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Phylogenetic structure and host abundance drive disease pressure in communities

Nature volume 520, pages 542–544 (2015)Cite this article

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Abstract

Pathogens play an important part in shaping the structure and dynamics of natural communities, because species are not affected by them equally1,2. A shared goal of ecology and epidemiology is to predict when a species is most vulnerable to disease. A leading hypothesis asserts that the impact of disease should increase with host abundance, producing a ‘rare-species advantage’3,4,5. However, the impact of a pathogen may be decoupled from host abundance, because most pathogens infect more than one species, leading to pathogen spillover onto closely related species6,7. Here we show that the phylogenetic and ecological structure of the surrounding community can be important predictors of disease pressure. We found that the amount of tissue lost to disease increased with the relative abundance of a species across a grassland plant community, and that this rare-species advantage had an additional phylogenetic component: disease pressure was stronger on species with many close relatives. We used a global model of pathogen sharing as a function of relatedness between hosts, which provided a robust predictor of relative disease pressure at the local scale. In our grassland, the total amount of disease was most accurately explained not by the abundance of the focal host alone, but by the abundance of all species in the community weighted by their phylogenetic distance to the host. Furthermore, the model strongly predicted observed disease pressure for 44 novel host species we introduced experimentally to our study site, providing evidence for a mechanism to explain why phylogenetically rare species are more likely to become invasive when introduced8,9. Our results demonstrate how the phylogenetic and ecological structure of communities can have a key role in disease dynamics, with implications for the maintenance of biodiversity, biotic resistance against introduced weeds, and the success of managed plants in agriculture and forestry.

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Figure 1: Pathogen spillover depends on abundance and relatedness.
Figure 2: The joint effects of abundance and phylogenetic signal on disease pressure.
Figure 3: Prediction of local disease from a global database.

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Acknowledgements

We thank C. Webb for development of the methods of phylogenetic ecology. We thank J. Velzy for greenhouse support and the many undergraduates and volunteers who helped with fieldwork and disease assessments from scanned leaves. Editorial comments were provided by M. Kilpatrick, B. Lyon, C. Fresquez, D. Gordon, S. Grove, S. Gulamhussein, J. Harrower, C. Ray, K. Ross and M. Shu; K. Tanner revised Fig. 1. This research was funded by National Science Foundation grants DEB-0842059 and DEB-1136626, and by a Cooperative Agreement 14-8130-1472-CA between G.S.G. and US Department of Agriculture APHIS-PPQ-CPHST PERAL funded from Section 10201 of the Farm Bill.

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Author notes

  1. Ingrid M. Parker and Gregory S. Gilbert: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, 95064, California, USA

    Ingrid M. Parker, Megan Bontrager & Andrew P. Weitz

  2. Smithsonian Tropical Research Institute, Apartado, 2072, Balboa, República de Panamá

    Ingrid M. Parker & Gregory S. Gilbert

  3. Department of Environmental Studies, University of California Santa Cruz, Santa Cruz, 95064, California, USA

    Megan Saunders, Rebecca Hendricks & Gregory S. Gilbert

  4. Center for Integrated Pest Management, North Carolina State University, Raleigh, 27606, North Carolina, USA

    Roger Magarey & Karl Suiter

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Contributions

I.M.P. and G.S.G. conceived and designed the study, managed the project, performed fieldwork and laboratory work, analysed the data, and wrote the paper. G.S.G. built the supertree R2G2_20140601 and the PhyloSusceptibility model. I.M.P. wrote the initial manuscript. M.S. helped design the study and performed laboratory work. M.B., A.P.W. and R.H. performed fieldwork and laboratory work. R.M. and K.S. contributed the database for the PhyloSusceptibility model.

Corresponding author

Correspondence to Ingrid M. Parker.

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The authors declare no competing financial interests.

Additional information

Data have been deposited in the Dryad Digital Repository (http://dx.doi.org/10.5061/dryad.kf401).

Extended data figures and tables

Extended Data Figure 1 Phylogenetic tree of grassland and experimental species.

Phylogenetic tree of 43 wild species (open circles) in the grassland habitat and 44 experimentally introduced species (filled circles) of plants. Phylogenetic topology and distances (in Myr) were derived from supertree R2G2_20140601 (see Supplementary Methods and Supplementary Data).

Extended Data Figure 2 Phylogenetic signal in host sharing.

a, b, The influence of phylogenetic relationship on the probability of sharing a pest between two hosts was modelled from a database of 1,670 pests on 210 angiosperm host genera in the US Department of Agriculture Global Pest and Pathogen Database (described in ref. 35). Phylogenetic distances are calculated from the updated phylogenetic tree R2G2_20140601 (Supplementary Information). The regression takes the form of logit(S) = β0 + β1 × log10(PD + 1), where S is the probability of susceptibility and PD is the phylogenetic distance (time of independent evolution, in Myr) between the source and target host genera. The probability that a target host is susceptible to a pest from a source host is then S = exp(logit(S))/[1 + exp(logit(S))]. Coefficients were calculated for nine groups of pests/pathogens: bacteria, fungi, oomycetes, insects, mites, molluscs, nematodes, viruses and plants (Extended Data Table 1). a, b, Coefficients were generated for all pests (a), and for the subset of (polyphagous) pests known from more than one host genus (b). The latter is how the analysis was originally done in ref. 35.

Extended Data Figure 3 Heuristic example of the joint influences of focal species abundance and phylogenetic isolation on disease.

a, b, A few examples can be used to illustrate the joint influences of focal host abundance and the presence of close relatives on disease. Most of the most common species, for example, Avena barbata (Ab), Bromus diandrus (Bd), Raphanus raphanistrum (Rr) and Geranium dissectum (Gd), experienced high levels of diseased tissue, regardless of the identity of their neighbours (a, b, yellow circles). In contrast, although both Medicago polymorpha (Mp) and Taraxacum campylodes (Tc) were very rare (a, blue diamonds), T. campylodes experienced three times as much disease as M. polymorpha, perhaps because it had many more close relatives in the local community (b). Similarly, Convolvulus arvensis (Ca) and Sonchus asper (Sa) were found at the same abundance (a, red squares), but S. asper had more close relatives than C. arvensis (b) and experienced over four times as much disease. At intermediate abundance, Eschscholzia californica (Ec) and Nassella pulchra (Np) had very similar cover, but the latter suffered more than six times as much disease as the former (a, green triangles). Again, the species with more disease co-occurred with several close relatives while the relatively disease-free species was phylogenetically isolated (b). Note that axes are on a log scale.

Extended Data Figure 4 Measuring phylogenetic dispersion of grassland communities.

a, b, Phylogenetic dispersion analysis from one representative site. Open circles show the observed phylogenetic distance among the 29 species found in grassland site 2, for each of 18 quantiles (from 0 to 50%). Grey circles show phylogenetic distance quantiles for random draws from the 529 species in the regional metacommunity (the UCSC campus) (a) or the 43 species found in the surrounding grassland community (b). Size of the grey circle is proportional to the number of times that distance was found in 10,000 independent random draws of 29 species. Solid blue lines show the observed phylogenetic distance at the 7% quantile; dotted lines show medians of the random draws. a, b, The observed distance was 74 Myr less than the median of the random draws from the regional metacommunity at the 7% quantile (that is, phylogenetic clustering, consistent with habitat filtering) (a), and the observed distance was 13 Myr greater than expected from random draws of grassland species only (that is, phylogenetic overdispersion, consistent with negative species interactions) (b). Statistical tests based on independent estimates of dispersion for each of the ten sites are shown in Extended Data Fig. 5.

Extended Data Figure 5 Test of phylogenetic dispersion of grassland communities.

a, Plant species in the grassland were consistently phylogenetically clustered compared to the pattern expected if species were drawn randomly from the regional pool (529 species on the 810-hectare UCSC campus). Values less than zero indicate phylogenetic clustering, while values greater than zero indicate species are more distantly related than random (overdispersed) (Extended Data Fig. 4). One-tailed t-tests (df = 9, P values given) showed species were more closely related than expected through the first quartile (25%). This is consistent with habitat filtering. b, In contrast, species were less closely related than expected if drawn randomly from the 43 species found in the grassland community, especially at shorter phylogenetic distances (through the 7% quantile). Box-and-whisker plots indicate median (dark line), 25th and 75th quartiles (box) and minimum and maximum (whiskers), with the ten sites as replicates.

Extended Data Table 1 Logistic regression coefficients of phylogenetic signal in pest sharing among plant hosts
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Parker, I., Saunders, M., Bontrager, M. et al. Phylogenetic structure and host abundance drive disease pressure in communities. Nature 520, 542–544 (2015). https://doi.org/10.1038/nature14372

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