Advertisement

Tools for Landscape Science: Theory, Models and Data

  • Marcel van OijenEmail author
Chapter
Part of the Innovations in Landscape Research book series (ILR)

Abstract

We review the different roles of theory, models and data in landscape science. The need for science at the landscape scale is argued. Landscape theory is considered as a repository of probabilistic patterns rather than as a collection of laws of nature. We present a typology of such patterns for five distinct landscape features: land cover, land use, patch properties, patch interactions and exogenous influences. We show how theory for these features can support landscape modelling, and we provide a checklist of questions for model developers. The limited availability of data on landscapes is discussed, and how that leads to uncertainties in theoretical patterns as well as models. We analyse how probability theory can be used to account for these uncertainties, strengthening the links between theory, models and data, and facilitating decision support.

Keywords

Agricultural landscapes Bayesian methods Data Ecosystem services Landscape theory Models Probability theory Uncertainties 

Abbreviations

ALS

Agricultural landscapes

ES

Ecosystem services

FLS

Forest landscapes

Notes

Acknowledgements

I thank the organizers of the Landscape 2018 meeting in Berlin for their invitation to participate, and for the stimulating discussions about the future of landscape science.

References

  1. Baker WL (1989) A review of models of landscape change. Landsc Ecol 2:111–133CrossRefGoogle Scholar
  2. Baker WL, Cai Y (1992) Ther.Le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landsc Ecol 7:291–302.  https://doi.org/10.1007/BF00131258CrossRefGoogle Scholar
  3. Baker W, Egbert SL, Frazier GF (1991) A spatial model for studying the effects of climatic change on the structure of landscapes subject to large disturbances. Ecol Model 56:109–125.  https://doi.org/10.1016/0304-3800(91)90195-7CrossRefGoogle Scholar
  4. Bealey WJ, Loubet B, Braban CF, Famulari D, Theobald MR, Reis S, Reay DS, Sutton MA (2014) Modelling agro-forestry scenarios for ammonia abatement in the landscape. Environ Res Lett 9:125001.  https://doi.org/10.1088/1748-9326/9/12/125001CrossRefGoogle Scholar
  5. Begg GS, Cook SM, Dye R, Ferrante M, Franck P, Lavigne C, Lövei GL, Mansion-Vaquie A, Pell JK, Petit S, Quesada N, Ricci B, Wratten SD, Birch AE (2017) A functional overview of conservation biological control. Crop Prot 97:145–158.  https://doi.org/10.1016/j.cropro.2016.11.008CrossRefGoogle Scholar
  6. Bertalanffy LV (1968) General system theory: foundations, development, applications. George BrazillerInc, New YorkGoogle Scholar
  7. Bertram J, Dewar RC (2013) Statistical patterns in tropical tree cover explained by the different water demand of individual trees and grasses. Ecology 94:2138–2144CrossRefGoogle Scholar
  8. Bormann FH, Likens GE (1967) Nutrient cycling. Science 155:424–429.  https://doi.org/10.1126/science.155.3761.424CrossRefPubMedGoogle Scholar
  9. Cabell J, Oelofse M (2012) An indicator framework for assessing agroecosystem resilience. Ecol Soc 17.  https://doi.org/10.5751/ES-04666-170118
  10. Chaplin-Kramer R, O’Rourke ME, Blitzer EJ, Kremen C (2011) A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol Lett 14:922–932.  https://doi.org/10.1111/j.1461-0248.2011.01642.xCrossRefPubMedGoogle Scholar
  11. Dalgaard T, Bienkowski JF, Bleeker A, Dragosits U, Drouet JL, Durand P, Frumau A, Hutchings NJ, Kedziora A, Magliulo V, Olesen JE, Theobald MR, Maury O, Akkal N, Cellier P (2012) Farm nitrogen balances in six European landscapes as an indicator for nitrogen losses and basis for improved management. Biogeosciences 9:5303–5321.  https://doi.org/10.5194/bg-9-5303-2012CrossRefGoogle Scholar
  12. Dinsmore KJ, Billett MF, Skiba UM, Rees RM, Drewer J, Helfter C (2010) Role of the aquatic pathway in the carbon and greenhouse gas budgets of a peatland catchment. Glob Chang Biol 16:2750–2762.  https://doi.org/10.1111/j.1365-2486.2009.02119.xCrossRefGoogle Scholar
  13. Duretz S, Drouet J, Durand P, Hutchings N, Theobald M, Salmon-Monviola J, Dragosits U, Maury O, Sutton M, Cellier P (2011) NitroScape: a model to integrate nitrogen transfers and transformations in rural landscapes. Environ Pollut 159:3162–3170.  https://doi.org/10.1016/j.envpol.2011.05.005CrossRefPubMedGoogle Scholar
  14. Erikstad L et al (2017) LandskapstyperiNorge: Nymetodikk for kartleggingavlandskap. https://www.regjeringen.no/contentassets/8eebe3a246ee4baa8b6eb94ef9827eab/erikstad.pdf. Accessed on 17 Mar 2019
  15. Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, Cambridge, New York, p 632CrossRefGoogle Scholar
  16. Gonzalez-Redin J, Luque S, Poggio L, Smith R, Gimona A (2016) Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes. Environ Res 144(Part B):15–26.  https://doi.org/10.1016/j.envres.2015.11.009 (The provision of ecosystem services in response to global change)CrossRefPubMedGoogle Scholar
  17. Holling C, Gunderson L, Ludwig D (2002) In quest of a theory of adaptive change. In: Gunderson L, Holling C (eds) Panarchy: understanding transformations in human and natural systems. Island Press, Washington, pp 3–22Google Scholar
  18. Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc: Ser B (Stat Methodol) 63:425–464CrossRefGoogle Scholar
  19. Kleidon A, Zehe E, Ehret U, Scherer U (2013) Thermodynamics, maximum power, and the dynamics of preferential river flow structures at the continental scale. Hydrol Earth Syst Sci 17:225–251.  https://doi.org/10.5194/hess-17-225-2013CrossRefGoogle Scholar
  20. Lee J, Bagheri B, Kao H-A (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23.  https://doi.org/10.1016/j.mfglet.2014.12.001CrossRefGoogle Scholar
  21. Levy P, Van Oijen M, Buys G, Tomlinson S (2018) Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach. Biogeosciences 15:1497–1513.  https://doi.org/10.5194/bg-15-1497-2018CrossRefGoogle Scholar
  22. Odum EP (1968) Energy flow in ecosystems: a historical review. Am Zool 8:11–18CrossRefGoogle Scholar
  23. Roschewitz I, Gabriel D, Tscharntke T, Thies C (2005) The effects of landscape complexity on arable weed species diversity in organic and conventional farming. J Appl Ecol 42:873–882.  https://doi.org/10.1111/j.1365-2664.2005.01072.xCrossRefGoogle Scholar
  24. Thenail C, Baudry J (2004) Variation of farm spatial land use pattern according to the structure of the hedgerow network (bocage) landscape: a case study in northeast Brittany. Agr Ecosyst Environ 101:53–72.  https://doi.org/10.1016/S0167-8809(03)00199-3CrossRefGoogle Scholar
  25. Van Oijen M (2002) On the use of specific publication criteria for papers on process-based modelling in plant science. Field Crop Res 74:197–205CrossRefGoogle Scholar
  26. Van Oijen M (2009) Theory and models for managed ecosystems: from confusion to certainty and back again. 40 Years Theory and Model at Wageningen UR 25Google Scholar
  27. Van Oijen M (2017) Bayesian methods for quantifying and reducing uncertainty and error in forest models. Curr For Rep 3:269–280.  https://doi.org/10.1007/s40725-017-0069-9CrossRefGoogle Scholar
  28. Van Oijen M, Rougier J, Smith R (2005) Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol 25:915–927.  https://doi.org/10.1093/treephys/25.7.915CrossRefPubMedGoogle Scholar
  29. Van Oijen M, Beer C, Cramer W, Rammig A, Reichstein M, Rolinski S, Soussana J-F (2013) A novel probabilistic risk analysis to determine the vulnerability of ecosystems to extreme climatic events. Environ Res Lett 8:015032.  https://doi.org/10.1088/1748-9326/8/1/015032CrossRefGoogle Scholar
  30. Van Oijen M, Cameron D, Levy PE, Preston R (2017) Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models. Environ Model Softw 94:157–165.  https://doi.org/10.1016/j.envsoft.2017.03.023CrossRefGoogle Scholar
  31. Van Oijen M, Bellocchi G, Höglind M (2018) Effects of climate change on grassland biodiversity and productivity: the need for a diversity of models. Agronomy 8:14.  https://doi.org/10.3390/agronomy8020014CrossRefGoogle Scholar
  32. von Thünen J (1826) Die isolierte Staat in Beziehung auf Landwirtshaft und Nationalökonomie. Perthes, HamburgGoogle Scholar
  33. West G (2018) Scale: the universal laws of life and death in organisms, cities and companies. W&N.320 pp, ISBN 9781780225593Google Scholar
  34. Whipple AV, Holeski LM (2016) Epigenetic inheritance across the landscape. Front Genet 7.  https://doi.org/10.3389/fgene.2016.00189

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Ecology & HydrologyPenicuikUK

Personalised recommendations