Implementing Green Infrastructure: integrating biodiversity, connectivity, and ecosystem services into landscape planning decisions in the Geneva region

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Thesis

Reference

Implementing Green Infrastructure: integrating biodiversity, connectivity, and ecosystem services into landscape planning

decisions in the Geneva region

HONECK, Erica Cristine

Abstract

Nature forms interdependent networks in a landscape, which is key to the survival of species and maintenance of genetic diversity. Nature also provides crucial socio-economic benefits to people, but are typically undervalued in political decisions. This has led to the concept of Green Infrastructure (GI), which defines an interconnected network of (semi-)natural areas designed and managed to preserve a wide range of ecological, social, and economic benefits.

GI is increasingly being recognized as a policy instrument to better integrate nature's values into landscape planning decisions, but there is no consensus in the scientific literature on how to map and implement GI, and its operationalization in spatial planning has never been done in Switzerland. Consequently, this thesis aims to examine how the concept of GI can be effectively implemented to support the integration of natural capital values into landscape planning decisions, with a focus on the canton of Geneva, Switzerland.

HONECK, Erica Cristine. Implementing Green Infrastructure: integrating biodiversity, connectivity, and ecosystem services into landscape planning decisions in the Geneva region. Thèse de doctorat : Univ. Genève, 2020, no. Sc. 5535

URN : urn:nbn:ch:unige-1478935

DOI : 10.13097/archive-ouverte/unige:147893

Available at:

http://archive-ouverte.unige.ch/unige:147893

Disclaimer: layout of this document may differ from the published version.

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UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES

Département F.-A. Forel des sciences Professeur Anthony Lehmann de l’environnement et de l’eau

Implementing Green Infrastructure:

integrating biodiversity, connectivity, and ecosystem services into landscape planning decisions in the

Geneva region

THÈSE

présentée à la Faculté des Sciences de l’Université de Genève

pour obtenir le grade de Docteur ès Sciences, mention Sciences de l’Environnement

par

Erica Cristine Honeck

de Genève (GE)

Thèse N° 5535

GENÈVE

Centre d’Impression de l’Université de Genève 2020

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“The Earth is what we all have in common.”

– Wendell Berry

“The proper use of science is not to conquer nature but to live in it.”

– Barry Commoner

Color pencil illustration by Erica Honeck, 2020

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Abstract

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Abstract

Biodiversity loss is one of the biggest challenges we must face in our epoch, labeled as the

‘Anthropocene’, in which habitat degradation and destruction by human activities continue to put major pressures on the stability and resilience of ecosystems worldwide.

Nature forms interdependent networks in a landscape, which is key to the survival of species and the maintenance of genetic diversity. Nature also provides crucial socio- economic benefits to people, but they are typically undervalued in political decisions.

Conservation of sites with high ecological value is one of the most effective ways to safeguard ecosystems functions and reduce biodiversity loss. Therefore, it is critical to locate those areas to be protected in priority. This has led to the concept of Green Infrastructure (GI), which defines an interconnected network of (semi-)natural areas designed and managed to preserve a wide range of ecological, social, and economic benefits.

GI is increasingly being recognized as a policy instrument to better account for nature’s values in landscape planning decisions, and is being incorporated into regional, national, and local biodiversity conservation action plans. However, the concept of GI has been used with widely diverging interpretations, and there is no apparent consensus in the scientific literature on how to map and implement GI.

This has resulted in GI maps with highly variable assessment approaches that do not cover the full spectrum of biodiversity and ecosystem services (ES). Another issue concerns the coordinated integration of various datasets to ensure that two GI maps created with different tools, data quality, and data sources are still comparable.

Consequently, this thesis aims to examine how the concept of GI can be effectively implemented to support the integration of natural capital values into landscape planning decisions.

This research focuses on the canton of Geneva, Switzerland. GI has never been fully implemented in Swiss planning strategies, despite growing interest among environmental research and policy circles. In response to economic and demographic drivers, the country introduced a Swiss Biodiversity Strategy in 2012, as a governance tool in line with the Convention on Biological Diversity’s (CBD) Aichi Biodiversity Targets, and developed a related action plan for its operationalization in 2017.

At the local level, the canton of Geneva reinforced its biodiversity law for natural reserves following the national Swiss Biodiversity Strategy with the elaboration of a cantonal Biodiversity Strategy and action plan 2020-2030, to promote a sustainable society and resilient economy that went beyond legal protection of biodiversity dimensions (i.e., threatened species and ecosystems).

The implementation of a GI network to optimize the protection of the country’s biodiversity and ES is promoted in both of these national and cantonal biodiversity strategies.

To this end, this research is structured into four main steps:

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(1) the review of comprehensive GI mapping approaches;

(2) the application of a GI mapping framework to the canton of Geneva;

(3) the generalization of GI mapping with Essential Variables;

(4) the implementation of GI into policymaking.

Chapter 2 provides a review of existing methods to construct an ‘effective’ (or

‘functional’) GI to preserve natural capital, integrating all three pillars: species and habitat diversity (biodiversity), ecological structure and connectivity, and ES supply.

Based on this review, inclusive approaches for GI mapping are then summarized in a structured catalog in five levels according to the representativeness and precision of each pillar’s assessment. This work would serve as a guide to select the most appropriate GI mapping approach according to the specific needs of each case study.

Chapter 3 evaluates the feasibility of a ‘three-pillar’ GI mapping framework using the spatial prioritization software, Zonation, applied to the canton of Geneva. Such inclusive framework can be applied to any region and scale to help local decision-makers optimally allocate limited resources for nature conservation, by visualizing priority areas and their potential threats in a spatially explicit manner.

Chapter 4 evaluates the added value of identifying Essential Variables (EV) for GI, to coordinate the integration of various datasets and ensure that two GI maps created with different tools, data quality, and data sources will still be comparable. A transboundary GI case study is demonstrated on the Greater Geneva, an agglomeration between Switzerland and France, and is used as a basis to propose a preliminary set of existing EV to define a GI mapping workflow.

Finally, Chapter 5 identifies key factors that could catalyze the integration of conservation science knowledge into policymaking by analyzing the case study of GE-21, a governance structure implicated in Geneva’s GI project. GE-21 represents a ‘boundary organization’

that aims to straddle disciplinary silos and institutional boundaries to foster reciprocal science-policy collaboration. The identified factors that have facilitated the exchange of ideas and the structure’s role as a link between academic research and public agencies could help implement GI in other regions.

This research represents a first step toward the operationalization of the GI concept into cantonal landscape management and planning decisions to optimize the preservation of key areas for wildlife and people. Many challenges are yet to be overcome before GI can effectively be integrated into cantonal decisions, and this thesis proposes different conceptual and methodological solutions to expand and adapt the implementation of GI to other region’s specific needs and objectives. Perspectives for future research include the evaluation of climate change and land use change scenarios, and the automation of the GI mapping workflow to facilitate data updates.

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Résumé

La perte de biodiversité est l'un des plus grands défis à laquelle nous devons faire face à notre époque de l’« Anthropocène ». La dégradation et la destruction des habitats naturels par les activités humaines continue d'exercer des pressions majeures sur la stabilité et la résilience des écosystèmes à l’échelle planétaire.

La nature forme des réseaux interconnectés et fonctionnels entre milieux naturels qui sont essentiels à la survie des espèces et au maintien de la diversité génétique. Les écosystèmes fournissent de nombreux services fondamentaux, mais ceux-ci sont généralement sous-représentés dans les décisions politiques. Cela a mené à l’émergence du concept d'infrastructure écologique (IE), qui définit un réseau interconnecté d'espaces (semi-)naturels conçus et gérés pour préserver un large éventail de bénéfices écologiques, sociétales, et économiques.

L’IE est de plus en plus reconnue comme un outil stratégique pour mieux intégrer les valeurs de la nature dans les décisions d'aménagement du paysage, et sont incorporés dans plusieurs plans d'action régionaux, nationaux et locaux pour la conservation de la biodiversité. Cependant, ce concept a été utilisé avec des interprétations divergentes, et il n’existe aucun consensus dans la littérature scientifique sur la manière de cartographier et mettre en œuvre une IE.

Cela a abouti à des cartes d’IE conçues avec des approches très variables qui ne couvrent pas le large spectre de la biodiversité et des services écosystémiques. Un autre enjeu concerne l'intégration coordonnée de divers ensembles de données pour garantir la comparabilité de deux IE créées avec différents outils, qualités de données, et sources de données.

Par conséquent, l’objectif de cette thèse consiste à examiner comment le concept d'IE peut être efficacement mis en œuvre pour intégrer les valeurs du capital naturel dans les décisions d'aménagement du territoire.

Le cas d’étude de cette recherche est basé sur le canton de Genève. En Suisse, une IE n'a jamais été mise en œuvre dans une stratégie de planification territoriale, malgré l'intérêt croissant des cercles académiques et politiques. En 2012, la Confédération a introduit une stratégie nationale pour la biodiversité conforme aux exigences des objectifs d’Aichi pour le plan stratégique de la Convention sur la Diversité Biologique, et a élaboré un plan d'action pour son opérationnalisation en 2017.

A l’échelle locale, le canton de Genève a renforcé sa loi sur la biodiversité pour la gestion des réserves naturelles à la suite de la Stratégie nationale suisse pour la biodiversité, avec l'élaboration d'une Stratégie cantonale pour la biodiversité et d'un plan d'action 2020- 2030, afin de promouvoir une société plus durable et une économie résiliente allant au- delà de la protection légale des espèces et écosystèmes menacés.

La mise en œuvre d’une IE pour promouvoir la biodiversité et les SE en Suisse est encouragée dans ces stratégies nationales et cantonales.

A cet effet, cette recherche est structurée en quatre étapes :

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(1) l'évaluation des approches intégrales de cartographie de l’IE;

(2) l'application d'une approche de cartographie de l’IE dans le canton de Genève;

(3) la généralisation de la cartographie d’IE avec des variables essentielles;

(4) la mise en œuvre de l'IE dans les politiques.

Le chapitre 2 passe en revue les méthodes existantes pour construire une IE ''efficace'' (ou ''fonctionnelle'') pour préserver le capital naturel, intégrant trois piliers: la diversité d’espèces et d’habitats, la structure et la connectivité écologiques, et les services écosystémiques (SE). Sur cette base, les approches inclusives de cartographie de l’IE sont ensuite structurées en cinq niveaux selon la représentativité et la précision de l'évaluation de chaque pilier. Ce travail servirait de guide pour sélectionner l'approche de cartographie d’IE la plus appropriée en fonction des besoins spécifiques de chaque étude.

Le chapitre 3 évalue la faisabilité d’une approche de cartographie de l’IE ''à trois piliers'' en utilisant le logiciel de priorisation spatiale, Zonation, appliqué au canton de Genève.

Un tel cadre peut être utilisé dans d’autres régions et à toute échelle pour aider les décideurs locaux à attribuer de manière optimale les ressources limitées pour la conservation de la nature, en exposant les zones prioritaires et leurs menaces potentielles de manière spatialement explicite.

Le chapitre 4 évalue la valeur ajoutée de l’identification des variables essentielles pour l’IE, afin de coordonner la collecte et l’intégration des données et d’assurer que deux cartes d’IE créées avec des outils et sources de données différents restent comparables.

Un cas d’étude sur le Grand Genève, une agglomération entre la Suisse et la France, a servi de base pour proposer un ensemble préliminaire de variables essentielles existantes, qui seraient utiles pour caractériser le processus de cartographie de l’IE.

Enfin, le chapitre 5 identifie les facteurs clés qui pourraient catalyser l’intégration des savoirs scientifiques dans l’élaboration des politiques, en analysant le cas de GE-21, une structure de gouvernance impliquée dans le projet d’IE du canton de Genève. GE-21 représente une structure de liaison qui vise à faire le pont entre les silos disciplinaires et les frontières institutionnelles pour favoriser la collaboration réciproque entre sciences et politiques. Les facteurs identifiés comme jouant un rôle central dans les échanges d’idées et favorisant à établir des liens entre la recherche académique et les organismes publics pourraient aider à mettre en œuvre l’IE dans d’autres régions.

Cette recherche représente la première étape vers une mise en œuvre d’une IE dans la gestion et les décisions de planification territoriale à l’échelle cantonale afin d’optimiser la préservation de sites clés pour la biodiversité et la société. De nombreux défis doivent encore être surmontés avant que l'IE puisse être intégrée dans les décisions politiques cantonales, et cette thèse propose différentes solutions conceptuelles et méthodologiques pour étendre et adapter l’opérationnalisation de l’IE aux besoins et objectifs spécifiques à d’autres régions. Les perspectives pour des recherches ultérieures comprennent l’analyse de scénarios de changement climatiques et de changement d’exploitations du sol, la définition d’indicateurs ''absolus'' pour monitorer la qualité des aires prioritaires, and l’automatisation du processus de cartographie de l’IE afin de faciliter la mise à jour des données.

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Acknowledgments

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Acknowledgments

First of all, I would like to express my gratitude to Professor Anthony Lehmann, director of this thesis, for giving me the opportunity to work on such an exciting project. I am very grateful to him for believing in my abilities and trusting me as a teaching assistant. He supported me to participate in multiple international conferences, which offered me enriching experiences and broadened my scientific perspectives. His passion, enthusiasm and constant encouragements have been source of motivation since the time I met him in my Bachelor’s.

I deeply thank Benjamin Guinaudeau for his invaluable contributions to the GI project, for his generous availability to help me treat any spatial data issues, and for being such a great friend and ‘desk-neighbor’.

I am very thankful to the jury members who kindly accepted to evaluate this PhD thesis, namely Adrienne Grêt-Regamey, Nicolas Ray, and Atte Moilanen. Atte is also the developer of the Zonation software used in this thesis, and helped our GI mapping team immensely with understanding and parameterizing Zonation for the objectives of Geneva’s GI network.

Thank you to the GEOEssential project members who have given me advice on my work during the GEOEssential workshop, especially Denisa Rodila, Ivette Soral, Aidin Niamir, Gregory Giuliani and André Mascarenhas who have continued to help me with essential variables after the workshop. André has also provided me with valuable inputs and feedbacks which greatly improved my article on essential variables.

I am very grateful to all the people who have contributed to the publications related to this thesis: Joëlle Massy, Loreto Urbina, Olga Villarubia, Louise Gallagher, Arthur Sanguet, Martin Schlaepfer, Bertrand von Arx, Frédéric Sandoz, Nicolas Wyler, Pascal Martin and Claude Fischer. Special thanks to Bertrand who carved out some time to answer my questions, thoroughly review my papers, and bring relevant insights, always with a contagious smile. Arthur has also been a great teammate for our joint-papers and joint- presentations, and his constant cheerfulness was a source of motivation to keep going. I will also cherish the memories created together with Arthur and Martin during our conference trips.

A big thank you to all my other colleagues at the University of Geneva, particularly the Carl-Vogt B4 office block, for creating a warm and welcoming atmosphere, and making my lunch breaks so enjoyable and energizing: Fleur Hierink, Charlotte Poussin, Zeyna Sy, Julie Fahy, Katia Vladimirova, Carlos Ochoa, Pablo Timoner, Martin Lacayo, Marc Fasel, Yaniss Guigoz, Pierre Lacroix, Tuo Wang, and Castro Gbedomon. I also treasure the memories from our multiple activities outside the office.

Finally, I owe it all to my parents who continuously supported and encouraged me along every step of my academic studies.

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Funding

I would like to acknowledge the European Commssion’s Horizon 2020 research and innovation program for funding this PhD thesis (Grant agreement No. 689443 for ERA- PLANET/GEOEssential project), as well as the Swiss Federal Office of the Environment who supported the Green Infrastructure project in the canton of Geneva (grant RPT).

This thesis was developed within the scope of ERA-PLANET’s GEOEssential project (Essential Variables workflows for resources efficiency and environmental management) launched in 2017. GEOEssential addresses the need for trusted data sources to monitor the progress toward environmental policy targets (http://www.geoessential.eu). With 14 partner institutes collaborating through seven interlinked work packages, the project aims to build concrete demonstration workflows using EV to derive policy-relevant indicators. Conceptual workflows stored in a dedicated knowledge base will be executable in the GEOEssential Virtual Laboratory developed within the project (Lehmann et al., 2020a).

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Chapter 1: Introduction

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1.1 Background and fundamental concepts

Climate change and biodiversity loss are two of the most urgent challenges of our time (IPBES, 2018a, 2019; IPCC, 2014; UN Environment, 2019). Biodiversity at all levels is declining worldwide at an unprecedented rate, due mainly to land and sea use changes, direct exploitation of organisms, climate change, pollution, and invasive alien species, and is expected to continue declining if no concrete actions are taken (IPBES, 2019; Newbold et al., 2015; Visconti et al., 2016). Ecosystems are losing their resilience to maintain their functions, which will ultimately jeopardize our food and water security, deteriorate our health, and threaten our social-economic well-being (Dawson et al., 2011; McGill et al., 2015; Scheffers et al., 2016).

Land degradation has been identified as one of the main factors threatening ecosystems and biodiversity (Arthington et al., 2016; Baur and Erhardt, 1995; IPBES, 2018b, 2019).

It has been estimated that 68% of the world’s growing population will live in urban areas by 2050 (United Nations, 2019), which will inevitably increase the pressure to develop the “grey” infrastructure for housing, mobility, and economic use. Along with other human activities, urbanization continues to have serious consequences for biodiversity and the provision of ecosystem benefits to people. Growing demand for new residential areas is a major policy driver in urban land use planning and management, and road constructions also represent a global threat to biodiversity (Meijer et al., 2018).

Despite numerous efforts devoted to nature conservation and the expansion of protected areas, we are failing to meet the Aichi Targets for 2020 set by the UN Convention on Biological Diversity (IPBES, 2019; Tittensor et al., 2014). We now face the urgent need for a credible agenda and well-defined action plans to safeguard the survival of species and restore the ecosystems on which we depend (Mace et al., 2018).

Nature conservation schemes traditionally focused on preserving species and intact wilderness, but have recently evolved to adopt a more holistic “people and nature”

approach (Mace, 2014), where the landscape is managed to support biodiversity and humanity in the long term (Kremen and Merenlender, 2018). This new paradigm takes into considerationthe numerous interactions between people and nature and analyzes social, economic, and ecological systems as a whole. Nature forms interdependent networks in a landscape, which is key to the survival of species and the maintenance of genetic diversity. Nature provides us with crucial socio-economic benefits, but they are often undervalued in political decisions. This novel framing of our relationship with nature illustrates our dependence upon ecosystems and emphasizes that people are part of nature, not apart from it (Mace, 2016).

1.1.1 The concept of Green Infrastructure (GI)

This has led to the concept of Green Infrastructure (GI), which defines an interlinked network of (semi-)natural areas with high ecological values for wildlife and people, to be conserved and managed in priority to preserve biodiversity and ecosystem services (ES) (European Environment Agency, 2014). Benedict and McMahon (2006) have called it the

“ecological framework for environmental, social, and economic health – in short, our natural life-support system”.

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GI is increasingly being considered as natural and cost-effective alternative to grey infrastructure to help mitigate environmental impacts, adapt to climate change, and build resilient societies. Considering environmental resources as infrastructure allows us to recognize their role in our livelihoods, and also to point out that ES also require maintenance to sustain their capacity to provide clean water and air, aesthetic benefits, physical and mental health, wildlife conservation, and other community values.

GI has gained credibility among land planners and policymakers as a policy tool to promote sustainable development and to assess synergies and trade-offs between conservation goals and other conflicting land use interests (Lanzas et al., 2019). In fact, visualizing priority areas to implement conservation actions will support decision- makers to optimize the allocation of limited resources for ecosystem preservation.

Having such priority areas mapped out in advance also saves time by avoiding conflicts when a key resource or environmental concern is brought up after a development project has been initiated (Firehock, 2015). GI has been incorporated into national, regional, and continental environmental agenda (DETA et al., 2018; European Commission, 2013;

FOEN, 2017a).

However, the translation of the concept of GI into concrete actions on the ground remains a hurdle (Li et al., 2020; O’Donnell et al., 2017), and it has rarely been fully operationalized in local planning strategies. A significant challenge resides in the disparity of data availability and quality among regions and countries to ensure the effective design and establishment of a GI network that preserves the ecological continuity across borders.

Besides, GI has been formulated and interpreted in divergent ways, and various terms refer to the same idea (Firehock, 2015), as there is no consensus, neither on its components nor on the method to identify and map GI (Wang and Banzhaf, 2018).

1.1.2 The three-pillar approach to GI mapping

Snäll et al. (2016) and the European Environment Agency (2014) among others have claimed that a functional GI network will require the integration of three main ‘pillars’:

(i) species and habitat diversity (biodiversity), (ii) ecological structure and connectivity, and (iii) ES supply (or nature’s contributions to people) (Díaz et al., 2018) (Figure 1).

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Biodiversity refers to the variability of biological life at various scales from genes, to species and landscapes (CBD, 1992). Perimeters of existing protected areas or (semi-)natural areas in a broad sense (such as forests) are sometimes used to represent biodiversity, but remains a rough estimation of diversity distribution in an area. Including a broad range of species surrogates and diversity indicators would enable to account for the multiple dimensions of biodiversity.

1.1.2 b) Ecological structural and connectivity

Ensuring species movement through a connected landscape helps increase the genetic diversity in a metapopulation, which raises the chances of species’ survival by improving their resilience against climate change and other perturbations (Pauls et al., 2013).

Species use the landscape’s structure in different ways according to their specific ecological niches, lifestyles, and dispersion abilities. GI builds on these principles to account for habitat shapes and sizes as well as edge areas surrounding a habitat serving as a buffer.

Spatial structure refers to the topological distance between landscape features (Tischendorf and Fahrig, 2000) or between the spatial arrangement of landscape elements, and determines the mosaic of contiguous land cover types (Benedict and McMahon, 2006). Functional connectivity refers to the relative ease of mobility between Figure 1: 'Three-pillar' approach to GI mapping, based on the separate assessment of species and habitat diversity, ecological structure and connectivity, and ecosystem services supply. (The concept of Essential Variables will be introduced in Chapter 4)

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landscape patches for a specific species (Taylor et al., 1993; With et al., 1997). For instance, spatially unconnected landscape elements (i.e. low connectedness) may represent strong constraints for species with low vagility (D’Eon et al., 2002), but may not necessarily reduce connectivity for flying species (Bélisle, 2005). Corridors structurally connecting two patches may also be too narrow to have any functional connectivity values for some species.

1.1.2 c) Ecosystem Services (ES) supply

Ecosystem services (ES) represent the benefits people obtain from nature (Costanza et al., 1997; MEA, 2005), and their value corresponds to the relative contribution of ecosystems to a community’s goal (Turner et al., 2016). In other words, ES refer to the flows of benefits from stocks of natural capital to people (Costanza et al. 1997), which may be combined with manufactured services (built capital and/or human and social capital) to satisfy people’s needs (de Groot, Wilson and Boumans 2002). ‘Natural capital’

incorporates the concept of ES or Nature’s Contributions to People (Díaz et al., 2018), and refers to the stock of natural resources consisting in geology, soil, air, water, and living organisms (Natural Capital Forum, 2020).

ES is a valuable concept to help policymakers and stakeholders adhere to ecosystem protection. By demonstrating the links between a healthy ecosystem and human well- being (e.g. heat island mitigation provided by tree canopy cover), people can value nature in novel ways and realize the importance of its preservation.

1.1.3 Spatial Conservation Prioritization (SCP) and its benefits

Methods commonly used for mapping GI include overlay analyses with Geographic Information Systems (GIS), morphological spatial pattern analysis, minimum path model, and landscape-functional units (Niedźwiecka-Filipiak et al., 2019). However, these are not well suited for maximizing synergies and minimizing trade-offs between ES and biodiversity, which is the aim of efficient conservation planning (Chan et al., 2006).

Spatial Conservation Prioritization (SCP) is a widely used approach in systematic conservation planning by conservation biologists who are also confronted with finding optimal areas to allocate protected areas or restoration actions (Kukkala and Moilanen, 2013; Moilanen et al., 2009). The main advantage of SCP tools is their capacity to account for trade-offs and synergies among multiple components in a landscape, and to present alternative solutions to spatial planning, which is not straightforward with other methods such as overlay analyses using Geographic Information Systems (GIS) (Bennett et al., 2009; Egoh et al., 2010; Reyers et al., 2012; Snäll et al., 2016).

SCP software use computational methods to optimize the selection of priority areas in a landscape for a given target. Weights can be attributed to some features to influence the outcome to account for factors such as species rarity and ecological connectivity. In addition, opportunity costs, opposing land use interests, land ownership, and other restrictions can be considered in the analysis to create more realistic solutions. As weights accorded to input data may significantly influence the prioritization result, expert knowledge and stakeholders’ consultation are strongly advised.

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The most widely used prioritization software include Marxan (Ball et al., 2009) and Zonation (Moilanen et al., 2009).

Marxan was created to identify a set of planning units to meet conservation targets for selected biodiversity features while minimizing the total cost. The tool’s optimization algorithm is based on simulated annealing for a fast and relatively simple way to solve minimum set problems of different types and sizes, and find the best fit among multiple alternative solutions (Ball et al., 2009).

Marxan can be used to analyze trade-offs between biodiversity features, boundary length, area, and costs by varying the representational targets in the input files (Regan et al., 2009). The tool takes into consideration connectivity between selected planning units, but cannot include species-specific connectivity requirements. It can account for ecological processes, site conditions, or socio-political influences (private parcels or culturally important sites).

Zonation was created to address the maximum utility problem, i.e. to maximize the conservation value for the selected species or biodiversity features within limited resources (Moilanen et al., 2009). The main output is a hierarchical map of ranked conservation priority. It does not require setting a specific target and can be used to evaluate the adequacy of proposed protected areas or to specify where to expand conservation or restoration areas (Moilanen et al., 2009).

Zonation uses information on different types of features such as species presence/absence, abundance, probabilities of occurrence, and costs/constraints to prioritize sites according to their representativeness and persistence. The tool’s cell removal rules are based on core-area zonation for emphasizing rare features, additive benefit function for selecting richer areas, or target-based planning for specifying specific conservation targets for each feature. The algorithm uses accelerated stepwise heuristic, which starts from the whole landscape and iteratively removes cells with the smallest marginal loss over the total conservation value (Moilanen et al., 2009).

Zonation can also account for corridors, using an additive penalty method in the spatial priority ranking. This method does not rely on habitat patches, resistance coefficients, or species targets, and uses two key parameters – penalty strength and corridor width – to control trade-offs between connectivity and other factors in conservation planning (Pouzols and Moilanen, 2014).

Other software include C-Plan and ConsNet Portal, which are used to solve the minimum area problem (representing all biodiversity surrogates with the minimum area) and the maximum representation problem (representing the maximum number of surrogates in a constrained area) (Moilanen et al., 2009).

The choice of prioritization tools depends on the objectives of the project as well as available data for inputs. However, several studies have indicated that different tools could lead to similar results and that the most important factor in SCP assessments is the quality of the input data (Delavenne et al., 2012).

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1.2 Research problem and questions

1.2.1 Research problem

This research focuses its study area mainly on the canton of Geneva, Switzerland. In Switzerland, the concept of GI is increasingly recognized as an important means to enhance biodiversity and ES conservation, but has never been fully implemented in local planning strategies. Currently, 12.5% of the country is protected as natural reserves (FOEN, 2017b), which means 4.5% of protected surfaces are still lacking to fulfill its requirements towards the 17% of nationally protected surfaces set by the Aichi Biodiversity Target 11 (CBD, 2014).

Therefore, the operationalization of GI is one of the principal objectives of the Confederation’s Swiss Biodiversity Strategy, introduced in 2012 along with its Action Plan released in 2017 (FOEN, 2017a). In 2018, the canton of Geneva followed this scheme and published the first cantonal Biodiversity Strategy to reinforce and implement the national biodiversity strategy at a local scale (Etat de Genève, 2018). The canton of Geneva ambitions to extend its protected areas from 4.4% (excluding the lake of Geneva) (OCAN, 2019) to 30%, and GI is a central element to achieve this target.

Despite growing interest among environmental research and policy circles, we must overcome many more challenges before GI can be implemented in the whole country and effectively contribute to the preservation of the territory’s natural capital.

Consequently, the overarching aim of this research is to:

Examine how the concept of GI can be effectively implemented to support the integration of natural capital values into landscape planning decisions.

1.2.2 Research questions

The associated research questions are the following:

Question 1:

What are the most comprehensive approaches to map an effective GI?

The GI concept has been interpreted in divergent ways in the scientific literature with no consensus on how to construct an effective GI network. Consequently, areas designated as GI may be based solely on vegetation types as proxies for biodiversity, or on a few ES of interest. Such approaches do not fulfill the ambition of the local and national Biodiversity Strategy to preserve the full spectrum of biodiversity and ES. This research aims to review existing methods to map an ‘effective’ (or ‘functional’) GI considering all three pillars (species and habitat diversity, ecological structure and connectivity, and ES supply).

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What are the potentials and limitations of implementing a comprehensive GI?

A particular challenge in designing GI is to establish the GI network in areas where they effectively provide conservation benefits for both wildlife and people. Based on GI mapping approaches reviewed in Question 1, this question addresses the feasibility of a comprehensive GI mapping framework applied to the canton of Geneva.

Question 3:

Which Essential Variables (EV) best describe GI mapping to improve the coordination of GI mapping efforts across scales and borders?

The GI mapping framework can be used from local scales and urban contexts (Capotorti et al., 2019a, 2019b) to regional/continental scales and in mosaic landscapes (Hermoso et al., 2020; Kopperoinen et al., 2014a; Liquete et al., 2015). However, such analyses and purposes widely differ from one another, making it difficult to compare their methods and the identified networks.

This raises the question of how to coordinate the integration of various datasets and ensure that two GI maps created with different tools and data sources will still be comparable. To this end, identifying a set of Essential Variables (EV) for GI mapping would reduce the risk of collecting disparate types of data in an uncoordinated way or missing useful inputs.

Question 4:

What are the contributions of boundary organizations to foster science-policy collaboration for GI implementation?

The science-policy gap in nature conservation is a widespread concern among researchers and practitioners in the field. Scientific knowledge and capacity sharing are encouraged and mentioned in many biodiversity strategies, including Aichi Biodiversity Target 19 at the international level. However, scientific evidence for nature conservation still struggles to overcome power structures and must often compete with other socio- economic interests influencing policymaking.

A proposed solution to help catalyze the implementation of scientific information into policy is the use of ‘boundary organizations’, which are structures that aim to straddle disciplinary silos and institutional boundaries to foster reciprocal science-policy demands. This research analyzes the case study of GE-21, a boundary organization developed to promote biodiversity and ES in Geneva, which has contributed to the emergence of the cantonal GI project. Identified factors that have led to the success of this boundary organization could help other regions build solid collaborations for GI mapping and implementation.

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1.3 Structure of the thesis

This thesis is structured in six chapters, contributing to fulfilling the research questions defined under section 1.2.

Chapter 1 introduces the thesis and sets the scene by describing the background situation and arguments for the necessity of this work, followed by some fundamental concepts and terminology underlying GI mapping (spatial prioritization, and the three GI pillars: species and habitat diversity; ecological structure and connectivity; and ES supply). The chapter then defines the overarching aim of this thesis as well as the four associated research questions.

Chapter 2 serves as a guide to select the most appropriate GI mapping approach for the needs of a given case study. Reviewing articles on creating GI networks, this chapter summarizes and evaluates commonly used methods and tools for mapping GI. Based on this literature review, the chapter proposes five theoretical levels towards a more complex, reliable, and integrative approach to identify GI networks, and discusses the applications and limits of such methods.

Chapter 3 demonstrates a ‘three-pillar’ GI mapping framework using the spatial prioritization software, Zonation, applied to the canton of Geneva, to support decision- makers optimally allocate limited resources for nature conservation. The chapter evaluates the relative influence of adding ES and ecological connectivity to a biodiversity- based approach to identify priority areas and the fraction of threatened species covered by the GI. It also analyzes the extent of existing protected areas covered by the proposed GI as well as the risks and implementation feasibility in terms of land property types.

Chapter 4 explores the added value of identifying Essential Variables (EV) for GI. A transboundary GI case study is demonstrated on the Greater Geneva, an agglomeration between Switzerland and France, and is used as a basis to propose a preliminary set of existing EV to define a GI mapping workflow.

Chapter 5 discusses key elements to catalyze the integration of conservation science knowledge into policymaking by analyzing the case study of GE-21, a governance structure (called ‘boundary organization’) implicated in Geneva’s GI project. This chapter analyses and identifies factors facilitating the exchange of ideas between academic research and public agencies, which could help implement GI in other regions.

Finally, Chapter 6 concludes this research by answering the four research questions and discusses perspectives for future directions of research.

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1.4 Contributing papers

Honeck, E., Sanguet, A., Schlaepfer, M.A., Wyler N., Lehmann A. (2020) Methods for identifying Green Infrastructure. SN Applied Sciences, 2(11), 1916.

https://doi.org/10.1007/s42452-020-03575-4

Honeck, E., Moilanen, A., Guinaudeau, B., Wyler, N., Schlaepfer, M. A., Martin, P., Sanguet, A., Urbina, L., von Arx, B., Massy, J., Fischer, C., Lehmann, A. (2020) Implementing Green Infrastructure for the Spatial Planning of Peri-Urban Areas in Geneva, Switzerland. Sustainability, 12(4), 1387. https://doi.org/10.3390/su12041387

Honeck, E., Mascarenhas, A., Guinaudeau, B., Moilanen, A., Rodila, D.D., Sanguet, A., Lehmann A. (in manuscript) Essential Variables for Green Infrastructure across scales and borders.

Honeck, E., Gallagher L., von Arx B., Lehmann A., Wyler N., Villarrubia O., Guinaudeau B., Schlaepfer M.A. (in review) On the use of boundary organizations to integrate conservation science into policymaking. Ecosystem Services

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Chapter 2 : Methods for identifying Green Infrastructure

Based on:

Honeck, E., Sanguet, A., Schlaepfer, M.A., Wyler N., Lehmann A. (2020) Methods for identifying Green Infrastructure. SN Applied Sciences, 2(11), 1916.

https://doi.org/10.1007/s42452-020-03575-4

Erica Honeck¹*, Arthur Sanguet ^1,2*, Martin A. Schlaepfer ^1,3, Nicolas Wyler ², Anthony Lehmann ^1,3

1 University of Geneva, Institute for Environmental Sciences, enviroSPACE Lab, Bd Carl-Vogt 66, CH-1211 Geneva, Switzerland

2 Conservatory and Botanical Garden of the City of Geneva, Switzerland, 1 ch. de l’Impératrice, CH-1292 Chambésy, Switzerland

3 University of Geneva, Department F.-A. Forel of Environmental and Aquatic Sciences, Bd Carl-Vogt 66, CH-1211 Geneva, Switzerland

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2.1 Introduction

2.1.1 The concept of Green Infrastructure (GI)

As land degradation is one of the major threats to natural habitats and biodiversity (Arthington et al., 2016; Baur and Erhardt, 1995; IPBES, 2018b, 2019), the importance of our natural capital in decision-making must be better emphasized to improve the sustainability of landscape management (Blaikie and Brookfield, 2015). This recognition has led to the concept of ‘Green Infrastructure’ (GI) (Benedict and McMahon, 2006) to help preserve a functional ecosystem network through land use planning. GI describes an interconnected network of natural and semi-natural areas designed and managed to deliver a wide range of ecological, social, and economic benefits (Benedict and McMahon, 2006; European Environment Agency, 2014).

One of the main assets of GI is its focus on landscape multifunctionality, i.e. promoting spatial areas that can serve more than one purpose, such as biodiversity conservation, climate change mitigation, the creation of recreational green spaces, and supplying employment opportunities (European Environment Agency, 2014). While grey infrastructure is often designed for a single function (e.g. habitation, transport, or economy), GI addresses multiple demands and contributes to finding solutions for a range of environmental, social, and economic pressures (Naumann et al., 2011).

Da Silva and Wheeler (2017) have traced the history of the concept of ecosystems as infrastructure, and synthesized the concept of GI as a network of natural, semi-natural areas that are designed and managed at different spatial scales for the preservation of biodiversity and a wide range of ecosystem services (ES), to ensure resilient ecosystems and societies.

To implement a conservation action, planners must know where the most urgent needs are and where actions will deliver optimal results. For this, it is necessary to identify areas where the landscape ensures ecological resilience and habitat quality, helps people and species adapt to climate change, and enhances people’s physical and mental health.

Visualizing priority conservation areas will support decision-makers to optimally allocate limited resources for ecosystem preservation. Having such priority areas mapped out in advance also minimizes conflicts when a key resource or environmental concern is brought up after a development project has been initiated (Firehock, 2015).

However, there is no consensus, neither on its components nor on the method to identify and map GI (Wang and Banzhaf, 2018). Consequently, the concept of GI has been formulated and interpreted in divergent ways, and various concepts and names have emerged to refer to the same idea (Firehock, 2015) e.g.:

- ecological infrastructure, - ecological networks, - greenprints,

- natural asset maps,

- green, blue, brown, black corridors.

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Operational definitions of GI vary not only in the type of habitat they include, but also the biological value-sets that are incorporated. A typology of GI could help provide an overview of this variation.

GI focusing only on biodiversity indicators fails to capture societal values that may resonate with a larger fraction of the population. While the supply of ES implies a minimal level of biodiversity, spatial synergies among different ES, or between ES and biodiversity may be weak (Cimon-Morin et al., 2013). In some specific eco-regions, areas with high species diversity provide more ES than areas with low levels of diversity (Maestre et al., 2012), but this is not always the case (Manhães et al., 2016a). In addition, implementing conservation actions based only on habitats or abiotic surrogates may lack coverage of rare or functionally important species, since similar habitats can be biologically different (Virtanen et al., 2018).

As ES locations may differ from where they are supplied to where they are consumed, their integration in GI requires special care on the type of connectivity involved in their treatment (Kukkala and Moilanen, 2017). Therefore, priority areas for ES supply and biodiversity distribution should be analyzed separately, as they are not appropriate surrogates for each other.

This chapter explores how a GI, integrating both multidimensional biodiversity and several ES indicators, could be implemented.

Snäll et al. (2016) and the European Environment Agency (2014) among others have argued that a functional GI network will require the integration of three main aspects, which will be referred to as ‘pillars’ in this thesis (Figure 1):

i. species and habitat diversity (referred to as “biodiversity”): biodiversity is the variability of living organisms at various scales from genes, to species and landscapes (CBD, 1992).

ii. ecological structure and connectivity: functional connectivity measures the relative ease of mobility between landscape patches for selected species (Taylor et al., 1993; With et al., 1997), whereas structural connectivity (also named “connectedness”) refers to the structural links or topological distance between landscape features (Tischendorf and Fahrig, 2000).

iii. ES supply: ES are nature’s benefits and contributions to our society, economy, and our well-being (Costanza et al., 1997; MEA, 2005).

2.1.2 Objectives

This chapter provides a structured catalog (typology) of existing GI methods and serves as a guide towards possible tool choices for the needs of each case study. The review focused on different GI identification approaches used in case studies that have the same GI definition as mentioned above.

Following the foundations and recommendations of GI identification by Snäll et al. (2016), this chapter analyzes if and how the case studies included the three pillars and how the

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areas were selected to be integrated into the GI network. The methods used to assess the three pillars and the identification of GI itself were evaluated.

These approaches were summarized in levels according to their representativeness and reliability to help future researchers identify the appropriate method for their own work.

This chapter also discusses some hypotheses explaining the observed tendencies in the method’s choice as well as future challenges for GI identification and mapping.

2.2 Methodology

2.2.1 Articles selection

The article search focused on results from Web of Science (searched on the 24.03.2020) using the following topic keywords: “ecosystem service*” AND “biodiversity” AND (“corridor*” or “connect*”) AND “green infrastructure*”. Articles defining GI as strictly urban greening methods or architectural elements were filtered out, to select only articles that interpret GI as a strategically planned network of interlinked natural and semi-natural areas.

The comparison of different GI identification approaches was performed on case studies that assessed each of the three pillars separately for their GI design. The full dataset of references is available in the additional resources (Appendix 1).

2.2.2 Evaluation methods

The analysis of different GI identification approaches focused on the review of the literature (Appendix 1). The methodological review consisted in analyzing for each pillar the type of data used as input, the software and methods used, the quality and quantity of items calculated, modeled, or mapped, the choice of surrogates, the conceptual approach, the representativity and reliability of the results in the context of nature conservation. Following the foundations and recommendations of GI identification by Snäll et al. (2016) and the review of the literature (Appendix 1 - articles with similar GI definition), GI identification approaches were then classified into five levels, according to the complexity of their methods.

The lower level methods represent a GI identification considering one or two pillars, a few surrogates and simplified analysis, and higher-level methods consider all pillars, many surrogates, and a complex methodology to identify GI. The discussion section examines these complexity levels and their relevance.

Having a common baseline to identify and map GI is necessary, since there are as many methods as articles in the literature. The aim of this work is not to evaluate the quality of GI identification method in each article, but to point out general, theoretical, conceptual, and methodological directions to assess each pillar to reach a more reliable, functional, and efficient GI network.

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2.3 Results

2.3.1 Bibliographic search

The topic keywords search in Web of Science resulted in 67 articles (Appendix 1). Those defining GI as strictly urban greening methods or architectural elements were filtered out, leaving 32 articles that interpret GI as a strategically planned network of interlinked natural and semi-natural areas. The search excluded reviews and conceptual papers, to only keep case studies for the evaluation of GI mapping methods. Among them, only seven case studies explicitly took all three pillars into account for their GI design (Table 1).

Once the three pillars are calculated, they must be compiled to perform a spatial selection of the most ecologically valuable areas to build a GI network. This kind of map highlights priority ecological areas where habitats should be conserved and land development avoided, and identifies areas where land changes would have minimal impact on the ecosystem.

Although many use conventional overlay analyses by combining GIS data, Snäll et al.

(2016) argue that priority areas could be optimized with a spatial conservation prioritization (SCP) method. Despite SCP tools being appropriate for GI network mapping, case studies applying them to solve the challenges of spatial planning remain scarce. In fact, among the seven selected case studies that identified GI based on all three pillars, only four used a prioritization method to identify their GI, including two using the SCP tool Marxan.

The literature shows a research gap regarding studies using SCP methods for GI identification: even when including conceptual GI papers, only nine papers used or mentioned “spatial prioritization” (Figure 2). However, among the 27 papers that have used all keywords except the term “green infrastructure”, some have similar approaches while using other terms such as “protected area(s) network” (Andrew et al., 2014; Balbar and Metaxas, 2019; Liang et al., 2018) instead.

Figure 2: Research gaps in GI studies using SCP. Numbers represent the number of results obtained in Web of Science using the corresponding combination of keywords. See Appendix 2 for the exact query.

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Table 1: Approaches used in the seven case studies identifying GI with all three pillars. The table lists the scale of the study, the resolution of the resulting maps, the approach used to compile the pillars into a final GI map, the type of data and methods used for the assessment of each pillar, as well as the surrogates used.

Article

(Hermoso et

al., 2020) (Capotorti et

al., 2019b) (Lanzas et al.,

2019) (Capotorti et al.,

2019a) (Hu et al.,

2018) (Cannas et al.,

2018) (Liquete et al.,

2015)

Information on the study

scale continental/

national local regional local (city) regional regional continental

resolution 10km² 2km² 1km² 1.5 km² 30m² 25m² 1km²

method for final GI network identification

prioritization prioritization prioritization overlap analysis prioritization

with overlay prioritization maximum value of pixels for the pillars

Species and habitat diversity pillar

type of data species occurrences, vegetation map

occurrences (trees, shrubs), species distribution maps, natura2000 protected sites, LULC map

Species distribution maps, LULC map

vegetation map habitat map habitat map habitats (for large mammals)

surrogates 767 vertebrate species, 229 habitats

species richness and conservation concerns of vascular plants, mammals, birds, Amphibians &

reptiles

birds of interest, habitats of interest

vegetation types habitat types habitat types large mammals

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software/

methods compilation of

existing data compilation of existing data

compilation of existing data

vegetation species recognized for their

performance in the provision of air purification service

InVEST’s

“habitat quality” tool

conservation value based on a regional report, natural value including

ecological integrity with InVEST's

"habitat quality"

tool

compilation of existing data

ES pillar

type of data raster maps LULC habitats map

CORINE LULC and Remote Sensing maps

vegetation map habitat map

LULC, habitat types, threats raster map, sources of degradation

maps of indicators

ES types considered

five ES supplies of cultural, supporting and regulating services

supply of cultural, supporting and regulating services

ten ES supplies of regulating, cultural and provisioning services

air purification service (supply and demand)

"biodiversity service equivalent"

including recreation and agriculture

cultural services (recreation, anthropic heritage)

eight ES supplies of regulating, supporting services

software/

methods

none, used the maps directly as inputs for Marxan

ES supplies of each

vegetation type (biophysical table method)

none, used the maps directly as inputs for Marxan

overlap between recognized critical ecosystems, population density, and the particulate matter mean annual concentrations

China ecosystem services evaluation indicator based on surveys

conservation value and natural value including levels of ecosystem functions and capacity to provide ES (InVEST’s

“habitat quality”

tool)

proxy measures

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Ecological structure and

connectivity pillar

type of data

species, ES supplies and habitat maps

LULC

results from biodiversity and ES assessments

vegetation map habitat map

habitat suitability map based on literature, resistance map based on the inverse of habitat suitability

habitats quality

structural

/functional structural structural and

functional structural structural and

functional structural functional-

>corridors (and

structural) functional

Implementing Green Infrastructure: integrating biodiversity, connectivity, and ecosystem services into landscape planning decisions in the Geneva region

Thesis

Reference

Implementing Green Infrastructure: integrating biodiversity, connectivity, and ecosystem services into landscape planning

decisions in the Geneva region

Implementing Green Infrastructure:

integrating biodiversity, connectivity, and ecosystem services into landscape planning decisions in the

Geneva region

THÈSE

Erica Cristine Honeck

Table of contents

Abstract

Abstract

Résumé

Acknowledgments

Acknowledgments

Funding

Chapter 1: Introduction

1.1 Background and fundamental concepts

1.2 Research problem and questions

1.3 Structure of the thesis

1.4 Contributing papers

Chapter 2 : Methods for identifying Green Infrastructure

2.1 Introduction

2.2 Methodology

2.3 Results