Landscape Ecology

, Volume 28, Issue 10, pp 1961–1974 | Cite as

Landscape structure mediates the effects of a stressor on field vole populations

  • Trine Dalkvist
  • Richard M. Sibly
  • Chris J. Topping
Research article


Spatio-temporal landscape heterogeneity has rarely been considered in population-level impact assessments. Here we test whether landscape heterogeneity is important by examining the case of a pesticide applied seasonally to orchards which may affect non-target vole populations, using a validated ecologically realistic and spatially explicit agent-based model. Voles thrive in unmanaged grasslands and untreated orchards but are particularly exposed to applied pesticide treatments during dispersal between optimal habitats. We therefore hypothesised that vole populations do better (1) in landscapes containing more grassland and (2) where areas of grassland are closer to orchards, but (3) do worse if larger areas of orchards are treated with pesticide. To test these hyposeses we made appropriate manipulations to a model landscape occupied by field voles. Pesticide application reduced model population sizes in all three experiments, but populations subsequently wholly or partly recovered. Population depressions were, as predicted, lower in landscapes containing more unmanaged grassland, in landscapes with reduced distance between grassland and orchards, and in landscapes with fewer treated orchards. Population recovery followed a similar pattern except for an unexpected improvement in recovery when the area of treated orchards was increased. Outside the period of pesticide application, orchards increase landscape connectivity and facilitate vole dispersal and so speed population recovery. Overall our results show that accurate prediction of population impact cannot be achieved without taking account of landscape structure. The specifics of landscape structure and habitat connectivity are likely always important in mediating the effects of stressors.


Epigenetics Population-level risk assessment Ecotoxicology Microtus agrestis Modelling Spatial dynamics 



This research has been sponsored by the Danish Natural Science Research Council and the Centre for Integrated Population Ecology (CIPE).

Supplementary material

10980_2013_9932_MOESM1_ESM.docx (22 kb)
Supplementary material 1 (DOCX 22 kb)
10980_2013_9932_MOESM2_ESM.docx (39 kb)
Supplementary material 2 (DOCX 38 kb)


  1. Agrell J, Erlinge S, Nelson J, Sandell M (1996) Shifting spacing behaviour of male field voles (Microtus agrestis) over the reproductive season. Ann Zool Fenn 33(2):243–248Google Scholar
  2. Andreassen HP, Ims RA (2001) Dispersal in patchy vole populations: role of patch configuration, density dependence, and demography. Ecology 82(10):2911–2926CrossRefGoogle Scholar
  3. Anway MD, Cupp AS, Uzumcu M, Skinner MK (2005) Epigenetic transgenerational actions of endocrine disruptors and mate fertility. Science 308(5727):1466–1469PubMedCrossRefGoogle Scholar
  4. Anway MD, Leathers C, Skinner MK (2006a) Endocrine disruptor vinclozolin induced epigenetic transgenerational adult-onset disease. Endocrinology 147(12):5515–5523PubMedCrossRefGoogle Scholar
  5. Anway MD, Memon MA, Uzumcu M, Skinner MK (2006b) Transgenerational effect of the endocrine disruptor vinclozolin on male spermatogenesis. J Androl 27(6):868–879PubMedCrossRefGoogle Scholar
  6. Baddeley AJ, Turner R (2005) Spatstat: an R package for analysing spatial point patterns. J Stat Softw 12(6):1–42Google Scholar
  7. Baddeley AJ, Moller J, Waagepetersen R (2000) Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Stat Neerlandica 54(3):329–350CrossRefGoogle Scholar
  8. Barrett LW, Bohlen PJ (1991) Landscape ecology. In: Hudson WE (ed) Landscape linkages and biodiversity. Island, Washington, DC, pp 149–191Google Scholar
  9. Bates DM, Chambers JM (1992) Nonlinear models. In: Chambers JM, Hastie TJ (eds) Statistical models in S: Wadsworth and Brooks/Cole Computer science series. Wadsworth and Brooks/Cole, Pacific Grove, pp 377–421Google Scholar
  10. Bates DM, Watts DG (1988) Nonlinear regression analysis and its applications. Wiley, New YorkCrossRefGoogle Scholar
  11. Benschop J, Stevenson MA, Dahl J, Morris RS, French NP (2009) Informing surveillance programmes by investigating spatial dependency of subclinical Salmonella infection. Epidemiol Infect 137(9):1348–1359PubMedCrossRefGoogle Scholar
  12. Brown JH, Kodricbrown A (1977) Turnover rates in insular biogeography—effect of immigration on extinction. Ecology 58(2):445–449CrossRefGoogle Scholar
  13. Cairns J (1993) Will there ever be a field of landscape toxicology. Environ Toxicol Chem 12(4):609–610CrossRefGoogle Scholar
  14. Cairns J, Niederlehner BR (1996) Developing a field of landscape ecotoxicology. Ecol Appl 6(3):790–796CrossRefGoogle Scholar
  15. Crocker DR, Hart ADM, Gurney J, McCoy C (2002) Estimation daily food intake of wild birds and mammals., Appendix I, accessed on 19 Aug 2013
  16. Dalkvist T, Topping CJ, Forbes VE (2009) Population-level impacts of pesticide-induced chronic effects on individuals depend more on ecology than toxicology. Ecotoxicol Environ Saf 72(6):1663–1672PubMedCrossRefGoogle Scholar
  17. Elkin CM, Possingham H (2008) The role of landscape-dependent disturbance and dispersal in metapopulation persistence. Am Nat 172(4):563–575PubMedCrossRefGoogle Scholar
  18. Erlinge S, Hoogenboom I, Agrell J, Nelson J, Sandell M (1990) Density-related home-range size and overlap in adult field voles (Microtus agrestis) in Southern Sweden. J Mamm 71(4):597–603CrossRefGoogle Scholar
  19. Ersboll AK, Ersboll BK (2007) Simulation of the K-function in the analysis of spatial clustering for non-randomly distributed locations-exemplified by bovine virus diarrhoea virus (BVDV) infection in Denmark. In GisVet 2007 Conference, Copenhagen, pp 64–71Google Scholar
  20. Fahrig L, Freemark K (1995) Landscape-scale effects of toxic events for ecological risk assessment. In: Cairns JJ, Niederlehner BR (eds) Ecological toxicity testing: scale, complexity, relevance. Lewis, Boca Raton, pp 193–208Google Scholar
  21. Fahrig L, Nuttle WK (2005) Population ecology in spatially heterogeneous environments. In: Lovett GM, Jones CG, Turner MG, Weathers KC (eds) Ecosystem function in heterogeneous landscapes. Springer, New York, pp 95–118CrossRefGoogle Scholar
  22. FOCUS (2001) FOCUS surface water scenarios in the EU evaluation process under 91/414/EEC. Report of the FOCUS working group on surface water scenarios. Report of the FOCUS working group on surface water scenarios, EC Document Reference SANCO/4802/2001-rev-2Google Scholar
  23. Frank K, Wissel C (1998) Spatial aspects of metapopulation survival—from model results to rules of thumb for landscape management. Landscape Ecol 13(6):363–379CrossRefGoogle Scholar
  24. Gaines KF, Boring CS, Porter DE (2005) The development of a spatially explicit model to estimate radiocaesium body burdens in raccoons (Procyon lotor) for ecological risk assessment. Sci Total Environ 341(1–3):15–31PubMedCrossRefGoogle Scholar
  25. Godfrey GK (1953) The food of Microtus agrestis hirtus (Bellamy,1839) in Wytham, Berkshire. Säugetierk Mitt 1:148–151Google Scholar
  26. Gray LE, Ostby J, Monosson E, Kelce WR (1999) Environmental antiandrogens: low doses of the fungicide vinclozolin alter sexual differentiation of the male rat. Toxicol Ind Health 15(1–2):48–64PubMedCrossRefGoogle Scholar
  27. Grimm V, Railsback SF (2005) Individual-based modeling and ecology. Princeton University Press, PrincetonGoogle Scholar
  28. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, DeAngelis DL (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310(5750):987–991Google Scholar
  29. Haddad NM (1999) Corridor and distance effects on interpatch movements: a landscape experiment with butterflies. Ecol Appl 9(2):612–622CrossRefGoogle Scholar
  30. Haddad NM, Bowne DR, Cunningham A, Danielson BJ, Levey DJ, Sargent S, Spira T (2003) Corridor use by diverse taxa. Ecology 84(3):609–615Google Scholar
  31. Hanski I (1994) A practical model of metapopulation dynamics. J Anim Ecol 63(1):151–162CrossRefGoogle Scholar
  32. Hanski I (1999) Metapopulation ecology. Oxford University Press, OxfordGoogle Scholar
  33. Hansson L (1971) Habitat, food and population dynamics of the field vole Microtus agrestis (L.) in South Sweden. Viltrevy 8:267–378Google Scholar
  34. Hansson L (1977) Spatial dynamics of field voles Microtus agrestis in heterogeneous landscapes. Oikos 29(3):539–544CrossRefGoogle Scholar
  35. Holt RD (1984) Spatial heterogeneity, indirect interactions, and the coexistence of prey species. Am Nat 124(3):377–406CrossRefGoogle Scholar
  36. Illian JB, Moller J, Waagepetersen RP (2009) Hierarchical spatial point process analysis for a plant community with high biodiversity. Environ Ecol Stat 16(3):389–405CrossRefGoogle Scholar
  37. Jensen TS, Hansen TS (2001) Effekten af husdyrgræsning på småpattedyr. In: Pedersen LB, Buttenschøn RM, Jensen TS (eds) Græsning på ekstensivt drevne naturarealer—effekter på stofkredsløb og naturindhold—Park-og landskabsserien 34. Skov og Landskab, Hørsholm, pp 107–121Google Scholar
  38. Kapustka LA (2003) Rationale for use of wildlife habitat characterization to improve relevance of ecological risk assessments. Hum Ecol Risk Assess 9(6):1425–1430CrossRefGoogle Scholar
  39. Kindlmann P, Burel F (2008) Connectivity measures: a review. Landscape Ecol 23(8):879–890Google Scholar
  40. Kooistra L, Huijbregts MAJ, Ragas AMJ, Wehrens R, Leuven R (2005) Spatial variability and uncertainty in ecological risk assessment: a case study on the potential risk of cadmium for the little owl in a Dutch river flood plain. Environ Sci Technol 39(7):2177–2187PubMedCrossRefGoogle Scholar
  41. Laan R, Verboom B (1990) Effects of pool size and isolation on amphibian communities. Biol Conserv 54(3):251–262CrossRefGoogle Scholar
  42. LeMay V, Pommerening A, Marshall P (2009) Spatio-temporal structure of multi-storied, multi-aged interior Douglas fir (Pseudotsuga menziesii var. glauca) stands. J Ecol 97(5):1062–1074CrossRefGoogle Scholar
  43. Levey DJ, Bolker BM, Tewksbury JJ, Sargent S, Haddad NM (2005) Effects of landscape corridors on seed dispersal by birds. Science 309:146–148PubMedCrossRefGoogle Scholar
  44. Myllymaki A (1977) Demographic mechanisms in fluctuating populations of field vole Microtus agrestis. Oikos 29(3):468–493CrossRefGoogle Scholar
  45. Nabe-Nielsen J, Sibly RM, Forchhammer MC, Forbes VE, Topping CJ (2010) The effects of landscape modifications on the long-term persistence of animal populations. PLoS One 5(1):e8932PubMedCrossRefGoogle Scholar
  46. Pe’er G, Heinz SK, Frank K (2006) Connectivity in heterogeneous landscapes: analyzing the effect of topography. Landscape Ecol 21(1):47–61CrossRefGoogle Scholar
  47. Pita R, Beja P, Mira A (2007) Spatial population structure of the Cabrera vole in Mediterranean farmland: the relative role of patch and matrix effects. Biol Conserv 134(3):383–392CrossRefGoogle Scholar
  48. Pulliam HR (1988) Sources, sinks, and population regulation. Am Nat 132(5):652–661CrossRefGoogle Scholar
  49. Revilla E, Wiegand T (2008) Individual movement behavior, matrix heterogeneity, and the dynamics of spatially structured populations. Proc Natl Acad Sci USA 105(49):19120–19125PubMedCrossRefGoogle Scholar
  50. Royer F, Fromentin JM, Gaspar P (2004) Association between bluefin tuna schools and oceanic features in the western Mediterranean. Mar Ecol Progr Ser 269:249–263CrossRefGoogle Scholar
  51. Saunders DA, Hobbs RJ, Arnold GW (1993) The kellerberrin project on fragmented landscapes—a review of current information. Biol Conserv 64(3):185–192CrossRefGoogle Scholar
  52. Sibly RM, Akcakaya HR, Topping CJ, O’Connor RJ (2005) Population-level assessment of risks of pesticides to birds and mammals in the UK. Ecotoxicology 14(8):863–876PubMedCrossRefGoogle Scholar
  53. Stenseth NC (1985) Why mathematical models in evolutionary ecology? In: Cooley JH, Golley FB (eds) Trends in ecological research for the 1980s. Plenum, New York, pp 239–287Google Scholar
  54. Thomas CD (2000) Dispersal and extinction in fragmented landscapes. Proc R Soc Lond Ser B 267(1439):139–145CrossRefGoogle Scholar
  55. Thomas CD, Hanski I (1997) Butterfly metapopulations. In: Hanski I, Gilpin ME (eds) Metapopulation biology. Academic Press, San Diego, pp 359–386CrossRefGoogle Scholar
  56. Tischendorf L, Fahrig L (2000) How should we measure landscape connectivity? Landscape Ecol 15(7):633–641CrossRefGoogle Scholar
  57. Topping C, Østergaard S, Pertoldi C, Bach LA (2003a) Modelling the loss of genetic diversity in vole populations in a spatially and temporally varying environment. Ann Zool Fenn 40(3):255–267Google Scholar
  58. Topping CJ, Hansen TS, Jensen TS, Jepsen JU, Nikolajsen F, Odderskaer P (2003b) ALMaSS, an agent-based model for animals in temperate European landscapes. Ecol Model 167(1–2):65–82CrossRefGoogle Scholar
  59. Topping CJ, Sibly RM, Akcakaya HR, Smith GC, Crocker DR (2005) Risk assessment of UK skylark populations using life-history and individual-based landscape models. Ecotoxicology 14(8):925–936PubMedCrossRefGoogle Scholar
  60. Topping CJ, Dalkvist T, Forbes VE, Grimm V, Sibly RM (2009) The potential for the use of agent-based models in ecotoxicology. In: Devillers J (ed) Ecotoxicology modeling, emerging topics in ecotoxicology. Springer, New York, pp 205–235CrossRefGoogle Scholar
  61. Topping CJ, Høye TT, Odderskaer P, Aebischer NJ (2010a) A pattern-oriented modelling approach to simulating populations of grey partridge. Ecol Model 221(5):729–737CrossRefGoogle Scholar
  62. Topping CJ, Høye TT, Olesen CR (2010b) Opening the black box-development, testing and documentation of a mechanistically rich agent-based model. Ecol Model 221(2):245–255CrossRefGoogle Scholar
  63. Topping CJ, Dalkvist T, Grimm V (2012) Post-hoc pattern-oriented testing and tuning of an existing large model: lessons from the field vole. PLoS One 7(9):e45872PubMedCrossRefGoogle Scholar
  64. Topping CJ, Odderskaer P, Kahlert J (2013) Modelling skylarks (Alauda arvensis) to predict impacts of changes in land management and policy: development and testing of an agent-based model. PLoS One 8(6):e65803PubMedCrossRefGoogle Scholar
  65. Turner MG, Ruscher CL (1988) Changes in landscape patterns in Georgia, USA. Landscape Ecol 1:241–251CrossRefGoogle Scholar
  66. Vos CC, Stumpel HP (1995) Comparison of habitat-isolation parameters in relation to fragmented distribution patterns in the tree frog (Hyla arborea). Landscape Ecol 11:203–214CrossRefGoogle Scholar
  67. Vuilleumier S, Wilcox C, Cairns BJ, Possingham HP (2007) How patch configuration affects the impact of disturbances on metapopulation persistence. Theor Popul Biol 72(1):77–85PubMedCrossRefGoogle Scholar
  68. Wiens JA, Stenseth NC, Vanhorne B, Ims RA (1993) Ecological mechanisms and landscape ecology. Oikos 66(3):369–380CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Trine Dalkvist
    • 1
    • 2
  • Richard M. Sibly
    • 3
  • Chris J. Topping
    • 1
  1. 1.Department of BioscienceUniversity of AarhusRøndeDenmark
  2. 2.Department of Environmental, Social and Spatial ChangeRoskilde UniversityRoskildeDenmark
  3. 3.School of Biological SciencesUniversity of ReadingReadingUK

Personalised recommendations