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:
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).