Species-based and community-level approaches to conservation prioritization
Anni Arponen
Metapopulation Research Group
Department of Biological and Environmental Sciences Faculty of Biosciences
University of Helsinki Finland
ACADEMIC DISSERTATION
To be presented, with the permission of the Faculty of Biosciences of the University of Helsinki, for public examination in Auditorium 1041 in Biocenter 2 (Viikinkaari 5)
on April 4th 2009, at 12 o’clock noon.
Helsinki 2009
© Anni Arponen (0, I)
© Wiley-Blackwell (II, III, VI)
© Elsevier B.V. (IV)
© Oxford University Press (V)
Layout: Anni Arponen
Cover illustration: Anni Arponen
Author’s address:
Department of Biological and Environmental Sciences PO Box 65
FIN-00014 University of Helsinki Finland
email: anni.arponen@helsinki.fi
ISBN 978-952-92-5280-0 (paperback) ISBN 978-952-10-5386-3 (PDF) http://ethesis.helsinki.fi
Helsinki University Printing House Helsinki 2009
Species-based and community-level approaches to conservation prioritization
Anni Arponen
The thesis is based on the following publications, which are referred to in the text by their roman numerals:
I Arponen, A. 2009. Prioritizing species for conservation.
– Submitted manuscript.
II Arponen, A., Heikkinen, R. K., Thomas, C. D. and Moilanen, A.
2005. The value of biodiversity in reserve selection:
representation, species weighting and benefit functions.
Conservation Biology 19: 2009–2014.
III Arponen, A., Kondelin, H. and Moilanen, A. 2007. Area-based refinement for selection of reserve sites with the benefit function approach. Conservation Biology 21: 527–533.
IV Moilanen, A., Arponen, A., Stokland, J. & Cabeza, M. 2009.
Assessing replacement cost of conservation areas: how does habitat loss influence priorities? – Biological Conservation 142:
575–585.
V Ferrier, S., Faith, D., Arponen, A. and Drielsma, M. 2009.
Community-level approaches to spatial conservation
prioritisation. In: Spatial conservation prioritisation: quantitative methods and computational tools. Editors: Moilanen, A.,
Possingham, H. & Wilson, K. Oxford University Press, pp. 94–
109, in press.
VI Arponen, A., Moilanen, A. & Ferrier, S. 2008. A successful
community-level strategy for conservation prioritization. – Journal
of Applied Ecology 45: 1436–1445.
Table of contributions
I II III IV V VI Original idea AA AM, AA,
CT
AA, AM AM SF, AA, DF, MD
AA, AM, SF
Study design AA AA AA AM, AA,
MC
SF, DF, AA, MD
AA, AM, SF Methods and
implementation
AA AM, AA, CT
AA, AM AM, AA SF, MD, DF, AA
AA, AM, SF
Empirical data AA RH HK JS - -
Manuscript preparation AA AA, AM, CT, RH
AA AM, AA,
MC, JS
SF, AA, DF, MD
AA, AM, SF
AA = Anni Arponen, AM = Atte Moilanen, RH = Risto Heikkinen, CT = Chris D. Thomas, HK = Hanna Kondelin, JS = Jogeir Stokland, MC = Mar Cabeza, SF = Simon Ferrier, DF=
Dan Faith, MD = Michael Drielsma
Supervised by Dr. Atte Moilanen
University of Helsinki, Finland
Reviewed by Prof. Raimo Virkkala
Finnish Environment Institute, Finland
Dr. Hanna Tuomisto
University of Turku, Finland
Examined by Prof. Stephen Polasky
University of Minnesota, USA
Custos Prof. Heikki Hirvonen
University of Helsinki, Finland
Contents
0 Summary
1. Introduction to systematic conservation planning 2. Thesis outline
3. Valuing biodiversity in conservation planning
4. Community-level strategies for conservation planning 5. Synthesis and future challenges
Acknowledgements References
I Prioritizing species for conservation
II The value of biodiversity in reserve selection: representation, species weighting and benefit functions
III Area-based refinement for selection of reserve sites with the benefit function approach
IV Assessing replacement cost of conservation areas:
how does habitat loss influence priorities?
V Community-level approaches to spatial conservation prioritisation VI A Successful community-level strategy for conservation prioritization
7 9 10
13 14 15 16 21
39
47
57
71
93
Abstract
One major reason for the global decline of biodiversity is habitat loss and fragmentation.
Conservation areas can be designed to reduce biodiversity loss, but as resources are limited, conservation efforts need to be prioritized in order to achieve best possible outcomes. The field of systematic conservation planning developed as a response to opportunistic approaches to conservation that often resulted in biased representation of biological diversity. The last two decades have seen the development of increasingly sophisticated methods that account for information about biodiversity conservation goals (benefits), economical considerations (costs) and socio-political constraints.
In this thesis I focus on two general topics related to systematic conservation planning. First, I address two aspects of the question about how biodiversity features should be valued. (i) I investigate the extremely important but often neglected issue of differential prioritization of species for conservation. Species prioritization can be based on various criteria, and is always goal- dependent, but can also be implemented in a scientifically more rigorous way than what is the usual practice. (ii) I introduce a novel framework for conservation prioritization, which is based on continuous benefit functions that convert increasing levels of biodiversity feature representation to increasing conservation value – using the principle that more is better. Traditional target-based systematic conservation planning is a special case of this approach, in which a step function is used for the benefit function. We have further expanded the benefit function framework for area prioritization to address issues such as protected area size and habitat vulnerability.
In the second part of the thesis I address the application of community level modelling strategies to conservation prioritization. One of the most serious issues in systematic conservation planning currently is not the deficiency of methodology for selection and design, but simply the lack of data. Community level modelling offers a surrogate strategy that makes conservation planning more feasible in data poor regions. We have reviewed the available community-level approaches to conservation planning. These range from simplistic classification techniques to sophisticated modelling and selection strategies. We have also developed a general and novel community level approach to conservation prioritization that significantly improves on methods that were available before.
This thesis introduces further degrees of realism into conservation planning methodology.
The benefit function -based conservation prioritization framework largely circumvents the problematic phase of target setting, and allowing for trade-offs between species representation provides a more flexible and hopefully more attractive approach to conservation practitioners. The community-level approach seems highly promising and should prove valuable for conservation planning especially in data poor regions. Future work should focus on integrating prioritization methods to deal with multiple aspects in combination influencing the prioritization process, and further testing and refining the community level strategies using real, large datasets.
Summary
Anni Arponen
Metapopulation Research Group, University of Helsinki, PO Box 65, FIN-00014 University of Helsinki, Finland
1 Introduction to systematic conservation planning
The current biodiversity crisis is unprecedented in the planet’s history: for the first time, a mass extinction event is caused by a living species.
Recent extinction rates are 100 to 1000 times larger than natural background rates and predicted to increase up to ten-fold in the near future (Pimm et al. 1995; Thomas et al. 2004;
Millennium Ecosystem Assessment 2005). We are driving species and populations to extinction: habitat loss and fragmentation, climate change, nutrient loading, direct exploitation and introduction of invasive species are the most important causes of extinctions (Millennium Ecosystem Assessment 2005).
Because we are the cause of this loss, we also have the possibility to take action to halt it.
Effective networks of protected areas are needed now more than ever. But as resources for nature conservation are limited, conservation efforts need to be prioritized in order to achieve best possible outcomes.
The field of systematic conservation planning (Margules & Pressey 2000) developed as a response to opportunistic approaches to conservation that often resulted in biased representation of biological diversity. In the past protected areas were typically established in areas with low economic costs and least potential for alternative land uses, for example, remote areas, high altitudes, steep slopes and low productivity soils. They were often created for reasons other than protection of biological diversity, such as for recreational purposes, or for scenic beauty (Pressey et al. 1993; Pressey 1994). The last two decades have seen the development of increasingly sophisticated planning schemes that account for information about biodiversity conservation goals (benefits), economical considerations (costs) and socio- political constraints. Various complications that influence the performance of a reserve network system, such as its spatial configuration, or the scheduling of conservation actions, can be taken into account with the modern tools (Figure 1).
A typical conservation planning process starts with setting general objectives, such as creating a reserve network with the goal of minimizing the loss of endangered species from the planning region. The goals may be influenced by perceived values of different biodiversity attributes. Because we rarely have data on all the attributes of interest, we must resort to the use of biodiversity surrogates.
There are two general types of surrogacy approaches: species-based and community- level modelling. Species distribution data can be used either directly for protected area prioritization, or in combination with environmental data to model the distribution data that are missing (Guisan & Zimmermann 2000; Guisan & Thuiller 2005; Elith et al. 2006).
The other approach is to model higher level diversity attributes: species richness patterns and turnover in species community composition (Ferrier et al. 2002; Ferrier et al. 2004; Ferrier &
Guisan 2006; Ferrier et al. 2007). With this approach good coverage of diversity features can be achieved without knowing the exact distributions of individual features (species).
Traditional conservation planning techniques require setting of quantitative targets for the optimization procedure. Early studies were simplistic variants of the minimum set or maximum coverage problems. In the minimum set problem the aim is to protect each species a given number of times with minimum cost (Underhill 1994; Csuti et al. 1997; Pressey et al. 1997), and in the maximum coverage approach to maximize the number of species occurring in the network when budget is limited (Camm et al. 1996; Church et al. 1996). Much of the literature revolved around optimality and sub-optimality of different programming techniques (Underhill 1994; Camm et al. 1996;
Church et al. 1996; Pressey et al. 1996; Csuti et al. 1997; Polasky et al. 2000; Rodrigues &
Gaston 2002; Önal 2003; Sarkar et al. 2004;
Fischer & Church 2005; Vanderkam et al.
2007). In the end it makes little difference
Figure 1 A schematic overview of the systematic conservation planning process. The roman numerals indicate which parts of the process are in focus in each chapter of this thesis. The planning process typically starts from identifying appropriate conservation objectives. Numerous factors can be incorporated into the optimization of
conservation actions, although most studies have focussed on only one or few of them at a time. The outputs of the optimization are used to guide decision making.
Expert opinion is presented here as an alternative to the quantitative planning tools, but in reality expert opinion can also be used to derive various inputs used in these analyses. Interests of stakeholders may influence decision-making and
implementation. Implementation should be followed by a monitoring programme to evaluate the successfulness of the project and to produce inputs for future planning.
whether the solution is optimal or slightly suboptimal if the problem definition is overly simplistic to begin with: ignoring relevant components such as habitat connectivity may lead to much larger sub-optimality than that of an inexact solution to a more realistic problem (Moilanen 2008).
With a simplistic problem definition such as the basic minimum set, there is no guarantee that the reserve network will actually be effective in the long term. A minimum set of protected areas without considering its spatial configuration may even correspond to maximizing the number of extinctions within the
network (Cabeza & Moilanen 2003). Long-term persistence of species in the network may be enhanced by aiming at higher representation levels (multiple or larger populations) and areas with better habitat quality, or by addressing the spatial configuration of the network to enhance dispersal between sites (Cabeza & Moilanen 2001).
Another common simplification in systematic conservation planning studies has been that protection is assumed to happen instantaneously. In reality funds allocated for conservation are rarely available all at once, but rather as smaller amounts over a longer
period of time. If we cannot protect everything at once, which are the areas that should be protected most urgently? Several dynamic (sequential) selection algorithms have been developed to address this scheduling problem (Costello & Polasky 2004; Meir et al. 2004;
Pressey et al. 2004; Snyder et al. 2004;
Drechsler 2005; Strange et al. 2006; Turner &
Wilcove 2006; Wilson et al. 2006; Moilanen &
Cabeza 2007; Harrison et al. 2008), and are typically guided by site vulnerability in addition to its contribution towards achieving the conservation goals (see also Figure 3).
Usually systematic conservation planning studies have considered sites simply as protected or unprotected, where protection is assumed to be beneficial for all species. In the real world there may be multiple alternative conservation actions available for each site, such as different kinds of restoration techniques or varying levels of protection. Few publications exist that consider the possibility of multiple actions (Wilson et al. 2007; van Teeffelen & Moilanen 2008; van Teeffelen et al.
2008), largely due to the complexity of the optimization problem, which is further complicated by the fact that what is beneficial for one species may be harmful for another (van Teeffelen et al. 2008).
Integration of socio-political factors into the quantitative prioritization is a difficult but important issue and a subject of ongoing work in the Metapopulation Research Group.
Considering mere economic costs emphasizes developing countries as conservation priorities due to low land acquisition costs and high levels of biological diversity. However, many developing countries have poor quality of governance which may in reality increase the total costs of conservation and decrease the effectiveness of protected areas. Considering such information in the prioritization process will change global conservation priorities (Eklund, unpublished results).
A common feature for all above mentioned components of the prioritization process is a high level of uncertainty. Distribution data are typically biased and incomplete in many ways, and factors such as habitat vulnerability or effects of different conservation actions on different species are at best rough estimates.
One of the recent advances in the field has been the integration of uncertainty analyses into conservation and management (Burgman et al. 2005; Regan et al. 2005; Moilanen et al.
2006).
Even though Figure 1 and this thesis focus on the quantitative tools for systematic
conservation planning, the other stages of the planning process are equally important.
Decision making and implementation on the ground are always influenced by a large number of social, political and economic factors, many of which are hard or impossible to account for within the optimization procedure (Knight et al. 2006a; Knight et al. 2006b;
Adams & Hutton 2007; Knight et al. 2008).
Public opinion, interests of different groups and stakeholders may cause an otherwise perfectly good conservation plan to be abandoned. This is why flexibility is required from the tools, so that when a solution turns out to be unfeasible to be implemented in practice, there are alternative solutions easily available.
An additional stage in the process that is often neglected should be the monitoring of the performance of the implemented network. The circular structure in Figure 1 refers to what is called “adaptive management”, which essentially means learning by doing (Shea et al. 2002; McCarthy & Possingham 2007).
Ideally, all implemented conservation plans should include a monitoring programme to follow up on changes in species abundances, habitat quality, or whatever would be the most suitable indicators of success relevant to the aims of the programme. The information collected should influence future decisions such that conservation actions that clearly are effective are favoured, and ineffective practices are changed.
2 Thesis outline
In this thesis I focus on two general topics related to systematic conservation planning. In the first part (chapters I-IV) I address two different aspects of the question about how biodiversity features should be valued. I investigate the extremely important but often neglected issue of differential prioritization of species for conservation (chapter I). Species prioritization can be based on various criteria, and is always goal-dependent, but can also be implemented in a scientifically more rigorous way than what is the usual practice. Then I introduce a novel framework for conservation prioritization, which is based on continuous benefit functions that convert increasing levels of biodiversity feature representation to increasing conservation value – using the principle that more is better (chapter II).
Traditional target-based systematic conservation planning is a special case of this
approach, in which a step function is used for the benefit function. We have further expanded the benefit function framework for area prioritization to address issues such as protected area size and habitat vulnerability (chapters III and IV).
In the second part of the thesis I address the application of community level modelling strategies to conservation prioritization (chapters V and VI). One of the most serious issues in systematic conservation planning currently is not the deficiency of methodology for selection and design, but simply the lack of data. We are remarkably ignorant about distributions of biodiversity features, which makes the application of even the best methods rather complicated and in the worst case perhaps even misleading. Community level modelling offers a surrogate strategy that makes conservation planning more feasible in data poor regions. It takes advantage of information on environmental variables together with available species distribution data to model higher level diversity attributes: species richness and spatial turnover in species community composition We have reviewed the available community-level approaches to conservation planning. These range from simplistic classification techniques to sophisticated modelling and selection strategies. We have also developed a general
and novel community level approach to conservation prioritization that significantly improves on methods that were available before.
3 Valuing biodiversity in conservation planning
Most often systematic conservation planning methods are applied at the level of occurrences of species at candidate sites. Species are considered equal, and the objective is to protect or prevent the extinctions of as many of them as possible. However, there are numerous reasons why species should not be treated as equals in conservation planning.
Chapter I addresses the infinitely complex issue of species prioritization for conservation.
The different criteria are summarized in Table 1. There is no single, correct way of assigning species priorities, and in the end everything depends on the goals determined by people involved in the planning process. However, prioritization can be made scientifically more rigorous by taking into account the various criteria preferably quantitatively rather than qualitatively whenever data availability allows that.
Table 1. A summary of different criteria used in species prioritization for conservation.
General criterion Specific criteria What is prioritized
Pattern Phylogenetic /
taxonomic uniqueness
Phylogenetically unique species with no close living relatives, relict lineages
Evolutionary potential “Evolutionary fronts”, lineages with active, ongoing speciation, opportunistic species, meta-species
Process
Ecological process Keystone species, structuring species, top predators, dispersal or pollination agents, indicators of ecosystem integrity
Need of protection Extinction risk Species with high extinction risk according to IUCN Red list status or similar, rare species (small geographical range, small population size, high habitat specificity), endemics Cost-effectiveness Conservation triage Species that benefit most from cheapest
conservation measures
The outcomes of typical conservation planning approaches are strongly dependant on targets that must be set in advance for each biodiversity feature (species). For example, the aim could be to protect one population of each species, or 10% of the range of each species.
Defining reasonable targets is difficult due to poor knowledge of species biology.
Often we do not know what is enough to ensure persistence of species in the long term.
Another problem is that with fixed targets the computational approaches designed to solve the problem are blind to differences in species representation levels that exceed the targets, although it seems obvious that higher representation levels should be better.
Similarly, they cannot distinguish between solutions where species are entirely absent or only barely below the target.
The use of continuous benefit functions for species representation introduced in chapter II provides a solution to this problem. The idea of a continuous benefit function for species representation is simply that protecting more individuals, more populations and more species is always better than fewer. The principle applies equally well at any level of organization of biological diversity, such as at the level of communities. Ideally, one would choose an appropriate form for the function based on species biology when such information is available. For example, species that are unlikely to persist if only few populations are protected could be represented by a sigmoid curve, where higher conservation benefit is obtained only when representation levels exceed some threshold value (type III, Figure 2), whereas a species that is likely to do well even if protected only at very few locations could be represented by an asymptotic function with decreasing marginal gains (type II, Figure 2).
The benefit function framework is especially suitable for implementing differential species weighting. In chapter II we apply the technique to conservation prioritization for herb rich forest vascular plants in Southern Finland. The species were prioritized according to IUCN threat status, national rarity and phylogenetic uniqueness. The use of continuous benefit functions in combination with species weights changed dramatically the identity of prioritized sites as compared with traditional target based planning. We also identified unprotected areas that would substantially improve the current protected area network.
Figure 2 Different forms of benefit
functions. (I) is the traditional step function, and forms (II) and (III) are continuous benefit functions appropriate for different types of species. The step function is actually the limit of the sigmoid function.
The following two chapters (III and IV) build on the benefit function approach for conservation planning. Chapter III addresses an issue commonly encountered in quantitative conservation planning when protected area candidates vary in size: Maximizing the numbers of species or populations occurring in the protected area network tends to result in selection of numerous small fragments that in combination contain many species. This is obviously problematic because species are less likely to survive in the long term at these small habitat fragments. We introduce two alternative refinements to the benefit function -based conservation planning approach that aim at correcting for site size.
Essentially, these refinements give higher weight to larger sites following the form of the species-area relationship; a well-known rule in ecology according to which the number of species increases with increasing area, taking the shape of a power function. In an application with Finnish rich fen vegetation data, we found that preferentially selecting larger areas led to only minor decreases in the numbers of
species or populations protected. Moreover, the decreases in representation applied mostly to species for which we had assigned lower weights because they were not classified as threatened or locally rare.
It has been commonly suggested that site prioritization should be based on two measures: Irreplaceability and vulnerability (Figure 3) (Eken et al. 2004; Pressey et al.
2004; Brooks et al. 2006; Linke et al. 2007).
Irreplaceability is a measure of site importance, typically defined as the extent to which options for a representative reserve system are lost if the site becomes unavailable, or as the potential contribution of the site to a conservation goal (Pressey et al. 1994).
Vulnerability describes the site’s susceptibility to (anthropogenic) threats, and is meant to indicate the urgency of protection of the site: a highly vulnerable site must be protected as soon as possible or otherwise it will be lost.
Even though these two measures are often suggested to be used in combination for site prioritization, they have never been quantitatively combined.
In Chapter IV we combine what corresponds to irreplaceability and vulnerability in the context of continuous benefit functions.
The measure of site importance is called
“replacement cost” (Cabeza & Moilanen 2006), and is defined as the loss in biological solution value (sum of values of species representations derived from their benefit functions) when the site cannot be included in the optimal solution.
Vulnerability is considered explicitly through implementing an algorithm for dynamic reserve selection (Moilanen & Cabeza 2007) which accounts for site-specific habitat loss rates. Our extended replacement cost measure considers the loss in biological value at the end of the planning period when a site cannot be included in the optimal selection sequence, and therefore combines not only “vulnerability” and
“irreplaceability”, but does so within a temporal dimension making the measure all the more realistic for real world planning, where implementation of conservation programmes tends to be developed painfully slowly over many years.
We investigated conservation priorities for fungus species in Norwegian forests using the extended replacement cost. We showed quantitatively with a real example how high site value does not necessarily mean the site should be protected early. We found that the sites most important for conservation were also highly expensive compared with young forest
stands, and using realistic cost estimates in the optimization procedure easily restricts the selection to sites with limited conservation value.
Figure 3 Conservation priorities determined by irreplaceability and
vulnerability. Highest priority sites are the ones in quadrant 1 where both vulnerability and
irreplaceability are high. The sites in quadrant 2 have high
conservation value but are not in imminent threat of being lost, and therefore not among the most urgent sites to protect. The sites in quadrant 3 are not among the most valuable, but are more likely to be lost if left unprotected.
Quadrant 4 represents the lowest priority sites that are neither particularly important for achieving the conservation goals nor at risk of being lost.
4 Community-level strategies for conservation planning
Currently the number one challenge in applying conservation planning techniques into practice seems to be the lack and poor quality of biodiversity data. We are in desperate need of surrogate techniques that best take advantage of that scarce data we have. Use of specific species groups for which data is available as surrogates for other species has been a popular idea, but as the tests have varied greatly in their implementation (Cabeza et al. 2008), no consensus has been reached about whether or not some groups work as surrogates, and if so, which ones (Rodrigues & Brooks 2007).
Species groups as surrogates do not seem an especially promising solution. Another popular approach has been to use species-level habitat modelling techniques to derive comprehensive species distribution maps from incomplete observational data (Guisan & Zimmermann 2000; Guisan & Thuiller 2005; Elith et al. 2006).
These techniques have evolved into rather functional options, but one can hardly model
distributions of all species, and therefore the question of surrogacy between species groups remains a problem.
A third group of approaches is what I call here community level modelling. The differences between species-based and community-level strategies are depicted in Figure 4. Chapter V provides a thorough introduction to and evaluation of community level approaches for systematic conservation planning. It is a chapter in the book “Spatial conservation prioritization: Quantitative methods and computational tools”, intended for a wide audience of advanced students, scientists, and conservation managers. The basic idea of community level approaches is that instead of modelling species distributions individually, we settle for modelling information on higher level diversity attributes: species richness and spatial turnover in species community composition. In this way we manage to identify locations that most differ from the others, and together contain most species without knowledge of the actual identities of species at each location.
Figure 4 Schematic of species-based vs. community-level approaches to conservation planning. Two types of data can be used (diamond boxes) either alone for site selection or in combination for modelling (square boxes). Different site selection approaches are described in the ovals with examples of specific techniques in italic.
The thick arrows connect different types of data to modelling approaches, and the thin arrows connect raw or modelled data to area selection approaches.
Various available community-level ap- proaches differ in their complexity. There is a continuum of approaches starting from simplistic classification techniques based on only environmental variables (Belbin 1993;
Faith et al. 2001): It is assumed that locations that differ from each other regarding some environmental variables differ also regarding their species community composition, which may or may not be true, and by selecting a diverse set of locations, one protects a diverse set of species.
The more advanced community level techniques use both environmental and species distribution data in the modelling, and operate in a continuous environmental space where the scaled distances between sites indicate dissimilarity in species community composition (Ferrier et al. 2004; Ferrier & Guisan 2006). In chapter VI we introduce an improved method where we take into account two important issues that have previously been ignored: (1) variation in species richness between locations, and (2) variation in species turnover rate across the environmental space. We demonstrated with simulated data how accounting for these two points significantly improves the performance of community level approaches in conservation planning.
Our method seems highly promising, and would be valuable especially in data poor regions, but also as a complementary strategy to other techniques in other regions. That said, as we used simulated data in our analyses for reasons of technical clarity, it is obvious that the technique needs to be further tested with real datasets to get a better picture of its value relative to other available approaches.
5 Synthesis and future challenges
This thesis introduces further degrees of realism into conservation planning methodology (Table 2). The benefit function -based conservation planning framework largely circumvents the problematic phase of target setting, and because it allows for trade-offs between different species, it provides a more flexible and hopefully more attractive approach to conservation practitioners. In addition to the RSW2 software (Moilanen & Arponen 2008) used in the analyses in this thesis, the benefit function approach has been implemented in the Zonation software (Moilanen et al. 2005;
Moilanen & Kujala 2006; Moilanen 2007). The
Zonation program has recently been implemented in several applications (Kremen et al. 2008; Leathwick et al. 2008; Moilanen et al.
2008; Thompson et al. 2009), of which especially a conservation plan for Madagascar has attracted considerable public attention (Kremen et al. 2008). This entitles us to opti- mism regarding the future and the imple- mentation of the fruits of our work in practical conservation planning projects.
There is still room for various methodo- logical developments in systematic conser- vation planning. Many issues have been addressed separately (scheduling of conser- vation action, spatial planning, multiple alter- native conservation actions, effects of costs and threats, effects of climate change) and future work should focus on how to take these various aspects into account in combination.
We have filled in one step by quantitatively combining irreplaceability with vulnerability of areas in chapter IV, but much remains to be done.
However, the major challenge seems to lie in the lack of data with which to use the increasingly sophisticated methods. Even though data quality and availability is constantly improving due to numerous global database projects, we are still remarkably far from having complete information on distribution of biological diversity. This is where the issue of surrogacy comes into picture. We have introduced a substantial methodological improvement for community level conservation planning. These techniques are especially valuable in situations where data is inadequate for species level conservation planning.
Often species-level modelling is also in- feasible due to the large amount of separate models required. Therefore, I anticipate that the best prospects for future work are in the further development of community level strategies. In addition to some technical improvements, most importantly, the methods should be tested and refined using real, large and comprehensive datasets to see how they perform at different geographical scales, grains and regions, with different types of data, and with data on different biodiversity features (taxa, functional species groups, phylogenetic diversity and so forth). Moreover, the methods should be modified for addressing issues such as temporal dynamics and climate change, and combined with species-based approaches.
Table 2. Main contributions of this thesis to the literature and the corresponding chapters.
Contribution Chapter 1. Clarifying and classifying the biologically relevant criteria for species
prioritization I
2. Introducing a new idea for phylogeny-based prioritization that is more
suitable for ”supertrees” than previous approaches I 3. Creating a framework for the use of continuous benefit functions in
conservation prioritization II
4. Providing practical examples of species weighting based on different criteria
(in the context of benefit functions) II, III, IV
5. Providing ways of accounting for site size in benefit function -based
planning III
6. Quantitatively combining equivalents of ”irreplaceability” and ”vulnerability”
in dynamic site prioritization IV
7. Summarizing and providing new insight into use of community level
modelling strategies in conservation prioritization V 8. Introducing a new, effective community-level strategy for conservation
prioritization that takes advantage of both richness and community turnover models
VI
Although I speak of systematic conservation planning or reserve selection methodology throughout this thesis, it should be noted that the methods that I have described are applicable to a much wider range of questions than mere selection of new protected areas.
They can be useful in any kind conservation prioritization, including targeting of restoration or habitat management actions, or even for the reverse problem of identifying the least valuable locations to sacrifice first for development. In the case of the community modelling approaches, an obvious application is the identification of potential survey sites that are most likely to contain highest numbers of new species, or in other words, the sites that differ most from the ones already surveyed.
Even though primarily methodological in nature, the contributions of this thesis are meant to be assimilated into practical conservation planning – if not directly through the use of software we are freely distributing in the internet, then at least through adopting the general principles, such as “more is better” in species representation. The ultimate objective of this type of research is to have an impact on real world planning practices, but its successfulness in this context remains to be seen in the future.
Acknowledgements
My deepest gratitude goes to Atte Moilanen, who has been an inspiring supervisor. He accepted me as a student to do research on topics that require some mathematical and programming skills, which I didn’t have in the beginning, and encouraged me to acquire those skills. He has also taught me the true meaning of one of the key words in conservation planning: efficiency. Also Mar deserves special thanks. She’s always there to help with just about anything, and discussions with her have often helped me put things into wider perspective when my own research seemed to get stuck in the detail.
The other conservation planners must be acknowledged: Astrid, Heini, Joona, Johanna, Laura and other students who have participated in our activities. Our journal clubs, a.k.a. the precious pulla-eating moments, have often been the highlight of the week, and hopefully continue to be so! In addition to the culinary experiences and lively discussions, the sugar- powered sessions have even resulted in writing articles – it has been fun to collaborate with you! Special thanks to Johanna who was sacrificed as a guinea-pig regarding my experience as a supervisor. I hope I have not caused any permanent traumas. Although not a conservation planner, Jonna was a part of the
team, and as the scientific coordinator of the faculty she has also been indispensable during the preparation for my defence.
I also wish to thank Professor Ilkka Hanski and the entire MRG for providing such an excellent learning environment for a PhD student. The wide range of scientific expertise in the group combined with always friendly, helpful characters of people makes life much easier for a student. The MRG has a group spirit that seems to be exceptional among research groups. I’ve had great fun in the annual meetings, karonkkas, canoeing trips and various other social activities.
Special thanks go to all the MRG office people who during these years have helped with the various necessary bureaucratic evils:
Mimma, Sami, Marika, Tuuli, Nina and Elina. I think my PhD would have lasted twice as long had I done all those things by myself.
I am obviously grateful to Prof. Polasky for agreeing to act as the opponent, and to Prof.
Heikki Hirvonen for being the Custos. Although Veijo could not be there in the final day, he was there for the last four years. I am also thankful to Professor Raimo Virkkala and Dr. Hanna Tuomisto for finding the time to review my thesis despite their busy schedules. This work would not have been possible without the collaboration of all my co-authors: Chris
Thomas, Risto Heikkinen, Hanna Kondelin, Simon Ferrrier, Jogeir Stokland, Dan Faith and Michael Drielsma.
I also want to acknowledge the LUOVA graduate school coordinators during these years: Anna-Liisa, Jonna and Anni.
I thank my husband Giampa, in addition to everything obvious, for his infinite patience and for keeping me well fed during these years. I also thank my family, in which science crept into everyday conversations, which made research seem like a normal thing to do.
Maintaining my mental health is largely the merit of Sani and Hera, my equine companions, who during these years took care of aerating my brain on a regular basis. They are the only thing that gets my mind off absolutely everything no matter what is happening with the rest of my life. And while I’m at it, I might as well thank Alma and Iiris as well, our two cats who have frequently helped me improve my writings:
when a cat walks over the keyboard and you realize the text makes about as much sense as it did before you know its time to go to bed.
This thesis was funded by Helsinki University Science Foundation grant for Young Researchers and LUOVA - Finnish School in Wildlife Biology, Conservation and Manage- ment.
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