Abstract
In nature, entangled webs of predator–prey interactions constitute the backbones of ecosystems. Uncovering the network architecture of such trophic interactions has been recognized as the essential step for exploring species with great impacts on ecosystem-level phenomena and functions. However, it has remained a major challenge to reveal how species-rich networks of predator–prey interactions are continually reshaped through time in the wild. Here, we show that dynamics of species-rich predator–prey interactions can be characterized by remarkable network structural changes and alternations of species with greatest impacts on community processes. On the basis of high-throughput detection of prey DNA from 1,556 spider individuals collected in a grassland ecosystem, we reconstructed dynamics of interaction networks involving, in total, 50 spider species and 974 prey species and strains through 8 months. The networks were compartmentalized into modules (groups) of closely interacting predators and prey in each month. Those modules differed in detritus/grazing food chain properties, forming complex fission–fusion dynamics of belowground and aboveground energy channels across the seasons. The substantial shifts of network structure entailed alternations of spider species located at the core positions within the entangled webs of interactions. These results indicate that knowledge of dynamically shifting food webs is crucial for understanding temporally varying roles of ‘core species’ in ecosystem processes.
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Data availability
The DNA sequencing data are available from the DNA Data Bank of Japan with the BioProject accession number PRJDB12701 and the sequence read archive numbers DRA016403-DRA016405. The community data are deposited at our GitHub repository (https://github.com/hiro-toju/spider_prey_1).
Code availability
All the scripts used to analyse the data are available at the GitHub repository (https://github.com/hiro-toju/spider_prey_1).
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Acknowledgements
We thank H. Fujita for his advice on statistical analyses. This work was financially supported by JSPS Grant-in-Aid for Scientific Research (18H04009) and JST FOREST (JPMJFR2048) to H.T.
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H.T. designed the work with S.S.S. S.S.S. performed experiments. S.S.S. analysed the data with H.T. S.S.S. and H.T. wrote the paper with Y.G.B.
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Extended data
Extended Data Fig. 1 Overview of the spider and prey compositions.
a, Rarefaction curves of the prey data. Relationship between the number of sequencing reads and that of detected Hexapoda OTUs is shown for each sample. b, Composition of collected spider species. The numbers of the collected spider specimens are shown for each month. c, Genus-level taxonomy of detected prey. The compositions of prey detection counts are shown across the sampling months in terms of the genus-level taxonomy. d, Order-level taxonomy of detected prey. The compositions of prey detection counts are shown across the sampling months in terms of the order-level taxonomy. e, Effects of spider species and sampling months on prey compositions. A PERMANOVA of family-level prey compositions (Fig. 1b) was performed by setting spider species, sampling months, and interactions between them as explanatory variables.
Extended Data Fig. 2 Counts of prey detection from respective spider species.
The compositions of prey detection counts are shown for each spider species across the sampling months. Top 19 spider species with highest numbers of specimens with prey sequences are presented.
Extended Data Fig. 3 Hunting type of spiders.
For each category (hunting type) of spiders (as defined in Fig. 3), the compositions of prey detection counts are shown with putative ecological guilds of prey. Based on family-level taxonomy, the prey guilds were classified into four categories: herbivores, predators/parasites, detritivores, and omnivores (variable feeding habits). The prey OTUs unidentified at the family level were omitted in the graphs.
Extended Data Fig. 4 Network topology.
Within the networks depicting the predator–prey interactions observed in respective months, the family-level taxonomy of prey vertices is indicated. The layout of the vertices was optimized based on the ‘stress’ algorithm of network ordination as implemented in the ggraph package of R.
Extended Data Fig. 5 Network modules.
Within each network depicting predator–prey interactions, vertices were classified into modules consisting of closely interacting spiders and prey based on the ‘Louvain’ algorithm.
Extended Data Fig. 6 Temporal variability in topological roles.
For the three spider species collected throughout the seasons (spider species that appeared in seven or eight months), temporal shifts of interaction generality and betweenness network centrality are presented. The size of symbols is roughly proportional to the number of spider individuals analysed. Colour indicates sampling months.
Extended Data Fig. 7 Original and bootstrap-standardized values of among-module connectivity.
For respective species within the meta-network representing all the interactions observed through the eight months (Fig. 5), the original and bootstrap-standardized values of among-module connectivity are shown on the horizontal and vertical axes, respectively. In the bootstrap analysis, the prey repertoire of each spider individual was bootstrap-sampled from the sample-level matrix in each month, subsequently converted into species-level matrices (1,000 interations). The bootstrapped species-level matrices of the eight months were then integrated as bootstrapped meta-network matrices for comparison with the original meta-network structure. Note that bootstrap-standardized values were unavailable for network vertices whose among-module connectivity were consistently zero across the bootstrapping.
Extended Data Fig. 8 Statistical significance in the bootstrapping analysis of among-module connectivity.
he relationship between bootstrap-standardized among-module connectivity (Fig. 6) and false discovery rate (FDR; one-tailed test) is shown.
Extended Data Fig. 9 Comparison of algorithms for modularity estimation.
For the estimation of modularity, three module-finding algorithms, namely, the ‘Louvain’, ‘fast greedy’, and ‘Infomap’ algorithms, were compared. Higher modularity scores represent more optimized module memberships. Based on the inferred memberships (Extended Data Fig. 5), Newman’s and Barber’s metrics of modularity were respectively calculated. a, Newman’s standard metric of modularity. b, Barber’s metric of modularity for bipartite networks.
Extended Data Fig. 10 Standardization of betweenness centrality based on bootstrapping.
To examine whether each species play disproportionately important topological roles as network cores (hubs) relative to its abundance, bootstrap-standardized betweenness index was calculated for each network node (spider species or prey OTU) in each month. Specifically, the prey repertoire of each spider individual was bootstrap-sampled from the sample-level matrix in each month (1,000 interations). The bootstrapped sample-level matrices were then converted into species-level matrices, subjected to the calculation of betweenness centrality. Bootstrap-standardized betweenness scores were then obtained as \(\frac{{X}_{{observed}}-{Mean}({X}_{{bootstrapped}})}{{SD}({X}_{{bootstrapped}})}\), where Xobserved represented the value calculated based on the original data matrix, and Mean(Xbootstrapped) and SD(Xbootstrapped) were the mean and standard deviation of the values calculated with the bootstrapped matrices, respectively.
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Suzuki, S.S., Baba, Y.G. & Toju, H. Dynamics of species-rich predator–prey networks and seasonal alternations of core species. Nat Ecol Evol 7, 1432–1443 (2023). https://doi.org/10.1038/s41559-023-02130-9
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DOI: https://doi.org/10.1038/s41559-023-02130-9