Agricultural intensification drives landscape-context effects on host–parasitoid interactions in agroecosystems
Version of Record online: 20 APR 2012
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society
Journal of Applied Ecology
Volume 49, Issue 3, pages 706–714, June 2012
How to Cite
Jonsson, M., Buckley, H. L., Case, B. S., Wratten, S. D., Hale, R. J. and Didham, R. K. (2012), Agricultural intensification drives landscape-context effects on host–parasitoid interactions in agroecosystems. Journal of Applied Ecology, 49: 706–714. doi: 10.1111/j.1365-2664.2012.02130.x
- Issue online: 31 MAY 2012
- Version of Record online: 20 APR 2012
- Received 20 June 2011; accepted 14 March 2012 Handling Editor: Yann Clough
Appendix S1. Selection of field sites and quantification of land-use patterns
Appendix S2. Resources for parasitoids
Appendix S3. Sensitivity analysis for the index of habitat disturbance
Appendix S4. Sensitivity analysis for structural equation models
Appendix S5. Confirmatory linear model analyses
Table S1. Summary statistics for landscape composition in circular landscapes with a 500-m radius around the centre of each study area.
Table S2. Summary statistics averaged across the 30 sites for proximate variables potentially explaining landscape effects.
Table S3. The estimated level of disturbance (k-value) used for different land-cover classes when calculating the habitat disturbance index.
Table S4. Standardized path coefficients from the structural equation models in Fig. 1, showing the direct effects, indirect effects and total effects of factors influencing parasitism.
Table S5. Standardized path coefficients of ‘maximal’ structural equation models for each response variable (carried out as a sensitivity analysis to test the robustness of causal hypotheses in the primary SEM models), showing the direct effects, indirect effects and total effects of factors influencing parasitism
Table S6. Confirmatory generalized linear mixed model (GLMM) analyses testing the relative influence of ultimate and proximate factors affecting parasitism.
Table S7. Coefficients for the best-fit ultimate-driver and proximate-driver GLMM models for parasitism rates.
Fig. S1. Map of New Zealand and the Canterbury region with the locations of the 30 study fields indicated with black circles.
Fig. S2. Examples of land-use maps of landscape sectors (500-m radius around study transect) with high and low habitat type diversity.
Fig. S3. Examples of land-use maps of landscape sectors (500-m radius around study transect) with high and low annual crop cover.
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