Wher eandhow t oc ons er ve: Ext endi ngt hes c opeof s pat i alr es er venet wor kdes i gn As t r i dJ . A.vanTeeffel en Where and how to conserve: Extending the scope of spatial reserve network design Astrid J.A. van Teeffelen Metapopulation Research Group Department of Biological and Environmental Sciences University of Helsinki Finland Academic dissertation To be presented, with permission of the Faculty of Biosciences of the University of Helsinki, for public criticism in the Auditorium of the Helsinki University Museum Arppeanum (Snellmaninkatu 3) on the 16th of June 2007 at 12 o’clock noon. Helsinki, 2007 © Astrid van Teeffelen (Summary, Chapter V) © Authors (Chapters III, IV, VI) © Springer (Chapter I) © Oikos (Chapter II) Cover design: Bart A. Smit & Astrid van Teeffelen Layout: Astrid van Teeffelen Author’s address: Land Use Planning Group Department of Environmental Sciences Wageningen University & Research Centre Droevendaalsesteeg 3 NL-6708 PB Wageningen The Netherlands E-mail: [email protected] ISBN 978-952-10-3955-3 (paperback) ISBN 978-952-10-3956-0 (PDF) http://ethesis.helsinki.fi Printed by: Yliopistopaino Helsinki 2007 Where and how to conserve: Extending the scope of spatial reserve network design Astrid J.A. van Teeffelen The thesis is based on the following articles and manuscripts, which are referred to in the text by their Roman numerals: I Van Teeffelen, A.J.A., M. Cabeza & A. Moilanen, 2006. Connectivity, probabilities and persistence: comparing reserve selection strategies. Biodiversity and Conservation 15(3): 899– 919. II Van Teeffelen, A.J.A. & O. Ovaskainen, 2007. Can the cause of aggregation be inferred from species distributions? Oikos 116(1): 4-16. III Van Teeffelen, A.J.A. & A. Moilanen. Where and how to manage: optimal selection of conservation actions for multiple species. Submitted. IV Van Teeffelen, A.J.A., M. Cabeza, J. Pöyry, K. Raatikainen & M. Kuussaari. Maximizing conservation benefit for grassland species with contrasting management requirements. Submitted. V Van Teeffelen, A.J.A. Taking account of species-specific connectivity requirements when selecting conservation actions. Manuscript. VI Moilanen, A., A.J.A. van Teeffelen, Y. Ben-Haim & S. Ferrier. How much compensation is enough? Explicit incorporation of uncertainty and time discounting when calculating offset ratios for impacted habitat. Submitted. Contributions I II III IV V VI Original idea AM, MC, AvT AvT, OO AvT AvT AvT AM, SF Study design AvT, AM AvT, OO AvT, AM AvT, MC AvT AM, SF Methods & implementation AM, MC, AvT AvT, OO AM, AvT AvT AvT AM, YBH Empirical data *, **, *** *, **, *** - JP, KR, MK, JPy - - Analysis AvT AvT AvT AvT AvT AM, AvT Manuscript preparation AvT, MC, AM AvT, OO AvT, AM AvT, MC, JP, KR, MK AvT AM, AvT, SF AvT Astrid van Teeffelen, AM Atte Moilanen, MC Mar Cabeza, OO Otso Ovaskainen, JP Juha Pöyry, KR Katja Raatikainen, MK Mikko Kuussaari, JPy Juha Pykälä, SF Simon Ferrier, YBH Yakov Ben-Haim. * Alterra - Wageningen University & Research Centre, the Netherlands. ** Province of Noord-Brabant, the Netherlands. *** SWEV, West-Brabant Association of Bird Study Groups, the Netherlands. Supervised by: Dr. Atte Moilanen Senior Researcher, University of Helsinki, Finland. Reviewed by: Dr. Risto Heikkinen Senior Researcher, Finnish Environment Institute, Finland. Dr. Kristina D. Rothley Assistant Professor, Simon Fraser University, Canada. Examined by: Dr. Michael McCarthy Senior Researcher, University of Melbourne, Australia. Table of contents Summary 7 1 Background 1.1 Where to conserve 1.2 How to conserve 7 7 12 2 Thesis outline 15 3 Results & Discussion 17 17 18 19 19 3.1 3.2 3.3 3.4 Spatial reserve network design From a single to multiple conservation options in design Connectivity in multi-option planning Effects of uncertainy on fair offset ratios 4 Synthesis & Perspectives 20 Acknowledgements 21 References 23 Connectivity, probabilities and persistence: comparing reserve selection strategies 29 II Can the cause of aggregation be inferred from species distributions? 47 III Where and how to manage: Optimal selection of conservation actions for multiple species 65 Maximizing conservation benefit for grassland species with contrasting management requirements 81 I IV V VI Taking account of species-specific connectivity requirements when selecting conservation actions 103 How much compensation is enough? Explicit incorporation of uncertainty and time discounting when calculating offset ratios for impacted habitat 123 About the author 137 Summary Astrid J.A. van Teeffelen 1 BACKGROUND Worldwide the impacts of human habitation and economic development have resulted in modification, destruction and fragmentation of our natural environment (Hanski, 2005). As a consequence, biodiversity is disappearing at an alarming rate, which is 100 to 1000 times larger than natural background rates of extinction (Smith et al., 1993; Pimm et al., 1995; Pimm & Lawton, 1998; Millennium Ecosystem Assessment, 2005). This rate is expected to increase even further due to human-induced climate change (McLaughlin et al., 2002; Thomas et al., 2004). In order to slow down the rate of biodiversity loss, conservation efforts are required. Habitat loss is considered to be one of the main driving forces of species extinctions (Saunders et al., 1991; Hanski, 2005). Preventing further habitat loss is therefore a key issue if the loss of biodiversity is to be slowed down. Due to a high demand for natural resources, as well as strong competition with other land use types (like agriculture, urban areas and industry), the pressure on natural areas is high. In combination with the tight budget for conservation, careful planning is required if we want to preserve as much of biodiversity as possible for future generations. Reserves have been a means of protection of natural heritage for centuries (Margules & Pressey, 2000). The reasons for protecting areas were mostly for their scenic beauty or for recreational purposes such as hunting and hiking, while biodiversity conservation was not much of an issue (Pressey, 1994; Lindenmayer & Burgman, 2005). Economic considerations resulted in the location of most reserves at economically uninteresting locations, such as at high altitudes, steep slopes or on poor soils (Adam, 1992; Pressey, 1994). These ad hoc and biased reserve selection practises have lead to a set of reserve networks that do not represent, let alone protect, the full range of biodiversity (Margules & Pressey, 2000). The insufficiency of current reserves, the increasing pressure on remaining natural areas and tight conservation budgets motivated conservationists to develop more systematic reserve selection methods for designing reserve networks in a cost-effective manner. The field of systematic conservation planning that emerged as a consequence, covers a wide range of questions about what facets of biodiversity to focus our conservations efforts on, where to conserve these biodiversity facets and how to conserve them (Fig. 1). Here, I mainly concentrate on the questions where and how to conserve, see Box 1 for a short note on what to conserve. 1.1 Where to conserve In order to handle the question “where to conserve?”, information is required on the distribution of (surrogates of) biodiversity. Henceforth, I will refer to “species” where I mean to say “surrogates of biodiversity”. This, because species are illustrative examples of biodiversity, they are commonly used as surrogates of biodiversity, and have been the subject of study in this thesis. Other studies have assessed the suitability of different species groups as surrogates (see Box 1 for references). Species distribution data Species distribution data (e.g. from field surveys or museum collections) are generally classified as being either presence-only or 7 presence-absence information, and can be binary or include abundance data (i.e. the number of individuals at a location). Such data are often incomplete and biased towards more accessible areas (Polasky et al., 2000). Obtaining a representative set of species distribution data to inform conservation planning is timeconsuming, during which biodiversity loss continues and options for conservation are reduced (Wilson et al., 2005b). The use of statistical models to predict the occurrence of species at un-surveyed sites, is a way to deal with incomplete distribution data (Margules & Stein, 1989; Wilson et al., 2005b). These models, referred to as species habitat distribution models (SHDMs; or habitat models) link species occurrence data to the distribution of environmental variables, such as vegetation, topographic and climatic variables (Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005; Elith et al., 2006). Due to the development of remote sensing technology, it is relatively easy and cheap to obtain coverage data for many environmental variables over large areas, compared to obtaining complete survey data for a large set of species. SHDMs allow the fitting of these environmental variables to the occurrence of a species, after which the model can be used to predict the probability of occurrence of that species at other sites in the landscape, based on the environmental suitability of these sites. Methods for modelling species’ habitat encompass – among others – generalised linear models (GLMs), generalised additive models (GAMs), and boosted regression trees (BRT) (Guisan & Zimmermann, 2000; Elith et al., 2006). What to conserve? How to conserve? • Surrogates for biodiversity • Goals o Prioritizing species o How much to conserve? • Protection • Restoration • Maintenance V VI III, IV Where to conserve? • Distribution of biodiversity • Site selection algorithms • Reserve network design I, II Figure 1. The three general topics (and a number of example subtopics) covered in systematic conservation planning. The Roman numerals indicate how the chapters of this thesis are related to those. 8 Summary Site selection problem: Definitions & solution methods In general, not all the locations where species are found (or are expected to occur) can be protected. Conservation planning therefore requires decisions on which sites to select for conservation. Constraints in conservation budget or the number of sites (or total area) that can be reserved have motivated the development of site-selection methods, which aim at finding efficient sets of sites to represent species. Initially, site-selection methods were scoring approaches that ranked sites according to their species richness, and selected as many sites from the top of the list until all species under consideration were represented in the set of selected sites (Wilson et al., 2005b). However, to design entire reserve networks, the properties of individual sites should be assessed in relation to each other, rather than individually – a concept known as complementarity (Vane-Wright et al., 1991; Margules & Pressey, 2000). Cost considerations are an explicit element of site-selection methods, although true cost of representation is oftentimes approximated by the total number or area of sites. Two problem definitions are commonly used in site-selection problems: 1) the minimum set covering problem, which aims at representing Box 1. What to conserve? Although the goal of conservation planning is the protection of biodiversity, it is impossible to obtain information on the distribution of all aspects of biodiversity (i.e. genes, individuals, demes, populations, metapopulations, species, communities, ecosystems and their interactions; Lindenmayer & Burgman, 2005) to direct conservation efforts. Instead, we need to rely on biodiversity surrogates to represent the full extent of biodiversity (Pearson, 1994; Faith & Walker, 1996; Margules et al., 2002). Potential surrogates encompass species assemblages, taxa subsets, vegetation communities and environmental diversity (Ferrier et al., 2004; Chiarucci et al., 2005; Trakhtenbrot & Kadmon, 2005). A large amount of literature is devoted to the testing of surrogate groups for their ability to represent other aspects of diversity (Howard et al., 1998; Andelman & Fagan, 2000; Williams et al., 2006). Although ideal surrogates probably do not exist, quantification of what we aim to conserve is essential for systematic conservation planning. The debate on “how much conservation is enough”, is decades old, and still ongoing (e.g. Rodrigues & Gaston, 2001; Svancara et al., 2005; Tear et al., 2005). For individual species of conservation concern, modelling approaches such as population viability analysis (PVA) can be used to estimate extinction risk and evaluate conservation scenarios (Burgman et al., 1993; Morris & Doak, 2003). Such models are typically parameter rich and therefore data-demanding, which complicates the application of PVA to large sets of species and large scale conservation planning. At these large scales, more general rules of thumb are needed to determine practical conservation goals. For example, The World Conservation Union advocates to set aside at least 10% of the land area of each nation for biodiversity conservation (IUCN, 1993), which has in turn been criticized for being insufficient for long term conservation (Soulé & Sanjayan, 1998; Svancara et al., 2005). Although there is consensus on the goals of conservation (the long term protection of species and biodiversity in general), transforming broad goals into quantitative objectives is essential for a decision-theoretic approach to the conservation problem (Westphal & Possingham, 2003; Nicholson & Possingham, 2006). 9 each species a given number of times while minimizing cost (Underhill, 1994; Csuti et al., 1997; Pressey et al., 1997). The proportional coverage problem is a variant of this, which aims at representing a particular proportion of vegetation types (Pressey et al., 1997; Leslie et al., 2003; Moilanen & Cabeza, 2005); 2) The maximal coverage location (or maximum coverage) approach, which maximizes the total number of species representations under a cost constraint (Camm et al., 1996; Church, 1996; Arthur et al., 1997; Arponen et al., 2005). Instead of including simply the number of representations, more meaningful measures of population viability are being sought by e.g., using probabilities of occurrence rather than presence-absence data (e.g. Araújo & Williams, 2000; Williams & Araújo, 2000, 2002; Wilson et al., 2005b), or by including measures of viability and persistence in the objective function directly (Hof & Raphael, 1993; Bevers et al., 1995; Araújo & Williams, 2000; Nicholson & Possingham, 2006). From site selection to reserve network design Initially, site selection methods did not consider the size or spatial configuration of the set of selected sites. Consequently, a small and spatially scattered set of sites was typically selected, because such a set is inexpensive and thus efficient. This would not be problematic, if the sites remain embedded in a landscape consisting of natural vegetation. However, with ongoing land use change, reserves are likely to become islands of natural habitat in a matrix of intensive land use. In landscapes with small and scattered habitat patches, local populations are sensitive to demographic stochasticity due to small population size and therefore have an increased risk of extinction. Edge effects can cause habitat quality at the edge of a patch to be lower than at the core of the patch. Examples of edge effects are the influx of high nutrient levels or pesticides from neighbouring agricultural land; lower availability of resources such as food and shelter; a higher predator density in the matrix, which increases predation risk at the edge (Andren & 10 Angelstam, 1988; Woodroffe & Ginsberg, 1998; Debinski & Holt, 2000). Since small patches have relatively more edge habitat than large patches, edge effects are more pronounced in smaller patches. Though local populations at small patches are more likely to go extinct, a species could persist in a fragmented landscape as a metapopulation, if the exchange of individuals between patches through dispersal is sufficient (Hanski, 1998, 1999). Dispersing individuals can found a new population in empty patches, and reduce extinction probability of existing populations via the so-called rescue effect (Brown & KodricBrown, 1977). However, exchange of individuals between patches is typically reduced with increasing distance between patches. Simply because the likelihood of finding another patch is reduced, and also because mortality risk is usually higher in the matrix: due to higher predator density, the presence of barriers such as roads and build-up areas and a lower resource density. Consequently, a lower dispersal success decreases colonisation success and increases the extinction probability of populations, which both decrease the probability of metapopulation persistence. Species living in larger, better connected networks therefore have a larger probability of persistence than species living in small, fragmented networks (Hanski, 1998). From these concepts of metapopulation ecology follows that the size and the configuration of networks should be larger and more compact in order to increase the probability of species persistence in the reserve network. Also from a management perspective clustered reserve sites can be preferential, as management costs typically increases with increasing reserve edge length (Possingham et al., 2000). The approaches that take the spatial configuration of reserve networks into account can be divided into 1) methods that implicitly take account of species’ connectivity requirements by aiming at a structurally connected reserve network; and 2) methods that explicitly adapt the configuration of the network to species-specific Summary requirements, hence selecting connected reserve networks. functionally Structurally connected reserve networks can be obtained by applying a penalty for the total boundary length of the reserve network, which causes a preference for selecting sites with a clumped spatial configuration (Possingham et al., 2000; Nalle et al., 2002; Cabeza et al., 2004b). Other approaches to decrease the distance between sites encompass methods that minimize the maximum distance between sites or the sum of inter-site distances (Briers, 2002; Önal & Briers, 2002), and the selection of adjacent sites for habitat restoration, which together would form a corridor between patches (Williams & Snyder, 2005). The interior point search method introduced by Moilanen (2005a) is yet another method, which selects a given number of spatially contiguous reserves. One potential disadvantage of forcing structural connectivity into the network configuration is that sites will be included that have low habitat quality or few species, for the sake of clustering alone. The reservation of such sites requires resources, which precludes the selection of other sites, which are perhaps more valuable for conservation. Another drawback of Box 2. Solution methods for site selection Small site selection problems with few species can be solved by hand, but conservation planning problems typically encompass large areas and many species, for which finding efficient and effective solutions is no longer straightforward. Computer algorithms have therefore been developed to aid the site selection for large problems, which can be divided into exact and non-exact (or heuristic) methods. Exact methods, such as linear and integer programming are guaranteed to find globally optimal solutions (Underhill, 1994; Church, 1996; Rodrigues & Gaston, 2002; Williams et al., 2004), but have two limitations: 1) The objective function and constraints need to be linear. When the value of a site depends on the value of other sites in the set of selected sites, however, non-linearities arise (see last paragraph of section 1.1); 2) Linear programming may run into computational difficulties for large reserve selection problems (> ~ 10,000 selection units) due to the enormous increase in the search space size (Williams et al., 2004). As conservation problems typically encompass many sites and species, and are increasingly having non-linear problem formulations the use of exact algorithms is not always feasible. Non-exact methods encompass iterative heuristics (i.e. local search methods based on stepwise maximization of marginal gain) and stochastic global search techniques. Iterative heuristic algorithms are quick , easy to implement and hence widely used (Kirkpatrick, 1983; Pressey et al., 1993; Pressey et al., 1996; Williams et al., 1996; Araújo & Williams, 2000), although they have been criticized for returning suboptimal solutions (Underhill, 1994; Rodrigues & Gaston, 2002; Önal, 2003). Stochastic global search techniques, such as simulated annealing (Possingham et al., 2000; McDonnell et al., 2002; Westphal & Possingham, 2003; Westphal et al., 2007) and genetic algorithms (Moilanen & Cabeza, 2002; Moilanen, 2005a, b) can be demonstrated to find near-optimal solutions for large, complex (e.g. non-linear) optimization problems. Whether or not solutions from site selection algorithms are mathematically optimal is no longer a central debate in conservation planning, as conservation is more than the output of an algorithm (Pressey et al., 1996). Moreover, the effects from incomplete data, model assumptions and problem formulations are likely to have a larger influence than an optimal versus a slightly suboptimal site selection solution. 11 these methods that work directly on the spatial pattern of reserve sites is that they do not account for species-specific requirements for spatial configuration. As species differ in their habitat requirements and dispersal capacity, the optimal configuration of reserve networks will be different for different species. To explicitly account for species-specific connectivity requirements, one can parameterize a species habitat distribution model that incorporates a measure of connectivity as one of the covariates, resulting in a spatial SHDM. The neighbourhood over which the connectivity is calculated should be related to the species requirements for connectivity, which could come from e.g. dispersal capacity, sensitivity to edge effects or home range size (see also Box 3). Examples of spatial SHDMs are found in Ferrier et al. (2002), Binzenhofer et al. (2005) and Wintle et al. (2005). Such spatial SHDMs can next be incorporated in reserve selection, to account for species’ connectivity requirements through the predicted species habitat distribution (Cabeza, 2003; Westphal & Possingham, 2003; Cabeza et al., 2004a; Moilanen, 2005b; Moilanen et al., 2005; Moilanen & Wintle, 2006, 2007; Westphal et al., 2007). As a result of this, the model will predict better connected sites to have higher occupancy levels than equally suitable, but less well connected sites. The reserve selection algorithm will prefer sites with high predicted occupancy levels, and will thus select better connected sites. Besides accounting for the spatial configuration in reserve sites, it is also important to account for the possibility of landscape change around reserve networks. As metapopulation theory predicts, the occurrence of nearby populations affects the probability of occupancy of a site (Hanski, 1998). When selecting reserve networks, it is therefore important to acknowledge the possibility of habitat loss outside reserve sites, to prevent the selection of small sink sites, where the occurrence of species depends on source populations outside the network (Cabeza & Moilanen, 2003). One way to do this is by letting the predicted 12 probabilities of occurrence within reserve sites depend explicitly on the set of reserve sites only, as was introduced by Cabeza (2003) under the concept of the dynamic probability approach (see also Moilanen, 2005b; Westphal et al., 2007; Chapters I & V). This approach requires the probabilities of occurrence to be re-computed during the site selection process, to account for changes in the set of selected sites. Note that this results in a non-linear problem formulation (Box 2), which cannot be solved by exact solution methods. Other approaches that explicitly account for habitat loss around reserve networks are described by Moilanen et al. (2005) and Moilanen & Wintle (2006; 2007). 1.2 How to conserve Though the protection of remaining natural habitat is vital for biodiversity conservation, it is not the only conservation practise (Fig. 1; How to conserve). Especially in humandominated landscapes around the world vast areas of natural habitat have been lost or are degraded (Hanski, 2005; Moilanen & Wintle, 2007). Protecting only remaining habitat may not be sufficient for conservation if there is too little high quality habitat available for species to persist in the long term. Habitat restoration can be defined as the management of degraded sites, with the aim to improve site quality in terms of biodiversity (Gilbert & Anderson, 1998). By restoring degraded sites, or applying agri-environment schemes, the conservation value of a landscape can be improved, which makes habitat restoration a complementary tool for biodiversity conservation (Dobson et al., 1997; Young, 2000; Donald & Evans, 2006). Next to restoration and protection measures, also maintenance and management of reserved or restored sites may be required to maintain optimal site conditions for biodiversity conservation (Margules & Pressey, 2000). As described above, the field of reserve design has a relatively long tradition in the use of optimization tools to select optimal reserve networks for a large number of species in a Summary cost-effective manner (Margules & Pressey, 2000; Cabeza & Moilanen, 2001; Williams et al., 2004). The field of restoration planning has a different tradition however, mostly concentrating on a single species or habitat type for which a number of land use planning scenarios are evaluated. Population viability analysis (PVA) is a frequently used method to evaluate restoration scenarios for a single species (Root, 1998; Haines et al., 2005; Schtickzelle et al., 2005). Other single-species restoration planning approaches aim at ranking sites by restoration value (Nikolakaki, 2004; Schultz & Crone, 2005). The application of optimization algorithms to restoration planning problems has however been limited (Hof et al., 2002; Newbold & Eadie, 2004; Crossman & Bryan, 2006; Westphal et al., 2007). This is surprising, as restoration planning problems can be formulated like reserve selection problems (Westphal & Possingham, 2003), which allows for (near-) optimal planning solutions rather than the comparison of a few scenarios only. Towards multi-action conservation planning Reserve selection problems can be considered single-action conservation planning problems, as the application of a single conservation action (i.e. protection) is considered per site. Restoration planning problems that consider a single action (e.g. revegetation), fall also into this category. Many conservation planning problems however, are characterized by the availability of multiple alternative conservation actions per site. For example, the restoration and maintenance of grasslands or moorland can require methods like grazing (with varying intensity), sod cutting or prescribed burning. Also reserve planning problems could have multiple alternative conservation actions per site, for example when planning different protection or zoning levels (e.g. strict reserves, buffer areas, and recreation areas). Despite the apparent commonness of such conservation planning problems, methods in the field of reserve network design have not been adjusted to incorporate multiple, alternative conservation actions. A few studies related to forest harvest scheduling and landscape planning have considered multiple alternative actions per site (Hof et al., 1994; Bevers et al., 1995; Holzkämper et al., 2006). However, these studies did not consider the effects of action costs and budget availability on species representations. In contrast, cost constraints motivated the development of systematic conservation planning tools and are hence a core element of systematic planning tools as used for reserve selection. Restoration as compensation measure Although the protection of natural vegetation needs to be preferred above restoration as a conservation tool (Young, 2000; Hilderbrand et al., 2005), the degradation or loss of existing habitat cannot always be prevented, for example when urban expansion or infrastructural works are considered necessary. When habitat loss is unavoidable, compensation for the loss through restoration of habitat elsewhere is oftentimes required by law (Ten Kate et al., 2004). Restoration activities to compensate for the loss of biodiversity at the impacted site are generally referred to as biodiversity offsets (Ten Kate et al., 2004). Besides the importance of planning where and how to restore, the main question in this context is “how much to restore?” (Fig. 1) (Race & Fonseca, 1996; Bakker et al., 2000). Quantifying the value of the lost and to-be-restored habitat is hard, and hence defining offset ratios is difficult. Whether restoration practises will result in the desired outcome for biodiversity remains to be seen, due to e.g. uncertainties in restoration action success and establishment of the species community, and time delays (Hilderbrand et al., 2005; Morris et al., 2006; Roach & Wade, 2006). Habitat Equivalency Analysis (Dunford et al., 2004; Bruggeman et al., 2005) and the concept of “No Net Loss” (Harper & Quigley, 2005) are often used in this context to aim at sufficient compensation. However, none of the methods incorporates the various sources of uncertainty in finding appropriate offset ratios, for which the question of “how much to restore?” remains largely unanswered. 13 Species distribution data Environmental variables Understanding species distribution pattern II Species habitat distribution model I, II, IV, V Predicting effects of conservation actions Site (and action) selection algorithm I, III, IV, V Prioritising species IV Valuing sites and actions III, IV, V Goal setting VI Selecting sites and actions Uncertainty VI Implementation of conservation plan Conservation Figure 2. A schematic overview of where the topics of this thesis (in dark grey boxes, referred to by their Roman numerals) are placed within a conservation planning framework. The light grey box depicts an algorithm for the selection of sites (chapter I) and actions (chapters III, IV &V). 14 Summary 2 THESIS OUTLINE This thesis relates to several aspects that come into play in the planning of conservation effort such as reservation and restoration (Fig. 2). The chapters in this thesis can be linked in different ways. Three chapters concern spatial issues in the planning of conservation effort (I, II, V). I will first introduce chapters I and II, chapter V will be introduced together with chapters III and IV in the context of multi-action conservation planning, followed by chapter VI which concerns the estimation of robust biodiversity offset ratios. Spatial issues in planning conservation actions Since species differ in habitat requirements and dispersal abilities, the optimal location and configuration of reserve networks is expected to be different for different species. As conservation efforts cannot be targeted to each species individually, solutions are sought that are efficient in terms of area and cost, while still aiming at protecting the species. To aim for more robust reserve networks in terms of species persistence, measures that are expected to correlate with species viability have been incorporated in site selection methods. For example, using probabilities of occurrence rather than presence-absence data, and enhancing connectivity of reserve networks. In chapter I is investigated how the size and spatial configuration of reserve networks changed with different input data (presenceabsence data and probabilities of occurrence from both non-spatial and spatial SHDMs), when assuming 1) a static landscape or 2) complete habitat loss beyond the reserve network. The different reserve networks were evaluated in terms of the predicted occurrence levels of species inside the reserve networks, while accounting for habitat loss. Species requirements for connectivity can be estimated from the level of aggregation in species distribution data. However, different factors (e.g. dispersal, disturbances) can cause similar levels of aggregation in species distribution data, which may call for very different spatial designs of ecosystem networks (Box 3). Hence, it is important to investigate what factors drive the spatial distribution of species. Existing methods that account for aggregation in species distribution data, such as autoregressive models (Lichstein et al., 2002), have not been used to examine what factors caused aggregation in the species distribution data. In chapter II is investigated whether one can distinguish between different potential causes of aggregation through the use of different spatial variables in SHDMs. Such information would be valuable to conservation planning, as the spatial configuration of reserve networks can be adjusted depending on the variables that drive the spatial distribution of species. From a single to multiple conservation options Conservation actions such as habitat protection or restoration are carried out in an attempt to improve habitat conditions for species that might not persist without taking action. Planning of one such conservation action therefore focuses on these species that are likely to benefit from it. As action can not be taken at all locations, the problem concentrates on which locations are best targeted for conservation, in order to maximize the gain for the set of species of interest. Hence, the main question dealt with in conservation planning thus far is “where to conserve” (Fig. 1). When considering alternative conservation actions per site, the question extends to “how to conserve” (Fig. 1). Planning of alternative actions at sites can be relatively straightforward, when all species of concern have similar requirements. For example, planning which protection level to apply to sites (e.g. recreational area, national park, strict reserve), can be expected to have similar effects on species (stricter protection encompasses less disturbance, hence higher occurrence probabilities for species vulnerable to disturbance). The expected representation of species under different actions (such as protection levels) can be predicted with a 15 SHDM that incorporates the variables that will change with different actions. When species have contrasting requirements however, a planning problem with multiple actions becomes more difficult, as certain actions can benefit particular species, but could be harmful to other species. Effects of actions can vary with the locations where they are applied, as site conditions are usually not homogenous over space. In order to make decisions on which actions are best applied to which sites, the effects of different actions on species representation have to be valued. Benefit functions can be used to translate the changes in species representation, as a result of applying an action, into a conservation value (Hof & Raphael, 1993; Arponen et al., 2005; chapter III). In chapter III conservation planning problems are defined with multiple actions per site, from which the single-action selection problem (such as reserve selection) can be considered a special case. We describe a site-action selection algorithm for multiple species and explore ways to value effects of actions on species representation, which could have a strong effect on the outcome of selected conservation actions (III, V). Chapter IV contains an application of the method described in chapter III, to investigate the trade-offs that can occur when Box 3. Causes of aggregation in species distributions Species commonly show an aggregated spatial distribution. The aggregation in species distributions causes observations at sites that are close together to be spatially dependent on one another, while most statistical methods require observations to be independent (Besag, 1972; Legendre, 1993; Guisan & Thuiller, 2005). To correct for this, autoregressive models have been developed, which include a variable that measures the amount of occupied sites in the neighbourhood of a site (Besag, 1972; Lichstein et al., 2002). This autocovariate resembles the connectivity measures typically employed in metapopulation modelling (Ferrier et al., 2002; Moilanen & Nieminen, 2002). The importance of connectivity for dispersal and metapopulation persistence motivated the use of spatial covariates in species habitat distribution models (spatial SHDMs) for reserve design (Cabeza et al., 2004a). Dispersal limitation is an endogenous cause of aggregation, which relates to the behaviour of the species, just like gregarious behaviour for example (Augustin et al., 1996). Exogenous factors that can cause aggregation in species distributions include spatially aggregated environmental variables such as climatic, soil or vegetation variables, regional stochasticity (Hanski, 1991), and the occurrence of other species such as predators, prey or competitors. These different factors can lead to similar levels of aggregation in species distributions. Spatial covariates in SHDMs can capture spatial aggregation in species distributions but do not distinguish between different processes that can cause aggregation. Some of these processes however, call for a reserve network design that is dispersed (for factors like regional stochasticity, disease, predators, fire) rather than aggregated, in order to sustain species at the long term. To adjust reserve configurations to species’ spatial requirements it is important to be able to disentangle what variables drive aggregation of species distributions. Thus far, autoregressive models have not been used to investigate what factors caused the aggregation in species distribution data. 16 Summary seeking appropriate sets of sites and conservation actions to benefit a set of species with contrasting requirements. We examine the robustness of the set of selected actions to budget constraints and the relative priorities assigned to species. Where chapter I concerns the design of a reserve network only, chapter V allows for multiple alternative conservation actions per site (Fig. 3). I describe a method to explicitly account for a species’ habitat configuration requirements, when selecting among multiple protection and restoration measures per site. Effects of uncertainty on fair offset ratios Biodiversity offsets are a policy instrument to compensate for habitat loss due to economic development. To prevent net loss of biodiversity value, an answer is required to the question of how much restoration is enough to compensate the loss of biodiversity value at the impacted area. Several sources of uncertainty are associated with the expected conservation outcome of habitat restoration (Suding et al., 2004; Hilderbrand et al., 2005). Chapter VI addresses the influence of such uncertainties on the calculation of fair offset ratios that are unlikely to result in a net loss of biodiversity value. Since restoration is increasingly used to compensate habitat loss because of economic development activities, it is increasingly relevant that restoration practices result in equal biodiversity value at the restored sites as was lost from the developed sites. In this chapter we investigate how much restoration effort is required to match the realised value at restored sites with the value lost from developed sites, when taking into account that restoration failure at particular sites is possible. 3 RESULTS & DISCUSSION 3.1 Spatial reserve network design Habitat loss and connectivity Selecting reserves based on SHDMs that included spatial variables, resulted in more aggregated reserves than when selection is based on non-spatial SHDMs or presenceabsence data directly (Chapter I). These more aggregated reserves retained higher probabilities of occurrence inside reserve networks, when habitat outside the reserve network would be lost completely. Explicit accounting for habitat loss at unprotected sites during the site selection process, resulted in the selection of larger and better connected reserve networks. This occurred, because the probabilities of occurrence of a species within the reserve network decrease as a result of habitat loss. Consequently, if we aim at protecting species for which habitat is likely to degrade or disappear without protection, we have to set higher targets than for species for which habitat is less likely to degrade or disappear. The assumption of 100% habitat loss outside reserves may sound overly conservative, but in human-dominated landscapes this is not implausible as the expansion pressure from urban areas and industry is large. If preferred, land use change modelling could be employed to provide other scenarios of habitat loss. Disentangling causes of aggregation Several exogenous and endogenous processes and variables can cause aggregation in species distributions (Box 3). Chapter II investigated the possibility to disentangle causes of aggregation from snapshots of species distribution data by comparing the performance of spatial SHDMs with different spatial covariate structures. Species distribution data with similar aggregation patterns were simulated with different causes of aggregation (distance dependent dispersal, edge effects and 17 aggregated distributions of environmental factors, which were static as well as dynamic in space and time). Next, different spatial SHDMs were fitted to these data, varying in the type of spatial covariate. We observed differences in the relative model fit of various spatial SHDMs, which can be understood based on the cause of aggregation that we used to model simulated data. We also observed differences in model fit of various spatial SHDMs when analysing spatial occurrence patterns of several bird species, indicating that information can be gained from species distribution patterns in this way. The use of spatial SHDMs in this manner provides a possibility to study what variables drive a species distribution, to aim for biologically more meaningful habitat models. More meaningful models will in turn be better predictors of species occurrence, and hence be more informative to conservation planning. 3.2 From a single to multiple conservation options in design In three chapters of this thesis I allowed for multiple alternative conservation options per site, instead of just one option as has hitherto been employed in the field of reserve design and in the few earlier applications of reserve selection tools in restoration planning. In chapters III, IV and V conservation actions were valued based on the change in the conservation value of the landscape (summed over all species considered) relative to action cost, in order to decide which action returns the highest marginal gain in benefit. The shape of the benefit function had a large impact on the capacity of a forward stepwise heuristic algorithm to find globally optimal solutions (III). We showed that reserve selection formulations with either concave or convex benefit functions properties can be optimally solved (III) even with a simple iterative algorithm. As the computation time of the iterative algorithm is short, this approach could successfully be used for large conservation problems with many sites, species and actions. 18 It is important to note however, that the choice for a particular benefit function influences the set of actions and sites that is selected by the algorithm, and its consequences for the expected representation of the species in the landscape. Although all benefit functions aim at maximizing the value obtained by conservation actions, they value changes in representations differently depending on the level of representation (Hof & Raphael, 1993; Arponen et al., 2005; III, IV). Arponen et al. (2005) demonstrated that selecting reserve networks with different benefit functions results in rather different representation levels of for example rare species. Where Arponen et al. (2005) studied how benefit functions affect the selection of reserve sites, this was extended here to the selection of both sites and actions. Even when optimizing conservation actions for a single species, the shape of the benefit function had a large effect on which actions are preferred by an algorithm (V). Which benefit function is considered better is not straightforward to answer, as it depends on the characteristics of the planning problem at hand and the aim of the study (III, IV). When the group of species in need of conservation actions has diverse habitat requirements, the species are likely to benefit from different actions. For example, with the disappearance of semi-natural pastures due to changes in agricultural practices, preventing succession through restoration is required to benefit the species that depend on such pastures. The species can differ however in how intensive restoration is preferred (Köhler et al., 2005; Pöyry et al., 2005; Pykälä, 2005; IV). The planning of actions to cater for these different species can be handled with the same problem formulation and algorithm as presented in chapter III. The algorithm selects an action at each site, to maximize the summed benefit over all species, while accounting for action cost. As a result of species’ contrasting requirements however, the level at which individual species were represented differed widely when all species were given equal priority. Although the average representation over all species did not Summary appear very sensitive to the budget available, the individual species representations differed substantially, indicating a strong trade-off between different species groups (IV). The results from chapter IV emphasize the importance of using sensitivity analysis to test the robustness of conservation planning solutions to the relative priorities assigned to species, when planning for species with different requirements. Consequently, on top of the importance of the choice for a particular benefit function to value changes in species representation levels as a result of different actions (III), it becomes also important to determine how to prioritize between different species (IV, Fig. 1). 3.3 Connectivity in multi-option planning The method presented in chapter III and applied in chapter IV is non-spatial. It is possible though to employ for example a penalty for boundary length or for distance to existing habitat to induce structural connectivity into the set of selected sites and actions, like in single-action planning problems (e.g. Possingham et al., 2000; Cabeza et al., 2004a; Crossman & Bryan, 2006; I). An approach that incorporates functional (i.e. species-specific) connectivity in planning with multiple conservation options per site for a single species, is presented in chapter V (Fig. 3). The algorithm, which is a reverse stepwise heuristic, starts from the ‘ideal’ landscape for a species by assigning the best action at each location, followed by the downgrading of actions at sites, until the remaining set of actions can be afforded. When selecting which actions to replace by cheaper (but less beneficial) actions, the effect of replacing an action is calculated for the local site, as well as for the neighboring sites, through a spatial SHDM, similar to the dynamic probability approach described by Cabeza (2003) for a single-action problem formulation. Hence, the effects of restoration and protection actions are evaluated based on species-specific requirements for habitat and connectivity. Again, the shape of the function used to evaluate the effect of actions on the species representation had a large effect on the priority assigned to actions. The method as presented in chapter V works well for a single species with simple habitat requirements, and could also be applied to multiple species that respond similarly to particular actions (as in the example of different protection levels with increasing strictness). For species with contrasting requirements however, the responses to actions can be contradictory, for which the ‘ideal’ landscape to start the action removal from, is not easily defined. 3.4 Effects of uncertainy on fair offset ratios Figure 3. Classification of conservation planning problems, and how the chapters of this thesis (in Roman numerals) relate to this. One way to determine the offset ratio for compensation of a damaged site is by dividing the present conservation value of the site that will be lost, by the expected conservation value of the sites to be restored. This approach, which is termed ‘matching of mean utility’ in chapter VI, ignores uncertainty and time lags in the establishment of conservation value at restoration sites. If the realized conservation 19 value at restored sites turns out lower than expected, this results in a net loss of biodiversity value, whereas the objective of offset legislation is the maintenance of biodiversity (Ten Kate et al., 2004). Instead of matching mean utility, chapter VI employed uncertainty analysis, to calculate so-called ‘robustly fair offset ratios’ (Fig. 2). These offset ratios are robust in the sense that they guarantee a high enough probability that the compensation does not result in net loss of biodiversity value. We accounted for several factors which are likely to affect the expected value of restored sites: (i) uncertainty in the effectiveness of restoration action; (ii) uncertainty about the establishment and growth of conservation value at compensation areas; (iii) correlation between success of different compensation areas; and (iv) time discounting. When accounting for these possible causes of restoration failure, the offset ratio required to obtain at least the same conservation value in the restored sites as currently present in the development site, increased steeply. The key point of this paper is that higher offset ratios are needed for a robustly fair exchange between destroyed and restored habitat, when the aim is to have a low probability that the exchange turns out unfavourably. For maintaining biodiversity value in the long run, the calculation of offset ratios requires explicit accounting of various, but common, sources of uncertainty. 4 SYNTHESIS & PERSPECTIVES In the struggle for biodiversity conservation in a world where natural habitat becomes sparse and conservation budgets are tight, methods for systematic conservation planning have come a long way (Margules & Pressey, 2000; Cabeza & Moilanen, 2001; Williams et al., 2004). Besides continuous efforts on delineating appropriate conservation goals and biodiversity surrogates (What to conserve, Fig. 1), reserve network design methods are increasingly dealing with direct and indirect measures of species viability 20 to increase the likelihood of persistence (Gaston et al., 2002; Wilson et al., 2005a; Moilanen & Wintle, 2006; Nicholson et al., 2006). With the aim of long-term conservation on the one hand, and data limitations on the other, trade-offs have to be made with respect to model complexity, and applicability of methods at large scales for many species (Verboom et al., 2001; Vos et al., 2001; Opdam et al., 2003; Moilanen et al., 2005). Within this trade-off, species habitat distribution models (SHDMs) can be a means to overcome the problem of incomplete survey data to inform conservation planning, by predicting species occurrence probabilities based on the habitat suitability of sites, which has been considered a measure of viability (Araújo & Williams, 2000; Williams & Araújo, 2000; but see Wilson et al., 2005b). SHDMs also allow for the inclusion of spatial covariates (also termed connectivity measures or contextual variables), which can be useful for several reasons demonstrated in this thesis: In the context of selecting a network for conservation, spatial SHDMs allow for the evaluation of configuration of reserve networks and other conservation actions for either structural or functional connectivity (I, V). Spatial SHDMs also facilitate incorporating effects of landscape change, such as habitat loss, around sites targeted for conservation (Cabeza, 2003; Moilanen, 2005b; Westphal et al., 2007; I, V). The use of spatial SHDMs has sometimes been criticized as a quick fix for correcting for spatial dependency in the observations, caused by a failure to include the relevant variables as covariates in the model (Lichstein et al., 2002; Guisan & Thuiller, 2005). The results from chapter II indicate that spatial SHDMs can be used successfully for studying causes of aggregation in species distribution patterns. This can lead to a better understanding of factors driving the spatial distribution of species, and subsequently to biologically more meaningful SHDMs. These findings can be relevant for planning reserve networks with appropriate levels of aggregation for species, depending on the factors that drive their Summary distribution. Further work can encompass more complex species-habitat relationships and the study of time-series data to disentangle static from dynamic exogenous variables. Planning with multiple conservation options per site is still in its infancy in reserve network design and restoration planning literature, despite related work in other fields such as forest harvest scheduling (Hof & Raphael, 1993; Hof et al., 1994; Bevers et al., 1995). This thesis shows that the reserve selection problem can be framed in a large context of single- and multiaction conservation planning problems, which can incorporate several levels of spatial complexity (Fig. 3). Reserve design algorithms already found some applications in restoration planning, but these were defined as singleaction problems (Newbold & Eadie, 2004; Crossman & Bryan, 2006; Westphal et al., 2007). This thesis provides novel straightforward methods to solve multi-action planning problems, a non-spatial method for multiple species (III, IV), and an explicitly spatial method for a single species or species with similar requirements (V). Identifying ways to explicitly account for the spatial requirements of species with different habitat needs, in conservation planning with alternative management options (central area of Fig. 3), is a challenging but common conservation planning issue. Redirecting the methodology employed in this thesis and theory from reserve network design towards an integrated planning framework, which allows for multiple actions in protection, restoration and habitat maintenance, provides ample opportunity for future work. ACKNOWLEDGEMENTS This thesis would not have been written without the help and support of many people. First of all, I would like to thank Atte, for giving me the opportunity to conduct my PhD research in the Metapopulation Research Group under his supervision. You have introduced me to the world of reserve network design methodology, thought me much about computational issues and coding, and always found time for answering questions. You gave me the freedom to develop my own work, regardless of when and where I was working, which I very much appreciate. As a result, not all of my projects have been your core business. Nevertheless, you have always tried to keep me on track and on schedule to make this PhD a success, thank you! I would like to thank all my co-authors for being a great source of inspiration, knowledge and reflection. Mar, although supervision has not been your duty, you have always shown interest in my work. You made me focus on the questions and our discussions helped me realise and understand what I was trying to do. Thank you for taking the effort! Swimming in the unfamiliar sea of spatial statistics, it took a mathematician to retrieve the track. Otso, thank you for your interest, for explaining me complex things in simple terms (or silly drawings), and for many constructive comments. Katja, Juha and Mikko familiarised me with mesic grassland communities, and gave me the opportunity to consider the strengths and limitations of my methods in a practical context, which was a very educational experience to me. Though not having coauthored the work in this thesis, other people have given an invaluable contribution to my education. From them, I am most grateful to Jane Elith and Brendan Wintle, who taught me a great deal about habitat modelling, without which I would not have been able to complete this work. Thank you for sharing your knowledge, and for being such wonderful hosts during my visit to the Environmenal Science Group in Melbourne. I would also like to thank Henk Sierdsema and Rogier Pouwels, for providing data for two of my chapters and for answering numerous questions on model parameters and result interpretation. Risto Heikkinen and Kris Rothley I would like to thank for their encouraging reviewer statements, and Risto for 21 his efforts to comment the thesis in detail and for providing me with food for thought. These years would not have been so enjoyable, if I would not have worked with great colleagues, many of which became friends. The MRG is an exceptional group to work in, and I would like to thank Ilkka for providing us with excellent research conditions. My sincere thanks go to all MRG-members for your willingness to discuss ideas, comment manuscripts and presentations, and for the pleasant daily atmosphere. I’ve also very much enjoyed the reserve design reading club, thanks Mar, Anni, Heini and Evgeniy! I would like to thank Tapio, Anu, Mimma, Nina, Elina, Sami and Tuuli for taking good care of the MRGsecretariat over the years - you have made my life a lot easier. Of the very many social events that have taken place I have warm memories, thanks to Sofia, Ace, Itsuro, Luisa and all other MRGideae for making these years such a great experience! The department has been a pleasant place to work in, for there work a great number of fine persons. The Wednesday’s coffee breaks, the spring symposiums and research seminars were inspiring events. Thanks to Andrés, Hanna K, Katja B, Johan, Bob, Kata, Daniel, Outi and many others for creating this ambience. Since my office and the coffee room were far apart, I oftentimes ran through the corridor. I apologise to those who I have scared when I almost bumped into them around the corner! Veijo Kaitala, Ilkka Teräs and the coordinators of the LUOVA graduate school: Anna-Liisa Laine, Tomas Roslin and Heikki Hirvonen, thank you for bridging the bureaucratic hurdles. A number of people have made my life in Finland unforgettable, for which I would like to express them my deepest gratitude. Tapio and Mar have taken me on numerous trips berryand mushroom picking, camping, sailing, skiing – experiences of the Finnish countryside I would not have liked to have missed, I hope that many more will follow. Mar & Tero, thank you also for giving me a home during my visits. 22 Jonna has pulled me through the last difficult stages of completing this work. I owe you for the times you answered the phone even though it made you miss your bus! Thanks for being a friend, I sincerely miss your irony and face expressions! Evgeniy, thank you for the pulla we had, for the heaps of silly jokes and for coping incredibly well with the angelcharacters in 5619, YM! Jenni, thanks an elephant for the many board games where you let me win(!), but most for your kindness, support and for appreciating my baaad sense of humour! Hanna, Elina, Annukka and Esko, thanks for dragging me out of the office once in a while and make me talk about something else than work. Karin, Dieuwke, Elma, Karin, Marc, Maarten and many others, thanks for your support and friendship over the years, I’m looking forward to catching up. Ton en Efi, Mireille, Maurice, Fleur en Koen, Henny en Hans, jullie wil ik heel hartelijk bedanken voor jullie steun en betrokkenheid bij ons Finlandavontuur, super! Mams en paps, bedankt voor de warmte, het vertrouwen en de vrijheid die ik altijd van jullie kreeg. Bedankt voor jullie steun en medeleven tijdens mijn promotieonderzoek en de ontelbare lieve kaartjes die op de deurmat vielen! Ik kan me geen lievere ouders wensen. Niels and Yvonne, thank you for your warm hospitality that provides a wonderful home to us in family holidays; those are days with a golden rim. Lieve Emma, thank you being such as sunshine, this thesis is for you. We have not been able to visit as often as we would have liked, I hope we can make up for that in the time to come. Lieve Bart, your encouragement and support have been indispensable during all this time. Doing a PhD and living a large part of the time in two countries requires trade-offs and sacrifices. Thank you for your unconditional love and sincerity, for the perseverance that I sometimes lacked but needed so badly, and for the many beautiful moments we’ve shared these years. I’m looking forward to more! Summary My work has been generously funded by the Academy of Finland (grant #202870 to A. Moilanen), and I have been able to present my work at several conferences with financial support from to the Chancellor’s travel grant. The University of Melbourne has kindly funded my stay at the Environmental Science Group. REFERENCES Adam, P. 1992. The end of conservation on the cheap. National Parks Journal 36:19-22. Andelman, S.J., and W.F. Fagan. 2000. Umbrellas and flagships: Efficient conservation surrogates or expensive mistakes? 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