Landscape Ecology

, Volume 30, Issue 10, pp 1975–1986 | Cite as

Using multi-level generalized path analysis to understand herbivore and parasitoid dynamics in changing landscapes

  • Tatiane Beduschi
  • Teja Tscharntke
  • Christoph Scherber
Research Article



In patchy environments, such as agricultural landscapes, both spatial and temporal scales of habitat heterogeneity can affect population dynamics and trophic interactions. As a result of crop rotation, landscapes and local resource availability may change dramatically within and between years.


We used a tritrophic interaction constituted by pollen beetles, their host plant oilseed rape, and their parasitoids, as a model system to investigate how the effect of landscape composition on insect abundance changes with time and whether system dynamics showed carry-over effects of previous years. We employ path analysis models that allow us to study whole networks of hypotheses rather than univariate cause–effect relationships.


We exposed pan traps in a 5 × 5 grid design within 10 landscapes in June 2011 (after oilseed rape flowering) and May 2012 (at peak oilseed rape flowering). Additionally, we assessed parasitism rates of pollen beetle larvae in May 2011 and measured changes in landscape composition.


The effect of the oilseed rape proportion on beetle abundance changed with time from negative (during flowering) to positive (after flowering). Parasitism had a negative effect on the number of newly emerged pollen beetles, but only in landscapes with a low proportion of oilseed rape. Interestingly, our path analysis showed that landscape composition affected herbivore abundance 1 or 2 years later, mediated by changes in parasitism.


Our results suggest that plant–herbivore–parasitoid interactions in dynamic agricultural landscapes can show interannual carry-over effects, as they are affected by landscape composition and top-down effects in previous years.


Crop rotation Multitrophic interactions Grid-based landscape analysis Biological control Structural equation model Regular (systematic) sampling 



We are indebted to the Landesamt für Geoinformation und Landentwicklung Niedersachsen for providing information on land-use and to the farmers for allowing us to perform this study on their fields. We thank Bill Shipley for inputs on the model and Thorsten Wiegand and two anonymous reviewers for their helpful comments on the manuscript. Funding was provided by the Deutsche Forschungsgemeinschaft (DFG) within the frame of the Research Training Group 1644 “Scaling Problems in Statistics”. RapidEyeTM satellite images were obtained from the DLR (Deutsches Zentrum für Luft- und Raumfahrt e. V.), RapidEye Science Archive, grant number RESA 464, funded by the German BMBF (Federal Ministry of Education and Research).

Supplementary material

10980_2015_224_MOESM1_ESM.docx (334 kb)
Supplementary material 1 (DOCX 334 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Agroecology, Department of Crop ScienceGeorg-August-University GöttingenGöttingenGermany

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