The predictability of phytophagous insect communities: Host specialists as habitat specialists

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Abstract

The difficulties specialized phytophagous insects face in finding habitats with an appropriate host should constrain their dispersal. Within the concept of metacommunities, this leads to the prediction that host-plant specialists should sort into local assemblages according to the local environmental conditions, i.e. habitat conditions, whereas assemblages of host-plant generalists should depend also on regional processes. Our study aimed at ranking the importance of local environmental factors and species composition of the vegetation for predicting the species composition of phytophagous moth assemblages with either a narrow or a broad host range. Our database consists of 351,506 specimens representing 820 species of nocturnal Macrolepidoptera sampled between 1980 and 2006 using light traps in 96 strict forest reserves in southern Germany. Species were grouped as specialists or generalists according to the food plants of the larvae; specialists use host plants belonging to one genus. We used predictive canonical correspondence and co-correspondence analyses to rank the importance of local environmental factors, the species composition of the vegetation and the role of host plants for predicting the species composition of host-plant specialists and generalists. The cross-validatory fit for predicting the species composition of phytophagous moths was higher for host-plant specialists than for host-plant generalists using environmental factors as well as the composition of the vegetation. As expected for host-plant specialists, the species composition of the vegetation was a better predictor of the composition of these assemblages than the environmental variables. But surprisingly, this difference for specialized insects was not due to the occurrence of their host plants. Overall, our study supports the idea that owing to evolutionary constraints in finding a host, host-plant specialists and host-plant generalists follow two different models of metacommunities: the species-sorting and the mass-effect model. © 2011 Müller et al.

Figures

  • Figure 1. Distribution of the 154 strict forest reserves in Bavaria. The histogram shows the distribution of the area covered by each reserve (ha). The 96 reserves included in the analyses are shown as black dots; the reserves not included in the analyses are shown as grey dots. Grey shading indicates forested area. Lines indicate borders between the forest ecoregions used in Fig. S3. doi:10.1371/journal.pone.0025986.g001
  • Figure 2. Number of species of nocturnal Macrolepidoptera occurring in Bavaria with different levels of specialization with respect to the host plant of the larvae (monophagous: 1 plant genus; oligophagous: 2–3 plant genera; polyphagous: more than 3 plant genera). Black bars indicate groups classified as specialists in the present study. doi:10.1371/journal.pone.0025986.g002
  • Figure 3. Cross-validatory fit of the raw data set of the composition of moth species and of three types of transformed data using local environmental factors as the predictor. The fit is plotted against the number of ordination axes. The blue symbols show the fit for specialists, the red symbols show the fit for generalists. Black lines are 100 assemblages with 79 randomly selected generalists, used as a comparison to specialists when the same number of species of specialists and generalists are used. doi:10.1371/journal.pone.0025986.g003
  • Figure 4. Cross-validatory fit of the raw data set of the composition of moth species and of three types of transformed data using the composition of the vegetation as the predictor. The fit is plotted against the number of ordination axes. The blue symbols show the fit for specialists, the red for generalists. Black lines are 100 assemblages with 79 randomly selected generalists, used as a comparison to specialists when the same number of species of specialists and generalists are used. The green line in (b) presents the mean cross-validatory fit of the correspondence analyses in which the host species of each moth was successively removed. doi:10.1371/journal.pone.0025986.g004
  • Figure 5. Cross-validatory fit of the log(x+1) transformed data of moth (a) generalists and (b) specialists plotted against the number of ordination axes for two sets of predictor variables: composition of the vegetation (squares) using co-correspondence analysis, and environmental variables (circles) using predictive canonical correspondence analysis. The filled symbols indicate significant axes according to permutation tests. For differences in the cross-validatory fit between data sets, we permuted residuals between data sets. doi:10.1371/journal.pone.0025986.g005
  • Table 1. Cross-validatory fit of co-correspondence models for various subsets of our matrix of moth abundances (species occurring in at least 5 reserves) across 96 forest reserves in Bavaria (see Fig. 1).
  • Figure 6. Mean explained variance (%) of single species cocorrespondence analyses of 79 host-plant specialists (black line) compared to the mean explained variance expected from co-correspondence analyses with a randomly selected plant or plants as predictors. Each bar represents 100 runs. doi:10.1371/journal.pone.0025986.g006

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Müller, J., Stadler, J., Jarzabek-Müller, A., Hacker, H., ter Braak, C., & Brandl, R. (2011). The predictability of phytophagous insect communities: Host specialists as habitat specialists. PLoS ONE, 6(10). https://doi.org/10.1371/journal.pone.0025986

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