Legacies, socio-economic and biophysical processes and drivers: the case of future forest cover expansion in the Polish Carpathians and Swiss Alps

Abstract

Mountain forest areas are key for providing a wide range of ecosystem services and are hot spots for land use change processes, in particular, increase in forest cover at the expense of mountain pastures and meadows. Mountain forest systems in eastern and western Europe have likely similar future socioeconomic situations but significantly different socioeconomic history. Using a scenario-based land use modelling approach (Dyna-CLUE framework) we model three scenarios (trend, liberalisation and self-sufficiency) of future land use in the Polish Carpathians and the Swiss Alps, focussing on forest cover change. We find that forest cover increase can be expected to continue in European mountainous regions under all likely scenarios, limited only by relatively strict policy interventions. Biophysical factors, rather than socioeconomic ones, are key for defining the suitability for, and therefore likely locations of future forest cover, but land use legacy plays a very important role in the spatial patterns of future forest cover, particularly in eastern Europe.

Land use changes and their catchment-scale environmental impact in the Polish Western Carpathians during transition from centrally planned to free-market economics

“…Between 2004 and 2015, reduced area of LULC change resulted mainly from already abandonment of most of the cultivated land and the rising role of forest succession (Kolecka et al 2017). CAP as a new form of agricultural support also facilitated stabilization of LULC.…”

“…Most of this area also experienced a gradual decline in agricultural land use during that time which accelerated after the collapse of the centrally planned economy in 1989 (Kuemmerle et al 2008;Ostafin 2009;Kozak 2010;Bucała-Hrabia 2017a). Such LULC dynamics can be characterised as a period of relative stability followed by rapid changes with potentially long-lasting effects in the society and environment (Dearing et al 2010;Lambin & Meyfroidt 2010;Hostert et al 2011;Price et al 2017). Several studies have shown spatio-temporal variability of this phenomenon during the transformation period at scales of the entire Polish Carpathians (Kozak 2005(Kozak , 2010Ciołkosz et al 2011;Kolecka et al 2017), their subregions (Kozak 2003;Kaim 2009;Ostafin 2009) and administrative units (Dec et al 2009;Kolecka et al 2015Kolecka et al , 2016.…”

“…Such LULC dynamics can be characterised as a period of relative stability followed by rapid changes with potentially long-lasting effects in the society and environment (Dearing et al 2010;Lambin & Meyfroidt 2010;Hostert et al 2011;Price et al 2017). Several studies have shown spatio-temporal variability of this phenomenon during the transformation period at scales of the entire Polish Carpathians (Kozak 2005(Kozak , 2010Ciołkosz et al 2011;Kolecka et al 2017), their subregions (Kozak 2003;Kaim 2009;Ostafin 2009) and administrative units (Dec et al 2009;Kolecka et al 2015Kolecka et al , 2016. However, environmental consequences of recent LULC changes are still not sufficiently understood, particularly at the catchment scale (Bucała 2014;Bucała--Hrabia 2017a, b).…”

Current practices and challenges for modelling past and future land use and land cover changes in mountainous regions

“…Price et al (2017) used past LULCC to model future forest cover changes by 2060 over large areas in two mountain regions, the Swiss Alps and the Polish Carpathians. They considered three scenarios of land cover changes in both regions relying on identified drivers determining previous forest cover trends and showed that for any scenario, forest cover will increase by 2-3% in the Swiss Alps and 6-7% in the Polish Carpathians.…”

“…The modelling approaches ranged from traditional methods, for instance logistic regression , GAMs (Loran et al 2017) or GLMs (Price et al 2017), to more novel approaches in land change modelling such as ensemble modelling . Ensemble modelling was proved to be a powerful tool to assess uncertainty of historical forest change and quantify projection robustness by considering a suite of models rather than a single model type.…”