Associations between forest characteristics and socio-economic development: A case study from Portugal

Journal of Environmental Management 90 (2009) 2873–2881

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Associations between forest characteristics and socio-economic development: A case study from Portugal So´nia Carvalho Ribeiro*, Andrew Lovett Zuckerman Institute for Connective Environmental Research (ZICER), School of Environmental Sciences, University of East Anglia, NR4 7TJ Norwich, UK

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 April 2007 Received in revised form 11 February 2008 Accepted 21 February 2008 Available online 10 October 2008

The integration of socio-economic and environmental objectives is a major challenge in developing strategies for sustainable landscapes. We investigated associations between socio-economic variables, landscape metrics and measures of forest condition in the context of Portugal. The main goals of the study were to 1) investigate relationships between forest conditions and measures of socio-economic development at national and regional scales, 2) test the hypothesis that a systematic variation in forest landscape metrics occurs according to the stage of socio-economic development and, 3) assess the extent to which landscape metrics can inform strategies to enhance forest sustainability. A ranking approach and statistical techniques such as Principal Component Analysis were used to achieve these objectives. Relationships between socio-economic characteristics, landscape metrics and measures of forest condition were only significant in the regional analysis of municipalities in Northern Portugal. Landscape metrics for different tree species displayed significant variations across socio-economic groups of municipalities and these differences were consistent with changes in characteristics suggested by the forest transition model. The use of metrics also helped inform place-specific strategies to improve forest management, though it was also apparent that further work was required to better incorporate differences in forest functions into sustainability planning. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Forest sustainability Forest transition Landscape metrics Landscape planning

1. Introduction Implementing sustainable development at spatial scales from the international to the local is nowadays a key goal for many researchers, planners, governments and non-governmental organisations. Since the Brundtland report defined sustainable development as ‘‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’’ (WCED, 1987: p. 8) there has been an extensive discussion in the literature about the integration of socio-economic and environmental issues in order to attain sustainability (Ahern, 2005; Antrop, 2006; Hodge, 1997; Lele, 1991; O’Riordan and StoolKleemann, 2002; WCED, 1987). Recent work has suggested that sustainable development strategies should focus on encouraging ‘‘virtuous circles’’ in landscapes (Matthews and Selman, 2006) so that the linkages between the socio-economic sphere and environmental functions are reinforced. However the form of these ‘‘virtuous circles’’ varies between rural and urban areas. Selman (2006) cites Antrop (2004) as arguing that landscapes in Europe ‘‘can broadly be categorised as

* Corresponding author. Tel.: þ44 1603 593911; fax: þ44 1603 591327. E-mail address: [email protected] (S.C. Ribeiro). 0301-4797/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2008.02.014

urban centre, urban fringe, rural of urban and deep rural, and that these display characteristic structures, functions and conflicts’’ (Selman, 2006: p. 146). In an urban area where land-use is becoming more intensive the policy emphasis is likely to be on ‘‘guiding’’ processes of change, while in rural regions experiencing depopulation there is a need to focus on economic and social regeneration (Antrop, 2006). Landscape ecology offers theories and methods that can contribute to the formulation of sustainability strategies through a better understanding of processes and functions in different environmental settings (Ahern, 2005; Potschin and Haines-Young, 2006; Wu, 2006). A key tool in landscape ecology is the use of metrics that describe the spatial structure of a landscape in terms of both composition and configuration (McGarigal et al., 2002). A number of researchers (e.g. Botequilha Leita˜o and Ahern, 2002) have discussed possible relationships between landscape metrics and sustainability (see summary in Table 1), but it is generally recognised that there needs to be more empirical assessment of such associations, particularly at different spatial scales. This paper aims to contribute to the ongoing landscapes and sustainability debate (Antrop, 2006; Blaschke, 2006; Potschin and Haines-Young, 2006; Selman, 2006; Wu, 2006) through an examination of relationships between indicators of socio-economic development, landscape metrics and measures of forest condition

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Table 1 Associations between landscape metrics and sustainability Metric type

Metric

Relation with sustainability

Area/density/edge metrics

Percentage of landscape (PLAND)

‘‘If one class dominates completely the landscape then it will provide little support for multi-habitat species’’ (Botequilha Leita˜o and Ahern, 2002) p. 75. ‘‘At its lowest limit, there is only one land-use type and landscape lacks diversity’’ ‘‘The arrangement of coarse/fine grained areas within the landscape is doubtless key factor to achieve a sustainable environment’’ (Forman, 1995) p. 489.

Number of patches (NP), Patch size (PS) and Patch density (PD), Patch richness (PR)

‘‘If mean patch size is small and number of patches is high it can indicate a fragmented landscape’’ (Botequilha Leita˜o and Ahern, 2002) p. 75. ‘‘To increase sustainability the obvious solution to shortcomings of both coarse/fine grained landscapes is to vary in grain size’’ (Forman, 1995) p. 491.

Diversity

Patch richness (PR)

‘‘The heterogeneity provided by patches and corridors in an area plays a key role in sustainability’’ (Forman, 1995) p. 488.

Shape metrics

Perimeter area ratio distribution (PARA)

‘‘Heterogeneity per se appears useful to planning a sustainable environment, but more important is the actual arrangement of patches and corridors’’ ‘‘Geometry patterns are indicators of human disturbance (roads, urban areas)’’ (Forman, 1995) p. 489.

Isolation/proximity

Nearest neighbour distance (MNND) Proximity (PROXIM)

‘‘greenways offer a promising planning strategy to address the challenge of making landscape planning sustainable’’ (Ahern, 1995) p. 152. ‘‘The spread of disturbances such as diseases and fire are greater when MNND is low and when PROXIM values are high’’ (Botequilha Leita˜o and Ahern, 2002) p. 75. Consensus is emerging: some form of ecological infrastructure is necessary to achieve a sustainable landscape condition (Rescia et al., 2006).

Contagion/interspeciation and Contagion, dominance, Fractal dimension, Isolation/proximity metrics Lacunarity, Diffusion rates, Percolation

Indicators for landscape stability and resilience at water catchments level were developed in order to describe/represent condition and trends of change in water catchments with the goal to manage towards more sustainable condition (Aspinall and Pearson, 2000).

Largest Patch index (LPI), Fractal dimension Indicators for change in landscape structure caused by urbanization provided index (FRAC) Euclidean nearest neighbour (ENN) information about specific aspects of landscape structure and thus were helpful and Interspersion and Justaposition Index (IJI) to ‘‘guide’’ process of urbanization towards sustainability (DiBari, 2007; Ji et al., 2006).

in Portugal. There have been major socio-economic changes in this country since it joined the EU in 1986 and forests are an important landscape feature whose management has faced a number of challenges (Firmino, 1999; Pinto-Correia, 2000). At present, some 38% of land is occupied by forests, but there are major threats to sustainability arising from wildfires and an increase in the area of non-native tree species such as eucalyptus (DGF, 1996). Rudel et al. (2005) suggest that economic factors linked to labour scarcity have been a key driver of the forest transition in Portugal and at present the stage reached (pre-industrial, industrial or post-industrial, see Mather, 1992) appears to vary across the country (particularly on a transect from the coast to the inland mountains) (Rudel et al., 2005). Comparing the 1995 and 2005 forest inventories indicates that many coastal areas had changes in forest area in the range 10 to þ10%, whereas adjoining the Spanish border the values were typically 30 to 50% (DGRF, 2007). There are still rural areas with common lands where forests provide a variety of products typical of the pre-industrial stage, as well as districts where timber production dominates and other urban regions or national parks where the service functions characteristic of post-industrial forestry are apparent. This diversity in turn raises questions as to how criteria for sustainable forest management should be defined (Shifley, 2006). Many different approaches to this issue exist including the application of indicators for Sustainable Forest Management (Kangas and Kangas, 2005; Lopez-Ridaura et al., 2005; Munda, 2005) the adoption of practices such as continuous cover forestry and ‘‘back to nature’’ management strategies (Gamborg and Larsen, 2003), the use of certification tools, and the valuation of goods and services provided by the forests (Mangold, 1995; Sheppard, 2005). However, the use of such techniques needs to be sensitive to socio-economic circumstances so that the ‘‘virtuous circles’’ underpinning sustainable development are reinforced. Given the importance and diversity of forests in Portugal, as well as the range of socio-economic settings, we felt that the country

provided one appropriate example in which to investigate issues of sustainable development with regard to forestry. In developing our analysis we sought to consider both national and regional scales and to combine variables from several different research fields (e.g. landscape metrics and socio-economic characterisation) to bridge the social, economic, and environmental dimensions of sustainability. The need for this type of integrative effort (interdisciplinary or transdisciplinary) has been increasingly recognised in studies of environmental and landscape change (Tress and Tress, 2001) and we see our research as an example of this wider approach. Three specific goals arose from these considerations, namely to 1) investigate relationships between forest conditions and measures of socio-economic development at national and regional scales, 2) test the hypothesis that a systematic variation in forest landscape metrics occurs according to the stage of socio-economic development and, 3) assess the extent to which landscape metrics can inform strategies to enhance forest sustainability. 2. Data and methods 2.1. Data sources We used data on forest and socio-economic characteristics at national and regional scales (Table 2). The national scale analysis was based on the 28 Nomenclature of Territorial Units for Statistics level III (NUTS III) areas on the Portuguese mainland, the regional analysis focused on 83 Northern Portuguese municipalities. The latter was selected due to its diversity in both forest types and socio-economic characteristics. Regional analysis was also necessary because it was not practical to calculate detailed landscape metrics at a national scale. Socio-economic data were obtained primarily from the website of the Portuguese national statistic office, Instituto Nacional de Estatı´stica (INE, 2001). This information included a development

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Table 2 Variable descriptions and data sources Scale

Category

Variable

Description

Source

Supporting references

National and regional

Socio-economic

UN91 UN98 CPOP N.soc Pry_91 Sec_91 Ter_91 DOM91 IND91 Illi_91 NDOC Dev_Id

INE

Gamborg and Larsen (2003)

INE INE INE

Lopez-Ridaura et al. (2005) Vora (1997), Noss (1999)

Ba80/05 N.F Brod. Conif

Unemployment rate 1991 Unemployment rate 2001 Change in population between 1991_ and 2001 Number of enterprises 1996 (societies) Percentage of economic activity primary sector 1991 Percentage of economic activity secondary sector 1991 Percentage of economic activity tertiary sector 1991 Domestic electricity consumption 1991 Industrial electricity consumption Illiteracy in 1991 Number of medical doctors/1000 inhabitants Development Index 98/99 (Included in the ranking but not in PCA) Area burnt in the period 1980/2005 Number of fires that occurred between 1980/2005 Percentage of broadleaved trees Percentage of Coniferous trees

Forest

Percentage of forests

NP

Number of Patches. Equals the number of patches of the corresponding patch type. Equals the sum, across all patches in the landscape, of the corresponding patch metric values, divided by the total number of patches. Equals the number of patches of the corresponding patch type divided by total landscape area (m2) converted to hectares. Percentage of landscape quantifies the proportional abundance of each patch type in the landscape.

Forest condition

Regional

Landscape metrics

PS

PD

PLAND

index for Portugal that quantifies the level of economic and social development for NUTS III regions and municipalities using 1998 and 1999 data (Fonseca, 2000). Details of forest conditions and other characteristics at NUTS III level were obtained from the two most recent Portuguese forestry inventories (1995–1998) and (2005–2006). This information is available for download from the website of the major forestry institution in Portugal, Direcça˜o Geral dos Recursos Florestais (DGRF, 2007). Data at municipal level were derived from 1:25.000 scale land-use map sheets for 1990 (Carta de ocupaça˜o do solo COS’90) downloadable from the Portuguese Geographic Institute (IGEO, 1990). The land-use sheets were appended and matched with the boundaries of the municipalities using union commands in the ArcGIS software. Detailed land cover categories were combined to produce urban, unproductive, agriculture, water bodies, broadleaved and coniferous forest classes. In subsequent analysis the broadleaved and coniferous classes were separated into different tree species namely maritime pine (Pinus pinaster), oak (Quercus robur) and eucalyptus (Eucalyptus globulus).

INE INE INE Fonseca

DGRF DGRF DGRF IGEO DGRF IGEO COS’90- IGEO COS’90- IGEO

Moreira et al. (2001), Niskanen and Lin (2001)

Botequilha Leita˜o and Ahern (2002), Selman (2006), Vora (1997)

COS’90- IGEO

COS’90- IGEO

2.2. Analysis techniques

2.2.1. Ranking method The first approach used to compare forest characteristics in different development situations was a ranking method (Malczewski, 1999; Munda, 2005). Each NUTS III area was ranked on five socio-economic development and five forest condition variables (see Table 3). At the regional scale it was also possible to rank each municipality on five landscape metrics. In all the rankings the lowest value (i.e. 1) was given to the area with the poorest performance and the highest (i.e. 28 in the case of the NUTS III and 83 in the case of the municipalities areas) to that with the best. Table 3 summarises how the end points of each ranking scale were defined. Once the individual variables had been ranked, overall measures for socio-economic development, forest conditions and landscape metrics were obtained by calculating the average rank for each observation on the different sets of variables. This meant that there were two final average rankings for each NUTS III area and three for each municipality. Simple exploratory analyses of the extent to which these assessments coincided were then performed by dividing the overall measures into two classes (above and below the median values) and displaying the results on maps or scatterplots. The degree to which the average rankings corresponded was also assessed by calculating Spearman rank correlation coefficients.

Landscape metrics were calculated at the regional scale using the FRAGSTATS program version 3.3. (McGarigal et al., 2002). The run parameters in FRAGSTATS were set with a pixel size of 30 m and calculations were performed accounting for eight neighbouring pixels. From the set of metrics proposed by Botequilha Leita˜o and Ahern (2002) we calculated class metrics such as percentage of landscape (PLAND), patch size (PS) and patch density (PD). These metrics were selected on the basis of their ease of interpretation, anticipated relationships with sustainability (see Table 1) and to provide coverage of both composition and configuration dimensions. Summaries of the metrics and land-use profiles for each municipality were subsequently exported to the SPSS software for statistical analysis.

2.2.2. Principal Component Analysis (PCA) and Cluster Analysis (CA) One limitation of the ranking approach was that it took no account of intercorrelations between the original input variables. To tackle this and extend the analysis we undertook a Principal Component Analysis (PCA) and k-means cluster analysis (CA) at the regional scale. Values for the 83 municipalities on the eleven socioeconomic variables in Table 2 provided the input to the PCA and the scores on the most important components were then used to classify the areas. This approach to socio-economic grouping was adopted in preference to a reliance on the Fonseca (2000) development index because we wanted to ensure that more account was taken of social characteristics (e.g. population change) as well as

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Table 3 Characteristics used to rank variables Category

Variable

Good

Poor

Socio-economic

Change in population Development index Primary sector Unemployment Number of enterprises

Highest value Highest value Lowest values Lowest values Highest values

Lowest value Lowest value Highest values Highest values Lowest values

Forest condition

Burnt area Eucalyptus area Broadleaved area Coniferous area Percentage of forest in the spatial unit

Smallest area of burnt forests Smallest area of eucalyptus Largest area of broadleaved trees Largest area of coniferous trees Highest forested area

Largest area of burnt forest Largest area of eucalyptus Smallest area of broadleaved trees Smallest area of coniferous trees Lowest forested area

Class metrics

Number of broadleaved patches Number of coniferous patches Mean area of broadleaved patches Mean area of coniferous patches Broadleaved patch density

High High High High High

Low Low Low Low Low

number of patches number of patches mean patch size mean patch size patch density

number of patches number of patches mean patch size mean patch size patch density

Note: High patch density of broadleaved tree species was interpreted as ‘‘good’’ due to its association with the presence of native tree species such as oak.

3. Results 3.1. National scale trends Fig. 1 plots the 28 NUTS III areas using their average rankings for forest and socio-economic conditions. The distribution shows no obvious relationship between the two variables and the Spearman rank correlation of 0.20 was not significant (p ¼ 0.31). All of the areas were also classified into four groups according to whether they were above or below the median values on the two variables. Fig. 2 maps the results and indicates some clear geographical blocks with the coastal areas generally having higher levels of socioeconomic development. There was also an obvious group of areas with below median forest conditions and socio-economic development in the north east of Portugal. 3.2. Regional scale trends The plot in Fig. 3 shows a much stronger association between forest and socio-economic characteristics at the regional scale than was apparent in Fig. 1 for the national analysis. For the 83 municipalities in the Northern region there was a significant negative Spearman rank correlation of 0.60 (p < 0.01) indicating better forest conditions in the less developed municipalities. The Ordinary Least Squares (OLS) regression line in Fig. 3 shows the general trend, but it is also apparent that there was considerable variation around this (r2 ¼ 33.9%). At the municipality scale it was also possible to compare an average ranking derived from five landscape metrics (see Table 3) with that for forest conditions. The plot in Fig. 4 shows a positive association, with a significant Spearman rank correlation of þ0.44

(p < 0.01). Compared to Fig. 3 there is a greater variation around the OLS regression line (r2 ¼ 19.1%). 3.2.1. Socio-economic classification The result of the PCA (with varimax rotation) on the 11 socioeconomic variables indicated that two components explained 70% of the original variance. Loadings stronger than þ/0.50 were considered high loadings. Principal Component 1 (PC1) had high loadings for change in population between 1991 and 2001 (0.84), percentage of economic activity in the primary sector (þ0.76), illiteracy (þ0.93) and rates of domestic and industrial electricity consumption, 0.93 and 0.66 respectively. Positive scores on this component were interpreted as an indicator of rurality. PC2 had high loadings on the percentage of economic activity in secondary (0.84) and tertiary sectors (þ0.89) and the number of medical doctors per inhabitant (þ0.69). The highest positive scores on this component were for the largest urban centres such as Porto. To further understand the meaning of the PCs the two sets of scores were correlated with Fonseca (2000) index of development (not

25.0

Mean rank of 5 forest condition variables

economic ones. Nevertheless, to help with the interpretation of the component scores we also correlated them with the Fonseca (2000) development index. Following the CA, key characteristics of the groups of municipalities were assessed by calculating mean values for variables such as population density (MPOP), change in population (MCPOP), percentage of primary sector activity (MPS) and percentage of tertiary sector activity (MTS). Analysis of Variance was then used to compare landscape metrics for three different tree species (eucalyptus, maritime pine and oak) across the socio-economic groups of municipalities. These tree species were selected to represent the native broadleaf cover (oak), coniferous plantations (maritime pine) and species introduced from the 1970s onwards for economic reasons (eucalyptus).

Alentejo Litoral 20.0

15.0

Minho-Lima

Grande Lisboa

10.0

5.0 5.00

10.00

15.00

20.00

25.00

Mean rank of 5 socio-economic variables Fig. 1. Mean ranks of Portuguese NUT III areas on forest condition and socio-economic variables.

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Mean rank of 5 forest condition variables

80.00

70.00

60.00

50.00

40.00

30.00 R Sq Linear = 0.191 20.00 20.00

30.00

40.00

50.00

60.00

70.00

80.00

Mean rank of 5 class metrics Fig. 4. Mean ranks of Northern region municipalities on forest condition and class metric variables.

Fig. 2. Classification of Portuguese NUT III areas on the basis of forest condition and socio-economic development.

Mean rank of 5 forest condition variables

80.00

70.00

60.00

50.00

40.00

30.00 R Sq Linear = 0.339

20.00 20.00

30.00

40.00

50.00

60.00

70.00

80.00

Mean rank of 5 socio-economic variables Fig. 3. Mean ranks of Northern region municipalities on forest condition and socio-economic variables.

included in the PCA input). The results were a significant negative correlation of 0.88 (p < 0.01) between the index and PC1 and an insignificant positive association of þ0.11 (p ¼ 0.34) with PC2. These results indicate that, as intended, the PCA-based approach covered broader socio-economic dimensions than Fonseca (2000) index. A k-means cluster analysis was used to classify the municipalities into five groups on the basis of their component scores on PC1 and PC2. The five-group solution was selected because this was felt to provide the best substantive representation of socio-economic contrasts within the region. Table 4 lists the number of municipalities in each group and their mean values with respect to population density (MPOP), change in population (MCPOP), primary (MPS) and tertiary (MTC) sector economic activity. These statistics indicate some clear contrasts that can be readily interpreted as an urban–rural gradient. This classification was also different from that obtained using Fonseca (2000) index alone where there were very uneven numbers of municipalities across the five groups. Fig. 5 maps the results of the classification. Porto was identified as the sole urban centre. The coastal areas around Porto and the most developed municipalities in Northern Portugal can be described as an inner urban fringe. Beyond this there is a similar sized set of municipalities that can be termed the outer urban fringe. The two remainder groups can be denoted as developing rural and deep rural. Within the latter there are also a number of municipalities that contain land designated as National Parks. Tourism is important in these areas, as reflected in the increase in the MTC percentage in Table 4. 3.2.2. Variations in class metrics Three class metrics (percentage of landscape, mean patch size, and patch density) for three tree species (eucalyptus, maritime pine and oak) were compared across the five socio-economic groups discussed above. These three metrics were included in the set proposed by Botequilha Leita˜o and Ahern (2002) and their importance has also been highlighted in earlier studies such as Forman (1995). Percentage of landscape (PLAND) quantifies the proportional abundance of each patch type in the landscape. It approaches 0 when the class type becomes increasingly rare in the studied area

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Table 4 Characteristics of the five socio-economic groups Classification

Number of MPOP MCPOP (%) MPS (%) MTS (%) municipalities (inhabitants)

Urban centre Inner urban fringe Outer urban fringe Developing rural Deep rural

n¼1 n ¼ 17 n ¼ 17 n ¼ 12 n ¼ 36

5787 959 498 119 52

13.0 9.5 11.2 0.3 10.3

0.6 1.7 1.5 5.9 11.2

88 73 54 52 65

Note: MPOP, Mean population density; MCPOP, Mean change in Population; MPS, Mean Primary sector; MTS, Mean Tertiary sector.

species it was also the case that the lowest mean PD occurs in one of the two rural categories. Analysis of variance was used to assess the significance of differences in the class metrics across the socio-economic groups. Since there was only one urban centre municipality this category had to be excluded from the analysis and the comparisons in Table 5 are based on the remaining four groups. The results indicate that all three tree species had significant differences in either PLAND or PD, while none of the contrasts in PS were significant at the 0.05 level. 4. Discussion

and 100 when the entire area consists of a single patch type (McGarigal et al., 2002). Fig. 6 shows how the mean values of PLAND varied across the socio-economic groups. Maritime pine stands out as the dominant class in all socio-economic categories while the value for eucalyptus was highest in the outer urban fringe group and that for oak in the deep rural areas. Fig. 7 illustrates how patch size (PS) varied across the groups. The mean patch area equals the sum of the area across all patches in the municipality, divided by the total number of patches in the municipality (McGarigal et al., 2002). Patch sizes for oaks increased across the urban–rural gradient, those for maritime pine displayed a more mixed trend, and the values for eucalyptus were higher in the urban fringe than the rural categories. Patch density (PD) values for individual tree species are generally interpreted as a measure of fragmentation (McGarigal et al., 2002). Fig. 8 indicates that across all five socio-economic groups the PDs were generally highest for maritime pine, while those for oak and eucalyptus were similar in several categories. For each tree

The first goal of this study was to investigate relationships between forest conditions and measures of socio-economic development at national and regional scales in Portugal. At the national scale there was only a weak correlation between socioeconomic development and forest conditions in NUT III areas. However, a stronger and statistically significant negative correlation was identified at the regional scale. A weaker association at the national scale can be attributed to the interaction of several factors. As Fig. 2 indicates, there was a clear tendency for poorer forest conditions to occur in a number of the most urbanised areas (e.g. around Porto, Lisboa and on the Algarve). In rural regions the situation is more variable, with a particular contrast in forest conditions between the inland parts of southern Portugal (e.g. the plains of Alentejo) and the more mountainous areas further north. Alentejo is dominated by a livestock-based agro-forestry system known as ‘‘montado’’ with scattered cork trees which are relatively easy to maintain in good condition (Firmino, 1999). Forests further north are characterised by relatively dense stands of pines, eucalyptus or oaks which are

Fig. 5. Socio-economic classification of Northern region municipalities.

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14.00

Mean PLAND (%)

12.00

Maritime pine Eucalyptus Oak

10.00 8.00 6.00 4.00 2.00 0.00 Urban centre

Inner urban fringe

Outer urban fringe

Developing rural

Deep rural

Socio-economic groups Fig. 6. Variations in percentage of landscape (PLAND) metrics for three tree species across the socio-economic groups of municipalities.

more challenging to manage and vulnerable to wildfires. With hindsight, variables such as the percentage of broadleaved trees used in the national analysis (although the best available) were not sufficiently sensitive to these regional differences in tree species or forestry systems. In addition, the absence of landscape metrics meant that factors such as contrasts in stand structure were not taken into account. Further analysis could be conducted to tackle these problems, but the data and processing requirements (e.g. to generate landscape metrics at a national scale) would be considerable. Almost all of the Northern region included in the municipality analysis had below median forest conditions on the national scale (see Fig. 2). However, restricting the analysis to this region helped to control for some of the key differences in tree species and forestry systems. The outcome was a stronger trend for higher levels of socio-economic development (mainly near the coast) to coincide with poorer forest conditions (Fig. 3). A second objective was to examine whether there was a systematic variation in forest landscape metrics according to the stage of socio-economic development. The initial ranking analysis at the regional scale indicated a positive association between measures of forest condition and a set of landscape metrics (Fig. 4). More detailed assessment of differences in specific metrics for particular tree species was then conducted. The results suggested a number of trends and contrasts (see Figs. 6–8 and Table 5), supporting the conclusion of Botequilha Leita˜o and Ahern (2002) that metrics such as PLAND, PS and PD are of value in evaluating sustainability issues.

Interpretation of the differences in the tree species metrics can be facilitated by reference to the forest transition concept (Mather, 1992). In essence, there has been a transformation of natural Portuguese broadleaf (particularly oak) forest into more productive tree species alongside the broader process of socio-economic development. The deep rural category of municipalities still has the largest proportion (2.5%) of its area with oak trees. By contrast, the outer urban fringe group has much higher percentages of landscape with non-native species such as eucalyptus (Fig. 6). The patch sizes for pine and eucalyptus species are also much larger (Fig. 7), often being over 15–20 hectares. Both these characteristics reflect planned initiatives (e.g. state funded development of coniferous plantations) in municipalities such as Bragança, Chaves, Viana do Castelo and Vila Real during the 1940s (Brouwer, 1999; Roche, 1998) and more generally these areas have features typical of ‘‘industrial’’ forestry. The presence of larger patches and higher percentages of area occupied by the same tree species would, at first sight, suggest a connected landscape. However, the interpretation of the patch density metric (Fig. 8) shows a more fragmented landscape in urbanised areas. This is a common trend and reflects a number of other studies (Antrop, 2004; Forman, 1995; Selman, 2006). The values of patch size show some signs of increasing across the urban–rural gradient (Fig. 7), but the differences were not statistically significant (Table 5) and the change is not as marked as might be anticipated in some cases (e.g. for oaks). Several factors help to explain this result. One is that the deep rural group (where forests might be expected to cover larger areas in bigger patches) includes some mountainous regions where the environmental

30.00

Mean PS (ha)

25.00

Maritime pine Eucalyptus Oak

20.00 15.00 10.00 5.00 0.00 Urban centre

Inner urban fringe

Outer urban fringe

Developing rural

Deep rural

Socio-economic groups Fig. 7. Variations in patch size (PS) metrics for three tree species across the socio-economic groups of municipalities.

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0.80 0.70

Mean PD (patch/ha)

0.60 0.50 0.40

Maritime pine Eucalyptus Oak

0.30 0.20 0.10 0.00 Urban centre

Inner urban fringe

Outer urban fringe

Developing rural

Deep rural

Socio-economic groups Fig. 8. Variations in patch density (PD) metrics for three tree species across the socio-economic groups of municipalities.

conditions are unfavourable for large areas of trees. A second factor is the presence of a number of National Parks with distinct features such as absence of eucalyptus trees and an emphasis on tourism i.e. ‘‘post-industrial’’ forestry. Taken together, these results suggest that the different socioeconomic groups have contrasting forest characteristics which reflect the pre-industrial, industrial and post-industrial categories embedded in the forest transition concept. It is also worth noting that the metrics used in this study are quite effective in distinguishing the ‘‘industrial’’ from other categories of forestry, but less so in separating pre- and post-industrial features. Within the deep rural group there are still common lands where forests provide a variety of products characteristic of pre-industrial forestry (Brouwer, 1999), as well as protected areas where the service functions characteristic of post-industrial forestry are apparent. In addition, although there has been a reversal of forest loss in many developed areas, the characteristics of these new forests are not especially appropriate to fulfil the increasing needs of recreation and tourism activities. This highlights that it is important to consider issues of functions as well as composition/ configuration in assessing issues of landscapes and sustainability (Blaschke, 2006; Perz, 2007). The third goal of this study was to assess the extent to which landscape metrics can inform strategies to enhance forest sustainability. From the previous discussion it is clear that landscape metrics can be used to identify some of the different problems and issues in urban and rural areas. However, it is also evident that they have some limitations in terms of distinguishing some of the different functions that forests can perform. This highlights that recommendations to improve forest condition need to be placespecific and take account of other surrounding land-uses (since these will influence the functions that forests need to perform). In other words, forests need to be better integrated into the continuum of land-uses that encompasses landscapes. Planning strategies have been defined by Ahern (1995: p. 139) as protective, defensive, offensive and opportunistic. Acknowledging such a view, a key protective step in the deep rural areas would be an increase in both the percentage of landscape and patch size of oaks and other broadleaved trees. This would help tackle a critical problem in these areas, namely the disappearance of the ‘‘idyllic

Table 5 Significance of differences in class metrics across the socio-economic groups Tree species

Percentage of landscape

Patch size

Patch density

Maritime pine Oak Eucalyptus

0.01 0.40 0.01

0.39 0.15 0.15

0.18 0.03 0.01

Note: Values in bold are statistically significant at the 0.05 level.

landscape’’ shaped by ‘‘traditional farmers’’ with a mixture of low intensity crop, grazing and forest land-uses (Firmino, 1999). Enlarging the area and patch sizes of broadleaves would help to support economic and social regeneration through other means (e.g. tourism), but needs to be accompanied by measures to maintain other, potentially conflicting, activities such as livestock grazing. A challenge for management of forests in these rural areas is thus to create adjacent uses with low level of conflict. Contagion/ interspersion metrics could play a role in helping to identify compatible land-use mosaics that would provide sustainability benefits in such areas. By contrast, according to Ahern (1995) the most appropriate strategy for an urban centre is a defensive one. Consequently, the priority in urban areas should be to enhance landscape connectivity in order to assure provision of environmental goods and services. To achieve this it seems sensible to propose an increase in both percentage of landscape and patch size of oaks and other broadleaved trees. This is a similar recommendation to rural areas, but the silvicultural techniques (e.g. tree spacing and stand age composition) would need to vary according to management priorities (i.e. to connect the landscapes in urban areas and promote economic and social regeneration in rural areas). Thus, in urban areas management priorities should focus on enhancing vegetation structure in existing woodlands as well as improving connectivity between them. These recommendations are similar to those presented by Hedblom and Soderstrom (2008) in their study of urban woodlands in Sweden. They also noted that many of the challenges to forest management in urban areas are concerned with reconciling ‘‘management for biodiversity’’ (Hedblom and Soderstrom, 2008) and aesthetics, for instance dealing with the fact that dead wood both ‘‘enhances biodiversity’’ and is ‘‘not aesthetically attractive’’. Diversity and connectivity measures are therefore examples of the types of landscape metrics that can help in planning enhancements to the sustainability benefits associated with forests in urban areas. 5. Conclusions The approach presented in this study is innovative in correlating ecological landscape metrics for tree species with socio-economic indicators for the areas in which the forests occur. It therefore provides an example of a simple interdisciplinary method for investigating sustainability issues and demonstrates that landscape metrics can be used as a measure of socio-economic change. The results of this study have several implications for the wider landscapes and sustainability debate. Our study suggests that it is possible to identify a positive association between landscape metrics and measures of better condition for forests and that the calculation of metrics for individual tree species can help inform

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strategies for sustainable forest management in urban and rural areas. The research also demonstrates how forest characteristics vary over an urban–rural gradient and therefore reinforces the point that criteria for sustainable management or strategies to establish ‘‘virtuous circles’’ need to be context specific. Landscape metrics clearly have value in detecting patterns of land cover or environmental change, but those used in this study are limited in their ability to distinguish the different functions or services of forests. This capability is an important aspect in any assessment of sustainability at the landscape scale (Selman, 2006) and it therefore suggests that future research should either evaluate more refined metrics to represent such functions or examine how basic metrics can be best supplemented by other indicators in the toolbox for sustainable landscape management. Acknowledgements We would like to thank the organisers of the PhD master class ‘‘Environmental and Landscape Change: Addressing an Interdisciplinary Agenda’’ namely Ba¨rbel and Gunther Tress and David Miller. This event was of great help in developing this manuscript. We are especially grateful to George Hess, Marc Antrop, Sandrine Petit, Jim Palmer and to three anonymous reviewers for their comments and suggestions. To all PhD students and professors that participated in this initiative we are thankful. It was hard work but a great week! Thanks are also due to the Fundaça˜o para a Cieˆncia e a Tecnologia (FCT-Portugal) for their financial support. References Ahern, J., 1995. Greenways as a planning strategy. Landscape and Urban Planning 33 (1–3), 131–155. Ahern, J., 2005. Theories methods and strategies for sustainable landscape planning. In: Tress, B., Tress, G., Fry, G., Opdam, P. (Eds.), From Landscape Research to Landscape Planning: Aspects of Integration, Education and Application. Springer, pp. 119–131. Antrop, M., 2004. Landscape change and the urbanization process in Europe. Landscape and Urban Planning 67 (1–4), 9–26. Antrop, M., 2006. Sustainable landscapes: contradiction, fiction or utopia? Landscape and Urban Planning 75 (3–4), 187–197. Aspinall, R., Pearson, D., 2000. Integrated geographical assessment of environmental condition in water catchments: linking landscape ecology, environmental modeling and GIS. Journal of Environmental Management 59, 299–319. Blaschke, T., 2006. The role of the spatial dimension within the framework of sustainable landscapes and natural capital. Landscape and Urban Planning 75 (3–4), 198–226. Botequilha Leita˜o, A., Ahern, J., 2002. Applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning 59, 65–93. Brouwer, R., 1999. Changing name-tags: a legal anthropolological approach to communal lands in Portugal. Journal of Legal Pluralism 43, 1–30. DGF, 1996. Lei de bases da polı´tica florestal. Lei n. 33/96. Direcça˜o-Geral das Florestas, Lisbon, pp. 3–5. DGRF, 2007. Inventario florestal nacional. Direça˜o Geral dos Recursos Florestais. Available from: http://www.dgrf.min-agricultura.pt/ifn/Tabelas.htm (accessed 02.08.08). DiBari, J.N., 2007. Evaluation of five landscape-level metrics for measuring the effects of urbanization on landscape structure: the case of Tucson, Arizona. USA. Landscape and Urban Planning 79 (3–4), 308–313. Firmino, A., 1999. Agriculture and landscape in Portugal. Landscape and Urban Planning 46 (1–3), 83–91. Fonseca, P.A.L., 2000. Indices de desenvolvimento concelhio. Volume II. 2. quadrimestre de 2002. Instituto Nacional de Estatistica, Lisbon, pp. 1–34. Forman, R.T.T., 1995. Land Mosaics: the Ecology of Landscapes and Regions. Cambridge University Press, Cambridge. Gamborg, C., Larsen, J., 2003. Back to nature – a sustainable future for forestry? Forest Ecology and Management 179, 559–571.

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