Using multi-level generalized path analysis to understand herbivore and parasitoid dynamics in changing landscapes
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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.
KeywordsCrop 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).
- Bates D, Maechler M, Bolker B, Walker S (2014) lme4: linear mixed-effects models using Eigen and S4. R package version 1.0-6Google Scholar
- Bivand R, Piras G (2015) Comparing implementations of estimation methods for spatial econometrics. J Stat Softw 63(18):1–36Google Scholar
- Bivand RS, Pebesma EJ, Gómez-Rubio V (2008) Applied spatial data analysis with R. Springer, New YorkGoogle Scholar
- BMBF: Bundesministerium für Bildung und Forschung (2012) Schub durch Doppel-Null-Raps. Available from http://www.biosicherheit.de/basisinfo/271.schub-doppel-null-raps.html. Accessed Feb 2014
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
- Crawley MJ (2013) The R book. Wiley, West SussexGoogle Scholar
- Eurostat (2013) Agriculture, forestry and fishery statistics pocketbook. Publications Office of the European Union, LuxembourgGoogle Scholar
- Fox J (2003) Effect displays in R for generalised linear models. J Stat Softw 8(15):1–27Google Scholar
- Fritzsche R (1957) Zur Biologie und Ökologie der Rapsschädlinge aus der Gattung Meligethes. J Appl Ecol 40(2):222–280Google Scholar
- Holzschuh A, Dormann CF, Tscharntke T, Steffan-Dewenter I (2011) Expansion of mass-flowering crops leads to transient pollinator dilution and reduced wild plant pollination. Phil Trans R Soc Lond B 278(1723):3444–3451Google Scholar
- Honěk A, Štys P, Martinková Z (2013) Arthropod community of dandelion (Taraxacum officinale) capitula during seed dispersal. Biologia 68(2):330–336Google Scholar
- Jourdheuil P (1960) Influence de quelques facteurs écologiques sur les fluctuations de population d’une biocénose parasitaire. Dissertation, Institut national de la recherche agronomiqueGoogle Scholar
- Keil M, Bock M, Esch T, Metz A, Nieland S, Pfitzner A (2010) CORINE Land Cover Aktualisierung 2006 für Deutschland. OberpfaffenhofenGoogle Scholar
- Nilsson C (1985) Impact of ploughing on emergence of pollen beetle parasitoids after hibernation. Z Angew Entomol 100(1–5):302–308Google Scholar
- Nilsson C (1988) The pollen beetle (Meligethes aeneus F.) in winter and spring rape at Alnarp 1976–1978, 1: Migration and sex ratio. VaextskyddsnotiserGoogle Scholar
- Pinheiro J, Bates D, DebRoy S, et al (2013) nlme: linear and nonlinear mixed effects models. R package version 3.1-113Google Scholar
- R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Ryszkowski L, Karg J, Kujawa K, Goldyn H, Arczynska-Chudy E (2001) Influence of landscape mosaic structure on diversity of wild plant and animal communities in agricultural landscape of Poland. In: Ryszkowski L (ed) Landscape ecology in agroecosystems management. CRS Press, Boca Raton, pp 185–217CrossRefGoogle Scholar
- Scherber C, Lavandero B, Meyer KM, Perovic D, Visser U, Wiegand K, Tscharntke T (2012) Scale effects in biodiversity and biological control: methods and statistical analysis. In: Gurr GM, Wratten SD, Snyder WE (eds) Biodiversity and Insect Pests: key issues for sustainable management. Wiley, West Sussex, pp 137–153Google Scholar
- Shipley B (2002) Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inference. Cambridge University Press, CambridgeGoogle Scholar
- Thies C, Roschewitz I, Tscharntke T (2005) The landscape context of cereal aphid–parasitoid interactions. Phil Trans R Soc Lond B 272(1559):203–210Google Scholar
- Tiemann LK, Grandy AS, Atkinson EE, Marin-Spiotta E, McDaniel MD (2015) Crop rotational diversity enhances belowground communities and functions in an agroecosystem. Ecol Lett. doi: 10.1111/ele.12453
- Wratten SD, Van Emden HF (1995) Habitat management for enhanced activity of natural enemies of insect pests. In: Glen DM, Greaves MP, Anderson HM (eds) Ecology and integrated farming systems. Wiley, Chichester, pp 117–145Google Scholar