A quantitative indicator framework for stand level evaluation and monitoring of environmentally sustainable forest management

Ecological Indicators 11 (2011) 468–479

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Original article

A quantitative indicator framework for stand level evaluation and monitoring of environmentally sustainable forest management Wouter Hendrik Maes a,1 , Maximilien Fontaine b,c,1 , Kris Rongé b,d , Martin Hermy b , Bart Muys b,∗ a

Ghent University, Department of Applied Ecology and Environmental Biology, Coupure Links 653, BE-9000 Ghent, Belgium Katholieke Universiteit Leuven, Division Forest, Nature and Landscape, Celestijnenlaan 200 E Box 2411, BE-3001 Leuven, Belgium Stad Brussel – Cel Groene Ruimten, Werkhuizenkaai 97, BE-1000 Brussel, Belgium d Vlaamse overheid, Departement Leefmilieu, Natuur en Energie, Koning Albert II-laan 20 Box 8, BE-1000 Brussel, Belgium b c

a r t i c l e

i n f o

Article history: Received 13 November 2009 Received in revised form 30 June 2010 Accepted 2 July 2010 Keywords: Sustainability Index Criteria and indicators Forest composition Forest structure Forest function Forest management

a b s t r a c t An indicator framework was designed as an operational science-based tool for the evaluation of the environmental aspects of sustainable forest management at stand level in Flanders (Belgium). The framework aims to assess the effects of forest management on forest composition, structure and functioning. It consists of seven principles and 19 criteria, to which 157 potential indicators, selected from literature, were assigned; 40 of these were considered as suitable by an expert panel, based on 10 operational selection criteria. All indicators were quantitative variables measurable in the field. After elaboration of a measurement protocol, the indicator framework was validated in 115 forest stands, distributed over the three main forest types of Flanders. The new indicator framework exhibited greater sensitivity to forest management practices and demonstrated better discriminating power than the method that is currently used by the Flemish forest administration to estimate the naturalness and environmental quality of a forest stand. Following a detailed cost calculation of each indicator and based on the sensitivity of each indicator to forest management practices, the indicator framework was further reduced to a final set of 29 indicators. This framework can also be applied in other regions, provided that local threshold values are defined to convert indicator values to indicator scores. The selection procedure and the possible contribution of this set to the forest management in Flanders are discussed. © 2010 Elsevier Ltd. All rights reserved.

1. Introduction Sustainability is a well-known concept in forestry since the 18th century (Wiersum, 1995). At first, sustainable forest management was interpreted as sustained yield management, based on the equilibrium between annual regrowth and annual harvest (Luckert and Williamson, 2005; Shvidenko et al., 2005). Later, the concept evolved towards sustainable yield, in which annual harvest is limited to sustained yield and in which the forest management maintains ecosystem productivity. Nowadays, the notion of sustainability embraces a much wider range of meanings (Luckert and Williamson, 2005; Wiersum, 1995). Rempel et al. (2004) defined sustainable forest management as a forest management that maintains the ecological integrity of forest landscapes so that the forest continues to provide the social, cultural and economic needs of people. This definition agrees with the well-established concept

∗ Corresponding author. Tel.: +32 16 329726; fax: +32 16 329760. E-mail address: [email protected] (B. Muys). 1 These authors contributed equally. 1470-160X/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2010.07.001

of sustainability having an environmental, social and economic dimension (e.g. Adams, 2006; Edwards, 2005). The evolving concept of sustainable forest management has made it increasingly more difficult to evaluate whether the management is indeed sustainable (Wiersum, 1995; Shvidenko et al., 2005). The large amount of aspects related to sustainability requires the use of several indicators, bundled in a framework (Holvoet and Muys, 2004). The C&I framework, using principles, criteria and indicators, is by far the most popular and most commonly applied standard to evaluate the forest management at national, regional or forest management unit (FMU)-scale (Hickey and Innes, 2008; Sherry et al., 2005). In this article, we discuss the development of an indicator framework for the quantitative assessment of the environmental aspects of sustainable forest management at the stand level in Flanders, Belgium. Environmentally sustainable forest management was defined as forest management that achieves the conservation or improvement of the compositional, structural and functional aspects of the forest. This definition is inspired by Franklin et al. (1981) and Noss (1990), who considered that biodiversity in a broad sense has three primary aspects or components; a compositional, structural and functional component.

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The framework must allow the Flemish government, who commissioned it, to evaluate the effectiveness of its policy and subsidy system and must deliver a monitoring tool for the assessment of the effects of forest management changes. The framework had to fulfil the following conditions: - The set had to exist of measurable and quantitative indicators. - The set had to be applicable at forest stand scale. - As the framework was to be applied at large scale, the costs for measuring a stand had to be limited. - The set had to give meaningful results when measured once in a forest stand (i.e. when used to evaluate the current environmental sustainability of the stand) and when it was repeated in time (i.e. the set had to be applicable in monitoring studies, with score changes reflecting the sustainability of the forest management). These specific conditions distinguishes the framework from other existing C&I frameworks, such as the Pan-European Forest Process on Criteria and Indicators for Sustainable forest management, consisting a set of 6 criteria with 35 quantitative indicators at national level (MCPFE, 2003), and the framework using the same 6 criteria defining indicators at stand level in the FORSEE-project (Tome and Farrell, 2009). These and most other C&I framework sets are developed with a view to forest certification. This requires the often difficult definition of threshold values of sustainability, needed to give a verdict on whether the management is sustainable or not (Bertrand et al., 2008; Lindner et al., in press). Other sets aim to provide information on how policies contribute to sustainable development, usually resulting in an overall indicator score or a score per indicator, rather than in a sustainability verdict; a recent example of such a framework set is ToSIA (Tool for Sustainable Impact Assessment; Lindner et al., in press). The new framework is of the last type and provides an environmentally sustainability score of the forest management. At stand scale, this score must allow forest managers to compare the sustainability of the management of differently managed stands directly as well as to monitor the management, when the application of the framework is repeated in time. In addition, the systematic assessment of the indicator score in a large number of Flemish forests must allow the forest administration to evaluate the forest management and to adjust its policy if required. In the second part of this paper, we deal with the development of the indicator framework as such. We relied on a small expert panel to build the basic framework of principles and criteria and on a second and larger expert panel to select the appropriate indicators from a large list of potential indicators. In the third part of the paper, we report on the evaluation of the provisional indicator framework, based on an extensive field campaign in the three prevailing forest types in Flanders, after which the provisional indicator framework is fine-tuned and a final set is presented.

2. Construction of the indicator set C&I frameworks owe part of their popularity to the versatility of the standard; in essence, they are nothing but a structured list of principles and criteria that can be moulded for any given scale or management goal (Van Cauwenbergh et al., 2007). However, this lack of rigidity is also the major disadvantage of C&I frameworks, as mistakes are very common in two crucial steps: (i) in the set-up of the framework structure and (ii) in the selection or weighing of the indicators (Failing and Gregory, 2003). As a consequence, numerous C&I frameworks have failed (Failing and Gregory, 2003; Garcia and Lescuyer, 2008).

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2.1. Structure of the hierarchical framework A lack of consistency in the use of terminology and concepts and the assignment of elements on a wrong hierarchical level are among the most frequent and important errors in the set-up of the C&I framework (Holvoet and Muys, 2004). Mistakes in the framework structure can lead to the incomplete cover of some aspects of sustainability, to redundancy of elements and to an overall lack of transparency of the framework (Lammerts van Bueren and Blom, 1997). To overcome these problems, Lammerts van Bueren and Blom (1997) formulated guidelines for the development of the framework (see also Van Cauwenbergh et al., 2007), which were closely followed in this study. A good framework should be horizontally and vertically consistent (Lammerts van Bueren and Blom, 1997). Horizontal consistency is reached when the elements on a given level do not overlap, while all aspects of sustainable management are dealt with. Vertical consistency means that all elements are placed at the right hierarchical level, are formulated correctly and are connected with the corresponding element at a higher hierarchical level. The principles, the first hierarchical level, are the overall conditions required for sustainability, and should be formulated as general objectives. The second hierarchical level is made up of the criteria. A criterion is an aspect that needs to be fulfilled for a principle to be achieved. It should be formulated as a simple verdict (achieved or not achieved) and should be a more concrete and specific objective than the principle. The indicators form the third hierarchical level; these are variables indicating the level of compliance with a criterion (Lammerts van Bueren and Blom, 1997; Van Cauwenbergh et al., 2007). The set-up of the framework structure (i.e. the selection, assignment and formulation of the principles and criteria) was carried out by a small expert panel. This panel consisted of the authors of this article and of the members of the project’s steering committee, in total 10 persons. The steering committee consisted of representatives of the forest administration, public forestry sector, forest groups and forest ecology scientists. The structure was decided upon in general consensus as the result of a series of meetings. The three key components of forest biodiversity in the broad sense – composition, structure and functioning (see definition of environmentally sustainable forest management in Section 1) – were considered equally important. These key components were translated into principles following the above mentioned guidelines. For the compositional and structural component, only one principle was formulated; the functional component was covered by four principles (Table 1). A set of criteria was assigned to each principle, resulting in a total of 19 criteria. Each component was considered equally important to reach environmental sustainability. Similarly, each principle was considered equally important within the functional component and each criterion equally important for the achievement of the principle (Table 1). 2.2. Selecting suitable indicators 157 potential indicators were selected from a literature study (Maes et al., 2005) and assigned to a criterion. As far as possible, this list consisted of endpoint indicators which could be measured at stand scale. Endpoint indicators are indicators which measure the outcome or result of a certain input or of a management practice (process) (Lammerts van Bueren and Blom, 1997). The further selection procedure involved four steps: - Formation of the extended panel of experts.

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Table 1 The key components, principles, criteria and suitable indicators as selected by the expert panel. Indicators in italic were not measured during the field campaign; indicators in grey were omitted from the final indicator set (see Section 3.5). Component composition 1. The forest management achieves the conservation or improvement of the biodiversity. 1.1. The woody vegetation is native and species-rich. 1.1.1. Percentage of surface area of native species 1.1.2. Number of native woody species 1.2. The non-woody vegetation is native and species-rich. 1.2.1. Number of red-listed non-woody vascular plant species 1.2.2. Number of native non-woody vascular plants 1.2.3. Fagers’ NMS index of diversitya 1.3. The other taxa are native and species-rich. 1.3.1. Number of fungi species 1.3.2. Number of lichen species 1.3.3. Number of functional groups of bacteria Component structure 2. The forest management achieves a natural forest structure. 2.1. The horizontal structure is well developed. 2.1.1. Percentage of open spaces 2.1.2. Stem distribution 2.1.3. Mixing index of Von Gadow 2.2. The vertical structure is well developed. 2.2.1. Number of vegetation layers 2.2.2. Height differentiation index of Von Gadowb 2.3. The general structure is well developed. 2.3.1. Total aboveground biomass of the woody vegetation 2.3.2. Leaf Area Index 2.4. Thick trees are present. 2.4.1. Number of thick trees per hectare 2.4.2. Number of very thick trees per hectare 2.5. There is a sufficient stock of dead wood. 2.5.1. Number of dead thick and fallen trees per hectare 2.5.2. Percentage of surface area consisting of dead standing trees Component function 3. The forest management achieves the conservation or improvement of soil quality and soil processes. 3.1. The soil volume is conserved. 3.1.1. Soil cover per forest layer 3.1.2. Soil sealing per hectare 3.2. The physical soil properties are conserved. 3.2.1. Penetrable depth for roots 3.3. The chemical soil properties are conserved. 3.3.1. pH 3.3.2. Ellenberg mR × mN ecological spectrum 3.4. The biological soil activity is conserved. 3.4.1. Earthworm biomass per hectare 3.4.2. Humusindex of Pongec 3.4.3. Functional bacterial diversity 4. The forest management achieves optimal water flows in the forest ecosystem. 4.1. The natural water flows are maintained. 4.1.1. Total volume of drainage and/or irrigation channels per hectare 4.1.2. Ellenberg mF ecological spectrum 4.2. The quality of the water is maintained or improved. 4.2.1. Intensity of biocide use 4.2.2. Intensity of fertilization and/or liming 4.3. The water flows are sufficiently buffered. 4.3.1. Soil cover per forest layer 5. The forest management achieves optimal energy flows in the forest ecosystem. 5.1. The microclimate is maintained or improved. 5.1.1. Ellenberg mL ecological spectrum 6. The forest management achieves the conservation or improvement of the biotic processes. 6.1. Natural regeneration takes place. 6.1.1. Number of naturally regenerating native woody species 6.1.2. Cover and abundance of the natural regeneration per species 6.2. Biotic processes are maintained. 6.2.1. Regeneration and resilience, based on CSR-strategy (Competitive, Stress-tolerant or Ruderal) 6.2.2. fNPP (free net primary production) 6.3. The forest is stable. 6.3.1. Health condition of forest crowns 6.3.2. Height/diameter ratio 6.3.3. Cover of pest species a b c

See Fager (1972). See Von Gadow (1993). See Ponge et al. (2002).

Table 2 Overview of the selection criteria for indicator selection. Suitability 1. Is the indicator suitable for evaluating environmental aspects of sustainable forest management? 2. Is the indicator suitable for quantifying the specific criterion? Distinguishing power 3. Can differences in forest management between stands be detected by this indicator? 4. Can changes in forest management over time within a stand be detected by this indicator? 5. Is the indicator only influenced by forest management and PNV? Scientific correctness 6. Is the indicator scientifically founded and accepted? 7. Is the proposed measurement method scientifically correct? Measurability 8. Is the proposed measurement method efficient in time and costs? 9. Can the indicator easily be applied by non-scientists? Scale 10. Is the indicator applicable at the proposed scale level?

- Weighing by the experts of the selection criteria to evaluate the indicators. - Scoring of the indicators by the experts. - Selection of the best indicators based on a transparent procedure. 2.2.1. Expert panel Similar to the small expert panel, the major expert panel consisted of 19 experts selected from the forest administration, public forestry sector, forest groups and forest ecology scientists. Each expert only gave feedback on the component(s) she or he was most familiar with. Seven, ten and eight experts evaluated the compositional (with the inclusion of the biotic processes (principle 6)), structural and functional (without the biotic processes) component. Each expert received a manual with the procedure to evaluate the selection criteria and indicators, as well as a list with a clear description of the indicators and their proposed measurement method. 2.2.2. Selection criteria Instead of assigning one general score to an indicator, the selection of the indicators was based on 10 selection criteria (Table 2), which should not be confused with the sustainability criteria. The ten selection criteria belonged to five categories: the suitability of the indicator, its discriminating power, scientific correctness, measurability and appropriateness of scale. Given the importance of the selection criteria for the further selection procedure, the experts had the opportunity to evaluate the relevance of each criterion, by assigning a score between 1 (not relevant at all for the selection process) and 5 (very relevant). However, since this relevance score did not significantly differ between the selection criteria (P = 0.09), equal weights were assigned to each selection criterion. 2.2.3. Selection of suitable indicators The indicators were evaluated by assigning them a score between 1 (very unsuited) and 5 (very suited) for each selection criterion. In addition, the experts had the opportunity to add comments on each indicator. The selection occurred in three phases: (i) In a first phase, the poorly performing indicators were filtered out. Indicators that received a very low evaluation (<2 for all selection criteria) from at least one expert were discarded from the set. In addition, indicators presenting severe shortcomings, pointed out by at least one expert, were discarded. (ii) In a second phase, all remaining indicators were ranked according to their average score (equal weights were assigned to all experts and all selection criteria, see previous section).

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The two highest ranked indicators per sustainability criterion were then selected for the indicator set, unless these indicators were very similar, in which case the next best ranked indicator was taken in stead of the second ranked. Two indicators were considered very similar if they were essentially the same indicator but measured with different measurement methods or in different units. For three sustainability criteria (criteria 3.2, 4.3 and 5.1—see Table 1), only one indicator was selected, because only one indicator remained after the first selection phase or because all indicators which remained after the filter procedure contained very similar information. (iii) In a third phase, some pertinent indicators were added to the set. These indicators were not included in the original indicator list but were suggested by experts because of the additional information they could provide. The final list of suitable indicators is given in Table 1. 3. Validating the indicator set: from suitable to feasible indicators After the indicator set was selected, an extensive field campaign was set up. This campaign had several purposes, namely (i) to elaborate a measurement protocol; (ii) to calculate the local reference values for the three most important forest types in Flanders; (iii) to assess the feasibility and the measurement cost of all indicators separately and of the entire indicator set; (iv) to verify if the indicators and the indicator set as a whole were sensitive to differences in forest management; (v) to compare the performance of the new indicator framework with the currently used Authenticity Index (Van Den Meersschaut et al., 2001). (vi) to further reduce the indicator set, by deleting redundant indicators and indicators which were not sensitive to the forest management. 3.1. Measurement and calculation of the indicators 3.1.1. Selection of the forest stands and assessing forest management history The selected forest stands belonged to the three main forest types in Flanders: (i) 31 forest stands were selected on wet alluvial soils, where the potential natural vegetation (PNV) is Alno-Padion. Almost all forests on these soils and all selected stands are privatelyowned poplar plantations. Stands were selected based on age (young, pole stage, adult) and the presence or absence of an understorey layer. (ii) 49 forest stands were selected on well-drained loamy soils, where the PNV is Milio-Fagetum or Querco-Carpinetum. The most common forests on these soils are deciduous high forests, in which European beech (Fagus sylvatica L.) and Pedunculate or Sessile oak (Quercus robur L. or Quercus petraea L.) make up the canopy layer. In this forest type, stands were selected based on stand age (young, pole stage, adult), mixture (homogeneous or mixed) and forest age (old forest or not—old forest was defined as having remained permanently under forest cover since at least 1850 and was derived from Vandermaelen cartography). (iii) 35 forest stands were selected on dry sandy soils, where the PNV is Querco-Betuletum. A large part of the forests found on these soils are high forests of Scots pine (Pinus sylvestris L.) or Corsican black pine (Pinus nigra ssp. laricio var. corsicana (Poir.)

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Table 3 Overview of the forest management practices which were evaluated in the study, and their abbreviation. Regeneration of forest 1. Are the trees planted? 2. Was the soil tilled? 3. Was the soil limed or fertilized? 4. Were soil improving species planted? 5. Was natural recruitment of species allowed? Forest nursing 6. Was the forest mowed? 7. Was the forest thinned? 8. Were damaging exotic woody species actively removed? 9. Were drainage canals dug or maintained? (drainage) 10. Were measures taken to prevent soil disturbance? 11. Are diseased or dead trees left in the stand? Harvesting 12. Is/was the wood harvested? 13. Is/was a coppice system established? 14. Did the harvest occur through clearcutting?

(planted) (tillage) (liming/fertilization) (soil improving spec) (natural recruitment)

(mowing) (thinning) (removal exotics)

(soil disturbance prevented) (dead wood allowed)

(harvest) (coppice) (clearcut)

Maire), although these are currently being converted to mixed or deciduous oak forests (Kint et al., 2009). Forest stands were selected based on age (young, adult) and species (coniferous, mixed or deciduous). At least three replications were selected per class and forest type. The purpose of this stand selection protocol was to guarantee that the measurements covered a broad range of possible management classes within the stand type. In some cases, additional forest stands were included in the measurements to expand the dataset. Species and mixture classes were only used to assess a representative overview of the different development stages and management practices, not for later statistical analysis between the classes. The indicators Leaf Area Index (2.3.2., see Table 1), number of functional groups of bacteria (1.3.3.) and bacterial diversity (3.4.3.) required more intensive measurements and were assessed in a subset of 18 forest stands consisting of 3 young and 3 adult stands per forest type. To evaluate the sensitivity of the indicators to forest management, a list of 14 questions concerning forest management practices was filled in by the forest managers (Table 3). All questions were simple yes-or-no questions.

3.1.2. Measurements and calculations All field work took place between spring and autumn 2006. A measurement protocol was established which closely resembled the protocol used in the permanent plots of the Flemish regional forest inventory (Waterinckx and Roelandt, 2001). This system works with vegetation and dendrometric inventory plots. The Authenticity Index (Van Den Meersschaut et al., 2001) is the indicator set currently used by the Flemish forest administration to estimate the naturalness and environmental quality of a forest stand. This index was calculated from our dataset in order to compare it with the new framework set. The materials and methods used to measure and calculate the indicators are described in detail in the Supplementary Material. The different steps involved in the measurements are summarized in Table 4.

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Table 4 Overview of the different measurements and analyses, the scale level, the indicators calculated from each measurement/analysis (see Table 1) as well as the time and cost required to perform each measurement/analysis. The time and costs were estimated for homogeneous stands, where two vegetation plots were laid out (see Section 1 of l). VP = vegetation plot; DP = dendrometric plot; SS = soil samples. No.

Measurement

Scale level

Field measurements Exploring the stand 1 Inventory vegetation plots

Stand VP

2

Dendrometric variables

DP

3 4 5 6 7

Cover and height of all layers Taking soil samples Measuring humus profile Measuring soil compaction Estimating open places, logging tracks Taking hemispherical photos Measuring size and condition of very thick and dead trees Estimating the length, width and depth of drainage and irrigation channels Inventory extra species

VP; Stand SS SS VP; Stand Stand

8 9 10

11

Stand Stand Stand

Indicators

All 1.1.2.; 1.2.3.; 5.1.1.; 6.2.1.; 1.1.1.; 2.1.3.; 2.4.1.; 6.3.1.; 2.2.1.; 1.3.3.; 3.4.2. 3.1.2.; 2.1.1.; 2.3.2. 1.1.1.; 2.5.2. 4.1.1.

1.2.1.; 3.3.2.; 6.1.1.; 6.3.3.; 1.1.2.; 2.2.2.; 2.5.2.; 6.3.2. 3.1.1.; 3.3.1.;

Time measurement (min)

Time importing and processing

Cost (D )

15 15 × 2

0 15 × 2

5.8 23.1

2.1.2.; 2.3.1.; 6.2.2.;

45

15

23.1

4.3.1. 3.4.3.

5 10 × 3 5 15 15

5

3.9 11.5 3.9 11.5 7.7

1.2.2.; 4.1.2.; 6.1.2.;

3.2.1. 3.1.2.

2.4.2.;

2.5.1.;

20 15



5 15 5

5

10

7.7 7.7 3.9

20

5

225

85

119.5

20 60 15

5 10 10

14.6 41.9 9.6

Total laboratory work

95

25

66.1

Overall sum

320

110

185.6

Stand

1.1.2.; 1.2.3.;

1.2.1.; 6.3.3.

Total field measurements Laboratory and image processing 12 Measuring pH 13 Measuring bacterial diversity 14 Calculating LAI

3.3.1. 1.3.3. 2.3.2.

3.4.3.

3.1.3. Indicators which were not assessed In total, 33 indicators were assessed. The following indicators could not be measured, because they were too demanding in terms of expert knowledge, measurement time or information requirements: - Indicator 1.3.1. Number of fungal species As discussed in detail by Cannon (1997), the estimation of fungal diversity can be problematic because of the very large number of species, the small proportion of present species that can adequately be characterized and the necessity of trained staff. The set is aimed to be assessable by the technical staff of the local forest management office, which most probably will lack the expert knowledge required. We therefore decided not to include this indicator in the measurements. - Indicator 1.3.2. Number of lichen species Monitoring the diversity of lichens, based on the European guidelines, also requires very high levels of taxonomic knowledge of the field staff, making it a very expensive indicator in terms of time and employment. Alternative measurement methods lack reliability (Giordani et al., 2009). Consequently, we decided not to include this indicator in the measurements. - Indicator 3.4.1. Earthworm biomass The most suitable method for earthworm sampling is a combination of chemical or electrical extraction and hand sorting (ISO, 2004; Römbke et al., 2005), both of which are very labourintensive. As earthworm populations can vary considerably in time and in space (Valckx et al., 2009), a large number of samples must be taken to attain reliable estimates of the total earthworm biomass. This would dramatically increase the measurement

1.2.2.;

9.6

costs. Due to practical and time restrictions, it was not feasible to sample earthworm biomass in this study. - Indicators 4.2.1. Intensity of biocide use and 4.2.2. Intensity of liming These indicators estimate the amount of biocide or liming applied, not the environmental consequence. Hence, they are not endpoint indicators. In addition, no detailed information was available allowing a reliable estimation in most forest stands. - Indicator 6.2.2. Free net primary production (fNPP) The free net primary production, proposed by Lindeijer (2000), is the annual amount of biomass which is not extracted from the forest for human use. In forestry, acquiring a precise estimate of this indicator is problematic and was not feasible in the field work; in addition, there will only be an impact in the years of harvesting or thinning, making the indicator discontinuous and less suited for year-to-year monitoring of the sustainability of the forest management. 3.2. Calculating the Sustainability Index Each indicator value was converted into an indicator score based on a conversion function, yielding an indicator score between 0 (worst score) and 100 (best score). The conversion score used to calculate most indicator scores was a simple linear function of the indicator value, with a maximal score for the maximal value which could be obtained (type 1 in Fig. 1). However, seven other different types of conversion functions were defined, depending on whether an increase of the indicator value resulted in a positive (types 1 and 2) or a negative score (types 3–5); alternatively, some indicators had an optimal score for an indicator value or range of values (types

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Fig. 1. Overview and mathematical expression of the different conversion functions.

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6–8). The eight different types of conversion functions are given in Fig. 1. For each indicator, target indicator values (TV) or worst indicator values (WV) were derived. The goal of the indicator framework was to evaluate the forest management, not the local richness in species, because this latter aspect is also influenced by the location. As such, where appropriate, the target indicator or worst values were specific for the vegetation type (Failing and Gregory, 2003; Van Cauwenbergh et al., 2007); this was the case for 22 of the 34 indicators. An overview of the target and/or worst indicator values is given in Table 5. Data were imported in Microsoft Access® (Microsoft Corporation, USA) and all indicators were calculated automatically using pre-defined queries, after which the scores for the criteria, principles, components and the overall set were derived. Ultimately, the overall score, which will further be referred to as ‘Sustainability Index’, was calculated as the mean of the three component scores. 3.3. Cost estimation The time required to perform each measurement and to later import and process all data was recorded. The time estimates are calculated for a homogeneous stand in which two inventory plots were laid out. The total cost of one hour of labour of a technician at the K.U.Leuven with 5 years of experience in 2009 was 23.1D per hour and was used to estimate the labour cost. Additional costs were also included and comprised the ecoplates, needed for the measurement of the functional bacterial diversity (indicators 1.3.3. and 3.4.3.) (15D per ecoplate, one per stand), and the pH measurements (5D per stand). Measurement instruments and software licenses were considered to be available in every standard forestry service and were not included in the cost estimation. Also transport costs were not considered because they differ between the forest stands. Assessing the Sustainability Index of a stand requires 430 min of labour, of which 225 min are needed for measurements in the stand. The costs per measurement step and for assessing the entire set are given in Table 4. In Table 5, the cost per indicator is given when the indicator is measured separately and when it is measured as part of the set. The total cost is 185.6D per stand, of which 89% (165.6D ) were labour costs. The most expensive indicators are those assessing the microbial diversity (1.3.3. and 3.4.3.), each costing 25D , followed by pH (18.7D ) and LAI (17.5D ). 3.4. Results and discussion of the validation section It is not within the scope of this article to present a full data analysis of all results, but rather to illustrate the performance of the indicator framework, as well as to identify indicators which could be left out of the set in order to reduce the costs and to improve its operationality. 3.4.1. General distribution of the Sustainability Index and the Authenticity Index The distributions of the Sustainability Index and the Authenticity Index for all measured stands are given in Fig. 2. The average score for the Sustainability Index was 49.5 (SD = 9.3, range: 20.3–72.5) and was equal to the median. The distribution was almost symmetrical and closely matching a normal distribution (Fig. 1a). The distribution of the Authenticity Index was positively skewed (Fig. 1b). Both indices were very significantly correlated (R2 = 0.53, P < 0.001), but this correlation was due to the strong correlation of the Authenticity Index with the structural component of the Sustainability Index (R = 0.87, P < 0.001) and, to a lesser extent, with the compositional component (R = 0.27, P < 0.01), whereas the

Authenticity Index was not significantly correlated with the functional component (R = −0.14; P = 0.14). The influence of stand type and stand age on the Sustainability and Authenticity Index and on the component scores was investigated by performing a 2-factor univariate ANOVA. For the analysis, the age classes were regrouped into young (young and pole stage) and mature stands: uneven-aged stands were only measured in the forests on well-drained loamy soils and dry sandy soils and were not included in the analysis. Young stands had on average a lower Authenticity and Sustainability Index score, due to their lower score on the structural component (Table 6). The poplar stands on the wet alluvial soils had the lowest score for all three components, which resulted in a lower Sustainability Index score than stands on the other soil types; in contrast, stands on wet alluvial soils had the highest Authenticity Index score (Table 6). The Sustainability Index score and the score of the three components of the uneven-aged stands on well-drained loamy and dry sandy soils was statistically not different from the scores of the adult stands (results not shown). 3.4.2. Influence of forest management on the Sustainability Index, component and indicator scores The influence of forest management practices on the Sustainability Index, on its components and on the indicator scores was estimated by calculating the point-biserial correlation (Kent and Coker, 1996) coefficients (Table 7). The Sustainability Index was correlated with 10 of the 14 management practices and all these correlations reflected more or less the expected pattern. The component scores supplemented each other in their correlations with the management practices. Planting did not seem to influence any component score or any indicator score. As not all correlations reflect causal relations, the interpretation of these results must be done with care. Drainage channels, for instance, were almost exclusively found in poplar stands. A strong correlation between drainage and several indicators reflects the typical characteristics of poplar plantations (e.g. lower variability in stem diameter, worse forest crown health due to Melampsora leaf rust) rather than the influence of drainage channels itself. Most observed correlations were straightforward (e.g. if natural recruitment is allowed, a larger number of native woody species is observed, and they cover a larger area); in addition, some interesting trends could be observed. Regular mowing, for instance, increased the plant diversity in the herbal layer as it prevents some species from dominating the vegetation. On the other hand, regular mowing had a negative influence on the establishment of woody vegetation and resulted in a decrease in the number of native woody species. This example demonstrates the need and benefits of including several criteria in a framework set. The correlation pattern with regard to the management practices differed between the different indicator scores (Table 6). This suggests that the scores are poorly correlated with each other, which was confirmed by the Pearson correlations between all indicator scores (results not shown). Only a few strong correlations, indicating possible redundancy, were found. Strongly correlated indicators were Fagers’ NMS diversity index (1.2.3.) and the number of indigenous non-woody plants (1.2.2.) (R = 0.94), the two indicators of soil microbial diversity (1.3.3. and 3.4.1.) (R = 0.99) and the number and cover of the naturally regenerating species (6.1.1. and 6.1.2.; R = 0.83). The lack of strong correlations between the other indicators proved that the indicators reflect different aspects of environmental sustainability. 3.5. Possible reductions of the indicator set As mentioned, one of the goals of the field campaign was to verify if the indicator set could be further reduced. Indicators which

Table 5 Overview of the conversion function type (see Fig. 1 for the different types), worst value (WV), target value (TV) and of measurement costs per indicator if the indicator is measured separately, is measured as part of the original and as part of the final indicator set. VT stands for the Vegetation Type; WA for forests on Wet Alluvial soils, WDL for forests on Well-Drained Loamy soils, DS for forests on Dry Sandy soils. Min(VT) and Max(VT) stands for the minimal and maximal value for the indicator observed within the stands of the vegetation type. Conversion type

Worst value (WV)

Target value (TV)

Cost (separately)

Cost (part of set)

Cost (final set)

1.1.1. Percentage of surface area with native species 1.1.2. Number of native woody species 1.2.1. Number of red listed non-woody plant species 1.2.2. Number of native non-woody vascular plants 1.2.3. Fagers’ NMS index of diversity 1.3.3. Number of functional groups of bacteria 2.1.1. Percentage open spaces

1 1 1 1 1 1 6

0% 0 0 0 0 0 0% or >45% (a )

36.5D 61.5D 38.5D 38.5D 38.5D 59.2D 13.5D

4.2D 6.3D 4.2D 4.2D 4.2D 25.0D 4.0D

4.2D 7.3D 5.2D 5.2D – – 4.1D

2.1.2. Stem distribution 2.1.3. Mixing index of Von Gadow 2.2.1. Number of vegetation layers 2.2.2. Height differentiation index of Von Gadow 2.3.1. Total aboveground biomass of the woody vegetation 2.3.2. Leaf Area Index (LAI) 2.4.1. Number of thick trees per hectare 2.4.2. Number of very thick trees per hectare 2.5.1. Number of dead thick standing and fallen trees per hectare

1 1 1 1 1 1 2 2 7

28.8D 28.8D 9.6D 28.8D 28.8D 23.1D 28.8D 13.5D 13.5D

2.3D 2.3D 1.5D 2.3D 2.3D 17.5D 2.3D 2.1D 2.1D

2.3D 2.3D 1.5D 2.3D 2.3D – 2.3D 2.1D 2.1D

2.5.2. Percentage of surface area consisting of dead standing trees 3.1.1. Soil cover per forest layer 3.1.2. Soil sealing per hectare 3.2.1. Penetrable depth for roots 3.3.1. pH

7 1 3 1 2

0 0 0 0 0 0 0 0 0 or >WVhigh (c ): WVhigh (WDL) = 14 WVhigh (WA) = 8.75 WVhigh (DS) = 17.5 0 or >7% (c ) 0 Max(all stands) 0 0

36.5D 9.6D 25D 17.3D 31.9D

4.2D 1.5D 9.8D 5.9D 18.7D

4.2D 1.5D 9.8D 6.0D 26.3D

3.3.2. Ellenberg mR × mN ecological spectrum

8

28.8D

2.3D

2.8D

3.4.2. Humus index of Ponge 3.4.3. Functional bacterial diversity 4.1.1. Total volume of drainage and/or irrigation channels per hectare 4.1.2. Ellenberg mF ecological spectrum

5 1 3 8

9.6D 59.2D 9.6D 28.8D

4.0D 25.0D 4.0D 2.3D

4.1D – 4.1D 2.8D

4.3.1. Soil cover per forest layer 5.1.1. Ellenberg mL ecological spectrum 6.1.1. Number of naturally regenerating native woody species 6.1.2. Cover and abundance of the natural regeneration per species 6.2.1. Regeneration and resilience, based on CSR-strategy 6.3.1. Health condition of forest crowns 6.3.2. Height/diameter ratio 6.3.3. Coverage of pest species

1 (1) 1 1 1 1 5 3

100% Max(VT) Max(VT) Max(VT) Min(VT) Max(VT) TV(WA, WDL) = 5% TV(DS) = 15% (a ) Max(VT) Max(VT) 4 Max(VT) Max(VT) Max(VT) 40 (b ) 10 (b ) TVlow and TVhigh (c ): WA: 2.5 ≤ TV < 6.25 WDL: 4 ≤ TV < 10 DS: 5 ≤ TV < 12.5 2% ≤ TV < 5% (c ) 100 0 80 TV(WA, WDL) ≥ 4.0 TV(DS) ≥ 3.5 (d ) TVlow and TVhigh (e ): WA: 26 ≤ TV < 32 WDL: 17 ≤ TV < 29 DS: 5 ≤ TV < 8 1 Max(VT) 0 TVlow and TVhigh (e ): WA: 7.9 ≤ TV < 9 WDL: 5.1 ≤ TV < 5.9 DS: 5.3 ≤ TV < 7 100 (Max(VT)) (f ) Max(VT) Max(VT) Max(VT) 100 ≤80 (g ) 0

9.6D 28.8D 28.8D 28.8D 28.8D 28.8D 28.8D 38.5D

1.5D 2.3D 2.3D 2.3D 2.3D 2.3D 2.3D 4.2D

1.5D – 2.8D 2.8D 2.8D 2.3D 2.3D 5.2D

≤WVlow or ≥WVhigh (e ): WA: 22.5≤ or ≥35.5 WDL: 11≤ or ≥34.5 WDL: 3.3≤ or ≥9.9 Max(VT) 0 Max(all stands) ≤WVlow or ≥WVhigh (e ) WA: 7.3≤ or ≥7.5 WDL: 4.7≤ or ≥6.3 DS: 4.3≤ or ≥7.9 0 (0) (f ) 0 0 0 0 ≥120 (g ) ≥20

475

a The optimal area of open spaces of forests on wet alluvial and well-drained loamy soils was 5%, or the natural amount of open spaces which is reached by dying of individual trees (Pontailler et al., 1997). This is relatively low because the most valuable vegetation in these stands consists of vernal or shade-tolerant species, which are very sensitive to competition from competitive species as Rubus sp. and Stinging nettle (Urtica dioica L.). This is much less the case in forests on dry sandy soils, which have a more open canopy and whose optimal value was derived from Bos & Groen (2001). b The optimal density of thick and very thick trees was calculated after Bos and Groen (2001). c The optimal amount of total dead wood and surface area of standing dead trees was calculated after Bos & Groen (2001). See Fontaine et al. (2008) for more details. d Below pHCaCl2 = 4.0, the pH is within the Aluminium buffer range; below pHCaCl2 = 3.5, pH is within the Iron buffering range and podzolization can take place. e Values of WVhigh , WVlow , TVhigh and TVlow were derived from the ecogram of Rogister (1985), in which for each forest type the range of mR × mN and of mF is given. The absolute range for each forest type was derived from this ecogram, and was used to define WVlow and WVhigh . An interval of 50% of the total range and centred around the mean of the total range was used to define TVhigh and TVlow . f There is no straightforward conversion of the Ellenberg mL-value into an indicator score. As a consequence, this indicator was not included in the set to calculate the principle or component score or the Sustainability Index. g After Wilson and Oliver (2000) and Wonn and O’Hara (2001).

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Indicator

476

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Fig. 2. Histogram distribution of (a) the Sustainability Index and (b) the Authenticity Index of all 115 stands measured in the field campaign.

are not correlated with forest management practices or indicators whose measurement is complicated, very expensive or problematic were candidates for omission from the set, provided that this would not lead to a significant loss of information. It was decided that the following indicators could be omitted from the set: - Indicators 1.3.1., 1.3.2., 3.4.1., 4.2.1., 4.2.2. and 6.2.2. As discussed in Section 3.1.3, the measurement of these six indicators, which were not recorded in the field campaign, is problematic. As the indicator set aims at being applied on a large scale in monitoring studies, their omission from the set seems justified. - Indicators 1.3.3. Number of functional groups of bacteria and 3.4.1. Functional bacterial diversity The indicators dealing with soil microbial diversity (1.3.3. and 3.4.1.) were very expensive to measure and were poorly correlated with management practices. Removing these indicators from the set would reduce the cost by 41.9D per stand or 23% of the total cost. - Indicator 2.3.3. Leaf Area Index (LAI) The LAI score (2.3.3.) was only related to mowing, an aspect already covered by several other indicators. LAI measurements require very specific measuring conditions, which complicates the organization of the field work. In addition, it is very hard to follow scrupulously the procedure described in literature (measured every 10 × 10 m—see Nackaerts et al., 2000). These problems make LAI a very expensive indicator to measure (17.5D per stand or 9.8% of the total cost) without it providing much additional information. Its deletion from the set seems therefore justified. - Indicator 1.2.3. Fagers NMS diversity index Fagers NMS diversity index (1.2.3.) was added by expert demand, but was closely correlated to the number of indigenous

non-woody plants and was furthermore not influenced by any management practice. - Indicator 5.1.1. Ellenberg mL ecological spectrum Converting the Ellenberg mL-indicator into a meaningful score is difficult and the indicator was not included in the calculations of the Sustainability Index. It can be deleted from the framework, but could also be useful in monitoring studies to detect differences in time, without it being part of the framework. Soil sealing (3.1.2.) was also relatively expensive to measure (9.8D /stand or 5.5% of the total cost) and its score was not correlated with any management practice. On the one hand, deletion seems justified, because recovery from severe soil compaction is a process which can take several decades (Horn et al., 2004). On the other hand, because it takes such a long time, severe soil compaction should be avoided and the indicator can be very useful to detect declines after logging, in the framework of a monitoring program. It was therefore decided not to omit the indicator. Finally, there are several other indicators which were not or poorly correlated with the management practices: indicators 1.2.1. Number of red listed non-woody species, 2.5.1. Total number of dead standing or lying trees, 3.1.1. Cover per canopy layer, 3.3.2. mR × mN Ellenberg value and 6.2.1. CSR-strategy. However, these indicators are useful when the indicator is applied as a monitoring instrument (i.e. when the measurements are repeated in time to evaluate the forest management practices in this period); furthermore, they can be readily calculated from the standard measurements and imply few extra costs. Therefore, we propose to keep these indicators in the indicator set. The proposed deletions reduce the total number of indicators from 40, of which 34 indicators were measured, to a final set of 29 indicators. The total cost for assessing the Sustainability Index would then be 124.6D , a reduction of 33%.

Table 6 The influence of stand type and stand age on the Authenticity Index, Sustainability Index and on the three components composition, structure and functioning. The upper part of the Table gives the ANOVA-output (2-factor Univariate ANOVA model) for each index or component (underlined: moderately significant (P < 0.05); bold: highly significant (P < 0.01). Below, the mean values ± standard deviations are given per stand type and per age class; significant differences between the groups (P < 0.05; Bonferroni was used for post-hoc testing) are indicated by the different letters in subscript. For clarity, the correlation coefficient of the Sustainability Index with the management practices is indicated in bold. Authenticity Index

Sustainability Index

Composition

Structure

Function

Model Type Age Type × age

<0.001 <0.001 <0.001 0.10

<0.001 <0.001 <0.001 0.11

0.034 0.020 0.82 0.18

<0.001 <0.001 <0.001 0.036

<0.001 <0.001 0.36 0.021

Wet alluvial Well-drained loamy Dry sandy

31.8 ± 1.3b 35.8 ± 1.4b 26.6 ± 1.2a

42.1 ± 1.3a 49.9 ± 1.3b 53.1 ± 1.3b

40.4 ± 3.2a 46.8 ± 3.1ab 53 ± 3.1b

19.7 ± 1.8a 34.8 ± 2.3b 30 ± 2.2b

62.7 ± 1.1a 66 ± 1a 75.1 ± 1b

Young Mature

26.7 ± 1a 36.3 ± 1.1b

44.9 ± 1.1a 51.8 ± 1b

46.4 ± 2.6 47.2 ± 2.5

18.8 ± 1.4a 38.6 ± 1.9b

67.4 ± 0.9 68.5 ± 0.8

Table 7 Influence of different management practices (see Table 3 for abbreviations) on overall score, component scores and all indicator scores to different management practices. Only significant Pearson correlations were given, values are point-biserial correlation coefficients, n = 115 for all correlations. For clarity, the correlation coefficient of the Sustainability Index with the management practices is indicated in bold. Planted Tillage Liming/ Soil improving fertilization spec

* **

−0.32** 0.18*

0.50*

0.34** 0.25** 0.25**

0.21*

0.27*

0.18* 0.34* 0.24**

0.28**

Mowing Thinning Removal Drainage Soil disturbance prevented −0.26** −0.30**

0.26** 0.31**

−0.40*

0.36**

−0.21*

−0.19* −0.20*

Dead wood allowed Harvest Coppice Clearcut

0.30** 0.26**

−0.34** −0.30**

0.42*

0.27**

0.25* 0.30**

−0.47** −0.48** −0.26** −0.49**

−0.24* −0.27** −0.24** −0.23*

0.26*

−0.54** −0.36**

−0.30*

0.31** −0.36** −0.30**

−0.20*

−0.38* 0.27*

0.32* 0.27*

−0.34**

0.32**

−0.28* −0.41**

0.33** 0.35**

0.25* −0.25* −0.37**

−0.29* −0.28*

0.31**

−0.44** −0.25*

−0.34*

−0.67** −0.25*

−0.25*

0.42** −0.46**

−0.45** 0.26*

−0.26** 0.303*

−0.32** −0.32** 0.27*

−0.27*

−0.44**

0.28*

−0.46**

0.37**

0.25*

0.26* 0.38** 0.29*

W.H. Maes et al. / Ecological Indicators 11 (2011) 468–479

Authenticity Index Sustainability Index Component 1: composition Component 2: structure Component 3: function 1.1.1. % of surface area of native species 1.1.2. Number of native woody species 1.2.1. Number of red listed non-woody vascular plant species 1.2.2. Number of native non-woody vascular plants 1.2.3. Fagers’ NMS index of diversity 1.3.3. Number of functional groups of bacteria 2.1.1. % open spaces 2.1.2. Stem distribution 2.1.3. Mixing index of Von Gadow 2.2.1. Number of vegetation layers 2.2.2. Height differentiation index of Von Gadow 2.3.1. Total aboveground biomass of the woody vegetation 2.3.2. Leaf Area Index 2.4.1. Number of thick trees per hectare 2.4.2. Number of very thick trees per hectare 2.5.1. Number of dead thick and fallen trees per hectare 2.5.2. % of surface area consisting of dead standing trees 3.1.1. Soil cover per forest layer 3.1.2. Soil sealing per hectare 3.2.1. Penetrable depth for roots 3.3.1. pH 3.3.2. Ellenberg mR × mN ecological spectrum 3.4.2. Humusindex of Ponge 3.4.3. Functional bacterial diversity 4.1.1. Total volume of drainage and/or irrigation channels per hectare 4.1.2. Ellenberg mF ecological spectrum 4.3.1. Soil cover per forest layer 5.1.1. Ellenberg mL ecological spectrum 6.1.1. Number of naturally regenerating native woody species 6.1.2. Cover and abundance of the natural regeneration per species 6.2.1. Regeneration and resilience, based on CSR-strategy 6.3.1. Health condition of forest crowns 6.3.2. Height/diameter ratio 6.3.3. Cover of pest species

Natural recruitment

0.36**

−0.29* 0.28* 0.48**

0.28*

−0.28* 0.27** 0.29*

−0.40** 0.25* 0.24* −0.27*

0.42**

0.33**

0.33** −0.33**

−0.34** 0.31*

−0.53** −0.31**

0.42**

−0.26*

0.38** 0.27*

0.36**

0.37**

0.31**

0.37**

−0.29*

−0.28*

−0.25*

0.44**

0.25*

−0.27*

0.27* −0.36**

0.26* 0.42**

P ≤ 0.05. P ≤ 0.01. 477

478

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4. Discussion 4.1. Evaluation of the indicator selection procedure The indicator selection procedure consisted of three very distinct steps. First, the hierarchical framework was established by a small group of experts in a series of meetings. This proved to be an efficient and workable approach. Second, we relied on a larger group of experts to select the indicators for each criterion. As this selection step can be performed by the experts individually, it is desirable to select as many experts as possible for this step. The experts were given the opportunity to define new indicators; of the four additional indicators, three were maintained as part of the final indicator set. In addition, the experts were given the opportunity to inform on practical aspects or potential problems of the proposed measurement procedure. This extra information was of great use in the development of the measurement protocol and we recommend that experts have the option to propose on new indicators or to comment on proposed indicators in future C&I framework constructions. In this second step, we provided detailed information on the background, measurement and indicator value calculation of each indicator, which we believe is absolutely required for a correct selection procedure. However, the experts were not informed on the conversion functions later used to convert the indicator values to indicator scores (see Fig. 1; Table 5). It would be interesting to provide this information to the expert panel as well, as this could influence the evaluation and would give the opportunity to fine-tune the conversion scores. The third step of the selection procedure consisted of the validation of the indicator framework. This is by far the most timeconsuming and most expensive step of the selection procedure and is therefore rarely applied in the construction of a C&I set (Noss, 1999; Rempel et al., 2004). However, our results showed that the validation is a crucial step in the construction of the indicator framework. First of all, it allows verifying whether the indicator framework meets its goals, in our case, whether the Sustainability Index was related with forest management and was performing better than the Authenticity Index (see next section). Second, it allows discarding indicators whose measurement turns out to be problematic or indicators that yield redundant or unwanted information. As such, the validation step reduces the total number of indicators and hence the cost for assessing the indicator framework. 4.2. Potential application of the Sustainability Index The Authenticity Index is currently used by the Flemish government to monitor the environmental sustainability of the forest management within the entire region. However, this index does not use target values or worst values based on the local situation; as such, it tends to favour species-rich communities. This is reflected in the fact that the poplar stands on the wet alluvial soils had the highest AI score, whereas they had the lowest score for the three components of the Sustainability Index (Table 6). The AI was poorly related with the functional component. In addition, the skewed distribution of the Authenticity Index (Fig. 2) makes this index less suited for separating badly managed stands from stands with an average score for environmentally sustainable management. In comparison with AI, the Sustainability Index covers more aspects of environmental sustainability, has a more symmetric distribution (Fig. 1a), has been corrected for local compositional diversity and is more highly correlated with forest management practices (Table 7). Therefore, we conclude that the replacement of the Authenticity Index by the Sustainability Index would improve the monitoring quality of sustainable forest management by the Flemish government. Single measurements of the SI reflect both

past and present forest management practices; repetitions of the SI over the years allow evaluating the forest management interventions which took place. In addition, the SI indicates which particular aspects need further attention in forest management. For instance, the average score for the structural component was low for all forest stands, particularly for the poplar stands and the young stands; hence, regional and local forest policies should focus on promoting the achievement of a more natural forest structure. Although the SI has been developed in Flemish forests, the index can be readily applied to evaluate the environmental aspects of forest management in other regions, provided local target and worst indicator values are derived. 5. Conclusion The entire design process of an indicator framework was presented. The development of the framework structure was performed with a small expert panel, the indicator selection was carried out by a larger expert panel following a well-defined and transparent procedure, resulting in 40 indicators. We demonstrated that the validation step is required for an indicator framework to be converted from a potential to a suitable set of indicators. Only a validation procedure can yield information on the feasibility of each indicator, the costs involved of each indicator, potential redundancy of several indicators and, most importantly, to the achievement of the goals the indicator framework was designed for. In this study, the validation step allowed to reduce the set from 40 to 29 indicators, without the loss of significant information (only the indicators which were not or poorly related to forest management interventions were discarded), which reduced the operational costs to assess the set with 33%. Acknowledgements The project “Ontwikkeling van integrale kwantitatieve indicatoren voor de ecologische aspecten van duurzaam bosbeheer (B&G/27/2002)” was financed by Agentschap voor Natuur en Bos of the Flemish government. We are very grateful to all members of the Steering Committee, in particular to its chairman, Mr. Carl De Schepper, and to all experts who were willing to join the expert panel. Furthermore, we would like to thank all private and public forest owners and forest managers involved in the project for their kind help and cooperation. We furthermore would like to thank Kathleen Gybels and Isabelle Deridder for their kind support and useful comments during the development of the set. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ecolind.2010.07.001. The supplementary material section contains a detailed description of the methods and materials used to measure and to calculate the indicator set and the Authenticity Index. References Adams, W.M., 2006. The Future of Sustainability: Re-thinking Environment and Development in the Twenty-First Century. International Union for Conservation of Nature, Zurich, Switzerland. Bertrand, N., Jones, L., Hasler, B., Osmodei-Zorini, L., Petit, S., Contini, C., 2008. Limits and targets for a regional sustainability assessment: an interdisciplinary exploration of the threshold concept. In: Helming, K., Pérez-Soba, M., Tabbush, P. (Eds.), Sustainability Impact Assessment of Land Use Changes. Springer, Berlin, pp. 405–424. Bos & Groen, 2001. Beheersvisie voor openbare bossen. Ministerie van de Vlaamse Gemeenschap, Bos & Groen, Brussels, Belgium.

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