.A., Marcogliese, D.J. & Tellervo Valtonen, E. (2014).
Vulnerability and diet breadth predict larval and adult parasite
diversity in fish of the Bothnian Bay. Oecologia , 1, 253–262.
Locke, S.A., McLaughlin, J.D. & Marcogliese, D.J. (2013). Predicting
the similarity of parasite communities in freshwater fishes using the
phylogeny, ecology and proximity of hosts. Oikos , 122, 73–83.
López-Carretero, A., Díaz-Castelazo, C., Boege, K. & Rico-Gray, V.
(2014). Evaluating the spatio-temporal factors that structure network
parameters of plant-herbivore interactions. PLoS One , 9, e110430.
Marshall, B.E. (2018). Guilty as charged: Nile perch was the cause of
the haplochromine decline in Lake Victoria. Can. J. Fish. Aquat.
Sci. , 75, 1542–1559.
Martínez, V., Berzal, F. & Cubero, J.C. (2016). A survey of link
prediction in complex networks. ACM Comput. Surv. , 49, 69.
Messu Mandeng, F.D., Bilong Bilong, C.F., Pariselle, A., Vanhove,
M.P.M., Bitja Nyom, A.R. & Agnèse, J.F. (2015). A phylogeny ofCichlidogyrus spp. (Monogenea, Dactylogyridea) clarifies a
host-switch between fish families and reveals an adaptive component to
attachment organ morphology of this parasite genus. Parasit.
Vectors , 8, 582.
Molloy, J.C. (2011). The Open Knowledge Foundation: open data means
better science. PLoS Biol. , 9, e1001195.
Morand, S., Simková, A., Matejusová, I., Plaisance, L., Verneau, O. &
Desdevises, Y. (2002). Investigating patterns may reveal processes:
evolutionary ecology of ectoparasitic monogeneans. Int. J.
Parasitol. , 32, 111–119.
Mouillot, D., Krasnov, B.R. & Poulin, R. (2008). High intervality
explained by phylogenetic constraints in host-parasite webs.Ecology , 89, 2043–2051.
Murtagh, F. & Legendre, P. (2014). Ward’s hierarchical agglomerative
clustering method: Which algorithms implement Ward’s criterion? J.
Classif. , 31, 274–295.
Nunn, C.L. & Altizer, S.M. (2005). The global mammal parasite database:
an online resource for infectious disease records in wild primates.Evol. Anthropol. Issues, News, Rev. , 14, 1–2.
Oliveira, J.B.B.S., Faria, M.L., Borges, M.A.., Fagundes, M. & Araújo,
W.S. (2020). Comparing the plant–herbivore network topology of
different insect guilds in Neotropical savannas. Ecol. Entomol. ,
45, 406–415.
Page, R.D.M. (2003). Introduction. In: Tangled trees. Phylogeny,
cospeciation, and coevolution (ed. Page, R.D.M.). The University of
Chicago Press, Chicage, USA & London, UK, pp. 1–21.
Paperna, I. (1960). Studies on monogenetic trematodes in Israel. 2.
Monogenetic trematodes of cichlids. Bamidgeh , 12, 20–33.
Paradis, E. & Schliep, K. (2019). Ape 5.0: An environment for modern
phylogenetics and evolutionary analyses in R. Bioinformatics , 35,
526–528.
Pariselle, A., Morand, S., Deveney, M.R. & Pouyaud, L. (2003). Parasite
species richness of closely related hosts: historical scenario and
“genetic” hypothesis. In: Taxonomie, écologie et évolution des
métazoaires parasites: Livre-hommage à Louis Euzet (eds. Combes, C.,
Jourdane, J., Ducreux-Modat, A. & Pages, J.-R.). Presses Universitaires
de Perpignan, Perpignan, France, pp. 147–166.
Patefield, W.M. (1981). Algorithm AS 159: An efficient method of
generating random R × C tables with given row and column totals.Appl. Stat. , 30, 91–97.
Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S. & Daniel, H. (2009).
On the challenge of treating various types of variables: application for
improving the measurement of functional diversity. Oikos , 118,
391–402.
Pennell, M.W., Eastman, J.M., Slater, G.J., Brown, J.W., Uyeda, J.C.,
Fitzjohn, R.G., et al. (2014). Geiger v2.0: an expanded suite of
methods for fitting macroevolutionary models to phylogenetic trees.Bioinformatics , 30, 2216–2218.
Pocock, M.J.O., Evans, D.M., Fontaine, C., Harvey, M., Julliard, R.,
McLaughlin, Ó., et al. (2016). The visualisation of ecological
networks, and their use as a tool for engagement, advocacy and
management. Adv. Ecol. Res. , 54, 41–85.
Poelen, J.H., Simons, J.D. & Mungall, C.J. (2014). Global biotic
interactions: an open infrastructure to share and analyze
species-interaction datasets. Ecol. Inform. , 24, 148–159.
Poulin, R. (2002). The evolution of monogenean diversity. Int. J.
Parasitol. , 32, 245–254.
Poulin, R. (2010). Network analysis shining light on parasite ecology
and diversity. Trends Parasitol. , 26, 492–498.
Poulin, R. (2014). Parasite biodiversity revisited: frontiers and
constraints. Int. J. Parasitol. , 44, 581–589.
Poulin, R., Krasnov, B.R. & Mouillot, D. (2011). Host specificity in
phylogenetic and geographic space. Trends Parasitol. , 27,
355–361.
R Core Team. (2021). R: a language and environment for statistical
computing . R Foundation for Statistical Computing, Vienna, Austria.
Available at: https://www.r-project.org/. Last accessed 5 July 2021.
Rezende, E.L., Lavabre, J.E., Guimarães, P.R., Jordano, P. & Bascompte,
J. (2007). Non-random coextinctions in phylogenetically structured
mutualistic networks. Nature , 448, 925–928.
Ronco, F., Matschiner, M., Böhne, A., Boila, A., Büscher, H.H., El
Taher, A., et al. (2021). Drivers and dynamics of a massive
adaptive radiation in cichlid fishes. Nature , 589, 76–81.
Royle, J.A. (2004). N ‐Mixture models for estimating population
size from spatially replicated counts. Biometrics , 60, 108–115.
Salzburger, W. (2018). Understanding explosive diversification through
cichlid fish genomics. Nat. Rev. Genet. , 19, 705–717.
Salzburger, W., van Bocxlaer, B. & Cohen, A.S. (2014). Ecology and
evolution of the African Great Lakes and their faunas. Annu. Rev.
Ecol. Evol. Syst. , 45, 519–545.
Sasal, P. & Morand, S. (1998). Comparative analysis: a tool for
studying monogenean ecology and evolution. Int. J. Parasitol. ,
28, 1637–1644.
Sasal, P., Trouvé, S., Müller-Graf, C. & Morand, S. (1999). Specificity
and host predictability: a comparative analysis among monogenean
parasites of fish. J. Anim. Ecol. , 68, 437–444.
Schedel, F.D.B., Musilova, Z. & Schliewen, U.K. (2019). East African
cichlid lineages (Teleostei: Cichlidae) might be older than their
ancient host lakes: new divergence estimates for the East African
cichlid radiation. BMC Evol. Biol. , 19, 1–25.
Seehausen, O. (2006). African cichlid fish: a model system in adaptive
radiation research. Proc. R. Soc. B Biol. Sci. , 273, 1987–1998.
Segar, S.T., Fayle, T.M., Srivastava, D.S., Lewinsohn, T.M., Lewis,
O.T., Novotny, V., et al. (2020). The role of evolution in
shaping ecological networks. Trends Ecol. Evol. , 35, 454–466.
Simard, S.W., Beiler, K.J., Bingham, M.A., Deslippe, J.R., Philip, L.J.
& Teste, F.P. (2012). Mycorrhizal networks: mechanisms, ecology and
modelling. Fungal Biol. Rev. , 26, 39–60.
Šimková, A., Desdevises, Y., Gelnar, M. & Morand, S. (2001).
Morphometric correlates of host specificity in Dactylogyrusspecies (Monogenea) parasites of European Cyprinid fish.Parasitology , 123, 169–177.
Šimková, A., Řehulková, E., Rasoloariniaina, J.R., Jorissen, M.W.P.,
Scholz, T., Faltýnková, A., et al. (2019). Transmission of
parasites from introduced tilapias: a new threat to endemic Malagasy
ichthyofauna. Biol. Invasions , 21, 803–819.
Soares, R.G.S., Ferreira, P.A. & Lopes, L.E. (2017). Can
plant-pollinator network metrics indicate environmental quality?Ecol. Indic. , 78, 361–370.
Thébault, E. & Fontaine, C. (2008). Does asymmetric specialization
differ between mutualistic and trophic networks? Oikos , 117,
555–563.
Thompson, R.C.A. (2013). Parasite zoonoses and wildlife: One Health,
spillover and human activity. Int. J. Parasitol. , 43, 1079–1088.
Timi, J.T., Rossin, M.A., Alarcos, A.J., Braicovich, P.E., Cantatore,
D.M.P. & Lanfranchi, A.L. (2011). Fish trophic level and the similarity
of non-specific larval parasite assemblages. Int. J. Parasitol. ,
41, 309–316.
Toju, H., Yamamichi, M., Guimarães, P.R., Olesen, J.M., Mougi, A.,
Yoshida, T., et al. (2017). Species-rich networks and
eco-evolutionary synthesis at the metacommunity level. Nat. Ecol.
Evol. , 1, 0024.
Upham, N., Poelen, J.H., Paul, D.L., Groom, Q., Simmons, N.B., Vanhove,
M.P.M., et al. (2021). Liberating host-virus knowledge from
COVID-19 lockdown [preprint]. EcoEvoRxiv ,
10.32942/OSF.IO/TXEKQ.
Vanhove, M.P.M., Hablützel, P.I., Pariselle, A., Šimková, A., Huyse, T.
& Raeymaekers, J.A.M. (2016). Cichlids: a host of opportunities for
evolutionary parasitology. Trends Parasitol. , 32, 820–832.
Vanhove, M.P.M., Pariselle, A., Van Steenberge, M., Raeymaekers, J.A.M.,
Hablützel, P.I., Gillardin, C., et al. (2015). Hidden
biodiversity in an ancient lake: phylogenetic congruence between Lake
Tanganyika tropheine cichlids and their monogenean flatworm parasites.Sci. Rep. , 5, 13669.
Vázquez, D.P., Melián, C.J., Williams, N.M., Blüthgen, N., Krasnov, B.R.
& Poulin, R. (2007). Species abundance and asymmetric interaction
strength in ecological networks. Oikos , 116, 1120–1127.
Vázquez, D.P., Poulin, R., Krasnov, B.R. & Shenbrot, G.I. (2005).
Species abundance and the distribution of specialization in
host-parasite interaction networks. J. Anim. Ecol. , 74, 946–955.
Vignon, M., Pariselle, A. & Vanhove, M.P.M. (2011). Modularity in
attachment organs of African Cichlidogyrus (Platyhelminthes:
Monogenea: Ancyrocephalidae) reflects phylogeny rather than host
specificity or geographic distribution. Biol. J. Linn. Soc. , 102,
694–706.
Vizentin-Bugoni, J., Maruyama, P.K., de Souza, C.S., Ollerton, J., Rech,
A.R. & Sazima, M. (2018). Plant-pollinator networks in the tropics: a
review. In: Ecological networks in the tropics . Springer
International Publishing, pp. 73–91.
Walker, S. (2014). funphylocom: Functional traits, phylogenies,
communities, simulations. R package version 1.1/r211 . Available at:
https://r-forge.r-project.org/projects/multitable. Last accessed 28
March 2021.
Wang, P., Xu, B.W., Wu, Y.R. & Zhou, X.Y. (2015). Link prediction in
social networks: the state-of-the-art. Sci. China Inf. Sci. , 58,
011101:1-011101:38.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis .
Use R! 2nd edn. Springer, New York, USA.
Wilson, J.R., Saunders, R.J. & Hutson, K.S. (2019). Parasites of the
invasive tilapia Oreochromis mossambicus : evidence for
co-introduction. Aquat. Invasions , 14, 332–349.
Wu, X.Y., Zhu, X.Q., Xie, M.Q. & Li, A.X. (2006). The radiation ofHaliotrema (Monogenea: Dactylogyridae: Ancyrocephalinae):
molecular evidence and explanation inferred from LSU rDNA sequences.Parasitology , 132, 659–668.
Wu, X.Y., Zhu, X.Q., Xie, M.Q. & Li, A.X. (2007). The evaluation for
generic-level monophyly of Ancyrocephalinae (Monogenea, Dactylogyridae)
using ribosomal DNA sequence data. Mol. Phylogenet. Evol. , 44,
530–544.
Yu, G., Lam, T.T.-Y., Zhu, H. & Guan, Y. (2018). Two methods for
mapping and visualizing associated data on phylogeny using ggtree.Mol. Biol. Evol. , 35, 3041–3043.
Yu, G., Smith, D.K., Zhu, H., Guan, Y. & Lam, T.T.-Y. (2017). ggtree :
An R package for visualization and annotation of phylogenetic trees with
their covariates and other associated data. Methods Ecol. Evol. ,
8, 28–36.
Zhao, Y., Wu, Y.-J., Levina, E. & Zhu, J. (2017). Link prediction for
partially observed networks. J. Comput. Graph. Stat. , 26,
725–733.
List of table headers
Table 1. Selection of 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 graph areCoptodon guineensis (Günther, 1862) and Cichlidogyrus
gallus Pariselle & Euzet, 1995.
Figure 2. Cichlid-Cichlidogyrus species network. (A) Whole
network with unweighted links and for the most species-rich communities
(n > 10) highlighted with 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), ‘Ophthalmotilapia ’
(Op) and ‘Tilapia sparrmanii’ (TS) cluster. Many small
(meta-)communities that fall outside these four groups (C-shaped
cluster) are completely unconnected to these large groups or each other.
(B) Chord diagrams of the LV cluster, the most species-rich
meta-community in the network (in terms of host and parasite species).
(C) Five other species-rich meta-communities involving species ofCichlidogyrus and Scutogyrus with links weighted by number
of observed infections communities. Unlike the LV cluster,
communities CZ, ON, He, and TS are characterised by
sampling bias towards few, economically relevant host species, e.g.Coptodon zillii , Oreochromis niloticus , Hemichromis
fasciatus, and Tilapia 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 ´Oreochromis niloticus ’ (ON),
‘Hemichromis ’ (He), and ‘Coptodon zillii ’ (CZ). Most
values 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 including
missingness map of input variables (a), and heat map of host-parasite
links with rows and columns order by numbers of observed interactions
(b) and bar plot of variable importance (c) predicted by theplug-and-play algorithm Dallas et al. (2017). Missingness
map illustrates significant gaps in the taxon coverage of phylogenetic
data and host standard lengths. Heat map shows that a large proportion
of host-parasite interactions likely remain undetected (highlighted in
colour) (for taxon labels, see Appendix S7). Variable importance graph
indicates that the ecosystem of the hosts is the most important
predictor of cichlid-Cichlidogyrus interactions.
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 six most species-rich meta-communities
presented in Fig. 2 with additional species labels. Host species names
are abbreviated with the first three letters of the genus name and the
first four letters of the species epithet. Parasite species names are
abbreviated with the first and first six letters respectively.
Appendix S5. Structural and phylogenetic host specificity indices of
species of Cichlidogyrus with more than four infected hosts
reported in peer-reviewed literature. Significance of these indices was
tested against null models NM1, NM2, and
NM3. Structural specificity is measured as
specialisation index di’, phylogenetic specificity is
measure as z-scores (standardised effect size) of average mean pairwise
distance (MPD) and mean nearest taxon distance (MNTD) of 100 parasite BI
tree topologies randomly selected from the post-burn in fraction.
Appendix S6. Functional phylogenetic distance (FPDist) plots of host
repertoires of all species of Cichlidogyrus not included in Fig.
4.
Appendix S7. Heat map of links predicted by the plug-and-playalgorithm with complete taxon labels. See Fig. 5c for simplified
version.
Table 1. Selection of 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. (2021). 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).