Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation

Forest Ecology and Management 258 (2009) 2439–2449

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Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation Joona Lehtoma¨ki a,*, Erkki Tomppo b, Panu Kuokkanen c, Ilkka Hanski a, Atte Moilanen a a

Metapopulation Research Group, Dept. of Biological and Environmental Sciences, P.O. Box 65, University of Helsinki, FI-00014 Helsinki, Finland Finnish Forest Research Institute, Vantaa Research Unit, Vantaa Unit, P.O. Box 18, FI-01301 Vantaa, Finland c Metsa¨hallitus Natural Heritage, Kalevankatu 8, FI-40100 Jyva¨skyla¨, Finland b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 29 January 2009 Received in revised form 15 August 2009 Accepted 20 August 2009

We apply a recently developed conservation prioritization method (Zonation algorithm) to a nationalscale conservation planning task. The Finnish Forest and Park Service (Metsa¨hallitus) was given the mandate to expand the current protected areas in southern Finland by 10 000 ha. The question is which areas should be selected out of the total area of 1 760 000 ha. The data available include a nation-wide GIS data set describing general features of forests at the resolution of 25 m  25 m for entire Finland and another data set about biodiversity features within the current state-managed conservation areas. Ecologically, the key information includes forest age and the volume of growing stock for 20 forest types representing different productivity classes and dominant tree species. Our analysis employs four different connectivity components to identify forest areas that are (i) locally of high quality and internally well connected, (ii) well connected to surrounding high-quality forests, (iii) well connected to existing conservation areas, and (iv) large enough to allow efficient implementation. Expert evaluation of the results suggested that the present quantitative analysis was helpful in identifying areas with high conservation value systematically across southern Finland. Our analysis also showed that the highest forest conservation potential in Finland is located on privately owned land. The present techniques can be applied to many large-scale planning and management projects. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Boreal forest Zonation Connectivity Conservation planning GIS

1. Introduction Boreal forests comprise the most important habitat type in Finland both in terms of the area covered and overall biodiversity (Hilde´n et al., 2005). Although the volume of the growing stock of Finnish forests has increased in the past decades and is still increasing (Finnish Forest Research Institute, 2008), intensive forestry practices have led to adverse ecological changes in forests (Tikkanen et al., 2006). The most important threat to forest biodiversity is the small area and low quality of the remaining natural and semi-natural forests that potentially host large numbers of specialized species (Puumalainen et al., 2003; Hanski, 2005). Although forestry practices have been somewhat modified during the past 20 years, timber production-oriented management has created forest structures that are far from natural: evenaged stands largely established with a single tree species, which in southern Finland is mostly the Scots pine (Pinus sylvestris L.). The main difference between forests available for wood supply

* Corresponding author. Tel.: +358 9 191 57753; fax: +358 9 191 57694. E-mail addresses: joona.lehtomaki@helsinki.fi (J. Lehtoma¨ki), erkki.tomppo@metla.fi (E. Tomppo), panu.kuokkanen@metsa.fi (P. Kuokkanen), ilkka.hanski@helsinki.fi (I. Hanski), atte.moilanen@helsinki.fi (A. Moilanen). 0378-1127/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2009.08.026

(FAWS) and natural forests is the homogeneous structure of and low volume of dead wood in the former, which furthermore are typically <100 years in age (Rassi et al., 2001; Penttila¨ et al., 2004). Strictly protected areas cover 4.5% of the forested land in Finland, but most of the protected forests are in northern Finland (16% of the forested land) with climate and soils not favourable for forest growth, while only 2.2% of forests in southern Finland are protected (Ha¨nninen et al., 2006; Finnish Forest Research Institute, 2008). The most significant deficiency in the Finnish forest reserve network is the low level of protection in hemiboreal and southern and middle-boreal forest vegetation zones (Virkkala et al., 2000). In these zones, only 0.4% of the forests are protected and classified as semi-natural old forests (140 years or older with features indicating naturalness; Punttila, 2008, pers. comm.). Much hope has been placed on achieving an adequate level of conservation through a network of small woodland key habitats (Annila, 1998). However, ecological studies show that these hopes are unwarranted (Hanski, 2006), because of high local extinction rates (Pyka¨la¨, 2004), edge effects (Murcia, 1995), and very low connectivity (Aune et al., 2005). In the future, climate change (Soja et al., 2007) and biomass harvesting for energy production (Lundmark, 2006; Antikainen et al., 2007) are likely to exert additional pressures on forest biodiversity.

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quality index, which has been employed with cost–effect analysis to provide an overall indicator of forest biodiversity (Juutinen et al., 2008). Kallio et al. (2008) encompassed similar indices from the same data into a spatial partial equilibrium model simulating the Finnish forest sector for optimal regional allocation of sites for forest conservation. Finally, Luque and Vainikainen (2008) have developed a tool for forest conservation in southern Finland based on the MS-NFI data. Their approach consisted of habitat quality assessment and construction of maps indicating suitability for conservation. The present study describes a new spatial conservation prioritization approach that can be used, among other things, for planning the expansion of conservation area networks. Our approach is based on decision-theoretic and optimization techniques that have been developed in conservation biology under the rubric of systematic conservation planning and spatial conservation prioritization (Margules and Pressey, 2000; Cabeza and Moilanen, 2001; Sarkar et al., 2006; Pressey et al., 2007). The key elements in this approach, which make it different from the previous analyses, are a complementarity-based forest quality measure, a measure of internal connectivity of forested areas, and a measure of connectivity to existing high-quality conservation areas. We demonstrate how an ecologically meaningful conservation prioritization can be implemented for very large planning areas with high-resolution GIS data. Fig. 1. The METSO region in Finland. State-owned forests available for wood supply (FAWS) are shown in grey and the location of the existing FFPS protected areas in black.

It is widely acknowledged that there is a need for additional forest protection in southern Finland. Recently, the Forest Biodiversity Programme for Southern Finland (METSO program; Finnish Government, 2008) was established in response to the nation-wide conservation needs. In this program, the Finnish Forest and Park Service (Metsa¨hallitus, hereafter FFPS) has been given the mandate to expand the area of the currently protected areas (Fig. 1) on state-owned land by 10 000 ha. Given that there are 1 760 000 ha of state-owned forests, it is not trivial to find the optimal or near-optimal set of new protected areas to maximize the benefits for conservation. Here, we present a formal solution to this problem. We identify the optimal or near-optimal set of additional protected areas given information about the relevant variables of forest structure and certain ecological assumptions (outlined below and described in more detail in the next section). The key information consists of nation-wide high-resolution geo-referenced estimates of forest structure, such as site fertility, the volume of the growing stock, tree species composition and the age of the forest stands (Tomppo et al., 2008). The expansion of the protected areas should be based on the best information available. Local expert knowledge is invaluable in the delineation and evaluation of particular forest stands, but using only expert knowledge makes objective comparison of candidate areas difficult, and it is hence desirable to employ quantitative decision support tools in decision making. In the past, a range of methods have been applied, including species richness extrapolation (O’Dea et al., 2006), species compositional similarity (Steinitz et al., 2005), gap analysis (Montigny and MacLean, 2005), multiple use management planning (Baskent et al., 2008), simple heuristic algorithms (Virolainen et al., 2001; Heikkinen, 2002), genetic algorithms (Ho¨lzkamper et al., 2006), simulated annealing techniques (Boyland et al., 2004; Rayfield et al., 2008) and linear programming optimization (Ricker et al., 2007). The multi-source national inventory of Finland (MS-NFI) used in our study has been previously used for the purpose of constructing a biodiversity

2. Materials and methods The objective of this study is to describe a quantitative analysis of where to place the proposed 10 000 ha extension of the current network of forest conservation areas managed by the FFPS in southcentral Finland. The criteria of a desirable solution (Finnish Government, 2008) include high local quality of the forest to be protected (Ministry of the Environment, 2008) and high connectivity both internally and to the existing conservation areas, which may often function as source areas for colonization. The new protected areas have to be selected among the FAWS managed by the FFPS. Individual new protected areas should be large enough to facilitate implementation, in practice implying preference for areas of 100 ha or greater. However, as many of the new protected areas are in fact extensions to existing protected areas, this criterion was not used as a strict rule. As some areas identified by the analysis might not be convenient for the FFPS (see Section 4 for limitations of the analysis), we set the target of 18 000 ha for the pooled area rather than 10 000 ha. For comparison we also carried out a comparable analysis for a significantly larger target area (48 000 ha). 2.1. Data We have used two primary data sets in our analysis (see Appendix, Table A1). The first one originates from the multi-source national forest inventory of Finland (MS-NFI) and consists of maps giving the predicted volume of growing stock by tree species, stand age, and site fertility (Tomppo, 2006a). The MS-NFI data integrate field data of the 10th national forest inventory of Finland (NFI) (Tomppo, 2006b; Tomppo et al., 2008) with satellite images, digital map data and statistical image analysis methods. This combination of information allows the calculation of the predicted values of selected forest variables with high spatial resolution as well as associated small-area statistics. The MS-NFI data represent a systematic survey of forest characteristics across Finland, with data collected and processed in the same way across the entire country. The original MS-NFI data are available at the very high resolution of 25 m  25 m. Our analyses are based on layers aggregated to 300 m  300 m resolution for computational reasons and because the original units are so small that they are not

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relevant in forest conservation planning. Furthermore, using the resolution of 9 ha (300 m  300 m) rather than 0.0625 ha (25 m  25 m) decreases the root mean square errors of the estimates and makes the analysis more reliable. The pixel level (25 m  25 m) quantities are predictions and hence include prediction error, but the predictions are nearly unbiased in the sense that when the aggregated area increases the error decreases (Tomppo et al., 2008). The second primary data set is the FFPS nature type inventory (NTI), which includes detailed stand and habitat level information about forest type, vegetation cover, the amount of dead wood and general ‘‘naturalness’’ of the forest plot. These data are available only for the current protected areas (611 000 ha in the METSO region, see Fig. 1). Though these data are not available for the areas among which the new protected areas must be selected, they provide relevant information about connectivity to high-quality forest areas that may serve as source areas for colonization to the new protected areas. Only those NTI features were selected that have been consistently surveyed across all the FFPS areas and are relevant for the conservation of biodiversity (Table A1). These features were used to provide a ranking of the FFPS conservation areas, but also to calculate connectivity from the FFPS areas to the surrounding landscape. The study area is south-central Finland, as defined by the METSO programme, covering approximately two-thirds of Finland (Fig. 1). Within this area, the MS-NFI data cover ca 15 million ha of forest land and the NTI data cover 611 000 ha, including forests, peatlands and small water bodies. Following the aggregation of the MS-NFI data up to the resolution of 300 m  300 m, the data consisted of a matrix of 2.3 million informative locations. The vector form NTI data were converted to raster data and sampled to the same resolution as the MS-NFI data. Aggregation inevitably leads to loss of some spatial information, but the original information about various forest features in the 25 m  25 m cells is retained as averages in the aggregated data. 2.2. The Zonation framework for conservation prioritization Conservation prioritization is concerned with efficient allocation of limited resources for conservation. The Zonation framework and software are intended for quantitative conservation prioritization across large landscapes using data sets that describe the distribution of biodiversity features such as species, habitat types, etc. (Moilanen et al., 2005; Moilanen and Kujala, 2008). The input data may be derived from various sources, including remotely sensed habitat mapping, empirical data on the distribution of species and statistical species distribution models (Elith et al., 2006). Zonation generates a ranked prioritization of the landscape via iterative removal of the least important remaining site, accounting for factors such as multiple biodiversity features and variable local habitat quality, priorities (weights) given to the features, land cost, and the locations of existing conservation areas. Important features of Zonation for the present work include the ability to handle species-specific connectivity requirements (Moilanen et al., 2005; Moilanen and Wintle, 2006, 2007) and the ability to include interactions between the forest features in the analysis (Rayfield et al., 2009). It is also important that Zonation can handle species-specific connectivity requirements on grids of millions of elements, which allows high-resolution analyses across very large areas. In the forest context Zonation has previously been used for targeting of habitat restoration in Australia (Thomson et al., 2009). Other major Zonation applications include conservation prioritization of biodiversity hotspot in Madagascar (Kremen et al., 2008) and the evaluation of the planned marine protection areas of New Zealand (Leathwick et al., 2008b).

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2.3. Connectivity Connectivity is a fundamental variable in spatial ecology. There is a large literature demonstrating that connectivity influences both local and regional population dynamics, including the risk of extinction (Murcia, 1995; Hanski, 2000; Rolstad et al., 2004; Moilanen and Wintle, 2006). In the case of networks of protected areas, regional viability of species may depend critically on connectivity (Aune et al., 2005). In this study, we took into account three components of connectivity, (1) the internal connectivity of forest areas, that is connectivity among different parts (grid cells) of the forest area, (2) connectivity to current high-quality conservation areas that may function as sources of immigrants to any new conservation areas, and (3) structural compactness of new conservation areas that would facilitate the implementation of conservation. The spatial scale of connectivity is selected to correspond to the likely dispersal capacity of the more specialized forest-dwelling species that are of main conservation concern. There are no data to rigorously estimate the spatial scale, but based on expert opinion and existing empirical studies (discussed below) we used the exponential kernel with mean dispersal distance of 2 km for internal connectivity within forested areas and 5 km for the connectivity to current high-quality conservation areas. The internal connectivity of forest areas was calculated using a variant of the kernel-type metapopulation connectivity measure (Hanski, 1994; Moilanen and Nieminen, 2002), which takes into account multiple features (habitat types) that contribute to the connectivity of each other. For example, spruce forest may contribute to the connectivity of species living in pine forest, because there is some overlap in the species composition of the respective communities. Multi-feature connectivity was implemented as a many-to-one generalization of the distribution smoothing and interaction connectivity techniques (Moilanen et al., 2005; Moilanen and Kujala, 2008; Rayfield et al., 2009), which have been previously applied to single features. To construct the multi-feature measure, we re-compute the value of feature k at location (grid cell) i via a transformation, in which the value of feature k is multiplied by its connectivity, p0ik ¼ pik C ik ;

(1)

where Cik is the connectivity of feature k in cell i, and pik and p0ik are the original and transformed values of the feature. Importantly, Cik is defined (below) so that it accounts not only for the distribution of the feature k but also for the distributions of all features that may contribute to the connectivity of feature k. In the Zonation algorithm, pik is first normalized to the fraction of the full distribution of feature k in cell I (see Moilanen et al., 2005). The connectivity component of transform (1) is described by 8 9 J F < = X X Snk p jn exp½ak dði; jÞ ; C ik ¼ : ; n¼1

(2)

j¼1

where F is the total number of features, J is the total number of cells, d(i,j) is the geographical distance between cells i and j, and ak is the parameter giving the spatial scale for feature k. We used ak = 0.001, corresponding to the mean dispersal distance of 2 km. Snk is a coefficient specifying how much feature n contributes to the connectivity of feature k. Table A2 specifies the matrix of Snk coefficients used for the 20 MS-NFI feature layers in this study. The mean dispersal distance of 2 km is a reasonable value for species like the Capercaillie (Tetrao urogallus) (Storch and Segelbacher, 2000) and the Siberian flying squirrel (Pteromys volans) (Selonen and Hanski, 2004), which require relatively large areas of suitable habitat and high level of landscape connectivity (Kurki et al., 2000; Reunanen et al., 2000). Many less mobile forest species do not

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necessarily require equally large areas, but are specialized on ephemeral resources such as dead wood that are scattered unevenly across the landscape. Studies conducted on two polypore species, Fomitopsis rosea and Phlebia centrifuga, have concluded that wooddecaying fungi regularly disperse over distances of 1–3 km in boreal forests (Norde´n and Larsson, 2000; Edman et al., 2004). Turning to the second connectivity component, we note that only small fragments of forests of high conservation value remain in southern and central Finland (Ha¨nninen et al., 2006) and many species living in natural forests have low dispersal ability (e.g. Komonen et al., 2000). This observation suggests that connectivity in general is an important feature of reserve networks (Hanski, 2006) and that the present FFPS conservation areas may function as sources of immigrants to any new conservation areas. To construct the relevant connectivity measure, a second copy of the MS-NFI layers (Fig. 2, step 5) was transformed by connectivity to existing high-quality conservation areas. This step was implemented using the interaction connectivity method (Moilanen and Kujala, 2008; Rayfield et al., 2009), which was applied between

particular forest types present both in the existing conservation areas and in their neighbourhood (Section 2.4, step 5). The parameter of the negative exponential dispersal kernel was selected to correspond to the mean dispersal distance of 5 km. The rational for using here a longer spatial scale than in the calculation of connectivity within forest stands is that uncommon dispersal events from the existing high-quality conservation areas may lead to successful colonization of the new conservation areas. The third connectivity component was added to address the requirement that the new protected areas should preferably be large enough for practical implementation. We applied the boundary length penalty technique to increase the structural compactness of the selected forest areas (Possingham et al., 2000; Moilanen and Wintle, 2007). 2.4. The course of the analysis Fig. 2 outlines the course of the analysis. The primary aim is to arrive at a recommendation as to where the new protected areas

Fig. 2. A flow chart of the analysis. Numbered steps in the flow chart indicate main analysis phases and correspond to the steps described in Section 2.4.

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should be located. The primary data are the MS-NFI data, which cover the entire country and therefore allow unbiased treatment of all forested areas. The additional data from the currently protected areas (Fig. 1) are helpful in identifying likely source areas for colonization to the candidate new protected areas. The analysis involves the following steps: (1) Construction of the MS-NFI feature layers. Twenty feature layers were extracted from the MS-NFI data, combining five forest productivity classes and four dominant tree species. Each cell in the grid was assigned to a combination layer AB if it represented productivity class A and dominant tree species B (see Appendix, Table A1 for all the combinations). These grids were then aggregated by summing up the values in individual grid cells to achieve the resolution of 300 m. At this resolution, each grid cell can contribute to multiple forest types. To assess the conservation value of each feature we calculated index pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi layers using the formula age  volume, where age is given in years and volume is total volume. This formula gives weight to forest areas with large volume of old trees. (2) Weighting of the habitat types. Weights were assigned to features in both the MS-NFI and NTI data sets based on their conservation value as determined by experts. Highest weights were given to highly productive forests areas and deciduous forests with a dominant tree species other than birch (Table A1). These criteria were used because species-rich forest types are underrepresented in the current conservation network and represent focal habitats in Finnish forest conservation (Virkkala and Toivonen, 1999). (3) Quality of existing forest reserves as source areas for colonization. We had additional data for the current reserves in the form of nature type inventory (NTI) reflecting the quality of the conservation areas. Here we employed weighted, range-size normalized richness layers, which represent the fraction of the total known biodiversity features that occurs in each cell. P Technically, these fractions are calculated as ai ¼ jw j f i j , where w j is the weight of feature j, fij is the fraction of the distribution of feature j in cell i, and the summation is taken across the relevant features j (Moilanen and Kujala, 2008). Source quality was estimated separately for the classes corresponding to the four dominant tree species (layers 72– 75 in Appendix Table A1). NTI features contributed to the quality of one or more of these source quality layers. (4) Connectivity within the forest area. One copy of the MS-NFI layers was entered into the analysis and transformed using the multi-feature connectivity operation to model internal connectivity of the forest area (Table A2; see also Section 2.3). Importantly, this operation accounts for partial similarity of forests that have different nominal classifications. (5) Connectivity to current reserves. Another copy of the MS-NFI layers was first transformed by internal connectivity (previous step) and next by interaction connectivity to the current conservation areas (Rayfield et al., 2009) This transformation increases the value of the forest areas in the MS-NFI layers that are well connected to high-quality current protected areas. The weighted, range-size normalized richness layers (step 3) were used as source area quality in the interaction connectivity method (Rayfield et al., 2009), meaning that connectivity was calculated between similar habitat types inside and outside the current conservation areas. (6) Complementarity-based iterative hierarchical analysis of spatial conservation priority. This analysis was conducted with the Zonation software that was run in the additive benefit mode (Moilanen, 2007), with value z = 0.25 for the parameter of the power function that scales conservation value as a function of representation. The boundary length penalty was used for extra

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structural connectivity (Moilanen and Wintle, 2007; penalty parameter b = 0.00003). (7) Forcing the selection to the state-owned FAWS. A two-level mask file was produced, with the current protected areas given the highest priority and the FFPS FAWS the second-highest priority. No extra priority was given to privately owned forests, meaning that the cell removal order in Zonation was forced to be first private land, then the FFPS FAWS and lastly the FFPS protected areas. Within each of these three classes, the cell removal order was free and determined by the rule of stepwise minimization of marginal loss of conservation value. (8) Post-processing and output. Post-processing analyses were done to identify optimal extensions of the current protected areas based on the priority rank maps produced by each Zonation run. The optimal set of new conservation areas is the highestranked set of areas outside the current conservation areas. The present analysis with 71 informative layers (Table A1) of feature data in a matrix of 2.3 million informative grid cells was close to the memory limit using the present version of Zonation operating on a 32-bit Windows platform. Computation times on an ordinary desktop PC were around 2 h per analysis. 3. Results The priority rank maps calculated for the METSO planning area are shown in Fig. 3. The proposed new protected areas are identified as the highest-ranked areas outside the current protected areas. Fig. 3A is the map for the baseline analysis, based on the most plausible assumptions and using the best-supported parameter values as described in the previous section. Fig. 3B shows a variant of the analysis, in which all the connectivity components have been omitted—this figure essentially gives the result of a non-spatial analysis plotted on a map. There is a big difference between the two solutions. While the highly ranked areas occur broadly in the same areas, the non-spatial solution (Fig. 3B) exhibits a high level of fragmentation of the priority areas. The consequences of connectivity at a more local level are illustrated in Fig. 4. Fig. 4A gives the rank map for the simplest Zonation analysis without any connectivity components, with the current protected areas shown in black. Adding the multi-feature connectivity aggregates the high-priority areas, because now well connected high-quality areas become more valuable (Fig. 4B). Taking into account connectivity to the current reserves moves the preferred areas closer to the current protected areas (Fig. 4C). Adding the boundary length penalty further aggregates highpriority areas and reduces their number, which may be advantageous for implementation and management (Fig. 4D). Fig. 4E is the rank map based on the full baseline analysis, with the FAWS managed by the FFPS shown in grey. Our proposed set of new protected areas is identified by taking the highest-ranked areas outside the current protected areas, shown by red color in Fig. 4F. Table 1 summarizes the statistics on the highest-ranked extensions to the current protected areas. The 18 000 ha extension consists of 524 individual forest areas, of which 26 are equal to or larger than 11 grid cells (99 ha) and thereby meet the desired size of 100 ha. However, since the number of these sites is small and their total area (8451 ha) is smaller than the target of 10 000 ha, the size limit was decreased to four grid cells (36 ha). Hundred and eight (108) areas were at least 36 ha in size with the pooled area of 12 627 ha. The largest individual area was 1953 ha, but it turned out that this area is already under some protection as a recreational area and additional conservation measures are not urgently needed. Thus the remaining areas >36 ha in size sum up to 10 700 ha, and this set of 107 forest areas largely satisfy the planning objectives of the FFPS.

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Fig. 3. Basic Zonation output, priority maps for the extension of the forest conservation areas of the Finnish Forest and Park Service. The present conservation areas are shown by black, best proposed extensions by red, and the priority rank for the rest of the landscape by the color scale indicated in the panel. Panel (A) shows the baseline solution with all connectivity components, and panel (B) shows the same but without any connectivity components.

Table 1 demonstrates the consequences of the connectivity components that are included in the analysis. Dropping the boundary length penalty (Moilanen and Wintle, 2007) from the analysis significantly increases the number individual areas to 845, most of which are small; this solution has fewer large areas than the baseline solution. Removing all connectivity components further increases the number of individual forest areas to 1270, of which only 5 areas meet the initial target of 100 ha and 80 the reduced target of 36 ha. The level of spatial overlap between the forest areas identified by the different versions of the analysis are given in Table 2. Not surprisingly, the smallest overlap is between the baseline solution and the non-spatial solution accounting for local habitat quality only (37%), whereas the solution without the boundary length penalty has the largest overlap with the baseline solution (66%). The version using the core-area Zonation (CAZ; Moilanen et al., 2005; Moilanen, 2007) method has the spatial overlap of 53% with the baseline solution. Apart from the rank maps (Figs. 3 and 4), the second main output of the Zonation analysis is performance curves (Fig. 5). In the present case we are interested in the level of representation of the four main forest types, with an emphasis on the high-priority groups of productive forests, herb-rich forests and forests with deciduous trees other than the birch. The curves in Fig. 5 can be interpreted as the level of features (grouped by the four main forest types) under protection (y-axis) when a given fraction of the landscape is under conservation management (x-axis). Fig. 5 shows graphically the relative qualities of the current FFPS protected areas, the proposed new protected areas, the stateowned non-protected forests managed by the FFPS, and the privately owned forests. The present analysis was constrained by the requirement that the new protected forests must be on stateowned land. Two conclusions can be drawn from Fig. 5. First, the class of deciduous forests is underrepresented within the FFPS protected areas as well as in the FAWS. Second, the FAWS managed by the FFPS are not of particularly high-quality at the national scale. This is evident from the fact that the performance curves rise very rapidly when selection moves to privately owned forests. Note that Fig. 5 also suggests that the current protected areas

managed by the FFPS are not of especially high quality either. This conclusion is however misleading, because the current protected areas include peatlands and other areas that are not protected because of their forests. The 10 000 ha addition that is the recommendation from the present analysis can be seen as a small kink upwards that is most visible in the minimum-performance curve for deciduous forests (Fig. 5). 4. Discussion We have developed and implemented a procedure for identifying a biologically well-justified extension to the current network of protected forests located within publicly owned forest land in southern Finland. Several characteristics of the data and the planning task strongly influenced the way the analysis was set up. First, there were two qualitatively different data sets, detailed data for the existing protected areas and less detailed data covering the entire planning region. Second, the new protected areas could only be located within certain areas—the forests currently managed for timber production by the FFPS. Third, neither land nor timber price were included in the optimization. Fourth, the new protected areas were required to have high connectivity both internally as well as to the current protected areas. Fifth, new areas larger than 36 ha were preferred for logistic reasons. Thus high or relatively high connectivity was required for both population dynamic reasons and to facilitate implementation and management—a high level of fragmentation of the selected areas would make a solution impractical (compare Fig. 3A and Fig. 3B). The representatives of the FFPS actively participated in the planning of the analysis and thus the present results directly address many of the planning needs of the FFPS. The results that were delivered included printed and numerical prioritization rank maps, a list of potential new protected areas as well as guidelines as to how to interpret the results. However, the set of areas that we have identified is only one input into the conservation planning process ongoing in the FFPS. Another very different type of input is a list of high-quality forest areas identified by NGO forest experts based on local-level knowledge about the occurrence of forest features that have high conservation value (list available in

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Fig. 4. Effects of connectivity at a local scale and the proposed conservation area extensions within this region. (A) With no connectivity components the solution is very fragmented. (B) Adding the multi-feature connectivity measure aggregates priorities, although local variation in habitat quality still leaves the best areas relatively fragmented. (C) Adding connectivity to existing high-quality conservation areas moves highly ranked areas closer to the protected areas. (D) Use of the boundary length penalty further aggregates priority areas to a level acceptable for decision making and management purposes. (E) The extensions of the conservation area network must be chosen among the forests available for wood supply (FAWS) and managed by the FFPS, shown with grey in the figure. (F) Our proposed extension of the conservation area network is shown with red.

Finnish: http://www.forestinfo.fi/etelasuomi/). The proposal made by the FFPS and NGO forest experts has the advantage that it incorporates detailed information about the occurrence of threatened species and habitat types within the proposed areas. Expert-based analysis may also include information about the occurrence of dead wood, an important feature for forest biodiversity that is not included in the MS-NFI data. The expertbased proposals may also include information about accessibility and recreational value, which considerations have not been included in the present analysis because relevant information is available only locally, not for the entire country.

The greatest advantage of the present analysis is objectivity, as the analysis is based on high-resolution data that cover systematically the entire planning area, originally at 25 m resolution but aggregated to 300 m resolution for the analysis. The nation-wide coverage means that poorly surveyed but high-quality areas can be identified. Importantly, our analysis identified solutions that are balanced in the sense that selected areas reflect the nation-wide distributions of biodiversity features, giving higher weights to features that are scarce (Leathwick et al., 2008a). Furthermore, our analysis allows the application of various connectivity-related planning criteria. The next planning step should involve an

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Table 1 Statistics for alternative solutions. Data are given for the baseline solution (base; Fig 3A), the baseline solution without the boundary length penalty (NoBLP), and the nonspatial solution without any connectivity components (non-spatial; Fig. 3B). For each solution there are two different target areas, 18 000 ha (A) and 48 000 ha (B). Base A

Base B

No BLP A

No BLP B

Non-spatial A

Non-spatial B

No. sites Mean A (ha) Median A (ha)

524 34.25 9

769 62.13 18

845 21.29 9

1886 25.45 9

1270 14.17 9

2879 16.67 9

N/total area (ha) of sites 900 ha 450 ha 216 ha 99 ha 36 ha

2/2952 6/5427 9/6318 26/8451 108/12 627

7/15 255 17/21 042 34/26 595 80/33 246 213/40 428

0/0 1/693 6/2313 20/4149 119/9297

3/4014 8/7209 21/11 178 59/16 830 277/28 287

0/0 0/0 0/0 5/702 80/4419

0/0 2/1188 9/3186 39/6813 247/17 073

informed combination of the expert-based and our quantitative analyses. If both analyses suggest that a location has high conservation priority, then it is likely to truly have high value. If the analyses disagree, further investigation is warranted. Preliminary comparisons indicate that the areas identified by the present quantitative analysis overlap significantly with areas of high conservation value as judged by experts in their assessment. Previous studies have attempted to identify the most valuable forest areas for additional conservation in the METSO region using the MS-NFI data and GIS-based habitat-suitability assessment (e.g. Luque and Vainikainen, 2008). Conventional landscape ecological approaches are usually based on habitat indices coupled with GIS operations like buffer distances to account for connectivity. To our knowledge, the approach we have described in the present paper is the first one to employ a large-scale complementarity-based prioritization method accounting for several types of connectivity. Apart from providing the solution to the original prioritization problem, the present analysis yielded a result that may have high policy relevance. Fig. 5 demonstrates that most of the remaining high-quality non-protected forests in southern Finland are located on privately owned land. This result is not entirely unexpected because most state-owned forests are located on unproductive soils and there are differences in the occurrence of many forest habitat types between privately owned and stateowned forests. Considering the limitations of the present analysis, several potentially important factors had to be ignored. More data about

the distribution of valuable biodiversity features, such as dead wood, would have benefited the analysis. The old age of a forest stand does not translate directly to a large amount of dead wood and species dependent on it (Martikainen et al., 2000; Siitonen et al., 2000), but the age of the forest stand can be used as a proxy in the absence of more direct information. We had to ignore forest dynamics, including natural succession and the predicted consequences of climate change. The present analysis provides a highlevel large-scale solution, while forest management operations, such as prescribed burning for restoration, can be targeted for the maintenance of the quality of the protected areas. Finally, our analysis was squarely focused on the distribution of biodiversity, and we ignored social, political and economical criteria that frequently influence conservation planning (Mikusin´ski et al., 2007; Humphries et al., 2008; Vierikko et al., 2008). In the present case, these latter criteria were not important, because in this case the decisions concerning protection will be made based on the biological criteria only. The situation would be much more complicated if a similar analysis would be made for privately owned forests. However, we emphasize that the present analysis could be extended to include the necessary economic and social factors as long as the relevant information can be quantified for the entire planning area. The present approach is expected to have wide applicability in many large-scale planning projects.

Table 2 The consequences of using different options in the analysis. The table gives a matrix of spatial overlaps between the solutions. Data are given for the baseline solution (base), the core-area Zonation variant of the baseline solution (+CAZ), the baseline solution without the boundary length penalty (BLP), the baseline solution without multi-feature connectivity (MC), the baseline solution without interactions (IA), and the non-spatial solution without any connectivity components (non-spatial). Table A is for the 18 000 ha extension and B for the 48 000 ha extension. Base

IA

MC

+CAZ

BLP

Non-spatial

(A) Base IA MC +CAZ BLP Non-spatial

1.00 0.63 0.58 0.53 0.66 0.37

0.63 1.00 0.47 0.67 0.74 0.49

0.58 0.47 1.00 0.46 0.52 0.41

0.53 0.67 0.46 1.00 0.66 0.54

0.66 0.74 0.52 0.66 1.00 0.52

0.37 0.49 0.41 0.54 0.52 1.00

(B) Base IA MC +CAZ BLP Non-spatial

1.00 0.65 0.66 0.56 0.52 0.32

0.65 1.00 0.52 0.67 0.64 0.45

0.66 0.52 1.00 0.50 0.45 0.33

0.56 0.67 0.50 1.00 0.59 0.45

0.52 0.64 0.45 0.59 1.00 0.58

0.32 0.45 0.33 0.45 0.58 1.00

Fig. 5. The second main Zonation output: solution performance. Performance curves show the mean fraction of the habitat type remaining when different fractions of the landscape are placed under conservation management. The x-axis corresponds to the top fraction of the landscape selected from the priority rank map (Fig. 3). The different parts of the performance curves corresponding to different land ownership are marked by vertical lines: (A) private lands, (B) FFPS forests managed for timber production, (C) conservation area extensions proposed by the analysis, and (D) current FFPS conservation areas. The insert shows the top fraction in greater detail.

J. Lehtoma¨ki et al. / Forest Ecology and Management 258 (2009) 2439–2449

Acknowledgments J.L. thank the University of Helsinki Science Foundation for funding. A.M, I.H. and J.L acknowledge the Academy of Finland and the Finnish Center-of-Excellence programme 2006–2011 for

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support (grants 129636 and 213457). We thank Pekka Punttila for valuable comments on the manuscript and Jyrki Ma¨a¨tta¨ for help with the GIS data. Appendix A. Data layers and connectivity coefficients

Table A1 Data used in this work. Groups (A) MS-NFI, (A2) MS-NFI transformed by interaction connectivity to existing protected areas, (B) FFPS NTI data for existing protected areas, (C) assisting supplementary layers, weighted range-size normalized richness layers corresponding to main tree species group and the mask for existing conservation areas. ID

Group

Species group

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21–40 41 42 43 44 45 46 47 48 49 50

A A A A A A A A A A A A A A A A A A A A A2 B B B B B B B B B B

Pine Dry upland forest site Pine Vaccinium site type Pine Fresh mineral soil forest sites Pine Upland forests with grass-herb vegetation Pine Herb-rich forest Spruce Dry upland forest site Spruce Vaccinium site type Spruce Fresh mineral soil forest sites Spruce Upland forests with grass-herb vegetation Spruce Herb-rich forest Birch Dry upland forest site Birch Vaccinium site type Birch Fresh mineral soil forest sites Birch Upland forests with grass-herb vegetation Birch Herb-rich forest Other broadleaves Dry upland forest site Other broadleaves Vaccinium site type Other broadleaves Fresh mineral soil forest sites Other broadleaves Upland forests with grass-herb vegetation Other broadleaves Herb-rich forest Layers 1–20 repeated and treated with connectivity to existing PAs Lichen Calluna site types Lichen-heath Dry upland Lichen-moss-heath Vaccinium site type Moss-heath Fresh mineral soil Moss-heath-grass Upland grass-herb vegetation Grass Herb-rich Lichen Grass-carex Moss Grass-carex Grass Grass-carex – Abandoned, reforesting or afforested agricultural land – Esker islands – Forested dunes – Limy rocky outcrops – Silicate rocky outcrops – Pioneer vegetation on rocky outcrops – Natural forests – Hardwood forests – Primary succession vegetation on postglacial rebound ground – Herb-rich forest – Esker forests – Pasturage, burn-beaten area – Flood forests – Ephemeral flood forests – Herb-rich forest on ravines and hillsides – Old oak forests – Forested swamps – Advanced thinning stand – Mature stand – Heterogeneous stand – Old forest – The volume of dead wood

51 52 53 54 55 56 57 58

B B B B B B B B

59 60 61 62 63 64 65 66 67 68 69 70 71

B B B B B B B B B B B B B

Additional supplementary layers used during the analysis 72 C –

73 74 75 76 77 78

C C C C C C

– – – – – –

Habitat type

Weighted range-size normalized richness (WSNR) layer for source areas within FFPS protected areas; birch forest type (BF) WRSNR layer; herb-rich forest (HRF) WRSNR layer; pine forest (PF) WRSNR layer; spruce forest (SF) Mask for Natural Heritage Services’ areas Mask for FFPS economically managed areas Mask for all areas managed by FFPS

Weight 1.0 1.0 1.0 1.5 2.0 1.0 1.0 1.0 1.5 2.0 1.0 1.0 1.0 1.5 2.0 2.0 2.0 2.0 3.0 4.0 1.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 0.5 10.0 2.0 10.0 2.0 2.0 5.0 10.0 5.0 5.0 2.0 5.0 5.0 5.0 5.0 10.0 2.0 1.0 2.0 2.0 5.0 10.0

NA

NA NA NA NA NA NA

J. Lehtoma¨ki et al. / Forest Ecology and Management 258 (2009) 2439–2449

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Table A2 Coefficients for connectivity calculations between habitat types (multi-feature connectivity). A full coefficient matrix of dimensions 20  20 was constructed as the product of two independent components, representing dominant tree species and productivity of location. Coefficients are given (A) between main tree species: pine (Pi), spruce (Sp), birch (Bi) and other broadleaves (Ob), and (B) between productivity of sites: dry upland forest site (Dr), Vaccinium type site (Vs), fresh mineral soil forest site (Fm), Upland forests with grass-herb vegetation site (Ug) and herb-rich forest (Hr). A coefficient of 1.0 at the diagonal indicates that a type always contributes fully to its own connectivity. A coefficient <1.0 indicates that the forest types help each other’s connectivity to a smaller degree. The connectivity contribution between habitat types does not have to be symmetrical and habitat type A may be better connected to habitat type B than the other way around. Pi

Sp

Bi

Ob

1.0 0.7 0.3 0.5

0.7 1.0 0.6 0.5

0.4 0.6 1.0 1.0

0.2 0.4 0.8 1.0

A Pi Sp Bi Ob Dr

Vs

Fm

Ug

Hr

1.0 1.0 0.9 0.7 0.4

0.9 1.0 1.0 0.9 0.7

0.7 0.9 1.0 1.0 0.7

0.4 0.7 0.9 1.0 1.0

0.2 0.4 0.7 0.9 1.0

B Dr Vs Fm Ug Hr

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