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Generalism in species interactions is more the consequence than the cause of ecological success

Nature Ecology & Evolution volume 8, pages 1602–1611 (2024)Cite this article

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Abstract

Generalism in resource use is commonly considered a critical driver of population success, species distribution and extinction risk. This idea can be questioned as generalism may be a result rather than the cause of species abundance and range size. We tested these contrasting causal hypotheses focusing on host use in three databases encompassing approximately 44,000 mutualistic (hummingbird–plant), commensalistic (lichen–plant) and parasitic (flea–mammal) interactions in 617 ecological communities across the Americas and Eurasia. Across all interaction types, our analyses indicated that range size and abundance influence the probability of encountering hosts and set the arena for species to express generalism potentials or adapt to new hosts. Hence, our findings support the hypothesis that generalism is a consequence of species ecological success. This highlights the importance of ecological opportunity in driving species characteristics considered key for their survival and conservation.

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Fig. 1: Different definitions of generalism allow the direction in the relationship between generalism and species success to be assessed.
Fig. 2: The hypothesis of generalism being a consequence of species success is better supported in SEMs.
Fig. 3: The geographical co-occurrence of hosts determines the resource space for hummingbirds and fleas, while host evolutionary history is the only determinant for lichens.

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Data availability

The data required to reproduce the results of this study are available via figshare at https://figshare.com/articles/dataset/Data_and_code_for_Hurtado_et_al_2024/26023627 (ref. 60).

Code availability

The code required to reproduce the results of this study is available via figshare at https://figshare.com/articles/dataset/Data_and_code_for_Hurtado_et_al_2024/26023627 (ref. 60).

References

  1. Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization? Front. Ecol. Environ. 9, 222–228 (2011).

    Google Scholar 

  2. Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).

    CAS  PubMed  Google Scholar 

  3. Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).

    Google Scholar 

  4. Carscadden, K. A. et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q. Rev. Biol. 95, 179–214 (2020).

    Google Scholar 

  5. Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124, 255–279 (1984).

    Google Scholar 

  6. Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Evol. Syst. 19, 207–233 (1988).

    Google Scholar 

  7. Schluter, D. The Ecology of Adaptive Radiation (OUP, 2000).

    Google Scholar 

  8. Poisot, T., Bever, J. D., Nemri, A., Thrall, P. H. & Hochberg, M. E. A conceptual framework for the evolution of ecological specialisation. Ecol. Lett. 14, 841–851 (2011).

    PubMed  PubMed Central  Google Scholar 

  9. Slatyer, R. A., Hirst, M. & Sexton, J. P. Niche breadth predicts geographical range size: a general ecological pattern. Ecol. Lett. 16, 1104–1114 (2013).

    PubMed  Google Scholar 

  10. Lancaster, L. T. On the macroecological significance of eco-evolutionary dynamics: the range shift–niche breadth hypothesis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20210013 (2022).

    PubMed  PubMed Central  Google Scholar 

  11. Song, C., Simmons, B. I., Fortin, M.-J. & Gonzalez, A. Generalism drives abundance: a computational causal discovery approach. PLoS Comput. Biol. 18, e1010302 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Ravigné, V. et al. Understanding the joint evolution of dispersal and host specialization using phytophagous arthropods as a model group. Biol. Rev. Camb. Philos. Soc. 99, 219–237 (2024).

    PubMed  Google Scholar 

  13. Fox, L. R. & Morrow, P. A. Specialization: species property or local phenomenon? Science 211, 887–893 (1981).

    CAS  PubMed  Google Scholar 

  14. Vázquez, D. P. et al. Species abundance and asymmetric interaction strength in ecological networks. Oikos 116, 1120–1127 (2007).

    Google Scholar 

  15. Fort, H., Vázquez, D. P. & Lan, B. L. Abundance and generalisation in mutualistic networks: solving the chicken‐and‐egg dilemma. Ecol. Lett. 19, 4–11 (2016).

    PubMed  Google Scholar 

  16. Johnson, C. N. Species extinction and the relationship between distribution and abundance. Nature 394, 272–274 (1998).

    CAS  Google Scholar 

  17. Gaston, K. J. et al. Abundance–occupancy relationships. J. Appl. Ecol. 37, 39–59 (2000).

    Google Scholar 

  18. Poulin, E., Palma, A. T. & Féral, J.-P. Evolutionary versus ecological success in Antarctic benthic invertebrates. Trends Ecol. Evol. 17, 218–222 (2002).

    Google Scholar 

  19. Gaston, K. J. The Structure and Dynamics of Geographic Ranges (Oxford Univ. Press, 2003).

  20. Wilson, R. J., Thomas, C. D., Fox, R., Roy, D. B. & Kunin, W. E. Spatial patterns in species distributions reveal biodiversity change. Nature 432, 393–396 (2004).

    CAS  PubMed  Google Scholar 

  21. Harnik, P. G., Simpson, C. & Payne, J. L. Long-term differences in extinction risk among the seven forms of rarity. Proc. Biol. Sci. 279, 4969–4976 (2012).

    PubMed  PubMed Central  Google Scholar 

  22. Futuyma, D. J. Food plant specialization and environmental predictability in Lepidoptera. Am. Nat. 110, 285–292 (1976).

    Google Scholar 

  23. Devictor, V. et al. Defining and measuring ecological specialization. J. Appl. Ecol. 47, 15–25 (2010).

    Google Scholar 

  24. Dalsgaard, B. et al. The influence of biogeographical and evolutionary histories on morphological trait‐matching and resource specialization in mutualistic hummingbird–plant networks. Funct. Ecol. 35, 1120–1133 (2021).

    Google Scholar 

  25. Cavender‐Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).

    PubMed  Google Scholar 

  26. Poulin, R., Krasnov, B. R. & Mouillot, D. Host specificity in phylogenetic and geographic space. Trends Parasitol. 27, 355–361 (2011).

    PubMed  Google Scholar 

  27. Cooper, N. et al. Phylogenetic host specificity and understanding parasite sharing in primates. Ecol. Lett. 15, 1370–1377 (2012).

    PubMed  Google Scholar 

  28. Calatayud, J. et al. Geography and major host evolutionary transitions shape the resource use of plant parasites. Proc. Natl Acad. Sci. USA 113, 9840–9845 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. USA 104, 19891–19896 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Gómez, J. M., Verdú, M. & Perfectti, F. Ecological interactions are evolutionarily conserved across the entire tree of life. Nature 465, 918–921 (2010).

    PubMed  Google Scholar 

  31. Galiana, N., Lurgi, M., Montoya, J. M., Araújo, M. B. & Galbraith, E. D. Climate or diet? The importance of biotic interactions in determining species range size. Glob. Ecol. Biogeogr. 32, 1178–1188 (2023).

    Google Scholar 

  32. Galiana, N., Arnoldi, J.-F., Mestre, F., Rozenfeld, A. & Araújo, M. B. Power laws in species’ biotic interaction networks can be inferred from co-occurrence data. Nat. Ecol. Evol. 8, 209–217 (2024).

    PubMed  Google Scholar 

  33. Jordano, P., Bascompte, J. & Olesen, J. M. Invariant properties in coevolutionary networks of plant–animal interactions. Ecol. Lett. 6, 69–81 (2003).

    Google Scholar 

  34. Krasnov, B. R., Shenbrot, G. I., Khokhlova, I. S. & Degen, A. A. Trait‐based and phylogenetic associations between parasites and their hosts: a case study with small mammals and fleas in the Palearctic. Oikos 125, 29–38 (2016).

    Google Scholar 

  35. Wilson, E. O. The nature of the taxon cycle in the Melanesian ant fauna. Am. Nat. 95, 169–193 (1961).

    Google Scholar 

  36. Janz, N. & Nylin, S. in Specialization, Speciation, and Radiation: the Evolutionary Biology of Herbivorous Insects (ed. Tilmon, K.) 203–215 (Univ. of California Press, 2005).

  37. Sonne, J. et al. High proportion of smaller ranged hummingbird species coincides with ecological specialization across the Americas. Proc. Biol. Sci. 283, 20152512 (2016).

    PubMed  PubMed Central  Google Scholar 

  38. Braga, M. P. et al. Host use dynamics in a heterogeneous fitness landscape generates oscillations in host range and diversification. Evolution 72, 1773–1783 (2018).

    PubMed  Google Scholar 

  39. Simmons, B. I. et al. Abundance drives broad patterns of generalisation in plant–hummingbird pollination networks. Oikos 128, 1287–1295 (2019).

    Google Scholar 

  40. Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).

    PubMed  PubMed Central  Google Scholar 

  41. Lamit, L. J. et al. Genotype variation in bark texture drives lichen community assembly across multiple environments. Ecology 96, 960–971 (2015).

    CAS  PubMed  Google Scholar 

  42. Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118–1123 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

    PubMed  PubMed Central  Google Scholar 

  44. Levin, B. R. & Bull, J. J. Short-sighted evolution and the virulence of pathogenic microorganisms. Trends Microbiol. 2, 76–81 (1994).

    CAS  PubMed  Google Scholar 

  45. Bordes, F. & Morand, S. Parasite diversity: an overlooked metric of parasite pressures? Oikos 118, 801–806 (2009).

    Google Scholar 

  46. Rigaud, T., Perrot-Minnot, M.-J. & Brown, M. J. F. Parasite and host assemblages: embracing the reality will improve our knowledge of parasite transmission and virulence. Proc. Biol. Sci. 277, 3693–3702 (2010).

    PubMed  PubMed Central  Google Scholar 

  47. Susi, H., Barrès, B., Vale, P. F. & Laine, A.-L. Co-infection alters population dynamics of infectious disease. Nat. Commun. 6, 5975 (2015).

    CAS  PubMed  Google Scholar 

  48. Calatayud, J., Madrigal-González, J., Gianoli, E., Hortal, J. & Herrero, A. Uneven abundances determine nestedness in climbing plant-host interaction networks. Perspect. Plant Ecol. Evol. Syst. 26, 53–59 (2017).

    Google Scholar 

  49. González-Varo, J. P. & Traveset, A. The labile limits of forbidden interactions. Trends Ecol. Evol. 31, 700–710 (2016).

    PubMed  Google Scholar 

  50. Lancaster, L. T. Host use diversification during range shifts shapes global variation in Lepidopteran dietary breadth. Nat. Ecol. Evol. 4, 963–969 (2020).

    PubMed  Google Scholar 

  51. Singer, M. C. & Parmesan, C. Colonizations cause diversification of host preferences: a mechanism explaining increased generalization at range boundaries expanding under climate change. Glob. Change Biol. 27, 3505–3518 (2021).

    Google Scholar 

  52. Agosta, S. J. & Klemens, J. A. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecol. Lett. 11, 1123–1134 (2008).

    PubMed  Google Scholar 

  53. Muñoz, J., Felicísimo, A. M., Cabezas, F., Burgaz, A. R. & Martínez, I. Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science 304, 1144–1147 (2004).

    PubMed  Google Scholar 

  54. Clarke, J. T., Warnock, R. C. M. & Donoghue, P. C. J. Establishing a time-scale for plant evolution. New Phytol. 192, 266–301 (2011).

    PubMed  Google Scholar 

  55. Calatayud, J. et al. Pleistocene climate change and the formation of regional species pools. Proc. Biol. Sci. 286, 20190291 (2019).

    PubMed  PubMed Central  Google Scholar 

  56. Calatayud, J. et al. Positive associations among rare species and their persistence in ecological assemblages. Nat. Ecol. Evol. 4, 40–45 (2020).

    PubMed  Google Scholar 

  57. Alzate, A. & Onstein, R. E. Understanding the relationship between dispersal and range size. Ecol. Lett. 25, 2303–2323 (2022).

    PubMed  Google Scholar 

  58. Hadfield, J. D., Krasnov, B. R., Poulin, R. & Nakagawa, S. A tale of two phylogenies: comparative analyses of ecological interactions. Am. Nat. 183, 174–187 (2014).

    PubMed  Google Scholar 

  59. Poisot, T. et al. mangal—making ecological network analysis simple. Ecography 39, 384–390 (2016).

    Google Scholar 

  60. Hurtado, P. Data and code for Hurtado et al. 2024—generalism in species interactions is more the consequence than the cause of ecological success. figshare https://doi.org/10.6084/m9.figshare.26023627.v2 (2024).

  61. Llimona, X. & Hladun, N. L. Checklist of the lichens and lichenicolous fungi of the Iberian Peninsula and Balearic Islands. Bocconea 14, 5–581 (2001).

    Google Scholar 

  62. Burgaz, A. R. Bibliografía botánica ibérica, 2016. Líquenes. Bot. Complut. 41, 109–113 (2017).

    Google Scholar 

  63. Burgaz, A. R. Bibliografía botánica ibérica, 2017. Líquenes. Bot. Complut. 42, 181–185 (2018).

    Google Scholar 

  64. Castroviejo, S. Flora Ibérica (Real Jardín Botánico, 1986–2012).

  65. Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).

    Google Scholar 

  66. Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M. & Rondinini, C. COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology 102, e03344 (2021).

    PubMed  Google Scholar 

  67. Jones, K. E. et al. PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648 (2009).

    Google Scholar 

  68. Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).

    PubMed  Google Scholar 

  69. Jin, Y. & Qian, H. V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).

    Google Scholar 

  70. Álvarez-Carretero, S. et al. A species-level timeline of mammal evolution integrating phylogenomic data. Nature 602, 263–267 (2022).

    PubMed  Google Scholar 

  71. Gaston, K. J. How large is a species’ geographic range? Oikos 61, 434–438 (1991).

    Google Scholar 

  72. Gaston, K. J. Measuring geographic range sizes. Ecography 17, 198–205 (1994).

    Google Scholar 

  73. He, F. & Gaston, K. J. Occupancy‐abundance relationships and sampling scales. Ecography 23, 503–511 (2000).

    Google Scholar 

  74. Rondinini, C., Wilson, K. A., Boitani, L., Grantham, H. & Possingham, H. P. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol. Lett. 9, 1136–1145 (2006).

    PubMed  Google Scholar 

  75. Gaston, K. J. & Fuller, R. A. The sizes of species’ geographic ranges. J. Appl. Ecol. 46, 1–9 (2009).

    Google Scholar 

  76. Letten, A. D. & Cornwell, W. K. Trees, branches and (square) roots: why evolutionary relatedness is not linearly related to functional distance. Methods Ecol. Evol. 6, 439–444 (2015).

    Google Scholar 

  77. Farage, C., Edler, D., Eklöf, A., Rosvall, M. & Pilosof, S. Identifying flow modules in ecological networks using Infomap. Methods Ecol. Evol. 12, 778–786 (2021).

    Google Scholar 

  78. Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

    CAS  PubMed  Google Scholar 

  79. Schleuning, M. et al. Ecological, historical and evolutionary determinants of modularity in weighted seed‐dispersal networks. Ecol. Lett. 17, 454–463 (2014).

    PubMed  Google Scholar 

  80. Pinheiro, J. & Bates, D. nlme: Linear and nonlinear mixed effects models. R package v.3.1-160 (R Core Team, 2022).

  81. Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).

    PubMed  Google Scholar 

  82. Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).

    Google Scholar 

  83. Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).

    PubMed  PubMed Central  Google Scholar 

  84. Legendre, P. Spatial autocorrelation: trouble or new paradigm? Ecology 74, 1659–1673 (1993).

    Google Scholar 

  85. Krasnov, B. R. et al. Phylogenetic signal in module composition and species connectivity in compartmentalized host–parasite networks. Am. Nat. 179, 501–511 (2012).

    PubMed  Google Scholar 

  86. Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).

    Google Scholar 

  87. Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).

    Google Scholar 

  88. Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).

    PubMed  Google Scholar 

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Acknowledgements

We thank L. T. Lancaster for constructive comments to improve the manuscript. We also thank R. Bernado, J. L. Cantalapiedra, P. Giordani, C. Gutiérrez-Cánovas, J. Hortal, J. Madrigal-González and S. Magalhães for their valuable comments on the manuscript. We thank G. C. Vega for her assistance with Fig. 3. We acknowledge the support provided by grant no. FJC2020-045923-I (Juan de la Cierva Formación grant from the Spanish Minister of Science and Innovation) to P.H., a Margarita Salas grant from the Spanish Minister of Universities to P.H., UNIPER grant no. PID2020-114851GA-100 (Spanish Minister of Science and Innovation) to J.C. and RARABUN grant no. 2022/00156/001 (Comunidad de Madrid Government) to J.C.

Author information

Authors and Affiliations

  1. DIFAR, University of Genoa, Genoa, Italy

    Pilar Hurtado

  2. Department of Biology and Geology, Physics and Inorganic Chemistry, Rey Juan Carlos University, Madrid, Spain

    Pilar Hurtado, Gregorio Aragón, Marina Vicente & Joaquín Calatayud

  3. Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales (MNCN-CSIC), Madrid, Spain

    Pilar Hurtado

  4. Global Change Research Institute, Rey Juan Carlos University, Madrid, Spain

    Gregorio Aragón & Joaquín Calatayud

  5. Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark

    Bo Dalsgaard

  6. Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, Israel

    Boris R. Krasnov

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  1. Pilar Hurtado
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Contributions

P.H. and J.C. contributed to the conceptualization, methodology, data analysis, visualization, funding acquisition and writing of the original draft. All authors actively participated in the investigation and data collection, providing valuable contributions, revisions and editing of the manuscript.

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Correspondence to Pilar Hurtado.

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Extended data

Extended Data Fig. 1 Results of the structural equation models testing both the pure causative aprioristic model (a, c, and e for hummingbirds, lichens, and fleas, respectively) and the pure consequent model (b, d, and f for hummingbirds, lichens, and fleas, respectively).

The marginal pseudo-R2 for each response variable is depicted using R2 below the respective variable. Arrows indicate the direction of causality for significant paths (yellow and blue for the causative and consequent model, respectively), and arrow width is proportional to the standardized regression coefficient of the path, as denoted by the numerical values on the lines.

Extended Data Fig. 2 Functional trait values of each host type for plants used by hummingbirds (a), mammals used by fleas (b) and plants used by lichens (c).

For plants used by hummingbirds, flower corolla length is depicted (a). For mammals used by fleas, the functional traits are body mass, density, shelter depth, and shelter complexity (b). In the case of plants used by lichens, bark texture and plant habit are the traits depicted (c). Boxplots illustrate the median (central line), the first and third quartiles (bounds of the box), the range within 1.5 times the interquartile range from the quartiles (whiskers), and outliers (points outside the whiskers). The numbers above the boxes indicate the number of species within each module.

Extended Data Table 1 Percentage of explained variance (pseudo-R2 * 100) of the models of local generalism as a function of abundance, and regional generalism and range size from the host perspective
Full size table

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Supplementary Figs. 1–7, Sections 1–3 and Tables 1–4.

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Hurtado, P., Aragón, G., Vicente, M. et al. Generalism in species interactions is more the consequence than the cause of ecological success. Nat Ecol Evol 8, 1602–1611 (2024). https://doi.org/10.1038/s41559-024-02484-8

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