impacts of land use on the vegetation in three rural...

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Impacts of land use on the vegetation in three rurallandscapes of NorwayAnders Bryn a & Lars Østbye Hemsing aa Norwegian Forest and Landscape Institute, PO Box 115, Raveien 9, NO-1430, Ås,NorwayVersion of record first published: 26 Oct 2012.

To cite this article: Anders Bryn & Lars Østbye Hemsing (2012): Impacts of land use on the vegetation in three rurallandscapes of Norway, International Journal of Biodiversity Science, Ecosystem Services & Management, 8:4, 360-371

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International Journal of Biodiversity Science, Ecosystem Services & ManagementVol. 8, No. 4, December 2012, 360–371

Impacts of land use on the vegetation in three rural landscapes of Norway

Anders Bryn* and Lars Østbye Hemsing

Norwegian Forest and Landscape Institute, PO Box 115, Raveien 9, NO-1430 Ås, Norway

Long-term and varied land use has had a major influence on the vegetation in rural Norway, and the traditional open land-scapes are now being replaced by forests. In the present investigation, we assess and quantify structural vegetation changescaused by changes in land use and climate. Up-to-date actual vegetation maps from three rural study areas were comparedwith interpreted historical vegetation maps and potential natural vegetation (PNV) models. Our findings indicate that thepresent vegetation structure is strongly influenced by land use. In the studied sites, 56–66% of the areas presently haveanother vegetation type than expected from a natural state (PNV). The mean turnover of vegetation types in the study areasduring the past 35–40 years was 25%. Our study highlights that the influence of land-use needs to be accounted for whenconsidering the effects of climate change.

Keywords: cultural landscape; forest re-growth; GIS modelling; potential natural vegetation; vegetation trajectories

Introduction

During the last c. 5000–6000 years, various forms of landuse have increasingly disturbed the vegetation in Norway(Kvamme 1988; Nilssen 1988; Bårdseth and Sandvik2010). The time of the introduction of agriculture var-ied throughout the country (Myhre 2004), and agricul-tural practices and technologies have constantly changedthroughout history and altered the physical properties ofthe landscape (Emanuelsson 2009; Hjelle et al. 2012). Thegeneral development of the last 50 years is that agriculturehas intensified in fertile and accessible areas in Norwayand Europe, whereas poor quality and inaccessible areashave been abandoned or extensified (MacDonald et al.2000). For Norway in particular, the landscape changesare dominated by re-growth of forests on former out-fields and abandoned agricultural land (Moen et al. 2006;Rössler et al. 2008; Bryn et al. 2012; Wehn et al. 2012).The ongoing landscape changes, in particular forest re-growth, are believed to affect biodiversity (Kålås et al.2010), landscape aesthetics and tourism (Fyhri et al. 2009),cultural heritage sites (Kuiper and Bryn 2012), agricul-ture and nature management (Almås 2004; Bryn et al.2010). Forest re-growth will probably continue in Norwayduring the next decades (Bryn et al. 2012), but therehave been very few studies of the extent of re-growthand vegetation-type trajectories. Studies of spatio-temporalvegetation changes caused by land use have mainly focusedon small-scale, species-diversity gradients in a limited partof a given area (e.g. Vandvik and Birks 2004), or thefindings have rested upon the results of frame surveys(e.g. Fjellstad and Dramstad 1999) or the studies docu-ment changes from only one study location (e.g. Lundberg2002, 2011). Therefore, even though forest re-growth and

*Corresponding author. Email: [email protected]

landscape changes are important for a number of socialand scientific sectors (Antrop 2005), little is known aboutthe regional extent and potential for further vegetationchanges related to abandoned land use. Thus, there is agreat need for more studies of vegetation trajectories at aregional scale, so that the regional variation can be betteraddressed.

The influence of different human activities on land-scape structure and vegetation pattern can be assessedin numerous ways (Farina 2007), either directly by mea-suring the varying effects of land use in situ throughtime and space (experimental or natural) or by compar-ing old vegetation maps with new ones from the samearea (Lundberg 2011), or indirectly through chronose-quence studies or by the use of potential natural vegetation(PNV) models (Moravec 1998). A combination of directand indirect methods, for example, comparing old vege-tation maps (interpreted from aerial photos or based onin situ mapping) with new ones as well as modellingof PNV, will provide information on both recent vegeta-tion trajectories and the potential for future changes (Bryn2008). The knowledge provided by the direct methods cansubsequently be used for training PNV modelling.

One of the main aims of producing PNV models isto eliminate the effects of previous land use on the veg-etation structure (Bryn 2008; Somodi et al. 2012). Thehuman influence on landscapes can then be assessed andquantified through comparison of actual vegetation withthe PNV models (Moravec 1998). The idea of the develop-ment and modelling of PNV was coined by Tüxen (1956)as a hypothetic natural state for vegetation in order toshow the biotic potential in nature. Tüxen (1956) definedPNV as:

ISSN 2151-3732 print/ISSN 2151-3740 online© 2012 Taylor & Francishttp://dx.doi.org/10.1080/21513732.2012.737373http://www.tandfonline.com

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International Journal of Biodiversity Science, Ecosystem Services & Management 361

The vegetation that would develop in a particular eco-logical zone or environment, assuming the conditions offlora and fauna to be natural, if the action of man on thevegetation mantle stopped and in the absence of substan-tial alteration in present climatic conditions. (translated byGallizia et al. 2001, p. 320)

A PNV model can be constructed based on actual vege-tation, where the existing vegetation serves as a referencepoint for potential extrapolation to sites with similar habi-tats, but where such vegetation is absent (Moravec 1998).The increased use of GIS tools for analysis, including rule-based envelope modelling (RBM), statistical techniquesand machine learning methods, has led to considerable useof GIS modelling in ecology (Franklin 2009), and PNV canbe modelled using different methods (Hemsing and Bryn2012). The construction and the use of PNV models areincreasing and evidently they are useful for determining theeffects of human impact on the vegetation (Rio and Penas2006; Lapola et al. 2008; Somodi et al. 2012).

In this study, we examine landscape changes over timeby comparing up-to-date actual vegetation maps (AVMs),interpreted historical vegetation (IHV) maps and PNVmodels. The main focus is the effect of previous land useon the present vegetation, studied through the potentialvegetation trajectories between the AVMs and the PNVs.

Materials and methods

Study sites

The study was carried out in three different rural districtsof Norway (Figure 1). These districts were specifically cho-sen to capture important variation in vegetation regions,climate sections, landscape patterns and land-use historythat appear along the south–north and east–west axes inNorway (Puschmann 2005; Bakkestuen et al. 2008). Thestudy areas were located in Beito, Vik and Hadsel. Generalinformation and climate data from the three study sitesare presented in Tables 1 and 2, respectively. Beito is a

–104725

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Beito

0 150 300km

N

Hadsel

7122

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6772

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195274 495274 795274 1095274

Figure 1. The three study areas in Norway (WGS84/UTM zone 33N).

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362 A. Bryn and L.Ø. Hemsing

Tabl

e1.

Gen

eral

info

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ree

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ysi

tes.

Stu

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ze(k

m2)

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(EU

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way

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ands

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(Pus

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to20

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34.2

676–

1607

6791

713

N/49

2043

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Nor

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mountain village located in Øystre Slidre municipalitywithin the south-central mountain region, and the studyarea reflects a gradient from the permanent settlementswithin the boreal vegetation zone up to the high-alpinezone of Bitihorn mountain. Vik is a municipality in thecounty of Sogn og Fjordane, and the study area covers aU-shaped, fiord-to-mountain transect, which is typical ofthe inner fiord districts in western Norway (Puschmann2005). Hadsel is a municipality in the district of Vesterålenin north-western Norway, and the study area reflects agradient from the flat coastlines (strandflat) up to thehighest mountains.

Agriculture was introduced in southern Norway around3700 BC (Emanuelsson 2009), and by 1000 BC agriculturehad consolidated in the coastal regions of northern Norwayas well, but then in combination with extensive fishing orhunting activities (Myhre 2004). The historical introduc-tion of agriculture thus varies among the study sites, butthey have in common a history of previously varied andextensive agricultural practices, from more than thousandat Vik to several hundred years old at Beito and Hadsel(Beitrusten 1987; Hovland 1995; Balvoll 2001). From alandscape perspective, the activities resulted in cultivatedland in low-lying locations and semi-natural vegetation inhigher areas that were previously used for outfield grazing,fodder collection and logging (Puschmann 2005).

Until the late 1960s, the inhabitants in Beito and Vikcombined lowland farming with mountain dairy farming(Beitrusten 1987; Balvoll 2001; Potthoff 2004), whereasthe inhabitants in Hadsel combined husbandry farmingwith coastal fishing (Hovland 1995). The number of farm-ers in the study areas has been greatly reduced since thelate 1960s, and the reduction is still ongoing (Table 1). Thereduction of farmers were paralleled by a substantial reduc-tion of outfield farming activities in all three study areassince the mid-twentieth century (Beitrusten 1987; Hovland1995; Balvoll 2001; Potthoff 2004; Tombre et al. 2005;Hemsing and Bryn 2012), whereas afforestation increasedslowly from the 1960s until the mid-1990s in Vik andHadsel (Bryn et al. 2012).

Mapping of actual vegetation

Actual vegetation was mapped using a survey system with44 vegetation types and 9 subordinate land-cover cate-gories (Bryn 2008). Unique vegetation types were sepa-rated based on homogenous species composition, indicatorspecies and vegetation physiognomy, or a combination ofthese. Symbols were used to add extra information to veg-etation types, such as ditched areas, sparse forest, soilcharacteristics and management status. The study areaswere mapped during summer 2009. All three researchersthat took part in the fieldwork were experienced mapperswith several seasons of fieldwork with vegetation mappingfollowing the survey system. With the use of portable lensstereoscopes in the field, vegetation types were drawn ontocolour aerial photos at a scale of approximately 1:35,000(Table 3). The smallest polygon size was ∼0.2 hectares.

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Table 2. Climate data for the three study sites.

� � � �Studyarea

Meteorologicalstation

Distance tostudy area AMT AMP AMP MJuT MJaT period

Beito Vollbu 17 km 1.6 590 +200 +1.5 +3 1971–2009521 m a.s.l. Northwest

Vik Vangsnes 10 km 6.7 1100 +100 +2 +0.3 1966–200951 m a.s.l. North

Hadsel Bø 7 km 4.4 1017 −200 +1 +3 1973–200912 m a.s.l. Northwest

Notes: Temperature is given in ◦C and precipitation in mm. AMT, annual mean temperature (1961–1990); AMP, annual meanprecipitation (1961–1990); �AMP, change in annual mean precipitation; �MJuT, change in mean July temperature; �MJaT, changein mean January temperature. The data were provided by eKlima (2012) and more climate details are given in the Appendix inSupplementary Material online.

Table 3. Year and reference scale for aerial photos used for AVM and IHV.

IHV AVM

Area Photo year Scale Photo serial no. Photo year Scale Photo ser. no.

Beito 1966 1:40,000 1830 2006 1:35,000 06057Vik 1971 1:30,000 0986 2006 1:35,000 06060Hadsel 1973 1:30,000 4341 2008 1:35,000 13609

In each study area, the vegetation mappers started withtwo days together to coordinate their understanding andinterpretation of vegetation types, as well as to coordinatethe technical drawing of outlines between vegetation types.The system used for mapping has been thoroughly testedduring the last 30 years, and almost 30,000 km2 has beenmapped following this system (Rekdal and Larsson 2005).In the mapping field-guide, there are detailed instructionsfor a number of difficult aspects, for example, drawinglines between fuzzy borders, deciding on a vegetation typeamong closely related types and recording the cover of spe-cific species important for the separation between types(Rekdal and Larsson 2005). All vegetation polygons havebeen observed and recorded in situ, and all polygons havebeen controlled by another researcher by visual inspectionof the outlines on the aerial photos that were used.

Interpreting previous vegetation from old aerial photos

Black and white aerial photos dating between 36 and42 years earlier than the AVMs were rectified to orthopho-tos and the previous vegetation was digitised using the soft-ware FYSAK (version G 1.7) (Table 3). The interpretationof previous vegetation from old orthophotos was carriedout by imposing outlines of vegetation polygons from theAVMs onto the old orthophotos. The actual vegetation-type polygon borders were then adjusted according to theinterpretation of the old photos, based on differences ingrey scales, structure and form from the new orthopho-tos. The interpretation of previous vegetation was therebyaided by the ecological information given from the actualvegetation, for example, variation in soil moisture, nutrientstatus and management status (Bryn 2008). This methodof interpreting previous vegetation is probably very con-servative, since new lines are only drawn where changes

could be detected, whereas all other polygon lines remainunchanged. The interpretation of previous vegetation wasdone by one of the authors for all areas and later controlledby the other.

Modelling of potential natural vegetation

All GIS analyses were run in ArcMap version 10.0. PNVmaps were compiled following RBM. However, perma-nently disturbed areas (cultivated land and built-up areas)were compiled following an expert-based manual mod-elling (EMM) method. The PNV modelling methods, usingtwo different approaches, have been thoroughly describedby Bryn (2008) and have also been discussed and com-pared in a broader methodological perspective by Hemsingand Bryn (2012). Thus, only a short introduction of themodelling methods will be provided here.

The RBMs were produced in GIS using the AVMs asa basis. General rules of change were dedicated to eachvegetation polygon at all altitudinal levels (envelopes). Therules for modelling were based on specific findings fromwithin the study areas (Bryn 2008). Vegetation types fromthe AVM were cut into 20 m altitudinal zones througha standard overlay procedure in ArcMap. An attributetable connected to the AVMs, with classes divided intoaltitudinal zones, was exported and reorganized for theimplementation of modelling rules in Microsoft Excel2010 and joined back using the join function. Amongother purposes, the model was designed to change apolygon with alpine vegetation, if it occurred lower thanthe identified upper forest limit, to the expected foresttype at that location. This is dependent on the propertiesstored in the AVM, such as vegetation type, soil properties(moisture, nutrients and substrate), human influence andaltitude.

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Manual PNV models based on expert knowledge(EMM) were produced simultaneously with the mappingof AVMs during fieldwork, using the same spatial frame-work as for the mapping of AVMs. The constructionof PNV extrapolates present vegetation types to similarhabitats, but where these classes do not presently exist(Moravec 1998). This expert-based method relies on thefieldworker’s judgement and understanding of ecology, dis-turbance and natural succession. Therefore, prior to thefieldwork, collective inspection of the area was performedto highlight any cases of doubt and to coordinate subjectivejudgements.

Results

Actual vegetation in the three study sites

The distribution of actual vegetation groups and typesvaries considerably among the three study sites (Table 5;Appendix in Supplementary Material online). Beito isdominated by deciduous forests (25%), wetlands (30%)and alpine heath communities (31%), but has few areaswith cultivated land (4.1%) and spruce forests (2.3%).Vik is dominated by cultivated land (28%), alpine heathcommunities (15.5%) and deciduous forests (30%), buthas few areas with wetlands (2.1%). Also, in Vik,spruce forests appear scattered around in the borealzone as plantations (11.1%), whereas thermophilic forestsappear scattered around in the nemoral lowlands (5.2%).Hadsel is also dominated by alpine heath communi-ties (16.5%), deciduous forests (25%) and cultivatedland (28%), but has more wetlands (5.2%) than Vik.Also, in Hadsel, the vegetation group, non-forested landbelow the forest limit appears in the lower coastalparts (9.8%).

Previous changes and deviances from the potentialnatural vegetation

The three different landscapes have changed considerablyduring the last c. 40 years, but more in Vik and Hadsel thanin Beito (Table 4). Between 3% and 12% of the changesduring these years have been in the form of forest re-growthand represent vegetation trajectories from IHV to PNV. Theincrease of deciduous forest in Hadsel was notably largerthan in Beito and Vik (Table 5), whereas the increase ofspruce forests was markedly larger in Vik than in Beitoand Hadsel. In Vik, the reduction of alpine heath communi-ties was far greater than in Beito and Hadsel. The average

Table 4. The spatial differences (#) among the vegetation mapsin percentage of total area.

IHV # AVM (%) IHV # PNV (%) AVM # PNV (%)

Beito 18 61 58Vik 31 69 66Hadsel 26 68 56Mean 25 66 60

vegetation-type deviation between AVM and PNV is 60%of the landscapes (Table 4), 6% less than between IHVand PNV.

General trends of vegetation trajectories

For all three study areas, a common trait of vegetationtrajectories from recent past (IHV) to present (AVM)was expanding deciduous forests types and a subsequentdecrease in mountainous vegetation types, mainly semi-natural boreal heaths and meadows (Table 5). Further,pastures and pasture land forests were decreasing, whereasbuilt-up areas were increasing from IHV to AVM.

Common traits of vegetation trajectories, expected toshow the largest advance from the AVMs to the PNVmodels, were blueberry spruce forests, deciduous forestsand rich thermophilic forests, whereas peatland forests andpine forests were expected to expand slightly.

Vegetation deviances between AVM and PNV in Beito

The vegetation groups that differed between AVM andPNV in Beito were alpine heath and meadow communi-ties, deciduous forests, wetlands, pastures, cultivated landand non-productive areas in the AVM (Figure 2). Mostof these vegetation groups originate from different typesof spruce forests and to a lesser degree deciduous orpeatland forest types (Figure 3). The vegetation deviancesbetween AVM and PNV in Beito differs notably from thetwo other study sites, with a much higher proportion ofdeciduous forests, less cultivated land and pastures, noareas with non-forested land below the forest limit, butwith higher proportions of wetlands and alpine meadowcommunities.

Vegetation deviances between AVM and PNV in Vik

The vegetation groups that differed between AVM andPNV in Vik were cultivated land, spruce and deciduousforest types, alpine heath communities, pastures and non-productive areas in the AVM (Figure 4). Most of thesevegetation groups originate from different types of decid-uous or thermophilic deciduous forests and to a lesserdegree peatland forest types (Figure 5). The vegetationdeviances between AVM and PNV in Vik differs markedlyfrom the two other study sites, with a much higher propor-tion of spruce forests, more cultivated land than in Beito,more deciduous forest than in Hadsel and no areas withnon-forested land below the forest limit.

Vegetation deviances between AVM and PNV in Hadsel

The vegetation groups that differed between AVM andPNV in Hadsel were cultivated land, pastures, alpine heathcommunities, non-productive areas and non-forested landbelow the forest limit in the AVM (Figure 6). Most of thesevegetation groups originate from different types of decid-uous forests and to a lesser degree peatland forest types

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Table 5. Area statistics of vegetation types in the three study areas representing past (IHV), present (AVM) and potential naturalvegetation (PNV).

Beito Vik Hadsel

Vegetation groups Vegetation types IHV AVM PNV IHV AVM PNV IHV AVM PNV

Snow-bed vegetation 1b Sedge and grasssnow-bed

1.26 1.26 1.18 1.87 1.87 1.24 0.26 0.26 0.26

Alpine heath communities 2c Lichen heath 3.62 3.62 3.5 2.36 2.36 1.88 1.14 1.14 1.12e Dwarf shrub heath 30.35 26.54 8.61 22.64 13.16 3.47 17.88 15.42 5.56

Alpine meadowcommunities

3a Low herb meadow 0.01 0.01 0.01 − − − − − −3b Tall forbs meadow 3.18 2.84 0.2 0.49 0.21 0 − − −

Deciduous forests 4a Lichen and heatherbirch forest

1.84 1.92 0.35 0 0 0.2 0.9 1.81 6.51

4b Blueberry birchforest

12.71 14.84 11.81 13.5 14.15 51.02 12.78 20.45 49.02

4c Meadow birch forest 7.2 8.52 0.87 4.43 2.86 11.21 1.09 2.12 2.094e Alder forest − − − 6.49 11.91 4.89 − − −4g Pasture land forest 0.88 0.25 0 4.07 1.48 0 1.78 0.79 0

Thermophilic forests 5b Rich thermophilicforest

− − − 4.89 5.25 20.02 − − −Pine forests 6a Lichen and heather

pine forest− − − 0 0 0.28 − − −

6b Blueberry pineforest

− − − 0 0.4 0.81 0.22 0.26 0

Spruce forests 7a Lichen and heatherspruce forest

0.25 0.42 2.66 − − − 0 0.06 0

7b Blueberry spruceforest

1.4 1.73 29.98 1.89 7.44 0 0.43 1.09 0

7c Meadow spruceforest

0.12 0.23 12.93 0.92 3.65 0 0 0.03 0

Peatland forests 8b Bog forest − − − 0.03 0.03 0.03 0 0.41 2.328c Poor swamp forest 0.03 0.03 2.35 0 0 0.34 0.44 2.42 8.718d Rich swamp forest 1.54 0.93 1.74 0 0 0.66 0.26 0.93 4.16

Wetlands 9a Bog 2.32 1.88 1.88 0.51 0.51 0.51 8.05 4.93 11.839c Fen 20.03 18.41 16.6 1.56 1.56 1.23 3.1 0.31 0.31

Non-forested land belowthe forest limit

10a Coastal heath − − − − − − 9.65 7.25 4.6610e Seashore and moist

meadows− − − − − − 2.3 2.47 0.23

10g Alluvial sand andgravel planes

− − − 0 0 0.39 − − −Cultivated land 11a Cultivated land 2.53 2.53 0 23.66 21.8 0 12.55 15.87 0Pastures 11b Pastures 4.66 1.6 0 7.23 6.13 0 20.26 12.26 0Non-productive areas 12d Built-up areas 0.09 3.32 0 0.75 1.82 0 1.84 3.33 0

12e Scattered housing 0.23 2.06 0 0.33 0.87 0 1.18 2.08 012f Artificial

impediment0.42 1.73 0 0.56 0.72 0 0.65 1.07 0

Unchanged types 5.33 5.33 5.33 1.82 1.82 1.82 3.24 3.24 3.24Sum 100 100 100 100 100 100 100 100 100

Notes: Area is given in percent of total area, without water. The following vegetation types did not change through time in any of the study areas: 1a, Mosssnow-bed; 2a, Mid-alpine heath; 2b, Dry grass heath; 2d, Mountain avens heath; 9b, Deer-grass fen; 9d, Mud-bottom fens and bogs; 9e, Sedge marsh; 12a,Barren land; 12b, Boulder fields; 12c, Exposed bedrock.

and wetlands (Figure 7). The vegetation deviances betweenAVM and PNV in Hadsel differs notably from the two otherstudy sites, with a much higher proportion of pastures andnon-forested land below the forest limit, whereas the extentof forests are far lower.

Discussion

Land-use impact on vegetation

Most landscapes in Norway are probably influenced byland use to varying degrees (Bryn and Daugstad 2001),

but very few studies have confronted the task of quan-tifying the spatial impacts of land use on the vegetationat a landscape level (Bryn 2008; Lundberg 2011; Brynet al. 2012). Our study shows that the three study areashave changed considerably during the last 35–40 years,with a spatial average of 25% turnover in vegetationtypes. Most of these changes, as well as all of the for-est re-growth, appeared at lower elevations than the upperclimatic forest limit. Furthermore, in the studied sites,56–66% of the areas presently (AVM) have another veg-etation type than expected from a natural state (PNV).

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Figure 2. Vegetation groups in AVM that differ from PNV inBeito.

The findings indicate that previous and contemporary landuse has played a major role in changing the vegetationwithin three rural districts in Norway. Reduced land useand a subsequent bush and forest encroachment followingland-use abandonment are dominating the outlying fieldsin all three study regions (Potthoff 2004; Tombre et al.2005; Hemsing and Bryn 2012), as well as in a numberof other rural regions throughout Norway (Moen et al.2006; Bryn 2008; Rössler et al. 2008; Lundberg 2011;Bryn et al. 2012; Wehn et al. 2012). Our findings thusdocument and strengthen the argument that re-growth offorests is a general trend within rural districts of Norwayand that the semi-natural landscapes are declining in extent(Fjellstad et al. 2010). Although the extent and type of for-est re-growth has varied among our three study areas, ageneral pattern is clear: previous semi-natural vegetation isundergoing a change towards secondary succession forests.Although the dominant changes appear as deciduous for-est expansion in previous semi-natural mountain vegeta-tion and pastures, the cultivation of mires, expansion of

Figure 4. Vegetation groups in AVM that differ from PNVin Vik.

built-up areas and afforestation of spruce have contributedconsiderably to the documented spatio-temporal changes(Figures 2–7).

In the three study sites, impacts of previous and con-temporary land use presently (2009) still influences onan average 60% of the terrestrial landscape (Table 4),traced as vegetation-type deviations between AVM andPNV. Therefore, in our opinion, Norway should no longerbe interpreted as the ‘last wilderness’ of Europe (Støreet al. 2003). On the contrary, the findings indicate that ruralNorway could be interpreted as ‘a semi-natural refugium’of northern Europe that still provides extensive landscapeswith a high proportion of semi-natural areas. The presentedstudy is, in our opinion, a conservative approach, reportingonly the main deviations between AVMs and PNVs. In asimilar study of a mountain region in south-east Norway,Bryn (2008) found that ∼50% of the terrestrial landscapewas structured by land use. Further, from a study site in

Figure 3. Vegetation trajectories from AVM to PNV in Beito.

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Figure 5. Vegetation trajectories from AVM to PNV in Vik.

Figure 6. Vegetation groups in AVM that differ from PNV inHadsel.

a nature reserve on the outer coastal region of rural mid-Norway, Bryn et al. (2010) documented that up to ∼85%of the terrestrial landscape would change following land-use abandonment. We therefore believe that previous landuse in Norway still affects a number of nature conservationareas and that such issues should be given more attention innature management conservation plans than they presentlyreceive (Lundberg 2002; Bryn et al. 2010; Fjellstadet al. 2010).

How representative are the study sites for Norway?

The three presented study areas do not capture the entirerange of landscape variation or different historical land-use practices within Norway (Puschmann 2005), and oneshould be careful to generalise the findings. Also, the lackof comparable studies from Norway is evident, and morestudies are needed to enable a generalisation of the human

impact on the vegetation. The three study sites capturemajor parts of variation in vegetation regions, ranging fromnemoral to high-alpine areas (Bakkestuen et al. 2008), butthe large site-specific differences in vegetation-type distri-butions and trajectories indicate that many more landscaperegions preferably could be included. However, when ourfindings are compared with those of other spatio-temporalvegetation-type modelling studies elsewhere in Norway(Bryn 2008; Bryn et al. 2010), as well as a potential for-est model that covers entire Norway (Bryn et al. 2012), theoverall indication is that a major part of the vegetation inmany rural districts is strongly structured by previous landuse.

Modelling uncertainty

The chosen methods for PNV modelling, RBM and EMM,will influence the presented models with a varying degreeof uncertainty (Hemsing and Bryn 2012). According toHemsing and Bryn (2012), the RBM method is highlyobjective and results in models almost similar to thoseresulting from statistical machine learning methods, suchas MaxEnt (Elith et al. 2010). The strong resulting corre-lation among the two modelling approaches is probably aconsequence of the semi-natural properties of the vegeta-tion in areas that are modelled using RBM and MaxEnt.Rural districts in Norway affected by land use are dom-inated by semi-natural outfield vegetation (Moen et al.2006; Potthoff 2009; Norderhaug and Johansen 2011).These areas have been disturbed by extensive grazing, log-ging and hay making, but are ecologically closely relatedto the PNV and with remnants of natural vegetation scat-tered around. Thus, the degree of PNV modelling certaintyis probably high for the parts of the landscapes that have

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Figure 7. Vegetation trajectories from AVM to PNV in Hadsel.

been modelled following the RBM approach (Hemsingand Bryn 2012).

The EMM method, however, is reported by the sameauthors to be highly subjective. In this study, the EMMmethod was only used to model PNV from permanentlychanged land-cover categories in the AVMs (cultivated andbuilt-up areas). For Beito, the EMM approach comprised11.1% of the study areas, whereas the numbers in Vikand Hadsel were 30.4% and 34.5%, respectively. Thereis no doubt that cultivated land and built-up areas com-prise land-cover categories that differ from their respectivePNV, so the interpretation of the spatial effect of land useon the vegetation can be taken for granted. The vegeta-tion trajectories resulting from models based on the EMMmethod, however, should be interpreted with caution. Thus,although the AVMs land cover is evident, the resulting veg-etation types of the parts of a PNV model using the EMMapproach is not, especially since the affected areas havebeen changed permanently through actions like drainage,ploughing and asphalting.

The effects of contemporary climate changes

The PNV models of this study are based on extrapolationof remnants of natural vegetation that appeared scatteredaround in the landscapes in 2009 (AVM). Therefore, futureclimate changes are deliberately excluded from the PNVmodelling (see next subsection). Previous climate changes,appearing in the period between the IHVs years and 2009(AVMs), however, can influence the resulting spatial effectsof land use in the IHVs, since this approach is based onthe difference between the IHVs and the PNVs. Indeed,the mean July temperatures have increased between 1◦Cand 2◦C during the period between the years of IHVs and

2009 in all three study sites. Also, the annual precipitationhas increased markedly in Beito, whereas the mean Januarytemperature has increased with ∼3◦C in Beito and Hadsel.We therefore believe that climate changes, especially in thelast decade when the mean July temperature has increasednotably (see Appendix in Supplementary Material online),have contributed to accelerated forest re-growth as wellas other successional vegetation trajectories. Acceleratedforest re-growth caused by higher summer temperaturesduring the last decade has also been substantiated fromother parts of Norway (Bryn 2008), whereas domestic andsemi-domestic grazing in many locations still suppress thepotential forest re-growth and forest expansion (Hofgaardet al. 2010; Speed et al. 2010; Lundberg 2011; Wehnet al. 2012).

However, we do not believe that the recent climatechanges have had any significant influence on the interpre-tation of the previous spatial effects of land use. In all threestudy areas, the forest re-growth between IHV and AVMhas appeared far below the previous and present climaticforest limits, so these forest changes could have taken placeregardless of the climate changes the last four decades(Bryn 2008; Lundberg 2011). A change in the upperclimatic forest limits, on the other hand, would have influ-enced the interpretation of the spatial effects of land use,but these have remained fairly stable within the study areas,as well as in other parts of Norway the last four decades(Dalen and Hofgaard 2005; Bryn 2008; Rössler et al. 2008;Aune et al. 2011). In our opinion, it is therefore likely thatthe main cause for vegetation changes from IHV to AVMwas land-use abandonment, but with a slowly increasinginfluence of changing climate throughout the period.

Also, from the type of vegetation trajectories (Table 5),we can conclude that climate changes can be excluded as a

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major cause for landscape changes for large parts of theapparent differences between IHV and AVM within thethree study sites. For example, the expansion of built-upareas in Beito, the afforestation in Vik or the cultivation ofmires in Hadsel cannot be caused by climate changes, butare bound to be a consequence of changed land use locally.

Implications for studies of climate change effects

The expected spread of boreal forests into alpine regionsat northern latitudes has been modelled at various spatialand temporal scales, mostly regional (Koca et al. 2006;Tang and Beckage 2010; Shuman et al. 2011). However,very few studies have attempted to spatially separate theeffects of future climate changes from the effects of veg-etation changes following land-use abandonment. Recentstudies have shown that it is both feasible and importantfor modelling results to account for historical land-use pro-cesses operating at landscape level (Rutherford et al. 2008;Morán-Ordóñez et al. 2011).

To enable a spatially explicit separation of future for-est expansion caused by land-use changes from that due toclimate changes, it is necessary to establish a PNV modelthat removes the spatial effects of land use (Rio and Penas2006; Lapola et al. 2008; Hemsing and Bryn 2012). A PNVmodel would then be a corrected baseline input map forpredictive modelling of the potential effects of climatechanges alone, thus avoiding the effects of previous landuse on progressing forests. Local models from Norwaythat have separated the spatial effects of climate from theeffects of land-use changes show that land-use effects mayhave greater impacts than climate changes alone regard-ing the expansion of forests into low-alpine regions (Bryn2008). Comparable findings have been documented by anumber of forest expansion studies based on other meth-ods for Norway (Rössler et al. 2008; Aune et al. 2011)as well as for the Alps (Gehrig-Fasel et al. 2007; Tasseret al. 2007; Rutherford et al. 2008; Chauchard et al. 2010;Morán-Ordóñez et al. 2011). Thus, we suspect that a num-ber of studies of climate change and forest expansion fromhuman-populated mountain regions might have exagger-ated the effects of future climate changes (see, e.g. Strand2002; Callaghan et al. 2005; Koca et al. 2006), simplybecause they have not accounted for the effects related toprevious deforestation.

AcknowledgementsThe authors are grateful to Catriona Turner, Finn-Arne Haugenand two anonymous reviewers. This article has been financiallysupported by the Research Council of Norway (No. 189977/I10).

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