Skip to main content

Long-term change in drivers of forest cover expansion: an analysis for Switzerland (1850-2000)

Abstract

The spatial distribution of forests in Europe represents the legacy of centuries of human land use decisions. Due to the limited availability of historical data, most studies on forest cover change focus only on analyzing recent decades, thereby overlooking the important long-term context. However, the latter is essential to improve our understanding of present landscape patterns. This study quantifies the spatiotemporal dynamics in drivers of forest gain in Switzerland. Specifically, we model forest gain in a long-term study covering 150 years (1850–2000) split into periods of similar length (∼30 years). This makes it possible to identify non-linear dynamics and whether drivers have changed over time. The rates of forest change are quantified based on analyzing historical maps and contemporary forest inventory data. Generalized additive models (GAMs) are fitted to examine the variation in the relative importance of socioeconomic and biophysical explanatory variables. Our results suggest that both biophysical and socioeconomic variables co-drive forest gain. Biophysical variables (such as temperature and slope) were identified as the major drivers explaining variations in forest gain. The most important socioeconomic driver was the change in the percentage of people employed per economic sector, although its effect came with a substantial time lag. Changes in employment per sector for the periods 1920–1941 and 1941–1980 were relevant for forest gain between 1980 and 2000. The identified time lag effect emphasizes the added value of long-term studies, since legacies may persist for decades, adding further complexity to contemporary land change processes. These findings are relevant to many temperate ecosystems that are experiencing increases in forest cover. Such insights can improve both future forest change predictions as well as the development of policies for sustainable landscape management.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  • Agresti A (2002) Categorical data analysis. Wiley, New Jersey

    Book  Google Scholar 

  • Anandhi A, Frei A, Pierson DC, Schneiderman EM, Zion MS, Lounsbury D, Matonse AH (2011) Examination of change factor methodologies for climate change impact assessment. Water Resour Res 47(3). doi:10.1029/2010WR009104

  • Antrop M (2005) Why landscapes of the past are important for the future. Landsc Urban Plan 70:21–34. doi:10.1016/j.landurbplan.2003.10.002

    Article  Google Scholar 

  • Baur P, Bebi P, Gellrich M, Rutherford GN (2006) WaSAlp—Waldausdehnung im Schweizer Alpenraum. Eine quantitative Analyse naturräumlicher und sozio-ökonomischer Ursachen unter besonderer Berücksichtigung des Agrarstrukturwandels. Swiss Federal Research Institute WSL, Birmensdorf

    Google Scholar 

  • Bertogliati M (2016) Agrarintensivierung—aus Wald wird Feld und Wiese. In: Mathieu J, Backhaus N, Hürlimann K, Bürgi M (eds) Geschichte der Landschaft in der Schweiz. Orell Füssli, Zürich

    Google Scholar 

  • Bezák P, Mitchley J (2014) Drivers of change in mountain farming in Slovakia: from socialist collectivisation to the common agricultural policy. Reg Environ Chang 14:1343–1356. doi:10.1007/s10113-013-0580-x

    Article  Google Scholar 

  • Bolliger J, Schmatz D, Pazúr R, Ostapowicz K, Psomas A (2017) Reconstructing forest-cover change in the Swiss Alps between 1880 and 2010 using ensemble modelling. Reg Environ Chang 1–13 doi:10.1007/s10113-016-1090-4

  • Borcard D, Legendre P, Drapeau P (1992) Partialling out the spatial component of ecological variation. Ecology 73:1045–1055

    Article  Google Scholar 

  • Brändli UBR (2010) Schweizerisches Landesforstinventar. Ergebnisse der dritten Erhebung 2004–2006. Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL, Birmensdorf. Bundesamt für Umwelt BAFU, Bern

  • Bürgi M, Hersperger AM, Schneeberger N (2004) Driving forces of landscape change—current and new directions. Landsc Ecol 19:857–868. doi:10.1007/s10980-004-0245-8

    Article  Google Scholar 

  • Bürgi M, Salzmann D, Gimmi U (2015) 264 years of change and persistence in an agrarian landscape: a case study from the Swiss lowlands. Landsc Ecol 30:1321–1333. doi:10.1007/s10980-015-0189-1

    Article  Google Scholar 

  • Chételat J, Kalbermatten M, Lannas KSM, Spiegelberger T, Wettstein J-B, Gillet F, Peringer A, Buttler A (2013) A Contextual Analysis of Land-Use and Vegetation Changes in Two Wooded Pastures in the Swiss Jura Mountains Ecol Soc 18 doi:10.1007/s10980-015-0189-1

  • Colombaroli D, Beckmann M, van der Knaap WO, Curdy P, Tinner W (2013) Changes in biodiversity and vegetation composition in the central Swiss Alps during the transition from pristine forest to first farming. Divers and Distrib 19:157–170. doi:10.1111/j.1472-4642.2012.00930.x

    Article  Google Scholar 

  • Conedera M (2009) Implementing fire history and fire ecology in fire risk assessment: the study case of Canton Ticino (southern Switzerland). Dissertation, University of Karlsruhe

  • Corbelle-Rico E, Crecente-Maseda R, Santé-Riveira I (2012) Multi-scale assessment and spatial modelling of agricultural land abandonment in a European peripheral region: Galicia (Spain), 1956–2004. Land Use Policy 29:493–501. doi:10.1016/j.landusepol.2011.08.008

    Article  Google Scholar 

  • Crawley MJ (2007) The R book. Wiley, Chichester

    Book  Google Scholar 

  • Dearing JA, Braimoh AK, Reenberg A, Turner BL, van der Leeuw S (2010) Complex land systems: the need for long time perspectives to assess their future. Ecol Soc 15(4):21 [online] URL: http://www.ecologyandsociety.org/vol15/iss4/art21/. Accessed 7 June 2016

    Article  Google Scholar 

  • Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D (2012) Qgraph: network visualizations of relationships in psychometric data. Journal Stat Softw 48(4):1–18 [online] URL: http://www.jstatsoft.org/v48/i04/. Accessed 6 June 2016

    Article  Google Scholar 

  • Fjellstad WJ, Dramstad WE (1999) Patterns of change in two contrasting Norwegian agricultural landscapes. Landsc Urban Plan 45:177–191. doi:10.1016/S0169-2046(99)00055-9

    Article  Google Scholar 

  • FOEN, Swiss Federal Office for the Environment (2013) Federal Act on Forest. [online] URL: http://www.bafu.admin.ch/wald/11352/14350/index.html?lang=en. Accessed 7 June 2016

  • Foster DR, Swanson FJ, Aber J, Burke I, Brokaw N, Tilman D, Knapp A (2003) The importance of land-use legacies to ecology and conservation. Bioscience 53:77–88. doi:10.1641/0006-3568(2003)053[0077:TIOLUL]2.0.CO;2

    Article  Google Scholar 

  • Fuchs R, Verburg PH, Clevers JGPW, Herold M (2015) The potential of old maps and encyclopaedias for reconstructing historic European land cover/use change. Appl Geogr 59:43–55. doi:10.1016/j.apgeog.2015.02.013

    Article  Google Scholar 

  • Gellrich M (2006) Natural forest re-growth on abandoned agricultural land in the Swiss mountains. An economic analysis of patterns and causes using spatial statistical models and interviews. Dissertation, Albert-Ludwigs-University

  • Gellrich M, Zimmermann NE (2007) Investigating the regional-scale pattern of agricultural land abandonment in the Swiss mountains: a spatial statistical modelling approach. Landsc Urban Plan 79:65–76. doi:10.1016/j.landurbplan.2006.03.004

    Article  Google Scholar 

  • Gellrich M, Baur P, Koch B, Zimmermann NE (2007a) Agricultural land abandonment and natural forest re-growth in the Swiss mountains: a spatially explicit economic analysis. Agric Ecosyst Environ 118:93–108. doi:10.1016/j.agee.2006.05.001

    Article  Google Scholar 

  • Gellrich M, Baur P, Zimmermann NE (2007b) Natural forest regrowth as a proxy variable for agricultural land abandonment in the Swiss mountains: a spatial statistical model based on geophysical and socio-economic variables. Environ Model Assess 12:269–278. doi:10.1007/s10666-006-9062-6

    Article  Google Scholar 

  • Ginzler C, Brändli U-B, Hägeli M (2011) Waldflächenentwicklung der letzten 120 Jahre in der Schweiz. Z Forstwes 162:337–343. doi:10.3188/szf.2011.0337

    Article  Google Scholar 

  • Gonseth Y, Wohlgemuth T, Sansonnens B, Buttler A (2001) Die biogeographischen Regionen der Schweiz. Erläuterungen und Einteilungsstandard. Umwelt Materialien Nr. 137 Bundesamt für Umwelt, Wald und Landschaft Bern. 48p

  • Grêt-Regamey A, Rabe S-E, Crespo R, Lautenbach S, Ryffel A, Schlup B (2013) On the importance of non-linear relationships between landscape patterns and the sustainable provision of ecosystem services. Landsc Ecol 29:201–212. doi:10.1007/s10980-013-9957-y

    Article  Google Scholar 

  • Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall, London

    Google Scholar 

  • Hersperger AM, Bürgi M (2009) Going beyond landscape change description: quantifying the importance of driving forces of landscape change in a Central Europe case study. Land Use Policy 26:640–648. doi:10.1016/j.landusepol.2008.08.015

    Article  Google Scholar 

  • Hirschi C, Widmer A, Zimmermann W (2012) Waldausdehnung im Berggebiet: Prozesse und Entwicklungen in der Schweizer Waldpolitik Schweizerische Zeitschrift fur Forstwesen 163:512–520. doi:10.3188/szf.2012.0512

  • Hunziker M (1995) The spontaneous reafforestation in abandoned agricultural lands: perception and aesthetic assessment by locals and tourists. Landsc Urban Plan 31:399–410. doi:10.1016/0169-2046(95)93251-J

    Article  Google Scholar 

  • Jepsen MR et al (2015) Transitions in European land-management regimes between 1800 and 2010. Land Use Policy 49:53–64. doi:10.1016/j.landusepol.2015.07.003

    Article  Google Scholar 

  • Kaim D, Kozak J, Kolecka N, Ziółkowska E, Ostafin K, Ostapowicz K, Gimmi U, Munteanu C, Radeloff VC (2016) Broad scale forest cover reconstruction from historical topographic maps. Appl Geogr 67:39–48. doi:10.1016/j.apgeog.2015.12.003

    Article  Google Scholar 

  • Kozak J, Estreguil C, Troll M (2007) Forest cover changes in the northern Carpathians in the 20th century: a slow transition. J Land Use Sci 2:127–146. doi:10.1080/17474230701218244

    Article  Google Scholar 

  • Lambin EF, Meyfroidt P (2011) Global land use change, economic globalization, and the looming land scarcity. Proc Natl Acad Sci U S A 108:3465–3472. doi:10.1073/pnas.1100480108

    CAS  Article  Google Scholar 

  • Leuschner C, Wulf M, Bäuchler P, Hertel D (2014) Forest continuity as a key determinant of soil carbon and nutrient storage in beech forests on Sandy soils in northern Germany. Ecosystems 17:497–511. doi:10.1007/s10021-013-9738-0

    CAS  Article  Google Scholar 

  • Levers C, Verkerk PJ, Müller D, Verburg PH, Butsic V, Leitão PJ, Lindner M, Kuemmerle T (2014) Drivers of forest harvesting intensity patterns in Europe. For Ecol Manag 315:160–172. doi:10.1016/j.foreco.2013.12.030

    Article  Google Scholar 

  • Liu F, Mladenoff DJ, Keuler NS, Schulte Moore L (2011) Broadscale variability in tree data of the historical public land survey and its consequences for ecological studies. Ecol Monogr 81:259–275. doi:10.1890/10-0232.1

    Article  Google Scholar 

  • Loran C, Ginzler C, Bürgi M (2016) Evaluating forest transition based on a multi-scale approach: forest area dynamics in Switzerland 1850–2000. Reg Environ Chang. doi:10.1007/s10113-015-0911-1

    Google Scholar 

  • Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H (2004) European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303(5663):1499–1503. doi:10.1126/science.1093877

    CAS  Article  Google Scholar 

  • MacDonald D, Crabtree JR, Wiesinger G, Dax T, Stamou N, Fleury P, Gutierrez Lazpita J, Gibon A (2000) Agricultural abandonment in mountain areas of Europe: environmental consequences and policy response. J Environ Manag 59:47–69. doi:10.1006/jema.1999.0335

    Article  Google Scholar 

  • Malek Ž, Boerboom L, Glade T (2015) Future Forest cover change scenarios with implications for landslide risk: an example from Buzau Subcarpathians. Romania Environmental management 56:1228–1243. doi:10.1007/s00267-015-0577-y

    Google Scholar 

  • Mather AS, Fairbairn J (2000) From floods to reforestation: the forest transition in Switzerland. Environ Hist 6(4):399–421. doi:10.3197/096734000129342352

    Article  Google Scholar 

  • Melendez-Pastor I, Hernández EI, Navarro-Pedreño J, Gómez I (2014) Socioeconomic factors influencing land cover changes in rural areas: The case of the Sierra de Albarracín (Spain) Applied Geography 52:34–45. doi:10.1016/j.apgeog.2014.04.013

  • Menard S (2002) Applied logistic regression analysis. Sage Publications, Thousand Oaks, 111 pp

    Book  Google Scholar 

  • Meyfroidt P (2015) Approaches and terminology for causal analysis in land systems science. J Land Use Sci 1–27. doi:10.1080/1747423x.2015.1117530

  • Mottet A, Ladet S, Coqué N, Gibon A (2006) Agricultural land-use change and its drivers in mountain landscapes: a case study in the Pyrenees. Agric Ecosyst Environ 114:296–310. doi:10.1016/j.agee.2005.11.017

    Article  Google Scholar 

  • Munteanu C, Kuemmerle T, Keuler NS, Müller D, Balázs P, Dobosz M, Griffiths P, Halada L, Kaim D, Király G, Konkoly-Gyuró É, Kozak J, Lieskovsky J, Ostafin K, Ostapowicz K, Shandra O, Radeloff VC (2015) Legacies of 19th century land use shape contemporary forest cover. Glob Environ Chang 34:83–94. doi:10.1016/j.gloenvcha.2015.06.015

    Article  Google Scholar 

  • Munteanu C, Nita MD, Abrudan IV, Radeloff VC (2016) Historical forest management in Romania is imposing strong legacies on contemporary forests and their management. For Ecol Manag 361:179–193. doi:10.1016/j.foreco.2015.11.023

    Article  Google Scholar 

  • Pauling AJ, Luterbacher C, Casty H, Wanner H (2006) Five hundred years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation. Clim Dyn 26(4):387–405. doi:10.1007/s00382-005-0090-8

    Article  Google Scholar 

  • Pazúr R, Lieskovský J, Feranec J, Oťaheľ J (2014) Spatial determinants of abandonment of large-scale arable lands and managed grasslands in Slovakia during the periods of post-socialist transition and European Union accession. Appl Geogr 54:118–128. doi:10.1016/j.apgeog.2014.07.014

    Article  Google Scholar 

  • Pellissier L, Anzini M, Maiorano L, Dubuis A, Pottier J, Vittoz P, Guisan A (2013) Spatial predictions of land-use transitions and associated threats to biodiversity: the case of forest regrowth in mountain grasslands. Appl Veg Sci 16:227–236. doi:10.1111/j.1654-109X.2012.01215.x

    Article  Google Scholar 

  • Perring MP, De Frenne P, Baeten L, Maes SL, Depauw L, Blondeel H, Caron MM, Verheyen K (2016) Global environmental change effects on ecosystems: the importance of land-use legacies. Glob Chang Biol 22:1361–1371. doi:10.1111/gcb.13146

    Article  Google Scholar 

  • Perz SG, Skole DL (2003) Secondary Forest expansion in the Brazilian Amazon and the refinement of Forest transition theory. Soc Natur Resour 16:277–294. doi:10.1080/08941920390178856

    Article  Google Scholar 

  • Plieninger T, Draux H, Fagerholm N, Bieling C, Bürgi M, Kizos T, Kuemmerle T, Primdahl J, Verburg PH (2016) The driving forces of landscape change in Europe: a systematic review of the evidence. Land Use Policy 57:204–214. doi:10.1016/j.landusepol.2016.04.040

    Article  Google Scholar 

  • Price B, Kienast F, Seidl I, Ginzler C, Verburg PH, Bolliger J (2015) Future landscapes of Switzerland: risk areas for urbanisation and land abandonment. Appl Geogr 57:32–41. doi:10.1016/j.apgeog.2014.12.009

    Article  Google Scholar 

  • Price B, Kaim D, Szwagrzyk M, Ostapowicz K, Kolecka N, Schmatz DR, Wypych A, Kozak J (2016) Legacies, socio-economic and biophysical processes and drivers: the case of future forest cover expansion in the Polish Carpathians and Swiss Alps. Reg Environ Chang 1–13 doi:10.1007/s10113-016-1079-z

  • Regos A, Ninyerola M, More G, Pons X (2015) Linking land cover dynamics with driving forces in mountain landscape of the northwestern Iberian peninsula. Int J Appl Earth Obs Geoinf 38:1–14. doi:10.1016/j.jag.2014.11.010

    Article  Google Scholar 

  • Rhemtulla JM, Mladenoff DJ (2007) Why history matters in landscape ecology. Landsc Ecol 22:1–3. doi:10.1007/s10980-007-9163-x

    Article  Google Scholar 

  • Rhemtulla JM, Mladenoff DJ, Clayton MK (2009) Legacies of historical land use on regional forest composition and structure in Wisconsin, USA (mid-1800s–1930s–2000s). Ecol Appl 19(4):1061–1078. doi:10.1890/08-1453.1

    Article  Google Scholar 

  • Rickebusch S, Gellrich M, Lischke H, Guisan A, Zimmermann NE (2007) Combining probabilistic land-use change and tree population dynamics modelling to simulate responses in mountain forests. Ecol Model 209:157–168. doi:10.1016/j.ecolmodel.2007.06.027

    Article  Google Scholar 

  • Rutherford GN, Bebi P, Edwards PJ, Zimmermann NE (2008) Assessing land-use statistics to model land cover change in a mountainous landscape in the European Alps. Ecol Model 212:460–471. doi:10.1016/j.ecolmodel.2007.10.050

    Article  Google Scholar 

  • Schneeberger N, Bürgi M, Hersperger AM, Ewald KC (2007) Driving forces and rates of landscape change as a promising combination for landscape change research—an application on the northern fringe of the Swiss Alps. Land Use Policy 24:349–361. doi:10.1016/j.landusepol.2006.04.003

    Article  Google Scholar 

  • Schulte LA, Mladenoff DJ (2001) The original US public land survey records. Their use and limitations in reconstructing presettlement vegetation. J For. [online] URL: http://landscape.forest.wisc.edu/PDF/Schulte_Mladenoff2001_JoF.pdf. Accessed 7 June 2016

  • Serneels S, Lambin EF (2001) Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model. Agric Ecosyst Environ 85:65–81. doi:10.1016/S0167-8809(01)00188-8

    Article  Google Scholar 

  • SFSO, Swiss Federal Statistical Office (1997) Statistisches Jahrbuch der Schweiz. Verlag Neue Zürcher Zeitung, Zürich

    Google Scholar 

  • Swetnam TW, Allen CD, Betancourt JL (1999) Applied historical ecology: using the past to manage for the future. Ecol Appl 9:1189–1206. doi:10.1890/1051-0761(1999)009[1189:AHEUTP]2.0.CO;2

    Article  Google Scholar 

  • Swisstopo (2015) Journey through time—maps. Federal office of topography. https://map.geo.admin.ch/?topic=swisstopo&X=190000.00&Y=660000.00&zoom=1&lang=en&bgLayer=ch.swisstopo.pixelkarte-farbe&catalogNodes=1392&layers=ch.swisstopo.zeitreihen&time=1864&layers_timestamp=18641231. Accessed 17 May 2016

  • Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–251. doi:10.1016/S0022-1694(96)03128-9

    Article  Google Scholar 

  • Thuiller W, Georges D, Engler R, Breiner F (2016) biomod2: ensemble platform for species distribution modeling. R package version 3:3–7 https://CRAN.R-project.org/package=biomod2

    Google Scholar 

  • van Strien M, Keller D, Holderegger R, Ghazoul J, Kienast F, Bolliger J (2014). Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow. Ecol Appl 24(2):327e339. doi::10.1890/13-0442.1.

  • van Vliet J, de Groot HLF, Rietveld P, Verburg PH (2015) Manifestations and underlying drivers of agricultural land use change in Europe. Landsc Urban Plan 133:24–36. doi:10.1016/j.landurbplan.2014.09.001

    Article  Google Scholar 

  • Verburg PH, Overmars KP (2009) Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landsc Ecol 24:1167–1181. doi:10.1007/s10980-009-9355-7

    Article  Google Scholar 

  • Verburg PH, Schot PP, Dijst MJ, Veldkamp A (2004) Land use change modelling: current practice and research priorities. GeoJournal 61:309–324. doi:10.1007/s10708-004-4946-y

    Article  Google Scholar 

  • Wilkenskjeld S, Kloster S, Pongratz J, Raddatz T, Reick CH (2014) Comparing the influence of net and gross anthropogenic land-use and land-cover changes on the carbon cycle in the MPI-ESM. Biogeosciences 11:4817–4828. doi:10.5194/bg-11-4817-2014

    Article  Google Scholar 

  • Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc (B) 73(1):3–36. doi:10.1111/j.1467-9868.2010.00749.x

    Article  Google Scholar 

  • Xoplaki E, Luterbacher J, Paeth H, Dietrich D, Steiner N, Grosjean M, Wanner H (2005) European spring and autumn temperature variability and change of extremes over the last half millennium. Geophys Res Lett 32(15). doi:10.1029/2005GL023424

  • Zimmermann NE, Edwards TC, Moisen GG, Frescino TS, Blackard JA (2007) Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol 44:1057–1067. doi:10.1111/j.1365-2664.2007.01348.x

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge support by the Swiss National Science Foundation (SNSF) in the project Forest dynamics in Switzerland (FORDYNCH)—pattern, driving forces, and ecological implications (Grant No. 200021-143242), the National Aeronautic Space Administration (NASA), and the NASA Earth System Science Fellowship Program. Thanks to Curtis Gautschi who kindly improved the English. N.E.Z. additionally acknowledges support from the Swiss National Science Foundation (grant 40FA40_158395).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christin Loran.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Loran, C., Munteanu, C., Verburg, P.H. et al. Long-term change in drivers of forest cover expansion: an analysis for Switzerland (1850-2000). Reg Environ Change 17, 2223–2235 (2017). https://doi.org/10.1007/s10113-017-1148-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10113-017-1148-y

Keywords

  • Long-term forest cover expansion
  • Socioeconomic and biophysical drivers
  • Switzerland
  • Time lag effect
  • Historical maps