Mapping and analysis of landscape diversity

Thesis for the degree for Philosophiae Doctor (PhD)

Mapping and analysis of landscape diversity

Trond Simensen

Natural History Museum

Faculty of Mathematics and Natural Sciences University of Oslo

2020

© Trond Simensen, 2021

Series of dissertations submitted to the

Faculty of Mathematics and Natural Sciences, University of Oslo No. 2385

ISSN 1501-7710

All rights reserved. No part of this publication may be

reproduced or transmitted, in any form or by any means, without permission.

Cover: Hanne Baadsgaard Utigard.

Print production: Reprosentralen, University of Oslo.

I

Contents

Acknowledgements ...III Abstract ... V List of papers ... VII

1 Introduction ...1

2 Aim and structure of the thesis ...4

3 Summary of papers and key findings ...6

4 Discussion ...10

5 Concluding remarks ...16

Glossary ...17

References ...19

Papers I–V ...27

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III

Acknowledgements

A PhD is a collaborative project, and I am grateful to many people for contributing to this thesis. This thesis could not have been written without the supervision of Rune Halvorsen at the University of Oslo’s Natural History Museum. I am grateful to him for his constant encouragement, support and guidance, for sharing his extensive knowledge of ecology and gradient analysis, and for teaching me scientific rigour in data collection, analysis and writing. Likewise, I wish to thank my co-supervisor Lars Erikstad at the Norwegian Institute for Nature Research, for sharing his extensive knowledge of physical geography and GIS analysis, for always providing fresh perspectives, and for his wise guidance throughout the project.

Since the start of the PhD in 2016, I have been affiliated with the Geo-Ecology research group at the Natural History Museum in Oslo. I am grateful to all past and present members of the group for their stimulating collaboration. I would particularly like to thank Anders Bryn and Olav Skarpaas for their inspirational mentoring in my PhD courses, and for introducing me to biogeography and distribution modelling. Furthermore, I would like to thank all my co-authors for their excellent teamwork. In addition to those mentioned above, they include Julien Vollering, Peter Horvath, Harald Bratli and Eva Lieungh.

My PhD endeavour would not have been possible without funding from the Research Council of Norway (RCN) and support from my employer, the Norwegian Environment Agency (NEA). I am particularly grateful to Bjørn Bjørnstad at the NEA for his support in the project’s application stage and its initial phase of development. I would also like to extend my gratitude to Eirin Bjørkvoll, my co-supervisor at the NEA, for her continuous backing and constructive comments at critical stages of my research. In addition, I would especially like to thank Ingunn, Ellen, Jo Halvard, Kjetil, Mehdi, Oddvar, Ole Torbjørn, Pål, Ragnvald, Stig and Terje, along with all my other excellent colleagues at the NEA, for providing such a great working environment.

I am thankful to Arild Lindgaard, Øyvind Bonesrønning and everyone at the Norwegian Biodiversity Information Centre for inspiring collaboration. Additionally, my research was significantly improved by feedback from scientists and professionals from several Norwegian universities, scientific institutions, governmental authorities, consultants and other stakeholders. I am highly grateful to each of them.

I am grateful to RCN for funding my six-month stay at the University of Wisconsin-Madison,

USA. I would like to extend a special thanks to Monica G. Turner for including me in the Ecosystem

and Landscape Ecology Lab during the summer and autumn of 2019, and for making it such a great

experience. I would also like to thank Tyler, Nathan, Kristin, Zak and other American friends and

IV

colleagues for research collaboration, and for the enjoyable time that I had at the lab in Wisconsin and during fieldwork in Montana and Wyoming.

I am deeply grateful to all my family and friends for their support and encouragement. I want to especially thank the most important pillars in my life, my wife, Ingunn, and our two wonderful daughters, Silje and Linnea, for always being such great company.

Trondheim, September 2020

Trond Simensen

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Abstract

There is an increasing need for planning and management strategies that combine the preservation of nature’s diversity with the sustainable use of land resources. Systematically structured knowledge about landscape variation is a prerequisite for knowledge-based spatial planning, nature management and the precision and credibility of global change assessments. Improved methods are required to meet the need for accurate and comprehensive distribution maps of ecosystem and landscape types at the local–regional scale.

The main aim of the thesis is to: 1) explore and, if possible, recommend, enhanced methods to analyse and map landscape diversity, and 2) increase the knowledge of landscape variation in Norway.

The thesis consists of five subprojects, presented in five papers. The five subprojects involve a gradual progression from the establishment of a conceptual and theoretical framework, to the analysis of empirical baseline data, and the use of the established conceptual framework to address research questions around landscape type mapping and the distribution modelling of ecosystem types.

In Paper I, we introduce a concept of ‘ecodiversity’ that simultaneously addresses biotic and abiotic aspects of nature’s variation. We present the EcoSyst framework, a set of general principles and methods for systematising ecological diversity at several levels and scales, from microhabitats and ecosystems to landscapes and ecoregions. The implementation of the EcoSyst framework in Norway for the ecosystem and landscape levels of ecodiversity is provided as a ‘proof of concept’.

Papers II–IV specifically address research questions at the landscape level of ecological diversity. Paper II is a systematic review of 54 contemporary landscape characterisation approaches worldwide. The analysis revealed how methodological approaches differed, and how they related. We found substantial differences between methods rooted in different disciplines and academic traditions.

We concluded that no single method can address all the dimensions of the landscape without important trade-offs, and that multiple landscape characterisation methods are needed to address different purposes and user needs.

Paper III demonstrates the value of gradient analytic methods, rooted in ecological continuum theory, in landscape analysis. We analysed landscape variation (co-occurring landscape elements and properties) from a sample of observation units (landscapes) throughout Norway. The multivariate analyses reveal very clear patterns in landscape element composition. The study showed that

geological diversity, biological diversity and human land use were tightly intertwined at the landscape

level of ecological complexity, and that predominantly abiotic processes controlled and constrained

both biotic processes and human land use. We built a tentative landscape type system, where each type

was defined by a fixed amount of landscape variation, that is the degree of similarity with respect to

landscape element composition.

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In Paper IV, we applied the theoretical principles described in Paper I and the tentative type system obtained in Paper III, to develop the first version of a complete, area-covering, evidence-based landscape type map of Norway. The results of the mapping are presented, including maps and tables summarising the distribution and abundance of each landscape type.

In Paper V, we explored the connection between the ecosystem level and the landscape level of ecological diversity. We used data from field-based ecosystem type mapping and a wide range of environmental variables to build distribution models for nine ecosystem types throughout Norway. We found that most ecosystem types could be predicted reliably and that variables derived from landscape type mapping have the potential to improve distribution modelling of ecosystem types.

The significance of the research is discussed, and limitations are addressed. A wide range of

current and potential applications are demonstrated, and their relevance for environmental monitoring,

spatial planning and nature management is examined. Possible directions for future research and

development are indicated. The thesis contributes to knowledge of the diversity and distribution of

ecosystem types and landscape types throughout Norway. The conceptual approach demonstrated in

the thesis may complement and enhance existing methods used to assess nature’s diversity.

VII

List of papers

The thesis is based on the following papers:

I. Halvorsen, R., Skarpaas, O., Bryn, A., Bratli, H., Erikstad, L., Simensen, T. & Lieungh, E.

2020. Towards a systematics of ecodiversity: The EcoSyst framework. Global Ecology and Biogeography, 29(11), 1887-1906. doi:10.1111/geb.13164

II. Simensen, T., Halvorsen, R. & Erikstad, L. 2018. Methods for landscape characterisation and mapping: A systematic review. Land Use Policy, 75, 557-569.

doi:10.1016/j.landusepol.2018.04.022

III. Simensen, T., Halvorsen, R. & Erikstad, L. 2020. Gradient analysis of landscape variation in Norway. Manuscript under review. Preprint available at bioRxiv, 2020.2006.2019.161372.

doi:10.1101/2020.06.19.161372

IV. Simensen, T., Erikstad, L. & Halvorsen, R. 2021. Diversity and distribution of landscape types in Norway. Norsk Geografisk Tidsskrift - Norwegian Journal of Geography, 1-22.

doi:10.1080/00291951.2021.1892177

V. Simensen, T., Horvath, P., Vollering, J., Erikstad, L., Halvorsen, R. & Bryn, A. 2020.

Composite landscape predictors improve distribution models of ecosystem types. Diversity and Distributions, 26(8), 928-943. doi:10.1111/ddi.13060

Supplementary material for all of the articles are available at the websites for the respective journals

and preprints.

VIII

1

1. Introduction

Landscapes and ecosystems worldwide are being transformed at an increasingly rapid rate (Díaz et al.

2019), to the extent that humans have issued a new geological time period – the Anthropocene – ‘a geology of mankind’ (Ellis 2015). Since the development of land-use policies often implies choices between irreconcilable views on the desired utilisation of a landscape, there is a growing demand for planning and management strategies that combine the protection of Natures diversity with sustainable use of land resources (Kremen & Merenlender 2018).

‘Landscape’

is often regarded as a unifying concept within integrated environmental research (Kienast et al. 2007; Sayer et al. 2013), and landscape approaches to integrated land management have recently gained considerable attention, both in the scientific literature and in other international fora (Leclère 2020). Landscape assessment, classification, and analysis are tools created for making wise decisions about the future of the land (Marsh 2005). Systematically structured knowledge about landscape diversity is a prerequisite for knowledge-based spatial planning and management and the precision and credibility of global change assessments (Hobbs 1997). It is also considered as essential to fulfil obligations set by international conventions such as the European Landscape Convention (Council of Europe 2000; ratified by Norway 2004) and the Norwegian Nature Diversity Act (2009).

The latter aims to protect ‘biological, geological and landscape diversity’ and promote conservation and sustainable use of the ‘full range of variation of habitats and landscape types throughout the nation’. This goal presupposes knowledge about the abundance and spatial distribution of landscape and ecosystem types. Nevertheless, existing maps of landscape variation with nation-wide coverage throughout Norway have generally lacked the thematic and spatial resolution necessary to serve as a relevant knowledge base for e.g., environmental impact assessments at the local–regional scale (i.e.,

~1:50 000, see, e.g., Helland et al. 2015). To ensure better quality and consistency of general landscape descriptions, several Norwegian scientists have called for a more systematic, observer- independent and repeatable framework as a reference and a knowledge base for a multitude of applied purposes (see, e.g., Moen 1999, Strand 2011, and Erikstad et al. 2015).

Grouping similar objects (e.g., ecosystems and landscapes) into types is a powerful way to communicate information effectively because affiliation to type alone will provide an extensive amount of information about any singular individual of that particular type. Since a type in a type system comprises an expected, ‘normal’ amount of variation, affiliation to type is also a useful reference and a good starting point for the assessment of condition, state and the unique character and properties of individual ecosystems and landscapes (see, e.g., Phillips 2007; Fairclough et al. 2018).

Type systems are also a reference and a necessary framework for global change assessments (Bland et al. 2017). Yet, although assigning ecosystems and landscapes to types suitable for mapping is an

1

Note that definitions of key terms and concepts are provided in the glossary at the end of the introductory chapter.

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essential step towards effective ecosystem management; there is no single, agreed-upon way to do this (Keith et al. 2015; Simensen et al. 2018). The establishment of rules and procedures for

systematisation of the elusive ‘higher levels of ecological diversity’ is a challenging process because landscapes share with ecosystems the property that, by and large, their composition, structure and processes vary in a gradual, continuous manner along multiple ‘directions of gradual variation’

(Whittaker 1967). Although classifications of nature must be considered a tool, not a goal in itself, any type-system developed to represent real systems or processes should be based upon a consistent theoretical framework and the best available empirical evidence (see Paper III).

The term ‘landscape’ is understood and applied differently within the various disciplines that lay claim to landscape as a one of their major subjects of interest (e.g., physical geography,

geomorphology, ecology, archaeology, human geography and landscape architecture; Goudie and Viles 2010). There are substantial differences between landscape characterisation methods, and no single method can address all dimensions of the landscape without important trade-offs (Simensen et al. 2018). Consequently, a few initial definitions and demarkations are neccesary. In this thesis we apply a landscape concept rooted in the natural sciences; concerned with the observable, material content of spatially heterogeneous areas, following the tradition of landscape ecology and physical geography (cf. Bailey 2009; Turner & Gardner 2015).

In general, ecological complexity increases in a nonlinear manner from finer towards broader spatial and temporal scales (McGill 2010a). Most landscape patterns result from processes that operate over longer time spans and affect broader spatial scales than ecosystems. Given that landscapes comprise complexity in addition to, and qualitatively different from, ecosystems, type hierarchies needs to be constructed specifically for each level, based on relevant empirical evidence (see Paper III). In this thesis, landscapes and ecosystems are recognised as separate levels within a hierarchy of ecodiversity levels, simultaneously addressing biotic variation (biodiversity) and abiotic variation (geodiversity) in heterogeneous areas (Noss 1990; see Figure 1).

The concept implies that each level of ecological diversity contains subsystems at the level below; landscapes contain ecosystems and other landscape elements, while ecosystems contain species and their environment (Allen & Hoekstra 1992). Importantly, these levels of ecological diversity are not distinct natural entities, but abstractions serving the purpose of describing and understanding complex multidimensional systems with gradual transitions at the spatial scale at which these

phenomena appear. In this context, landscapes contain biotic, abiotic and human-induced subsystems

such as landforms, ecosystems, meta-ecosystem complexes and other landscape elements at spatial

scales at kilometres-wide extents from 10

to 10

m² (often referred to as meso-scale) responding to

abiotic and biotic processes occurring over timespans from 10

to 10

years (Delcourt et al. 1982,

Dikau 1989). This ‘domain of spatial scales’ (see e.g., With 2019) address the ‘landscape’ as perceived

3

by a human observer (Allen & Hoekstra 1992). Hence, the research in the thesis focuses upon spatial scales broader than those traditionally addressed in field-based ecology (Estes et al. 2018), but finer than those applied in studies of ecoregions (Bailey 2014) and macroecology (Heffernan 2014; Rose et al. 2017).

Level Key characteristic

(response) Key source of variation (predictor)

Increasing spatial extent

100 km

Eco- region

Composition of regional-scale landscapes &

ecosystems

Variation along regional environmental gradients resulting

from large-scale geological processes, glacial-interglacial climatic cycles; evolution of biota,

biogeographical processes, etc.

10 km

Land- scape

Composition of landforms, ecosystem types &

other landscape elements

Variation along complex landscape gradients (inner-outer coast, topography, etc.) resulting from

geomorphological processes, regional climatic variation, successions in vegetation cover,

fire regimes, human land-use regimes, etc.

1 km

Eco- system

Species composition (e.g.,

vegetation)

Variation along local environmental complex-gradients

(moisture, pH, etc.), local disturbance regimes;

environmental stress, species competition, etc.

1 m

Micro- habitat

Micro- organisms

Variation in micro-environment

Figure 1. Levels of ecological diversity and corresponding domains of spatial scales. Complex entities

at any particular level in such a hierarchy contain entities one level down in the hierarchy; entities

which themselves are likely to be complex enough to need further reduction to their own component

parts. Spatial and temporal scales are correlated in the sense that observable patterns at higher levels

result from processes that operate over longer time spans and affect broader spatial scales than those at

lower levels. Key characteristic: characteristic of natural variation that provides response variables in

an ecodiversity model for a specific ecodiversity level. Key source of variation: source of variation

that provides predictors in an ecodiversity model for a specific ecodiversity level.

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An understanding of natural variation based upon studies of environmental gradients (e.g., soil properties, moisture, local climate, etc.) and species’ responses to these gradients is supported by evidence from ecosystems all over the world and has prevailed in plant and community ecology for more than 50 years (continuum theory; Whittaker 1967; McGill 2010b). This approach is, however, less commonly applied to understand and describe variation at the landscape level of organisation (Cushman et al. 2010; but see Skånes & Bunce 1997). In this thesis, we hypothesise that the principles derived from continuum theory can be extended to the landscape level of ecological diversity, by multivariate analyses of landscape elements (including ecosystem types) instead of species. We hypothesise that such landscape gradients may potentially be useful in landscape type mapping as well as predictors in distribution modelling (cf. Guisan et al. 2017). This thesis explore methods for

mapping and characterisation of landscape diversity (including the distribution of ecosystem types) rooted in ecological continuum theory.

2. Aim and structure of the thesis

The main aim of this thesis is to: 1) explore and, if possible, recommend, enhanced methods for analysis and mapping of landscape diversity, and 2) increase the knowledge about landscape variation in Norway.

The thesis consists of five subprojects, presented in five papers, each addressing different but related research challenges. The five subprojects involve a gradual progression from the establishment of a conceptual and theoretical framework, to the analysis of empirical baseline data, and the use of the established conceptual framework to address research questions around landscape type mapping and the distribution modelling of ecosystem types (Figure 2).

The first paper in the thesis is a conceptual paper, aiming to provide a set of general principles and methods for systematisation of natural variation at several levels and scales (from microhabitats, via ecosystems to landscapes and ecoregions). Paper I establishes the overarching framework for the following papers. Developed iteratively over almost a decade, Paper I also draws upon the work presented later in the thesis, where the results from project 3 and 4, in particular, is a part of the ‘proof of concept’. Hence, Paper I both sets the stage for the rest of the thesis, and at the same time,

summarises key parts of the thesis, framed within a broader context.

Paper II–IV specifically address research questions at the landscape level of ecological

diversity. Paper II is a review paper that aims to frame the research questions in the thesis in the

context of existing theory and prior landscape research. An essential starting point in this regard is to

understand the intellectual legacies underpinning the various traditions and methods for landscape

characterisation and mapping, and to understand how they differ and how they relate. We aim to

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accomplish this goal by a systematic review of 54 methods for landscape characterisation and mapping from all over the world.

Building on the principles outlined in Paper I, Paper III covers analyses of an extensive data set in great detail and provides the empirical keystone of the thesis. By analysing the total composition of landscape elements and properties from a sample of observation units (landscapes) throughout Norway, we aim to reveal general patterns of landscape variation, and to identify relationships between biodiversity, geodiversity and human land-use. Through these analyses, we also aim to establish the empirical basis for the construction of a landscape type system, according to the principles outlined in Paper I. We have chosen a monographic format for Paper III, to be able to document the analyses with greater depth and in more detail than the standard journal article format allows.

Although the analyses in Paper III reveal general patterns of landscape variation, these analyses are not spatially explicit. However, few patterns in ecology make sense unless viewed in an explicit geographic context (Lomolino et al. 2017). In Paper IV, we aim to apply the theoretical principles described in Paper I and the tentative type system obtained in Paper III, to map landscape diversity with full areal coverage (wall-to-wall) within a specific geographical area. More specifically, the goal of this paper is to create the first version of a landscape type map for Norway, covering coastal, marine and inland areas. Furthermore, we aim to present the data in a user-friendly, publicly available database, suitable for a multitude of applied purposes (e.g., landscape research, spatial planning and environmental management).

In the fifth and final paper, we explore the connection between the ecosystem and the

landscape levels of ecological diversity, outlined in Paper I. We use data from field-based ecosystem-

type mapping and a wide range of environmental variables to build distribution models for nine

ecosystem types throughout Norway. The aim is to test whether distribution models of ecosystem

types are improved by including information from landscape-type maps as additional predictors.

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Figure 2. The five sub-projects in the thesis make up a gradual progression from the establishment of a conceptual and theoretical framework, via analysis of empirical baseline data, to use of the concept in applied research questions such as landscape type mapping and distribution modelling.

3. Summary of papers and key results

Paper I

Background and aim: Although a standard taxonomy of organisms has existed for nearly 300 years, no consensus has yet been reached on principles for systematisation of ecological diversity (i.e., the co-ordinated variation of abiotic and biotic components of natural diversity). In a rapidly changing world, where nature is under constant pressure, standardised terms and methods for characterisation of ecological diversity are urgently needed (e.g., to enhance precision and credibility of global change assessments). The aim is to present the EcoSyst framework, a set of general principles and methods for systematisation of natural diversity that simultaneously addresses biotic and abiotic variation, and to discuss perspectives opened by this framework.

Innovation: EcoSyst provides a framework for systematising natural variation in a consistent manner across different levels of organisation. At each ecodiversity level, EcoSyst principles can be used to establish: (a) an extensive attribute system with descriptive variables that cover all relevant sources of variation; (b) a hierarchical-type system; and (c) a set of guidelines for land-cover mapping that is consistent across spatial scales. EcoSyst type systems can be conceptualised as multidimensional models, by which a key characteristic (the response) is related to variation in one or more key sources of variation (predictors). EcoSyst type hierarchies are developed by a gradient-based iterative

procedure, by which the ‘ecodiversity distance’ (i.e., the extent to which the key characteristic differs between adjacent candidate types) is standardised and the ecological processes behind observed patterns are explicitly taken into account.

Paper I

Concept

paper

of concept

Paper II

Systematic

review

logical context

Paper III

Empirical

basis

Gradient analysis of

landscape variation

Paper IV

Appli- cation I

Landscape type mapping

Paper V

Appli- cation II

ecosystem types

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Application: We present ‘Nature in Norway’ (NiN), an implementation of the EcoSyst framework for Norway for the ecosystem and landscape levels of ecodiversity. Examples of applications to research and management are given.

Conclusion: The EcoSyst framework provides a theoretical platform, principles and methods that can complement and enhance initiatives towards a global-scale systematics of ecodiversity.

Paper II

Background and aim: Due to the multidisciplinary nature of landscape research, many different systems and methods for landscape identification and classification exist. This paper provides a systematic review of 54 contemporary landscape characterisation approaches from all over the world, with the aim of identifying major methodological strategies.

Methods: Based on data obtained from a literature search, we used multivariate statistical analyses to identify relationships and differences between methods due to landscape concept applied, the degree of observer independence and various other factors involved in the landscape characterisation process.

Results: Our review confirmed a major distinction between approaches rooted in the natural sciences and approaches rooted in the arts and the humanities. Three substantially different methodological approaches or strategies were identified: 1) ‘holistic’ landscape character assessment approaches, by which visual perception and socio-cultural aspects of the landscape are emphasised; 2) landscape characterisation methods based on a priori selection of geo-ecological and land-use-related properties of the landscape; and 3) biophysical landscape characterisation approaches which rely strongly on statistical analyses in order to identify gradients of variation in the presence and/or abundance of landscape elements and properties. Assessment of landform and the composition of natural and human landscape elements was a central part of all of the reviewed methods. A trend towards increasing observer-independence over time was identified.

Conclusion: Although ‘landscape’ is often regarded as a unifying and interdisciplinary concept, our

review indicates that there are substantial differences between landscape characterisation methods, and

that no single method can address all dimensions of the landscape without important trade-offs. As the

choice of methodological approach will directly determine, and often constrain, the applicability and

usefulness of the resulting typologies for applied purposes, multiple landscape characterisation

methods are needed in order to address different purposes and user needs.

8 Paper III

Background and aim: A multitude of landscape characterisation and mapping methods exist, but few methods consider that landscapes properties vary in a gradual, continuous manner along multiple

‘directions of variation’. An understanding of natural variation based upon knowledge about environmental gradients and species’ responses to these gradients is supported by evidence from ecosystems all over the world and has prevailed in plant and community ecology for more than 50 years. Gradient analysis is, however, less commonly applied to understand and describe variation at the landscape level of organisation. In this study, we used gradient analytic methods, rooted in ecological continuum theory, to analyse landscape variation throughout Norway. The aim was to explain differences in landscape properties in the simplest possible way, by identifying ‘complex landscape gradients’ (CLGs), i.e., composite gradients of co-occurring landscape elements and properties.

Methods: We collected data by a stratified sampling of 100 test areas (20×20 km), in which we delineated a total of 3966 observation units (landscape polygons 4–30 km²) based on

geomorphological criteria. For each observation unit, 85 landscape variables were recorded. We identified patterns of variation in landscape element composition by parallel use of two multivariate statistical methods, detrended correspondence analysis (DCA) and global nonmetric multidimensional scaling (GNMDS).

Results: The analyses revealed that the most essential properties explaining differences in total landscape elements composition were the location of the landscape relative to the coastline (an inland- coastal gradient) and coarse-scale landform variation. Most landscape elements had distinct optima within specific segments along broad-scale complex-gradients in landscape properties. A tentative landscape-type hierarchy was built by an iterative procedure by which the amount of compositional turnover in landscape-element composition between adjacent types was standardised. Six ‘major landscape types’ were identified based on geomorphological criteria. Within each major type, we identified a unique set of 2–5 important CLGs, representing geo-ecological, bio-ecological, and land use-related landscape variation. These CLGs expressed variation in: 1) coastal– inland properties, 2) topography (e.g., relative relief), 3) soil properties; 4) freshwater lake properties; 5) abundance of wetlands; 6) vegetation cover; and 7) land use intensity. Minor landscape types were obtained by combining segments along two or more CLGs.

Conclusions: The study shows that geological diversity, biological diversity and human land-use are

tightly intertwined at the landscape level of ecological complexity, and indicate that predominantly

abiotic processes control and constrain both biotic processes and human land use.

9 Paper IV

Background and aim: Systematically structured information about observable landscape variation is required for knowledge-based management of landscape diversity. However, National landscape characterisation efforts have mostly lacked the thematic and spatial resolution necessary to serve as a relevant knowledge base for land-use policies and environmental impact assessments at the local–

regional scale. Here we present the development of the first version of a complete, area-covering, evidence-based landscape-type map of Norway, simultaneously addressing geo-ecological, bio- ecological and land-use related variation at the landscape level.

Methods: We applied map algebra operations on publicly available geographical data sets with full areal coverage for Norway. The type system used in the mapping is supported by systematically structured empirical evidence. We summarised key properties of landscape diversity (abundance and areal coverage) for each identified landscape type.

Results: The results provide new insights about the cover and distribution of marine, coastal and inland landscapes throughout Norway. We identified nine major landscape types based on coarse-scale landform variation. Within the inland and coastal major types, we mapped the distribution of 284 minor-landscape types based on the combined composition of geo-ecological, bio-ecological, and land use-related landscape properties. We discuss potential errors, uncertainties and limitations of the maps, and address the potential value of this new data set for research, management and planning purposes.

Main conclusions: We have demonstrated that the general theoretical principles presented by Halvorsen et al. (2016; 2020), with some adaptions, are applicable in semi-automated, spatially explicit mapping at a relatively detailed level across large regions encompassing a considerable amount of landscape variation.

Paper V

Background and aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent

gradients in landscape element composition derived from multivariate analyses, for example ‘inner-

outer coast’ and ‘land-use intensity’.

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Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data.

Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models.

Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.

4. Discussion

Key results and implications

In this thesis, a general and universal framework for systematisation of ecological diversity across different levels of organisation is presented (Paper I). This framework is applied in analyses of landscape variation throughout Norway (Paper III), and tested in applied purposes, such as landscape type mapping (Paper IV) and distribution modelling (Paper V). Key results include a series of maps indicating the spatial distribution of ecosystem types, landscape types, and variation along landscape gradients throughout the country at the local-regional scale (~ 1:50 000). Hence, the thesis contributes to general knowledge about the diversity and distribution of ecosystem and landscape types

throughout our country, with results of direct interest for applications in research, planning and nature management.

The statistical (Paper III) and the spatial (Paper IV) analyses revealed very clear patterns of

landscape variation throughout Norway. We have identified predictable, non-random patterns of co-

occurrence between various landscape elements and properties, and we have quantified these

relationships. We found that most landscape elements tend to have distinct performance optima

(probability of presence and/or abundance) along broad-scale compositional gradients. Hence, the

results demonstrate how geomorphology, ecology and human land use are tightly intertwined at the

landscape level of ecological diversity. The order of historical events and processes that have brought

about landscape variation is well established by geological, paleoecological and historical evidence

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(Delcourt et al. 1982, Birks 1993). Hence, Paper III indicate that predominantly abiotic conditions, largely control and constrain patterns derived from biotic processes as well as from human land use.

The methods presented and applied in the thesis comply with a general trend towards more observer-independent methods within biophysical landscape characterisation and mapping (see Paper II). The results provide a strong indication that gradient analysis, commonly applied in research at the ecosystem level, is a conceptual framework also appropriate for studies patterns in landscape element composition. Gradient analysis at the landscape level is, to a large extent, still unexplored (but see Skånes & Bunce 1997; Luck & Wu 2002). In landscape ecology, gradient analysis has mostly been applied to single continuous variables as an alternative to landscape metrics based on categorical landscape data (Cushman et al. 2010; Lausch et al. 2015), or multivariate analysis of variables representing landscape structure (e.g. Schindler et al. 2008). Paper III shows that ordination methods allowed for flexible analyses of landscape variables derived from a wide variety of data sources: (i) count data (e.g., buildings); (ii) areal coverage of various land-cover types; and (iii) continuous variables derived from direct measurements and remote sensing (e.g., the digital elevation model).

Multivariate analyses of landscape variation proved to be a suitable approach to reduce the dimensions in a complex data material and to reveal relationships inherent in the study material but otherwise unrecognisable. The approach allowed us to identify and explain variation in landscape properties in the simplest possible way, by identifying major, and largely independent gradients of landscape variation.

The ‘universality’ of the CLGs and their potential for model extrapolation in time and space (e.g., to Scandinavia and other boreal regions) is therefore largely unknown. The composite and ‘data- driven’ nature of the CLGs may theoretically limit their potential use for model projections and extrapolations in time and space (see Paper V). However, many of the landscape gradients recognised in our study are probably relevant over vast areas and likely to persist for long times, even in a

changing climate (e.g., terrain gradients, coast-inland gradients, gradients in human land-use intensity, gradual changes in vegetation cover, the abundance of lakes, etc.; see Paper II). We suggest that the distribution of ecosystems and other landscape elements along complex-gradients in the landscape should be explored across larger areas, as such studies may yield new insights of both theoretical and practical importance.

The results allowed for the construction of the first version of an evidence-based landscape

type system for Norway (Paper III) and application in semi-automated landscape type mapping (i.e.,

geocomputations; see Paper IV). The final maps describe Norwegian landscape variation at the local-

regional scale (~ 1:50 000). In contrast to the multivariate statistical analyses underpinning the

landscape type system; the landscape-type maps presented in the thesis are spatially explicit and

intended to be easy to interpret for planners, managers and the interested lay citizen. Accompanied by

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short textual descriptions of types, gradients and landscape elements, the database constitutes a publicly available knowledge base suitable for several applied purposes (see discussion below). As such, it is also one of the first ‘ecological base-maps’ with national coverage, in a series of maps of ecosystems, landscapes and environmental variables (cf. Ministry of Climate and Environment of Norway 2015).

The geographical result of the thesis add new details and new perspectives that complement classical studies of physical geography, regional geography and vegetation geography throughout Norway (see, e.g., Rudberg 1960; Moen 1999; Puschmann 2005; Bakkestuen et al. 2008). The thesis provides strong support for a division of the study area (Norway) into six ‘major landscape types’ by geomorphometric criteria: inland hills and mountains; inland valleys; inland fine-sediment plains;

other inland plains; coastal plains; and coastal fjords. In a global context, the major landscape types identified in our study largely resemble commonly applied classifications of macro- and meso-scale landforms (e.g., plains, hills and mountains) based on surface geometry, in the tradition of Hammond (1954), Dikau (1989) and Sayre et al. (2019). The complex landscape gradients show variation in landscape elemtent composition as a response to geomorphological processes (Ramberg et al. 2008), regional climatic variation (Bakkestuen et al. 2008), and variation in land-use (Moen 1999). Our quantitative approach provides strong empirical evidence for the relationship between the variables, i.e., the relative importance of each complex landscape gradient within various parts of the country.

Paper V establishes distribution modelling as a viable tool for spatial prediction of ecosystem types on a regional scale and at a relatively fine (100 m) spatial resolution, provided relevant predictor variables are available. The results support the conclusion of Ferrier & Guisan (2006) that distribution modelling of communities and ecosystems deserves to be used more often, and more widely, as an alternative or a supplement to modelling of individual species. Moreover, the study indicate a clear relationship between the landscape and the ecosystem level of ecological diversity, and show that CLGs extracted by ordination of landscape compositional data may be of predictive significance also for single ecosystem types that were not subject to prior ordination. The use of CLGs as predictors in distribution models for ecosystem types may constitute a route to statistically simpler models by reducing the number of model parameters, without introducing unsupported assumptions about simple cause–effect relationships (see Merow et al. 2014).

Limitations, errors and uncertainties

Challenges related to a possible application of the EcoSyst framework in a wider setting, are addressed

in Paper I, while other possible errors and uncertainties are discussed in detail in each paper. Here, a

few more general limitations that also relate to future directions in landscape research are highlighted.

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Both the process of assigning complex landscape variation to types, as well as mapping these types at a predefined spatial scale, imply a certain degree of generalisation (Hazeu et al. 2011).

Notably, the maps and analyses presented here must be considered as a systematisation of existing available knowledge, constrained by the scale of the analyses, the selection of data and the applied methods. Accordingly, any EcoSyst type hierarchy or mappresented in the thesis is a testable

hypothesis that can be challenged, subjected to new tests with new data, and improved over time (see Paper I). The maps and analyses presented in the thesis are models, i.e., abstract and imperfect representations of reality based on simplifying assumptions. Several steps in the analyses and the modelling processes are prone to errors, uncertainties, potential biases, incomplete data, scaling issues and other pitfalls in modelling (see Turner et al. 2015; Guisan et al. 2017). Despite strong efforts to make the approach to landscape analysis presented here observer-independent, the results of the analyses depend on the selection of data sources and other subjective choices made during the analytic procedure (Yang 2020).

Landscapes are shaped by the combined, interacting effects of multiple environmental controls and drivers (factors that directly or indirectly causes a change), including both deterministic and stochastic processes (Phillips 2007). While landscapes change continously over time (Plieninger et al.

2018), our analyses are static in the sense that they show patterns in landscape composition at one particular time-point. Hence, the CLGs and the landscape types identified in this study express variation in the distribution and abundance of observable landscape elements and properties, and do not explicitly address the processes behind these patterns. In this sense, they are parallel to the

‘coenoclines’ of Whittaker (1960), the species compositional gradients, which do not explicitly take the conditioning environmental complex-into account and therefore do not allow direct mechanistic modelling of the processes which give rise to the observed patterns. Nevertheless, the identification of CLGs opens for further studies of the relative magnitude of the drivers behind landscape variation (cf.

Bürgi et al. 2004), and the response to these drivers as expressed in landscape element composition (Paper III; see aslo ‘future perspectives’ below).

Our landscape models partition variation into ‘explained’ variation and residual, ‘unexplained’

or ‘apparently random’ variation (Paper III). This motivates for a standardised attribute system at the landscape level by which the residual variation can be described thoroughly in a standardised way (see Paper I). Data on the presence, absence and relative abundance of major ecosystem types (e.g.,

exposed ridge, open fen, mire and swamp forest) and microrelief landforms (e.g., gullies, dolines, sand dunes and terraces) can be included in an attribute system for the landscape level with better data.

Landscape pattern inludes both composition and configuration (Turner & Gardner 2015).

Since the studies in the thesis are mainly concerned with landscape-element composition, variables

that describe landscape structure and function (Turner & Gardner 2015) needs to be addressed

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separately. This can be accomplished by e.g. the calculation of ‘landscape metrics’ for quantification of the spatial configuration of categorical landscape patterns in (see Schindler 2008; Hesselbarth et al.

2019).

Since landscape type maps, as well as distribution models of ecosystem types represent indicative, rather than absolute knowledge, uncertainties and potential errors must be considered for any application in research, planning and management (Sofaer et al. 2019). In a broader perspective, it is also essential to acknowledge the implicit limitations associated with landscape characterisation approaches restricted to selcted material aspects and properties of the landscape (see Paper II).

Landscapes are characterised by complexity, change, and scale dependencies (Turner & Gardner 2015). Simple cause-effect relationships are unlikely in landscape systems. As indicated in Paper II, this argues for a pluralism of approaches that embraces this complexity and recognises the

complementary roles of different research methods.

Applications in research, management and planning

The distribution models of ecosystem types and the landscape data presented in the thesis, available at NBIC (2019), are already used for several applied purposes, and may have additional potential

applications. The distribution models of ecosystem types presented in Paper V are suitable for use as a supporting tool in red-list assessments (see NBIC 2018) as well as for the planning of field-based mapping of red-listed ecosystem types (Norwegian Environment Agency 2019).

The landscape data (Paper III an IV) constitutes a resource for basic landscape research and communication of knowledge about landscape variation. With some adaptions, the landscape type maps provide a framework for monitoring landscape changes, and to relate these changes to landscape policies and landscape quality objectives (cf. Kienast et al. 2015; Walz et al. 2015). As suggested for Norway by Erikstad et al. (2014), changes in the complex landscape gradients can be monitored directly (e.g., land-use intensity) or by relating landscape change (cf. Senf & Seidl 2020) to landscape type.

Landscape type maps are also a useful supporting tool for conservation planning (Beier et al.

2015; Baldwin et al. 2018), provided the introduction of explicit value criteria such as rarity, representativity, etc. (see Erikstad et al. 2008).

In line with Warnock & Griffiths (2014) and Yang et al. (2020), we suggest that landscape

type maps are a useful knowledge base for subsequent holistic landscape character assessments

(Fairclough et al. 2018). Landscape character assessment methods aim to integrate natural and cultural

aspects of landscapes, and people’s perceptions, whilst forming a spatial framework for planning and

conservation (see Paper II). Spatially explicit landscape-type maps open for interesting opportunities

15

to study human perception of landscapes (cf. Tveit et al. 2006; Hunziker et al. 2008). The landscape data are also suitable as a knowledge-base for spatial planning and environmental impact assessment at the local–regional level, often in combination with landscape character assessment (see, e.g., Norwegian Public Roads Administration 2018). Erikstad et al. (2020) have shown that the landscape type maps presented here can be applied successfully in strategic environmental assessments (SEA).

The scale of the landscape type maps and the extent of the spatial landscape units, make them well suited as a spatial framework for informed citizen participation including negotiating landscape values (Solecka 2018) and the development of landscape policies and setting landscape quality objectives (i.e., agreed-upon goals for the development a particular landscape, see Council of Europe 2000;

Kremen & Merenlender 2018).

Future perspectives

Future research on the systematisation of ecological diversity will likely benefit from conceptual and methodological improvements, as well as well as rapid development of computing power and available data. Standardised general scripts, tools and packages (cf. Vollering et al. 2019) might e.g., simplify and streamline future analyses and mapping processes and facilitate increased repeatability and observer-independence. Better availability of systematically sampled ecologically relevant field data (cf. Bryn et al 2018) will allow a better empirical basis for the establishment of type hierarchies as well as applications of mapping and distribution modelling at broader spatial extents. Furthermore, the elements that characterise landscapes are widely distributed and can be identified and analysed by high-quality remote sensing data such as satellite data and LIDAR ( Light Detection And Ranging; see Zarnetske et al. 2019). Variables derived from remote sensing may also be useful as additional predictors in distribution models for ecosystem types and various other landscape elements (cf.

Zimmermann et al. 2007). Better data from marine mapping programs (MAREANO; Buhl-Mortensen et al. 2015) will open for development of marine landscape type maps also at the minor-type level (see, e.g. Harris et al. 2014). Hence, fine-grained landscape-type maps with global extent may be in reach within a few years, as demonstrated by recent publications (Sayre et al. 2019; Jung et al. 2020).

Application of the principles applied in the thesis, may complement and enhance such initiatives.

Maps showing current observable patterns in landscape element composition, are also a good starting point and a reference for studies of the historical processes (the driving forces) that have caused these patterns, (see, e.g., Bürgi 2004; Turner & Gardner 2015; Plieninger et al. 2016), rate of change (cf. Schneeberger et al. 2007) or landscape change trajectories (cf. Käyhkö & Skånes 2005).

The increasing availability of spatially explicit historical data on geology and climate (Kriticos et al.

2012), historical biodiversity (Svenning et al. 2011) as well as human land-use (e.g., Klein Goldewijck

16

et al. 2011) open for exciting studies of present landscape variation as a response to historical abiotic, biotic and land-use related processes.

There are still significant gaps in our knowledge of the linkages between processes and patterns in long term global change studies (Alahuhta et al. 2019). Progress in understanding patterns, directions and tempos of landscape change requires strong integration of scientific approaches (theory, observations, experiments and process-based models) and high-quality empirical data (Myers-Smith et al. 2020; Turner et al. 2020). Use of dynamic, process-based simulation models will allow prediction of landscape change through time as a function of e.g., disturbances and succession (see e.g., Seidl et al. 2012). Such models may significantly improve our ability to develop scenarios for future landscape change, inlcuding large-scale ecological responses to global climate change (cf. Turner et al. 2019).

5. Concluding remarks

Research questions in geography and landscape ecology require interdisciplinary answers from several scales (Estes et al. 2018). In the study presented here, we have provided a synopsis of current

knowledge of landscape diversity in Norway by compiling and analysing data from comprehensive

sources of information at the ecosystem and landscape level. We have demonstrated that, with some

adaptions, the general theoretical principles proposed in Paper I can be operationalised as a semi-

automated, spatially explicit procedure for mapping at a relatively detailed level across large regions

encompassing a considerable amount of landscape variation. The research and development presented

in the thesis may have theoretical as well as practical applications, and may contribute to improved

knowledge about the diversity and distribution of ecosystems and landscapes. Such knowledge is

essential for planning and management strategies since the ecological diversity of our planet cannot be

managed species by species.

17

Glossary and definitions of terms and concepts

Abiotic the non-living chemical and physical environment that is not associated with living organisms

Biodiversity the biotic aspect of natural variation, on levels of organisation from biotic communities via species and populations to genes, and the processes that give rise to variation in their structure and composition

Biotic associated with, or derived from, living organisms

Continuum theory a unified theory of biodiversity, emphasising continuous variation in species composition along continuous environmental complex-gradients

Complex landscape gradient

abstract, continuous variable that expresses more or less gradual, co- ordinated change in a set of more or less strongly correlated landscape variables

Composition the relative performance of different observable objects within a spatial unit Diversity the richness and variety of object categories, including number of different

categories and their degree of presence

Ecology the study of the relationships between living organisms and their physical environment

Ecosystem a more or less uniform area, comprising all organisms, the total environment they live in and are adapted to, and the processes that regulate relationships among organisms and between organisms and the environment (natural, or dependent on or shaped by human activities)

Ecosystem type recurrent abstract units of assessment that represent complexes of organisms and their associated physical environment within an area

Ecodiversity diversity of units defined by biotic and abiotic components and their interactions, and the processes that give rise to variation in the structure and composition of these components.

Geodiversity the abiotic features of natural variation, including the lithosphere,

atmosphere, hydrosphere and cryosphere, with diversity levels exemplified by minerals, bedrock and landforms, and the processes that give rise to variation in their structure and composition

Geomorphometry the science of quantitative analysis of the Earth’s surface

Gradient the more or less gradual variation of a property of the environment, or in the composition of components

Hierarchy system of interconnections or organisation wherein the higher levels constrain and control the lower levels to various degrees depending on time constraints of the behaviour

Landscape a more or less uniform area including multiple ecosystems, aquatic and

terrestrial, characterised by its content of observable, natural and human-

induced landscape elements (i.e., natural or human-induced objects or

18

characteristics), including spatial units assigned to types at an ecodiversity level lower than the landscape level, which can be identified and observed on a spatial scale relevant for the landscape level of ecodiversity

Landscape diversity the richness and variety of landscapes, including the number and degree of presence of different landscape types

Landscape element natural or human-induced object or characteristic, including spatial units assigned to types at an ecodiversity level lower than the landscape level, which can be identified and observed on a spatial scale relevant for the landscape level of ecodiversity

Landscape level refers to the landscape as a ‘level of organisation’ within a hierarcically organised ecological system. The level of organisation is quantified by a rank ordering relative to other levels in the system

Landscape scale in this context, the landscape scale refers to spatial scales at kilometres-wide extents from 10

to 10

m² (often referred to as meso-scale) responding to abiotic and biotic processes occurring over timespans from 10

to 10

years Landscape type reoccurring and more or less uniform areas characterised by their content of

observable, natural and human-induced landscape elements Landscape type

mapping

the process of assigning spatial units (geographical areas) to abstract landscape types, based on similarity in landscape properties

Level of organisation the place within a hierarchy (e.g., cell – organism, population, community;

or microhabitat – ecosystem – landscape – ecoregion)

Observer-independent a transparent and repeatable method, in the sense that any person, accepting the method and the evidence, is likely to reach the same conclusion in the study

Pattern the non-random, repetitive variation of focal elements among units or along relevant gradients

Scale spatial or temporal dimension of an object or process characterised by both grain and extent. Grain refers to the finest spatial resolution within a given data set or a map (e.g., cell size in a raster grid or minimum mapping unit size for polygons). Extent refers to the size of the overall study area Spatial relating to or space and the position, size and shape of objects

Spatial planning the coordination of practices and policies affecting spatial organisation.

Synonymous with the practices of ‘land-use planning’ and ‘urban and regional planning’

Spatial unit geographically delimited area or site

Type category in a system established with the purpose of systematising variation,

defined as an abstract ideal

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