Advertisement

Landscape Ecology

, Volume 33, Issue 9, pp 1505–1517 | Cite as

Spatiotemporal variability in resources affects herbivore home range formation in structurally contrasting and unpredictable agricultural landscapes

  • W. Ullmann
  • C. Fischer
  • K. Pirhofer-Walzl
  • S. Kramer-Schadt
  • N. Blaum
Research Article

Abstract

Context

Movement is one of the key mechanisms for animals to deal with changes within their habitats. Therefore, resource variability can impact animals’ home range formation, especially in spatially and temporally highly dynamic landscapes, such as farmland. However, the movement response to resource variability might depend on the underlying landscape structure.

Objectives

We investigated whether a given landscape structure affects the level of home range size adaptation in response to resource variability. We tested whether increasing resource variability forces herbivorous mammals to increase their home ranges.

Methods

In 2014 and 2015 we collared 40 European brown hares (Lepus europaeus) with GPS-tags to record hare movements in two regions in Germany with differing landscape structures. We examined hare home range sizes in relation to resource availability and variability by using the normalized difference vegetation index as a proxy.

Results

Hares in simple landscapes showed increasing home range sizes with increasing resource variability, whereas hares in complex landscapes did not enlarge their home range.

Conclusions

Animals in complex landscapes have the possibility to include various landscape elements within their home ranges and are more resilient against resource variability. But animals in simple landscapes with few elements experience shortcomings when resource variability becomes high. The increase in home range size, the movement related increase in energy expenditure, and a decrease in hare abundances can have severe implications for conservation of mammals in anthropogenic landscapes. Hence, conservation management could benefit from a better knowledge about fine-scaled effects of resource variability on movement behaviour.

Keywords

Resource variability Resource availability Home range size European brown hare GPS tracking Telemetry Lepus europaeus 

Notes

Acknowledgements

This study was conducted in cooperation with and funds from the Leibniz Centre for Agricultural Landscape Research (ZALF), the long-term research platform “AgroScapeLab Quillow” (Leibniz Centre for Agricultural Landscape Research (ZALF) e.V.) and within the DFG funded research training group ‘BioMove’ (RTG 2118-1). Part of the telemetry material was also funded by the European Fund for Rural Development (EFRE) in the German federal state of Brandenburg. We thank the employees of the ZALF research station in Dedelow for their help and technical support. We also thank the Leibnitz Institute for Zoo and Wildlife Research Berlin—Niederfinow and Jochen Godt from the University of Kassel for providing the nets to catch hares. We also thank all students and hunters that helped with trapping and the land owners for allowing us to work on their land. All procedures for the research were obtained in accordance with the Federal Nature Conservation Act (§ 45 Abs. 7 Nr. 3) and approved by the local nature conservation authority (Reference Nos. LUGV V3-2347-22-2013 and 55.2-1-54-2532-229-13).

Supplementary material

10980_2018_676_MOESM1_ESM.doc (245 kb)
Supplementary material 1 (DOC 245 kb)

References

  1. Anderson DP, Forester JD, Turner MG, Frair J, Merrill E, Fortin D, Beyer HL, Mao JS, Boyce MS, Fryxell J (2005) Factors influencing female home range sizes in elk (Cervus elaphus) in North American landscapes. Landscape Ecol 20:257–271CrossRefGoogle Scholar
  2. Batáry P, Gallé R, Riesch F, Fischer C, Dormann C C, Mußhoff O, Császár P, Fusaro S, Gayer C, Happe AK, Kurucz K, Molnár D, Rösch V, Wietzke A, Tscharntke T (2017) The former Iron Curtain still drives biodiversity—profit trade-offs in German agriculture. Nat Ecol Evol 1:1279CrossRefPubMedGoogle Scholar
  3. Bayerische Vermessungsverwaltung (2014) Geobasisdaten zur tatsächlichen Nutzung. In: http://www.ldbv.bayern.de/produkte/kataster/tat_nutzung.html
  4. Bayerisches Landesamt für Statistik und Datenverarbeitung (2016) Erntemengenanteile der Fruchtartgruppen in Bayern 2015 in Prozent. In: https://www.statistik.bayern.de/statistik/landwirtschaft/#
  5. Benton TG, Vickery JA, Wilson JD (2003) Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol Evol 18:182–188CrossRefGoogle Scholar
  6. Bivand R, Keitt T, Rowlingson B (2014) rgdal: Bindings for the Geospatial Data Abstraction Library. R package version 0.8-16. In: Available at http://CRAN.R-project.org/package=rgdal
  7. Blaum N, Schwager M, Wichmann MC, Rossmanith E (2012) Climate induced changes in matrix suitability explain gene flow in a fragmented landscape—the effect of interannual rainfall variability. Ecography (Cop) 35:650–660CrossRefGoogle Scholar
  8. Boersma PD, Rebstock GA (2009) Foraging distance affects reproductive success in Magellanic penguins. Mar Ecol Prog Ser 375:263–275CrossRefGoogle Scholar
  9. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media, New YorkGoogle Scholar
  10. Burt WH (1943) Territoriality and home range concepts as applied to mammals. J Mammal 24:346–352CrossRefGoogle Scholar
  11. Calenge C (2006) The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecol Modell 197:516–519CrossRefGoogle Scholar
  12. Daan S, Deerenberg C, Dijkstra C (1996) Increased daily work precipitates natural death in the kestrel. J Anim Ecol 65:539–544CrossRefGoogle Scholar
  13. Duncan C, Nilsen EB, Linnell JDC, PettorelliI N (2015) Life-history attributes and resource dynamics determine intraspecific home-range sizes in Carnivora. Remote Sens Ecol Conserv 1:1–12CrossRefGoogle Scholar
  14. Edwards PJ, Fletcher MR, Berny P (2000) Review of the factors affecting the decline of the European brown hare, Lepus europaeus (Pallas, 1778) and the use of wildlife incident data to evaluate the significance of paraquat. Agric Ecosyst Environ 79(2–3):95–103CrossRefGoogle Scholar
  15. Eide NE, Jepsen JU, Prestrud PÅL (2004) Spatial organization of reproductive arctic foxes Alopex lagopus: responses to changes in spatial and temporal availability of prey. J Anim Ecol 73:1056–1068CrossRefGoogle Scholar
  16. Fahrig L, Girard J, Duro D, Pasher J, Smith A, Javorek S, King D, Lindsay KF, Mitchell S, Tischendorf L (2015) Farmlands with smaller crop fields have higher within-field biodiversity. Agric Ecosyst Environ 200:219–234CrossRefGoogle Scholar
  17. Fischer C, Schröder B (2014) Predicting spatial and temporal habitat use of rodents in a highly intensive agricultural area. Agric Ecosyst Environ 189:145–153CrossRefGoogle Scholar
  18. Fischer C, Thies C, Tscharntke T (2011) Small mammals in agricultural landscapes: opposing responses to farming practices and landscape complexity. Biol Conserv 144:1130–1136CrossRefGoogle Scholar
  19. Frylestam B (1992) Utilization by brown hares Lepus europaeus, Pallas of field habitats and complimentary food stripes in southern Sweden. In: Bobek B, Perzanowski K, Regelin W (eds) Global Trends in Wildlife Management. Swiat Press, Krakow-Warszawa, pp 259–261Google Scholar
  20. Handcock RN, Swain DL, Bishop-Hurley GJ, Patison KP, Wark T, Valencia P, Corke P, ONeill CJ (2009) Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing. Sensors 9:3586–3603CrossRefPubMedGoogle Scholar
  21. Hansen B, Herfindal I, Aanes R, Saether BE, Henriksen S (2009) Functional response in habitat selection and the tradeoffs between foraging niche components in a large herbivore. Oikos 118:859–872CrossRefGoogle Scholar
  22. Harestad AS, Bunnel FL (1979) Home range and body weight—a reevaluation. Ecology 60:389–402CrossRefGoogle Scholar
  23. Hijmans RJ, Van Etten J (2014) raster: Geographic data analysis and modeling. R package version 2.2-31. In: http://CRAN.R-project.org/package = raster
  24. InVeKoS (2014) Integriertes Verwaltungs- und Kontrollsystem—Landesvermessung und Geobasisinformation Brandenburg. In: https://www.geobasis-bb.de/dienstleister/gis_invekos.htm
  25. Johnson DDP, Kays R, Blackwell PG, MacDonald DW (2002) Does the resource dispersion hypothesis explain group living? Trends Ecol Evol 17:563–570CrossRefGoogle Scholar
  26. Jonzén N, Knudsen E, Holt RD, Sæther B-E (2011) Uncertainty and predictability: the niches of migrants and nomads. In: Milner-Gulland E, Fryxell JM, Sinclair ARE (eds) Animal migration: A synthesis. Oxford University Press, pp 91–109Google Scholar
  27. Kleijn D, Baquero RA, Clough Y, Diaz M, De Esteban J, Fernandez F, Gabriel D, Herzog F, Holzschuh A, Johl R, Knop E, Kruess A, Marshall EJP, Steffan-Dewenter I, Tscharntke T, Verhulst J, West TM, Yela JL (2006) Mixed biodiversity benefits of agri-environment schemes in five European countries. Ecol Lett 9:243–254CrossRefPubMedGoogle Scholar
  28. Leutner B, Horning N (2016) RStoolbox: tools for remote sensing data analysis. R Package version 0.1. 4. In: Available at https://CRAN.r-project.org/package=RStoolbox
  29. Lewandoski K, Nowakowski JJ (1993) Spatial distribution of brown hare (Lepus europaeus) populations in various types of agriculture. Acta Theriol (Warsz) 38(4):435–442CrossRefGoogle Scholar
  30. MacDonald DW (1983) The ecology of carnivore social behaviour. Nature 301:379–384CrossRefGoogle Scholar
  31. MacNab BK (1963) Bioenergetics and the determination of home range size. Am Nat 97:133–140CrossRefGoogle Scholar
  32. Marable MK, Belant JL, Godwin D, Wang G (2012) Effects of resource dispersion and site familiarity on movements of translocated wild turkeys on fragmented landscapes. Behav Process 91:119–124CrossRefGoogle Scholar
  33. Marboutin E, Aebischer NJ (1996) Does harvesting arable crops influence the behaviour of the European hare (Lepus europaeus)? Wildl Biol 2(2):83–91CrossRefGoogle Scholar
  34. McClintic LF, Taylor JD, Jones JC, Singleton RD, Wang G (2014) Effects of spatiotemporal resource heterogeneity on home range size of American beaver. J Zool 293:134–141CrossRefGoogle Scholar
  35. Mcloughlin PD, Ferguson SH, Messier F (2000) Intraspecific variation in home range overlap with habitat quality: a comparison among brown bear populations. Evol Ecol 14:39–60CrossRefGoogle Scholar
  36. McLoughlin PD, Morris DW, Fortin D, Vander Wal E, Contasti AL (2010) Considering ecological dynamics in resource selection functions. J Anim Ecol 79:4–12CrossRefPubMedGoogle Scholar
  37. Morales JM, Moorcroft PR, Matthiopoulos J, Frair JL, Kie JG, Powell RA, Merrill EH, Haydon DT (2010) Building the bridge between animal movement and population dynamics. Philos Trans R Soc London B Biol Sci 365:2289–2301CrossRefPubMedGoogle Scholar
  38. Mortelliti A, Boitani L (2008) Interaction of food resources and landscape structure in determining the probability of patch use by carnivores in fragmented landscapes. Landscape Ecol 23:285–298CrossRefGoogle Scholar
  39. Mueller T, Fagan WF (2008) Search and navigation in dynamic environments—from individual behaviours to population distributions. Oikos 117:654–664CrossRefGoogle Scholar
  40. Mueller T, Olson KA, Dressler G, Leimgruber P, Fuller TK, Nicolson C, Novaro AJ, Bolgeri MJ, Wattles D, DeStefano S, Calabrese JM, Fagan WF (2011) How landscape dynamics link individual- to population-level movement patterns: A multispecies comparison of ungulate relocation data. Glob Ecol Biogeogr 20:683–694CrossRefGoogle Scholar
  41. Naidoo R, Du Preez P, Stuart-Hill G, Weaver LC, Jago M, Wegmann M (2012) Factors affecting intraspecific variation in home range size of a large African herbivore. Landscape Ecol 27:1523–1534CrossRefGoogle Scholar
  42. Nilsen EB, Herfindal I, Linnell JDC (2009) Can intra-specific variation in carnivore home-range size be explained using remote-sensing estimates of environmental productivity? Ecoscience 12:68–75CrossRefGoogle Scholar
  43. Pettorelli N, Ryan S, Mueller T, Bunnefeld N, Jedrzejewska B, Lima M, Kausrud K (2011) The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Clim Res 46:15–27CrossRefGoogle Scholar
  44. Pettorelli N, Vik JO, Mysterud A, Gaillard J, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20:503–510CrossRefPubMedGoogle Scholar
  45. Pinheiro J, Bates D, DebRoy S, Sarkar, R Core Team (2014) nlme: linear and nonlinear mixed effects models. R package version 3.1-117. In: Available at http://CRAN.R-project.org/package=nlme
  46. R Core Team (2016) R: A language and environment for statistical computing. In: Vienna, Austria R Found. Stat. Comput. https://www.r-project.org/
  47. Relyea RA, Lawrence RK, Demarais S (2000) Home range of desert mule deer: testing the body-size and habitat-productivity hypotheses. J Wildl Manag 64:146–153CrossRefGoogle Scholar
  48. Requena-Mullor JM, López E, Castro AJ, Cabello J, Virgós E, González-Miras E, Castro H (2014) Modeling spatial distribution of European badger in arid landscapes: an ecosystem functioning approach. Landscape Ecol 29:843–855CrossRefGoogle Scholar
  49. Rouse Jr JW (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. In: Nasa Tech. Reports ServGoogle Scholar
  50. Rühe F, Hohmann U (2004) Seasonal locomotion and home-range characteristics of European hares (Lepus europaeus) in an arable region in central Germany. Eur J Wildl Res 50(3):101–111Google Scholar
  51. Saïd S, Gaillard J, Widmer O, Débias F, Bourgoin G, Delorme D, Roux C (2009) What shapes intra-specific variation in home range size? A case study of female roe deer. Oikos 118:1299–1306CrossRefGoogle Scholar
  52. Saïd S, Servanty S (2005) The influence of landscape structure on female roe deer home-range size. Landscape Ecol 20:1003–1012CrossRefGoogle Scholar
  53. Schai-Braun SC, Hackländer K (2014) Home range use by the European hare (Lepus europaeus) in a structurally diverse agricultural landscape analysed at a fine temporal scale. Acta Theriol (Warsz) 59:277–287CrossRefGoogle Scholar
  54. Schai-Braun SC, Peneder S, Frey-Roos F, Hackländer K (2014) The influence of cereal harvest on the home-range use of the European hare (Lepus europaeus). Mammalia 78(4):497–506Google Scholar
  55. Schmidt NM, Asferg T, Forchhammer MC (2004) Long-term patterns in European brown hare population dynamics in Denmark: effects of agriculture, predation and climate. BMC Ecol 4(1):15CrossRefPubMedPubMedCentralGoogle Scholar
  56. Smith RK, Jennings NV, Robinson A, Harris S (2004) Conservation of European hares Lepus europaeus in Britain: is increasing habitat heterogeneity in farmland the answer? J Appl Ecol 41:1092–1102CrossRefGoogle Scholar
  57. Smith RK, Vaughan Jennings N, Harris S (2005) A quantitative analysis of the abundance and demography of European hares Lepus europaeus in relation to habitat type, intensity of agriculture and climate. Mamm Rev 35:1–24CrossRefGoogle Scholar
  58. Strauß E, Grauer A, Bartel M, Klein R, Wenzelides L, Greiser G, Muchin A, Nösel H, Winter A (2008) The German wildlife information system: population densities and development of European Hare (Lepus europaeus PALLAS) during 2002–2005 in Germany. Eur J Wildl Res 54:142–147CrossRefGoogle Scholar
  59. Swihart RK (1986) Home range-body mass allometry in rabbits and hares (Leporidae). Acta Theriol (Warsz) 31:139–148CrossRefGoogle Scholar
  60. Tapper SC, Barnes RFW (1986) Influence of farming practise on the ecology of the brown hare (Lepus europaeus). J Appl Ecol 23:39–52CrossRefGoogle Scholar
  61. Tscharntke T, Klein AM, Kruess A, Steffan-Dewenter I, Thies C (2005) Landscape perspectives on agricultural intensification and biodiversity—ecosystem service management. Ecol Lett 8:857–874CrossRefGoogle Scholar
  62. van Moorter B, Bunnefeld N, Panzacchi M, Rolandsen C, Solberg E, Sæther BE (2013) Understanding scales of movement: animals ride waves and ripples of environmental change. J Anim Ecol 82:770–780CrossRefPubMedGoogle Scholar
  63. Vasseur C, Joannon A, Aviron S, Burel F, Meynard J-M, Baudry J (2013) The cropping systems mosaic: how does the hidden heterogeneity of agricultural landscapes drive arthropod populations? Agric Ecosyst Environ 166:3–14CrossRefGoogle Scholar
  64. Venables WN, Ripley BD (2002) Modern Applied Statistics with S. In: Available at http://CRAN.R-project.org/package=MASS
  65. Wegmann M, Leutner B, Dech S (2016) Remote sensing and GIS for ecologists: using open source software. Pelagic Publishing Ltd, ExeterGoogle Scholar
  66. Wikelski M, Kays R (2016) Movebank: archive, analysis and sharing of animal movement data. World Wide Web electronic publication. In: http://www.movebank.org (Accessed 1 Jun 2016)
  67. Wood SN (2001) mgcv: GAMs and generalized ridge regression for R. R news 1(2):20–25Google Scholar
  68. Worton BJ (1989) Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70:164–168CrossRefGoogle Scholar
  69. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New YorkCrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Plant Ecology and Nature ConservationUniversity of PotsdamPotsdamGermany
  2. 2.Institute for Landscape BiogeochemistryLeibniz-Centre for Agricultural Landscape Research (ZALF)MünchebergGermany
  3. 3.Restoration Ecology, Department of Ecology and Ecosystem ManagementTechnische Universität MünchenMunichGermany
  4. 4.Institute of Biology, Dahlem Center for Plant SciencesFreie Universität BerlinBerlinGermany
  5. 5.Department of Ecological DynamicsLeibniz-Institute for Zoo and Wildlife Research (IZW)BerlinGermany
  6. 6.Department of EcologyTechnische Universität BerlinBerlinGermany

Personalised recommendations