Spatial Planning and Connectivity Corpus - Technical Background Report

IPBES Spatial Planning and Connectivity Assessment

Abstract

To Be added

Disclaimer

LLM Usage Disclosure

Code and charts in this project have been generated with the assistance of the codex LLM tools in Positron. All content and code is based on conceptuaisation by the authors and has been thoroughly reviewed and edited by humans afterwards.

Contributors

Assessment Experts

• xxx, yyy

Data and Knowledge tsu

Working Title

IPBES_SPC_Corpus

Code repo

Github repository

Introduction

The literature search for the Spatial Planning and Connectivity assessment corpus was conducted using search terms provided by the experts and refined in co-operation with the IPBES task force for data and knowledge management. The search was conducted using OpenAlex, scripted from R to use the OpenAlex API. Search terms for the following searches were defined:

• Spatial Planning and Connectivity,
• Nature / Environment
• additional search terms for specific corpora

To assess the quality of the corpus, sets of key papers were selected by the experts to verify if these are in the corpus.

The following terminology is used in this document:

• Corpus: A body of works as based on a search on OpenAlex
• Spatial Planning and Connectivity Assessment Corpus: Short: SPC corpus; The corpus resulting from the search terms TO BE ADDED
• work: terminology used for a single document in their dataset. Each work has a unique OpenAlex id, but not necessarily a DOI.

The following searches are conducted on Title and Abstract only to account for fluctuating availability of full text searches and make the search more focussed..

Schematic Overview

This is a schematic overview of the search strategy. It is shown again in the Results section with the numbers of resulting hits. ### Overview

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart TD
    Start([Start literature search]) --> SPC["<u><span style='color:#1a0dab;'>spc_corpus.yaml<br/>Assemble base SPC corpus</span></u>"]
    click SPC "./input/search_terms/spc_corpus.yaml" "Open spc_corpus.yaml"
    SPC --> SPC_list["spc keyword set<br/>(planning & connectivity terms)<br/>(n = <<SPC_LIST>>)"
]
    SPC --> NATURE_list["nature keyword set<br/>(environmental context terms)<br/>(n = <<NATURE_LIST>>)"
]
    SPC_list --> BaseQuery["Level 1 query<br/>spc terms AND nature dictionary<br/>(n = <<BASE_QUERY>>)"
]
    NATURE_list --> BaseQuery

    BaseQuery --> ChapterSelect{Apply chapter / theme refinements}

    ChapterSelect --> CH1["<u><span style='color:#1a0dab;'>Chapter 1<br/>chapter_1.yaml<br/>Governance & planning principles</span></u>"]
    click CH1 "./input/search_terms/chapter_1.yaml" "Open chapter_1.yaml"
    ChapterSelect --> CH2["<u><span style='color:#1a0dab;'>Chapter 2<br/>chapter_2.yaml<br/>GBF targets, nexus themes, SDGs</span></u>"]
    click CH2 "./input/search_terms/chapter_2.yaml" "Open chapter_2.yaml"
    ChapterSelect --> CH3["<u><span style='color:#1a0dab;'>Chapter 3<br/>chapter_3.yaml<br/>Restoration & conservation planning</span></u>"]
    click CH3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"
    ChapterSelect --> CH4["<u><span style='color:#1a0dab;'>Chapter 4<br/>chapter_4.yaml<br/>Connectivity evidence & metrics</span></u>"]
    click CH4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"
    ChapterSelect --> CH5["<u><span style='color:#1a0dab;'>Chapter 5<br/>chapter_5.yaml<br/>Foresight & futures (sections, themes, cross-cutting)</span></u>"]
    click CH5 "./input/search_terms/Chapter_5_1_2.yaml" "Open Chapter 5 search terms"
    ChapterSelect --> CH6["<u><span style='color:#1a0dab;'>Chapter 6<br/>chapter_6.yaml (+ chapter_6_r2.yaml optional)<br/>Enabling environment</span></u>"]
    click CH6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

Chapter 1

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch1["<u><span style='color:#1a0dab;'>chapter_1.yaml<br/>Level 2 refinements</span></u>"]
    click Ch1 "./input/search_terms/chapter_1.yaml" "Open chapter_1.yaml"

    subgraph Chapter1Sets["Chapter 1 thematic searches"]
        direction TB
        C1_1["Set 1:<br/>land/spatial planning<br/>+ biodiversity goals<br/>+ societal needs/values<br/>(n = <<C1_1>>)"]
        C1_2.1["Set 2.1:<br/>???<br/>(n = <<C1_2.1>>)"]
        C1_2.2["Set 2.2:<br/>???<br/>(n = <<C1_2.2>>)"]
        C1_3["Set 3:<br/>evidence & precaution<br/>+ ILK knowledge base<br/>(n = <<C1_3>>)"]
        %% C1_4["Set 4:<br/>multilevel/transparent governance<br/>+ customary coherence<br/>(n = <<C1_4>>)"]
        %% C1_5["Set 5:<br/>participatory planning<br/>+ co-design / engagement<br/>(n = <<C1_5>>)"]
        %% C1_6["Set 6:<br/>equity / rights / tenure<br/>+ justice outcomes<br/>(n = <<C1_6>>)"]
        %% C1_7["Set 7:<br/>connectivity (land-sea/cross-scale)<br/>+ nexus & climate links<br/>(n = <<C1_7>>)"]
    end

    Ch1 --> C1_1
    Ch1 --> C1_2.1
    Ch1 --> C1_2.2
    Ch1 --> C1_3
    %% Ch1 --> C1_4
    %% Ch1 --> C1_5
    %% Ch1 --> C1_6
    %% Ch1 --> C1_7

Search Terms

Click chapter_1.yaml to download

params$search_terms$chapter_1 |>
  yaml::as.yaml() |>
  cat()

Set 1:
- ((indigenous AND (knowledge OR perspectives OR governance OR rights OR values OR
  practices OR land)) OR "local knowledge" OR biocultural OR customary OR (diverse
  AND ("knowledge systems" OR "world views" OR worldviews))) AND
- (planning OR governance OR policy OR tenure OR rights) AND
- (just OR inclusive OR fair OR effective OR equity OR equitable) AND
- (area OR territory OR "land use" OR "sea use" OR marine OR coastal OR "land-sea")
Set 2.1:
- ("multi-level" OR integrated OR accountable OR customary OR diverse OR "context-dependent"
  OR "cross-scale" OR polycentric OR pluralism OR adaptive) AND
- (governance OR policy OR framework) AND
- ("spatial planning" OR "marine planning" OR "land-use planning" OR "sea-use planning"
  OR "coastal planning" OR "territorial planning")
Set 2.2:
- ("legal pluralism" OR "institutional diversity" OR "polycentric governance" OR "multi-level
  governance" OR "customary governance" OR "co-management" OR "adaptive governance"
  OR "integrated governance" OR "cross-scale governance") AND
- (institution OR authority OR legitimacy) AND
- ("spatial planning" OR "marine planning" OR "land-use planning" OR "sea-use planning"
  OR "coastal planning" OR "territorial planning")
Set 3:
- (participatory OR community OR "co-design" OR stakeholder OR plurality OR tailored
  OR "different contexts" OR transformative OR adaptive OR "place-based" OR integrated
  OR multifunctional OR diverse) AND
- (approach OR strategy OR practice OR plan OR policy OR framework OR instrument)
  AND
- ("spatial planning" OR "marine planning" OR "land-use planning" OR "sea-use planning"
  OR "coastal planning" OR "territorial planning") AND
- (landscape OR seascape OR watershed OR ecosystem OR area OR region OR "land-sea"
  OR connectivity OR marine OR ocean OR maritime)

Chapter 2

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch2L2["<u><span style='color:#1a0dab;'>chapter_2.yaml<br/>Level 2 GBF bundles</span></u>"]
    click Ch2L2 "../search_terms/chapter_2.yaml" "Open chapter_2.yaml"

    subgraph L1_GBF["GBF contexts & targets"]
        direction TB
        GBF_Urban["GBF-1 Urban<br/>(n = <<GBF_Urban>>)"]
        GBF_Rural["GBF-1 Rural<br/>(n = <<GBF_Rural>>)"]
        GBF_Fresh["GBF-1 Freshwater<br/>(n = <<GBF_Fresh>>)"]
        GBF_Marine["GBF-1 Marine<br/>(n = <<GBF_Marine>>)"]
        GBF_Restore["GBF-2 Ecosystem restoration<br/>(n = <<GBF_Restore>>)"]
    end
    Ch2L2 --> GBF_Urban
    Ch2L2 --> GBF_Rural
    Ch2L2 --> GBF_Fresh
    Ch2L2 --> GBF_Marine
    Ch2L2 --> GBF_Restore

    subgraph L1_TARGET["GBF contexts & targets"]
        T3["Target 3<br/>(n = <<T3>>)"]
        T4["Target 4<br/>(n = <<T4>>)"]
        T5["Target 5<br/>(n = <<T5>>)"]
        T6["Target 6<br/>(n = <<T6>>)"]
        T7["Target 7<br/>(n = <<T7>>)"]
        T8["Target 8<br/>(n = <<T8>>)"]
        T9["Target 9<br/>(n = <<T9>>)"]
        T10["Target 10<br/>(n = <<T10>>)"]
        T11["Target 11<br/>(n = <<T11>>)"]
        T12["Target 12<br/>(n = <<T12>>)"]
        T13["Target 13<br/>(n = <<T13>>)"]
        T14["Target 14<br/>(n = <<T14>>)"]
        T15["Target 15<br/>(n = <<T15>>)"]
        T16["Target 16<br/>(n = <<T16>>)"]
        T17["Target 17<br/>(n = <<T17>>)"]
        T18["Target 18<br/>(n = <<T18>>)"]
        T19["Target 19<br/>(n = <<T19>>)"]
        T20["Target 20<br/>(n = <<T20>>)"]
        T21["Target 21<br/>(n = <<T21>>)"]
        T22["Target 22<br/>(n = <<T22>>)"]
        T23["Target 23<br/>(n = <<T23>>)"]
    end
    Ch2L2 --> T3
    Ch2L2 --> T4
    Ch2L2 --> T5
    Ch2L2 --> T6
    Ch2L2 --> T7
    Ch2L2 --> T8
    Ch2L2 --> T9
    Ch2L2 --> T10
    Ch2L2 --> T11
    Ch2L2 --> T12
    Ch2L2 --> T13
    Ch2L2 --> T14
    Ch2L2 --> T15
    Ch2L2 --> T16
    Ch2L2 --> T17
    Ch2L2 --> T18
    Ch2L2 --> T19
    Ch2L2 --> T20
    Ch2L2 --> T21
    Ch2L2 --> T22
    Ch2L2 --> T23

    subgraph L1_REL["Spatial Planning Related"]
        REL["Spatial Planning Related<br/>(n = <<REL>>)"]
    end
    Ch2L2 --> REL

    subgraph L1_NexusSets["Nexus"]
        direction TB
        Nexus_Water["Water<br/>(n = <<Nexus_Water>>)"]
        Nexus_Food["Food<br/>(n = <<Nexus_Food>>)"]
        Nexus_Health["Health<br/>(n = <<Nexus_Health>>)"]
        Nexus_Climate["Climate<br/>(n = <<Nexus_Climate>>)"]
    end
    Ch2L2 --> Nexus_Water
    Ch2L2 --> Nexus_Food
    Ch2L2 --> Nexus_Health
    Ch2L2 --> Nexus_Climate

    L1_GBF --> Ch2L4["<u><span style='color:#1a0dab;'>chapter_2_sdg.yaml<br/>Level 4 SDG filters</span></u>"]
    L1_NexusSets --> Ch2L4
    L1_REL --> Ch2L4
    click Ch2L4 "../search_terms/chapter_2_sdg.yaml" "Open chapter_2_sdg.yaml"
    subgraph SDGSets["SDG goal filters"]
        direction TB
        SDG1["SDG 1"]
        SDG2["SDG 2"]
        SDG3["SDG 3"]
        SDG4["SDG 4"]
        SDG5["SDG 5"]
        SDG6["SDG 6"]
        SDG7["SDG 7"]
        SDG8["SDG 8"]
        SDG9["SDG 9"]
        SDG10["SDG 10"]
        SDG11["SDG 11"]
        SDG12["SDG 12"]
        SDG13["SDG 13"]
        SDG14["SDG 14"]
        SDG15["SDG 15"]
        SDG16["SDG 16"]
        SDG17["SDG 17"]
    end
    Ch2L4 --> SDG1
    Ch2L4 --> SDG2
    Ch2L4 --> SDG3
    Ch2L4 --> SDG4
    Ch2L4 --> SDG5
    Ch2L4 --> SDG6
    Ch2L4 --> SDG7
    Ch2L4 --> SDG8
    Ch2L4 --> SDG9
    Ch2L4 --> SDG10
    Ch2L4 --> SDG11
    Ch2L4 --> SDG12
    Ch2L4 --> SDG13
    Ch2L4 --> SDG14
    Ch2L4 --> SDG15
    Ch2L4 --> SDG16
    Ch2L4 --> SDG17

Search Terms

Click chapter_2.yaml to download

params$search_terms$chapter_2 |>
  yaml::as.yaml() |>
  cat()

GBF:
  GBF-1 - Urban:
  - urban OR
  - city OR
  - periurban OR
  - metropolis
  GBF-1 - Rural:
  - rural OR
  - agriculture OR
  - forest OR
  - mountain
  GBF-1 - Freshwater:
  - freshwater OR
  - lake OR
  - stream OR
  - current OR
  - river OR
  - wetland OR
  - watershed OR
  - groundwater OR
  - aquifer OR
  - lagoon OR
  - peatland
  GBF-1 - Marine:
  - sea OR
  - marine OR
  - ocean OR
  - coast OR
  - mangrove OR
  - seagrass OR
  - saltwater OR
  - '"brackish water" OR'
  - '"tidal flats" OR'
  - '"intertidal zone" OR'
  - subtidal
  GBF - 2 (Ecosystem restoration):
  - restoration OR
  - degradation OR
  - regeneration OR
  - '"ecosystem rehabilitation" OR'
  - '"environmental rehabilitation" OR'
  - rewilding OR
  - abandonment OR
  - '"let nature do the job" OR'
  - renaturalization OR
  - reclamation OR
  - conservation OR
  - protection
Targets:
  Target 3 (Protected areas / conservation coverage):
  - '"protected areas" OR'
  - conservation OR
  - connectivity OR
  - '"biodiversity hot spots" OR'
  - OECMs
  Target 4 (Species extinction & genetic diversity):
  - extinction OR
  - '"human-wildlife conflict" OR'
  - '"genetic diversity" OR'
  - '"Red List of Threated Species" OR'
  - '"Red List of Ecosystems" OR'
  - RLE
  Target 5 (Wild species trade & use):
  - '"Wildlife trade" OR'
  - harvesting OR
  - overexploitation OR
  - poaching OR
  - CITES
  Target 6 (Invasive alien species):
  - '"invasive species" OR'
  - '"alien species" OR'
  - IAS
  Target 7 (Pollution):
  - pollution OR
  - '"nutrient runoff" OR'
  - '"excess nutrients" OR'
  - pesticide OR
  - plastic OR
  - PFAS OR
  - '"forever chemicals" OR'
  - contamination OR
  - eutrophication OR
  - '"industrial liquid waste" OR'
  - '"solid waste" OR'
  - waste OR
  - '"saline intrusion"'
  Target 8 (Climate change & resilience):
  - '"climate change" OR'
  - resilience OR
  - '"nature-based solutions" OR'
  - '"ocean acidification"'
  Target 9 (Wild species management & use):
  - '"wild species management" OR'
  - '"sustainable use of wild species" OR'
  - '"indigenous people" OR'
  - IPLC OR
  - '"community-based management"'
  Target 10 (Sustainability in agriculture, aquaculture, forestry & fisheries):
  - agriculture OR
  - agroecology OR
  - forestry OR
  - '"fisheries aquaculture" OR'
  - '"land use management" OR'
  - '"water use management" OR'
  - '"recreational fisheries"'
  Target 11 (Nature’s contributions to people / ecosystem services):
  - '"ecosystem services" OR'
  - '"nature’s contributions to people" OR'
  - NCP OR
  - '"people’s contributions to nature"'
  Target 12 (Green & blue spaces, urban planning):
  - '"green space" OR'
  - '"blue space" OR'
  - '"urban planning" OR'
  - '"green infrastructure" OR'
  - '"blue infrastructure" OR'
  - '"green areas"'
  Target 13 (Genetic resources & benefit sharing):
  - '"genetic resources" OR'
  - '"benefit sharing" OR'
  - ILK OR
  - '"traditional knowledge" OR'
  - '"Nagoya protocol"'
  Target 14 (Biodiversity integration in decision-making):
  - '"biodiversity policy" OR'
  - '"environmental impact assessment" OR'
  - EIA OR
  - '"strategic environmental assessment" OR'
  - SEA OR
  - '"biodiversity valuation" OR'
  - '"biodiversity action"'
  Target 15 (Business & financial institutions disclosure & risk):
  - company OR
  - '"corporate responsibility" OR'
  - business OR
  - '"supply chain" OR'
  - '"corporate biodiversity reporting" OR'
  - '"social responsibility" OR'
  - investment
  Target 16 (Sustainable consumption):
  - '"sustainable consumption" OR'
  - waste OR
  - footprint
  Target 17 (Biosafety):
  - biosafety OR
  - biotechnology OR
  - GMO OR
  - '"genetically modified organisms"'
  Target 18 (Harmful incentives / subsidies):
  - subsidies OR
  - incentives OR
  - PES OR
  - '"payments for ecosystem services"'
  Target 19 (Finance mobilisation):
  - '"finance mobilization" OR'
  - funding OR
  - '"private sector" OR'
  - '"biodiversity investments"'
  Target 20 (Capacity building, technology & cooperation):
  - '"capacity building" OR'
  - education OR
  - technology OR
  - cooperation OR
  - innovation
  Target 21 (Data, knowledge & monitoring):
  - '"data governance" OR'
  - monitoring OR
  - research OR
  - evaluation
  Target 22 (Participation & inclusion):
  - '"indigenous peoples" OR'
  - ILK OR
  - '"local communities" OR'
  - youth OR
  - '"participatory governance" OR'
  - minority OR
  - '"procedural justice" OR'
  - '"non-human" OR'
  - women OR
  - girls OR
  - rights OR
  - defender OR
  - activist OR
  - guardian
  Target 23 (Gender equality):
  - gender OR
  - women OR
  - woman OR
  - girls
Related to spatial planning process:
  Set Related:
  - '"planning phases" OR'
  - '"planning stages" OR'
  - '"planning steps" OR'
  - '"planning cycle" OR'
  - '"policy formulation" OR'
  - '"decision-making" OR'
  - '"local planning practices of indigenous people" OR'
  - '"traditional practices of indigenous people" OR'
  - '"planning guidelines"'
Nexus:
  Water:
  - Water OR
  - Hydrological OR
  - Catchment OR
  - '"blue infrastructure"'
  Food:
  - food OR
  - agriculture OR
  - nutrition OR
  - malnutrition OR
  - hunger OR
  - farm
  Health:
  - health OR
  - healthy OR
  - mortality OR
  - diseases OR
  - pandemics OR
  - '"well-being" OR'
  - wellbeing
  Climate:
  - Climate OR
  - energy OR
  - emission OR
  - GHG OR
  - '"greenhouse gas" OR'
  - flood OR
  - drought

Non implemented additional searches are:

• Click chapter_2_add.yaml to download
• Click chapter_2_sdg.yaml to download

Chapter 3

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch3["<u><span style='color:#1a0dab;'>chapter_3.yaml<br/>Level 2 refinements</span></u>"]
    click Ch3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"

    subgraph Chapter3Sets["Chapter 3 searches"]
        direction TB
        C3_1["Set 1:<br/>Protected/OECM + NBSAP + cases<br/>+ spatial prioritization + regional scales<br/>(n = <<C3_1>>)"]
        C3_3["Set 3:<br/>Restoration planning + connectivity + resilience<br/>(n = <<C3_3>>)"]
        C3_3.1A["Set 3.1 A:<br/>Objective<br/>(n = <<C3_3.1A>>)"]
        C3_3.1BCD["Set 3.1 BCD:<br/>Concept<br/>(n = <<C3_3.1BCD>>)"]
        C3_3.2D["Set 3.2 D:<br/>???<br/>(n = <<C3_3.2D>>)"]
        C3_3.2F["Set 3.2 F:<br/>???<br/>(n = <<C3_3.2F>>)"]
        C3_3.3["Set 3.3:<br/>???<br/>(n = <<C3_3.3>>)"]
        C3_4["Set 4:<br/>Systematic conservation planning / gap analysis<br/>(n = <<C3_4>>)"]
        C3_4.1AB["Set 4.1 AB:<br/>???<br/>(n = <<C3_4.1AB>>)"]
        C3_4.1CD["Set 4.1 CD:<br/>???<br/>(n = <<C3_4.1CD>>)"]
        C3_4.2["Set 4.2:<br/>???<br/>(n = <<C3_4.2>>)"]

        C3_5.1["Set 5.1:<br/>???<br/>(n = <<C3_5.1>>)"]
        C3_5.2["Set 5.1:<br/>???<br/>(n = <<C3_5.2>>)"]
    end
    Ch3 --> C3_1
    Ch3 --> C3_3
    Ch3 --> C3_3.1A
    Ch3 --> C3_3.1BCD
    Ch3 --> C3_3.2D
    Ch3 --> C3_3.2F
    Ch3 --> C3_3.3
    Ch3 --> C3_4
    Ch3 --> C3_4.1AB
    Ch3 --> C3_4.1CD
    Ch3 --> C3_4.2
    Ch3 --> C3_5.1
    Ch3 --> C3_5.2

Search Terms

Click chapter_3.yaml to download

params$search_terms$chapter_3 |>
  yaml::as.yaml() |>
  cat()

Set 1:
- (((protected OR conserved OR Indigenous) AND areas) OR OECM OR "other effective
  area-based conservation measures" OR ITT OR "indigenous and traditional territories")
  AND
- (NBSAP OR target OR (National AND ("Biodiversity Strategy")))  AND
- (regional OR national OR subnational OR marine OR interpretation OR interpret OR
  implement OR implementation OR adapt OR adaptation OR translate OR translation governance
  OR "decision-making" OR transboundary)
Set 3:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- (spatial AND (planning OR prioritization OR prioritisation))
Set 3.1A:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- (spatial AND (planning OR prioritization OR prioritisation)) AND
- (goal OR objective)
Set 3.1BCD:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- (spatial AND (planning OR prioritization OR prioritisation)) AND
- (concept OR principle)
Set 3.2CE:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- (spatial AND (planning OR prioritization OR prioritisation)) AND
- ("multi-criteria" OR "multi-objective" OR multicriteria OR (multiple AND (criteria
  OR objective OR goal)))
Set 3.2D:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- (spatial AND (planning OR prioritization OR prioritisation)) AND
- (method OR tool OR approach OR software)
Set 3.2F:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- (spatial AND (planning OR prioritization OR prioritisation)) AND
- (inclusivity OR participation OR "co-management" OR "Indigenous knowledge" OR "traditional
  ecological knowledge" OR ILK OR "local knowledge" OR "community-based restoration"
  OR "stakeholder engagement" OR "social equity" OR justice)
Set 3.3:
- (ecology OR ecological OR biodiversity OR habitat OR ecosystem) AND
- (restoration OR rewilding) AND
- planning AND
- (indicator OR index OR metric OR objective)
Set 4:
- (conservation OR biodiversity OR "protected area" OR OECM OR reserve) AND
- spatial AND
- (planning OR prioritization OR prioritisation)
Set 4.1AB:
- (conservation OR biodiversity OR "protected area" OR OECM OR reserve) AND
- spatial AND
- (planning OR prioritization OR prioritisation) AND
- (concept OR principle)
Set 4.1CD:
- (conservation OR biodiversity OR "protected area" OR OECM OR reserve) AND
- spatial AND
- (planning OR prioritization OR prioritisation) AND
- (method OR tool OR approach OR software)
Set 4.2:
- (conservation OR biodiversity OR "protected area" OR OECM or reserve) AND
- (indicator OR index OR metric OR objective)
Set 5.1:
- (conservation OR biodiversity OR "protected area" OR OECM OR reserve) AND
- restoration AND
- spatial AND
- (planning OR prioritization OR prioritisation)
Set 5.2:
- ("adaptive management" OR "adaptive co-management" OR "adaptive comanagement" OR
  "learning-based management" OR "iterative management") AND
- ("protected area" OR park OR reserve OR "conservation area" OR "biodiversity conservation"
  OR "ecological restoration" OR "habitat restoration" OR "ecosystem restoration")
  AND
- (planning OR implementation)

Chapter 4

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch4["<u><span style='color:#1a0dab;'>chapter_4.yaml<br/>Level 2 refinements</span></u>"]
    click Ch4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"

    subgraph Chapter4Sets["Chapter 4 searches"]
        direction TB
        C4_1["Set 1:<br/>Connectivity review articles<br/>(n = <<C4_1>>)"]
        C4_2["Set 2:<br/>Connectivity benefits vs risks<br/>+ ecosystem services<br/>(n = <<C4_2>>)"]
        C4_3["Set 3:<br/>Structural vs functional connectivity<br/>(n = <<C4_3>>)"]
        C4_4["Set 4:<br/>Connectivity modelling toolkits<br/>(n = <<C4_4>>)"]
        C4_5["Set 5:<br/>Connectivity indicators & metrics<br/>(n = <<C4_5>>)"]
        C4_6["Set 6:<br/>Policy & governance integration<br/>(n = <<C4_6>>)"]
        C4_7["Set 7:<br/>Multilevel / transboundary governance<br/>+ IPLC inclusion + land tenure<br/>(n = <<C4_7>>)"]
        C4_8["Set 8:<br/>Movement ecology<br/>(dispersal / migration / permeability)<br/>(n = <<C4_8>>)"]
    end
    Ch4 --> C4_1
    Ch4 --> C4_2
    Ch4 --> C4_3
    Ch4 --> C4_4
    Ch4 --> C4_5
    Ch4 --> C4_6
    Ch4 --> C4_7
    Ch4 --> C4_8

Search Terms

Click chapter_4.yaml to download

params$search_terms$chapter_4 |>
  yaml::as.yaml() |>
  cat()

Set 1: '"ecological connectivity"'
Set 2: ("ecological connectivity" OR "habitat connectivity" OR "landscape connectivity"
  OR "wildlife corridor" OR "biological corridor") AND (benefits OR advantages OR
  positive OR impacts OR opportunities) AND (risks OR drawbacks OR negative OR impacts
  OR challenges) AND ("ecosystem services" OR "ecosystem management")
Set 3:
- '"structural connectivity" OR'
- '"functional connectivity"'
Set 4:
- '"connectivity analysis" OR'
- '"network analysis" OR'
- '"circuit theory" OR'
- '"least-cost path" OR'
- '"graph theory" OR'
- '"resistant kernels" OR'
- '"agent-based models" OR'
- Conefor OR
- '"pattern-oriented model" OR'
- '"state-space model" OR'
- '"corridor model" OR'
- '"simulated individual" OR'
- '"movement paths" OR'
- '"hidden Markov models" OR'
- '"effective connectivity" OR'
- '"gene flow"'
Set 5: '"ecological connectivity" AND (indicator OR index OR "metric*" OR objective)'
Set 6:
- ("ecological connectivity" OR "landscape connectivity" OR "functional connectivity"
  OR "connectivity conservation") AND
- (policy OR framework OR governance OR "planning instrument" OR initiative OR agreement
  OR global OR international OR "regional strategy" OR "transboundary cooperation"
  OR "joint management" OR "cross-border conservation" OR "bioregional planning")
Set 7:
- ("ecological connectivity" OR "spatial governance") AND
- ("multi-level governance" OR "cross-scale governance" OR "transboundary governance")
  AND
- ("inclusive governance" OR "community-based management" OR "co-management") AND
- ("Indigenous Peoples" OR "Local Communities" OR IPLCs) AND
- ("policy coherence" OR "cross-sectoral integration") AND
- ("land tenure" OR "property rights" OR "private land conservation")
Set 8:
- dispersal OR
- migration OR
- nomadism OR
- '"landscape permeability"'

Chapter 5

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch5L2["<u><span style='color:#1a0dab;'>chapter_5.yaml<br/>Level 2 foresight framing</span></u>"]
    click Ch5L2 "./input/search_terms/chapter_5.yaml" "Open chapter_5.yaml"

    subgraph L2Sets["Section 5.1–5.2 searches"]
        direction TB
        C5_1["Set 1:<br/>Future + project*/predict*/scenario<br/>(n = <<C5_1>>)"]
        C5_2["Set 2:<br/>Future + pathway*/narrative*/vision*<br/>(n = <<C5_2>>)"]
        C5_3["Set 3:<br/>Future-proof*/anticipat*/scenario planning<br/>(n = <<C5_3>>)"]
        C5_4["Set 4:<br/>Foresight*/backcasting/simulat*/trend*<br/>(n = <<C5_4>>)"]
        C5_5["Set 5:<br/>Model* AND scenario<br/>(n = <<C5_5>>)"]
    end
    Ch5L2 --> C5_1
    Ch5L2 --> C5_2
    Ch5L2 --> C5_3
    Ch5L2 --> C5_4
    Ch5L2 --> C5_5

    subgraph L3Sets["Section 5.3 searches"]
        direction TB
        C5_3a["Set 1:<br/>Drivers of change<br/>(n = <<C5_3a>>)"]
        C5_3b["Set 2:<br/>Driver modelling approaches<br/>(n = <<C5_3b>>)"]
        C5_3c["Set 3:<br/>Driver gaps<br/>(n = <<C5_3c>>)"]
    end
    Ch5L2 --> C5_3a
    Ch5L2 --> C5_3b
    Ch5L2 --> C5_3c

    subgraph L4Sets["Section 5.4 searches"]
        direction TB
        C5_4a["Set 1:<br/>Interactions (synergy/trade-off/nexus)<br/>(n = <<C5_4a>>)"]
        C5_4b["Set 2:<br/>Response options (integrated planning / NbS)<br/>(n = <<C5_4b>>)"]
        C5_4c["Set 3:<br/>Cross-scale synergy & trade-off terms<br/>(n = <<C5_4c>>)"]
    end
    Ch5L2 --> C5_4a
    Ch5L2 --> C5_4b
    Ch5L2 --> C5_4c

    subgraph L5Sets["Section 5.5 searches"]
        direction TB
        C5_5a["Set 1:<br/>Adaptive / transformative management<br/>(n = <<C5_5a>>)"]
        C5_5b["Set 2:<br/>Uncertainty quantification<br/>(n = <<C5_5b>>)"]
        C5_5c["Set 3:<br/>Tipping points & thresholds<br/>(n = <<C5_5c>>)"]
        C5_5d["Set 4:<br/>Cascading risks & precaution<br/>(n = <<C5_5d>>)"]
    end
    Ch5L2 --> C5_5a
    Ch5L2 --> C5_5b
    Ch5L2 --> C5_5c
    Ch5L2 --> C5_5d

    subgraph L6Sets["Section 5.6 searches"]
        direction TB
        C5_6a["Set 1:<br/>Science-policy-practice pathways<br/>(n = <<C5_6a>>)"]
        C5_6b["Set 2:<br/>ILK integration & community planning<br/>(n = <<C5_6b>>)"]
        C5_6c["Set 3:<br/>Enabling factors & coordination<br/>(n = <<C5_6c>>)"]
        C5_6d["Set 4:<br/>Shocks, surprises, uncertainties<br/>(n = <<C5_6d>>)"]
    end
    Ch5L2 --> C5_6a
    Ch5L2 --> C5_6b
    Ch5L2 --> C5_6c
    Ch5L2 --> C5_6d

    subgraph CCSets["Cross-cutting searches"]
        direction TB
        CC1["Set 1:<br/>Scales & telecoupling<br/>(n = <<CC1>>)"]
        CC2["Set 2:<br/>Co-production & inclusion<br/>(n = <<CC2>>)"]
    end
    Ch5L2 --> CC1
    Ch5L2 --> CC2

Search Terms

Click chapter_5.yaml to download

params$search_terms$chapter_5 |>
  yaml::as.yaml() |>
  cat()

Sections 1 and 2:
  Set 1: Future AND (projection OR projected OR predicted OR prediction OR forecast
    OR scenario)
  Set 2: Future AND (pathway OR narrative OR storyline OR foresight OR vision OR horizon
    scan OR trajectory OR imagination)
  Set 3:
  - '"future-proof" OR'
  - anticipate OR
  - '"forward-looking" OR'
  - '"futures thinking" OR'
  - '"future-oriented" OR'
  - prospective OR
  - '"plausible future" OR'
  - '"scenario planning"'
  Set 4:
  - Foresight OR
  - backcasting OR
  - simulate OR
  - simulation OR
  - '"delphi method" OR'
  - storytelling OR
  - '"environmental scan" OR'
  - trend OR
  - megatrend
  Set 5: (model AND scenario)
Section 3:
  Set 1:
  - '"drivers of change" OR'
  - '"biodiversity driver" OR'
  - '"social-ecological driver" OR'
  - '"technological driver" OR'
  - '"perennial driver" OR'
  - '"emerging driver" OR'
  - '"cascading driver"'
  Set 2:
  - '"Integrated assessment" OR'
  - '"land-use change model" OR'
  - '"Scenario model" OR'
  - '"Social-ecological model" OR'
  - '"agent-based model" OR'
  - '"telecoupled systems model" OR'
  - '"hybrid model" OR'
  - '"participatory model" OR'
  - '"data-driven foresight model"'
  Set 3:
  - '"value-based driver" OR'
  - '"compound driver" OR'
  - '"interacting driver" OR'
  - '"distant driver"'
Section 4:
  Set 1:
  - Synergy OR
  - tradeoff OR
  - '"trade-off" OR'
  - nexus OR
  - '"win-win" OR'
  - '"cross-sectoral interaction" OR'
  - '"co-benefit"'
  Set 2:
  - '"Integrated planning" OR'
  - multifunctional OR
  - '"nature-based solution" OR'
  - '"adaptive governance" OR'
  - '"cross-sectoral governance"'
  Set 3:
  - (Spatial OR temporal OR thematic OR "cross-scale") AND
  - (Synergy OR tradeoff OR "trade-off" OR nexus OR "win-win")
Section 5:
  Set 1:
  - '"Adaptative management" OR'
  - '"Adaptative governance" OR'
  - '"Transformative adaptation" OR'
  - '"Adaptive systems" OR'
  - '"Adaptive foresight"'
  Set 2:
  - (Model OR data OR "decision-making under" OR planning OR scenario OR epistemic
    OR ontological) AND
  - uncertainty
  Set 3:
  - '"Tipping points" OR'
  - thresholds OR
  - '"Planetary boundaries" OR'
  - '"Nonlinear system dynamics" OR'
  - Surprise
  Set 4:
  - '"Cascading risk" OR'
  - '"Risk management" OR'
  - '"Environmental shocks" OR'
  - '"Environmental risks" OR'
  - '"Precautionary approach"'
Section 6:
  Set 1:
  - '"Science-policy-practice" OR'
  - '"transformative pathways" OR'
  - '"transdisciplinary co-production"'
  Set 2:
  - '"Knowledge co-production" OR'
  - '"community-based spatial planning" OR'
  - '"indigenous mapping" OR'
  - '"traditional knowledge" OR'
  - '"participatory scenario planning"'
  Set 3:
  - '"Multi-level governance" OR'
  - '"institutional capacity" OR'
  - '"cross-sectoral coordination"'
  Set 4:
  - '"Planning for uncertainty" OR'
  - '"adaptive planning" OR'
  - '"resilience planning" OR'
  - '"anticipatory governance"'
Cross-cutting sets:
  Set 1: ("Spatial scaling" OR "Temporal scaling" OR "Social scale" OR "Nested governance"
    OR "Multi-scale planning" OR "Cross-scale interaction" OR Telecoupling OR Teleconnection
    OR Metacoupling OR Spillover)
  Set 2:
  - ((Indigenous OR local OR ecological OR "co-production of") AND knowledge) OR
  - pluralism OR
  - epistemic OR
  - '"Community-based" OR'
  - Biocultural OR
  - Customary OR
  - tenure OR
  - Participatory OR
  - '"Cross-cultural" OR'
  - '"knowledge integration" OR'
  - ((Gender OR Social) AND Inclusion) OR
  - '"Gender-responsive" OR'
  - Intersectionality OR
  - equity OR
  - '"Women’s roles" OR'
  - '"Women''s roles" OR'
  - '"Women`s roles" OR'
  - Gendered OR
  - Empowerment OR
  - '"capacity-building"'

Chapter 6

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch6["<u><span style='color:#1a0dab;'>chapter_6.yaml<br/>Level 2 refinements</span></u>"]
    click Ch6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

    subgraph Chapter6Sets["Chapter 6 searches"]
        direction TB
        C6_1["Set 1:<br/>Institutional & governance structures<br/>(n = <<C6_1>>)"]
        C6_2["Set 2:<br/>Political & strategic leadership<br/>(n = <<C6_2>>)"]
        C6_3["Set 3:<br/>Socio-cultural & stakeholder engagement<br/>(n = <<C6_3>>)"]
        C6_4["Set 4:<br/>Collaboration, trust & networks<br/>(n = <<C6_4>>)"]
        C6_5["Set 5:<br/>Financial & economic mechanisms<br/>(n = <<C6_5>>)"]
        C6_6["Set 6:<br/>Legal & policy frameworks<br/>(n = <<C6_6>>)"]
        C6_7["Set 7:<br/>Human & institutional capacity<br/>(n = <<C6_7>>)"]
        C6_8["Set 8:<br/>Data, knowledge & decision support (8.1 + 8.2)<br/>(n = <<C6_8>>)"]
        C6_9["Set 9:<br/>Ecological & spatial planning tools<br/>(n = <<C6_9>>)"]
        C6_10["Set 10:<br/>Cross-cutting process enablers<br/>(n = <<C6_10>>)"]
    end
    Ch6 --> C6_1
    Ch6 --> C6_2
    Ch6 --> C6_3
    Ch6 --> C6_4
    Ch6 --> C6_5
    Ch6 --> C6_6
    Ch6 --> C6_7
    Ch6 --> C6_8
    Ch6 --> C6_9
    Ch6 --> C6_10

Ch6R2["<u><span style='color:#1a0dab;'>chapter_6_r2.yaml<br/>Optional Level 3 filter</span></u>"]
Chapter6Sets --> Ch6R2
click Ch6R2 "./input/search_terms/chapter_6_r2.yaml" "Open chapter_6_r2.yaml"
subgraph Chapter6Case["Chapter 6 searches"]
    R2["Case-study keywords<br/>(case stud*, example*, initiative*, etc.)"]
end
Ch6R2 --> R2

Search Terms

Click chapter_6.yaml to download

params$search_terms$chapter_6 |>
  yaml::as.yaml() |>
  cat()

Set 1:
- '"institutional arrangements" OR'
- '"governance structures" OR'
- '"cross-sector coordination" OR'
- '"multi-level governance" OR'
- '"inter-agency collaboration" OR'
- '"policy coherence" OR'
- '"environmental governance" OR'
- '"landscape governance" OR'
- '"seascape governance" OR'
- '"integrated landscape management"'
Set 2:
- '"political support" OR'
- '"environmental leadership" OR'
- '"ministerial alignment" OR'
- '"strategic prioritization" OR'
- '"national biodiversity strategy"'
Set 3:
- '"stakeholder engagement" OR'
- participation OR
- '"co-design" OR'
- '"co-production" OR'
- '"community-based management" OR'
- '"indigenous governance" OR'
- '"traditional knowledge" OR'
- '"Free Prior and Informed Consent" OR'
- FPIC OR
- '"stakeholder workshops" OR'
- '"stakeholder dialogues" OR'
- '"public consultation"'
Set 4:
- collaboration OR
- trust OR
- partnership OR
- '"multi-stakeholder platform" OR'
- coalition OR
- '"inter-organizational network" OR'
- cooperation
Set 5:
- '"biodiversity finance" OR'
- '"blended finance" OR'
- '"green finance" OR'
- '"conservation trust funds" OR'
- '"Payment of ecosystem services" OR'
- '"natural capital accounting" OR'
- '"biodiversity offsets"'
Set 6:
- '"environmental law" OR'
- legislation OR
- '"statutory framework" OR'
- '"policy instruments" OR'
- '"regulatory mechanisms" OR'
- '"strategic environmental assessment" OR'
- '"Environmental Impact Assessment"'
Set 7:
- '"institutional capacity" OR'
- '"technical expertise" OR'
- '"capacity building" OR'
- training OR
- '"professional development" OR'
- '"institutional strengthening" OR'
- '"workforce development" OR'
- education OR
- pipeline OR
- '"diversity and inclusion" OR'
- '"gender inequality" OR'
- '"girl’s education" OR'
- '"girl''s education" OR'
- '"girl`s education" OR'
- '"Indigenous knowledge" OR'
- '"generational knowledge" OR'
- '"institutional memory"'
Set 8.1:
- '"species distribution maps" OR'
- (("land cover" OR "protected area") AND data) OR
- '"Indigenous knowledge" OR'
- '"knowledge co-production" OR'
- '"knowledge and data management" OR'
- '"spatial data infrastructure" OR'
- ((CARE OR FAIR) AND principles) OR
- '"open science" OR'
- '"information delivery" OR'
- (data AND (standards OR stewardship OR "management plan" OR "sharing agreement"
  OR archive OR repository OR structure)) OR
- metadata OR
- (quality AND (assurance OR control))
Set 8.2:
- '"monitoring and evaluation" OR'
- '"decision-support tools" OR'
- '"ecological model" OR'
- '"scenario planning and analysis" OR'
- ("systematic conservation planning" AND ("and model" OR tools)) OR
- '"biodiversity targets" OR'
- '"science communication" OR'
- '"science-practice gap" OR'
- '"participatory mapping and model" OR'
- '"scenario narratives" OR'
- '"evidence bridge" OR'
- '"boundary spanner and organization" OR'
- '"early adopter"'
Set 9:
- '"spatial planning" OR'
- '"landscape governance" OR'
- '"territorial integration" OR'
- '"land-use planning" OR'
- '"ecological connectivity" OR'
- '"ecological networks" OR'
- '"ecological corridors" OR'
- '"green infrastructure" OR'
- '"nature-based Strategic spatial planning solutions" OR'
- '"restoration planning" OR'
- '"blue infrastructure" OR'
- '"blue-green infrastructure" OR'
- '"spatial decision-making" OR'
- '"strategic spatial planning"'
Set 10:
- '"adaptive management" OR'
- '"feedback mechanisms" OR'
- '"monitoring and learning" OR'
- '"institutional innovation" OR'
- '"continuous improvement"'

Non implemented additional searches are:

• Click chapter_6_r2.yaml to download

Type Selsection

OpenAlex contains more then 270 million works of different types. The following table shows and explains the available types and highlights which are selected to be included in the SPC Corpus.

params$types |>
  knitr::kable(
    caption = "OpenAlex Work Types and Inclusion in the SPC Corpus",
    booktabs = TRUE,
    align = c("l", "l", "l", "c")
  ) # |>

OpenAlex Work Types and Inclusion in the SPC Corpus

Type	Description	Included
article	Scholarly journal articles and related periodical works	TRUE
book	Monographs and other long-form published books	TRUE
book-chapter	Chapters published within edited books or proceedings	TRUE
dissertation	Doctoral or master level theses and dissertations	TRUE
editorial	Editorials and editor introductions	TRUE
preprint	Pre-publication manuscripts shared prior to peer review	TRUE
report	Technical, institutional, or policy reports	TRUE
review	Narrative or systematic review articles	TRUE
dataset	Published datasets and structured data releases	FALSE
erratum	Published corrections to previously released works	FALSE
grant	Summaries or descriptions of grant-funded projects	FALSE
letter	Correspondence, commentaries, and short letters	FALSE
libguides	Library research guides and curated bibliographies	FALSE
other	Works that do not align with a more specific OpenAlex type	FALSE
paratext	Prefaces, introductions, indexes, and other paratextual items	FALSE
peer-review	Formal peer review reports and evaluations	FALSE
reference-entry	Encyclopaedia or dictionary reference entries	FALSE
retraction	Notices retracting previously published works	FALSE
standard	Standards, protocols, and technical specifications	FALSE
supplementary-materials	Supplementary files accompanying primary publications	FALSE

# kableExtra::kable_styling(
#     full_width = FALSE,
#     position = "left"
# )

Methods

SPC Corpus

Assess of Individual Terms in spc and nature search terms

This assessment is done on the whole of the OpenAlex corpus and only filtered for types and not for the date range.

fn <- file.path(params$output_dir, "searchterm_assessment_spcc.rds")

if (!file.exists(fn)) {
  result <- list(
    spc = assess_search_term_both(
      st = params$search_terms$spc_corpus$spc,
      and_term = st_compact(params$search_terms$spc_corpus$spc_corpus$nature),
      types = params$types_filter,
      verbose = TRUE
    ),
    nature = assess_search_term_both(
      st = params$search_terms$spc_corpus$nature,
      and_term = st_compact(params$search_terms$spc_corpus$spc_corpus$spc),
      types = params$types_filter,
      verbose = TRUE
    )
  ) |>
    saveRDS(fn)
}

Get Key Paper

Here we get key papers in a parquet database which is partitioned by:

• found_in: the search term or openalex which is used as the filter, i.e. the key paper occurs in corpus which would result from the search term
• id_used: the id used to testing,m either the OpenAlex id (id) or the doi (doi)
• page: only for processing reasons

No filtering, neither by type nor by publication year is done.

The other columns are as returned by the OpenAlex API.

#|

dois <- params$key_papers$goldstandard$DOI[
  params$key_papers$goldstandard$DOI != ""
]
ids <- params$key_papers$goldstandard$openalex_id[
  params$key_papers$goldstandard$openalex_id != ""
]

#|

fn <- file.path(params$keyworks, "parquet", "found_in=openalex")
if (!dir.exists(fn)) {
  ### KP in OpenAlex
  openalexPro::pro_query(
    doi = dois,
    chunk_limit = 50
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_doi")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 50
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_oa_id")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

#|

fn <- file.path(params$keyworks, "parquet", "found_in=spc")
if (!dir.exists(fn)) {
  openalexPro::pro_query(
    title_and_abstract.search = st$spc,
    doi = dois,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_doi")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$spc,
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

#|

fn <- file.path(params$keyworks, "parquet", "found_in=nature")
if (!dir.exists(fn)) {
  ### KP in nature
  openalexPro::pro_query(
    title_and_abstract.search = st$nature,
    doi = dois,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_doi")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$nature,
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

#|

fn <- file.path(params$keyworks, "parquet", "found_in=spcc")
if (!dir.exists(fn)) {
  ### KP in spcc
  openalexPro::pro_query(
    title_and_abstract.search = st$spcc,
    doi = params$key_papers$goldstandard$DOI[
      params$key_papers$goldstandard$DOI != ""
    ],
    chunk_limit = 15
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_doi")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$spcc,
    id = params$key_papers$goldstandard$openalex_id[
      params$key_papers$goldstandard$openalex_id != ""
    ],
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

Keypaper in Search Terms

The in the previous step retrieved works are analysed here to get a table which shows where the key paper occur.

fn <- file.path(params$keyworks, "kp_found_in.rds")
if (!file.exists(fn)) {
  arrow::open_dataset(file.path(params$keyworks, "parquet")) |>
    dplyr::select(
      id,
      doi,
      type,
      found_in,
      title,
      citation
    ) |>
    dplyr::group_by(id, doi, title, citation, type) |>
    dplyr::summarise(
      in_openalex = base::max(found_in == "openalex", na.rm = TRUE),
      in_spc = base::max(found_in == "spc", na.rm = TRUE),
      in_nature = base::max(found_in == "nature", na.rm = TRUE),
      in_spcc = base::max(found_in == "spcc", na.rm = TRUE),
      .groups = "drop"
    ) |>
    dplyr::collect() |>
    saveRDS(fn)
}

Get Numbers from OpenAlex of the Search Terms

These data is gathered from OpenAlex directly, not downloaded any works. The data is used to assess the quality of the TCA Corpus.

The query contains: - the search term (nature, spc, spcc) - the types selected (article, book, book-chapter, dissertation, editorial, preprint, report, review) - the date range (from 1992-01-01 to 2025-12-31)

The following counts are retrieved:

Overall hits

#|

fn <- file.path(params$corpus, "st_hits.rds")
if (!file.exists(fn)) {
  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
  )

  results <- pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexPro::pro_request(
          count_only = TRUE,
          verbose = TRUE
        )
    }
  ) |>
    dplyr::bind_rows() |>
    dplyr::select(count)
  rownames(results) <- names(queries)
  saveRDS(results, file = fn)
}

Counts per Language

#|

fn <- file.path(params$corpus, "st_languages.rds")
if (!file.exists(fn)) {
  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to,
        group_by = "language"
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
    group_by = "language"
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          verbose = TRUE
        ) |>
        dplyr::bind_rows()
    }
  ) |>
    dplyr::bind_rows(.id = "source") |>
    dplyr::select(source, language = key_display_name, count) |>
    tidyr::pivot_wider(
      names_from = source,
      values_from = count,
      names_prefix = "count_",
      values_fn = list(count = sum),
      values_fill = list(count = 0)
    ) |>
    dplyr::select(
      language,
      count_openalex,
      count_spc,
      count_nature,
      count_spcc
    ) |>
    dplyr::arrange(desc(count_openalex)) |>
    saveRDS(file = fn)
}

Counts per Publication Year

fn <- file.path(params$corpus, "st_years.rds")
if (!file.exists(fn)) {
  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to,
        group_by = "publication_year"
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
    group_by = "publication_year"
  )

  result <- pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          verbose = TRUE
        ) |>
        dplyr::bind_rows()
    }
  ) |>
    dplyr::bind_rows(.id = "source") |>
    dplyr::select(source, year = key, count) |>
    dplyr::mutate(year = base::as.integer(year)) |>
    tidyr::pivot_wider(
      names_from = source,
      values_from = count,
      names_prefix = "count_",
      values_fn = list(count = sum),
      values_fill = list(count = 0)
    ) |>
    dplyr::select(
      year,
      count_openalex,
      count_spc,
      count_nature,
      count_spcc
    ) |>
    dplyr::arrange(dplyr::desc(year)) |>
    saveRDS(file = fn)
}

Chapter 1

# params$search_terms$chapter_1

fn <- file.path(params$chapter_1, "st_hits.rds")
if (!file.exists(fn)) {
  get_count_L1(
    sets = params$search_terms$chapter_1,
    types_filter = params$types_filter,
    publication_year_from = params$publication_date$from,
    publication_year_to = params$publication_date$to,
    st_AND = st$spcc
  ) |>
    saveRDS(file = fn)
}

Chapter 2

fn <- file.path(params$chapter_2, "st_hits.rds")
if (!file.exists(fn)) {
  # queries <- lapply(
  #   params$search_terms$chapter_2,
  #   function(section) {
  #     lapply(
  #       section,
  #       function(s) {
  #         openalexPro::pro_query(
  #           title_and_abstract.search = paste0(
  #             "(",
  #             st$spcc,
  #             ") AND (",
  #             st_compact(s),
  #             ")"
  #           ),
  #           type = params$types_filter,
  #           from_publication_date = params$publication_date$from,
  #           to_publication_date = params$publication_date$to
  #         )
  #       }
  #     )
  #   }
  # )

  # result <- lapply(
  #   names(queries),
  #   function(sect_nm) {
  #     result <- lapply(
  #       names(queries[[sect_nm]]),
  #       function(nm) {
  #         message("Retrieving ", sect_nm, " - ", nm, " ...")
  #         result <- c(
  #           count = NA_integer_,
  #           db_response_time_ms = NA_integer_,
  #           page = NA_integer_,
  #           per_page = NA_integer_
  #         )
  #         try(
  #           result <- queries[[sect_nm]][nm] |>
  #             openalexR::oa_request(
  #               count_only = TRUE,
  #               verbose = TRUE
  #             ) |>
  #             unlist(),
  #           silent = FALSE
  #         )
  #         return(result)
  #       }
  #     ) |>
  #       do.call(what = cbind) |>
  #       t() |>
  #       as.data.frame() |>
  #       dplyr::select(count)
  #     browser()
  #     rownames(result) <- names(queries[[sect_nm]])
  #     return(result)
  #   }
  # )
  # names(result) <- names(queries)

  get_count_L2(
    sets = params$search_terms$chapter_2,
    types_filter = params$types_filter,
    publication_year_from = params$publication_date$from,
    publication_year_to = params$publication_date$to,
    st_AND = st$spcc
  ) |>
    saveRDS(file = fn)
}

Chapter 3

fn <- file.path(params$chapter_3, "st_hits.rds")
if (!file.exists(fn)) {
  get_count_L1(
    sets = params$search_terms$chapter_3,
    types_filter = params$types_filter,
    publication_year_from = params$publication_date$from,
    publication_year_to = params$publication_date$to,
    st_AND = st$spcc
  ) |>
    saveRDS(file = fn)
}

Due to length limitations of the API call, the initial download is done without the types filter (parquet folder) and afterwards filtered using the types filter (corpus directory).

corpus_dir <- file.path(params$chapter_3, "corpus.openalex_pro", "corpus")
if (!dir.exists(corpus_dir)) {
  fetch_corpus(
    project_dir = file.path(params$chapter_3, "corpus.openalex_pro"),
    search_terms = params$search_terms$chapter_3,
    st_spcc = st$spcc,
    publication_date = params$publication_date,
    types_filter = params$types_filter,
    workers = 8
  )
}

#|

# fn <- file.path(
#   params$chapter_3,
#   "corpus.openalex_pro",
#   paste0("random_", n, ".xlsx")
# )
# if (!file.exists(fn)) {
#   sets <- list.dirs(
#     file.path(
#       params$chapter_3,
#       "corpus.openalex_pro",
#       "corpus"
#     ),
#     full.names = TRUE
#   )

#   names(sets) <- sets |>
#     basename() |>
#     utils::URLdecode() |>
#     gsub(
#       pattern = "page=",
#       replacement = ""
#     )

#   samples <- lapply(
#     sets,
#     openalexPro::sample_parquet_n,
#     n = params$n_samples,
#     select = c("id", "doi", "citation", "title", "abstract")
#   )

#   lapply(
#     seq_along(samples),
#     function(i) {
#       samples[[i]] |>
#         dplyr::mutate(
#           abstract = substr(abstract, 1, 5000)
#         ) |>
#         writexl::write_xlsx(
#           path = file.path(
#             params$chapter_3,
#             "corpus.openalex_pro",
#             paste0(
#               names(samples)[[i]],
#               "_",
#               n,
#               ".xlsx"
#             )
#           )
#         )
#     }
#   )
# }

fn <- file.path(
  params$chapter_3,
  "corpus.openalex_pro",
  "csl_json"
)

if (!file.exists(fn)) {
  sets <- list.files(
    file.path(
      params$chapter_3,
      "corpus.openalex_pro",
      "corpus"
    ),
    full.names = TRUE,
    recursive = FALSE
  )

  names(sets) <- sets |>
    basename() |>
    utils::URLdecode() |>
    gsub(
      pattern = "page=",
      replacement = ""
    )

  samples <- lapply(
    seq_along(sets),
    function(part) {
      message("Converting ", names(sets)[[part]], " ...")
      openalexConvert::corpus_to_csljson(
        corpus = sets[[part]],
        output = sets[[part]] |>
          dirname() |>
          dirname() |>
          file.path(
            "csl_json",
            names(sets)[[part]]
          ),
        chunk_size = params$csljson_batch_size,
        verbose = TRUE
      )
    }
  )
}

# TODO:
#   create groups
#   get API key
#

fn <- file.path(
  params$chapter_3,
  "corpus.openalex_pro",
  "csl_json.exported"
)
if (!file.exists(fn)) {
  sets <- list.files(
    file.path(
      params$chapter_3,
      "corpus.openalex_pro",
      "csljson"
    ),
    full.names = TRUE,
    recursive = FALSE
  )

  names(sets) <- basename(sets)

  lapply(
    seq_along(sets),
    function(part) {
      openalexConvert::csljson_to_zotero_upload(
        sets[[part]],
        group_id = params$zotero_groups$chapter_3[[names(sets)[[part]]]],
        api_key = keyring::key_get("API_zotero_IPBES_spc_corpus"),
        pause = 0.1
      )
    }
  )
}

Chapter 4

fn <- file.path(params$chapter_4, "st_hits.rds")
if (!file.exists(fn)) {
  get_count_L1(
    sets = params$search_terms$chapter_4,
    types_filter = params$types_filter,
    publication_year_from = params$publication_date$from,
    publication_year_to = params$publication_date$to,
    st_AND = st$spcc
  ) |>
    saveRDS(file = fn)
}

Chapter 5

fn <- file.path(params$chapter_5, "st_hits.rds")
if (!file.exists(fn)) {
  get_count_L2(
    sets = params$search_terms$chapter_5,
    types_filter = params$types_filter,
    publication_year_from = params$publication_date$from,
    publication_year_to = params$publication_date$to,
    st_AND = st$spcc
  ) |>
    saveRDS(file = fn)
}

Chapter 6

fn <- file.path(params$chapter_6, "st_hits.rds")
if (!file.exists(fn)) {
  get_count_L1(
    sets = params$search_terms$chapter_6,
    types_filter = params$types_filter,
    publication_year_from = params$publication_date$from,
    publication_year_to = params$publication_date$to,
    st_AND = st$spcc
  ) |>
    saveRDS(file = fn)
}

Results

Assessment of Search Terms

The individual terms are assessed with the second term as AND, e,g. each individual term in spc is assessed with AND nature. In addition,

SPC Term

readRDS(file.path(params$output, "searchterm_assessment_spcc.rds"))$spc |>
  dplyr::arrange(desc(count)) |>
  dplyr::mutate(
    count = format(count, big.mark = ","),
    count_excl = format(count_excl, big.mark = ","),
  ) |>
  knitr::kable(format = "html", escape = FALSE)

term	count	count_excl
(planning AND ("for ecosystem services" OR process OR scenario OR tools))	1,221,968	744,079
(scenario AND (analysis OR "based model" OR "based planning" OR building OR planning OR thinking OR "and models" OR "of land use"))	1,026,806	703,275
(spatial AND (composition OR configuration OR "conservation prioritisation" OR "conservation prioritization" OR decision OR development OR "forest planning" OR governance OR planning OR prioritisation OR prioritization OR transformation))	770,979	550,644
((anticipatory OR "community based" OR "forest management" OR "land-sea" OR participatory OR "place based" OR regional OR sectorial OR territorial OR urban OR "agricultural management") AND planning)	665,609	323,157
connectivity	556,389	504,070
restoration	545,919	483,532
(("land use" OR "sea use" OR marine) AND (decision OR planning OR governance OR management OR model OR scenario OR trade-offs OR tradeoffs))	457,807	239,564
(land AND (allocation OR governance OR system))	383,908	238,410
(adaptive AND (management OR planning))	363,907	235,182
(conservation AND (practice OR planning OR program OR strategy))	282,871	162,528
(("key biodiversity" OR protected OR "remote ocean") AND areas)	265,919	190,692
(landscape AND (complexity OR planning OR governance OR management OR "ecological planning"))	234,211	107,275
((integrative OR "trends and") AND scenarios)	198,464	54,153
(animal AND (migration OR movement))	88,083	75,793
(("cumulative impact" OR "environmental impact" OR "strategic environmental") AND assessment)	70,944	51,347
((functional OR working) AND landscapes)	69,190	40,190
(ecological AND (corridor OR networks))	62,327	35,913
zoning	56,775	40,046
(biodiversity AND (assessment OR indicators OR scenario))	56,511	23,042
"systems integration"	53,021	45,926
((blue OR green) AND infrastructure)	40,701	24,977
("ecosystem service" AND (mapping OR model OR planning))	32,708	7,422
((inclusive OR marine) AND governance)	26,940	13,492
"stepping stones"	18,549	16,027
(nature AND ("based solutions" OR futures))	17,758	12,201
(integrated AND ("assessment framework" OR "assessment model" OR "coastal zone management"))	11,695	6,581
((habitat OR wildlife) AND corridor)	10,691	3,050
(("ecosystem based" OR "sea use") AND management)	7,663	2,586
"multi-criteria decision analysis"	7,006	4,797
rewilding	2,223	1,494
IPBES	1,284	714
(("generalized dissimilarity" OR macroecological) AND model)	1,258	720
"reserve design"	806	321
"futures thinking"	678	463
OECM	374	229
"critical areas for biodiversity"	20	8

Nature Term

readRDS(file.path(params$output, "searchterm_assessment_spcc.rds"))$nature |>
  dplyr::arrange(desc(count)) |>
  dplyr::mutate(
    count = format(count, big.mark = ","),
    count_excl = format(count_excl, big.mark = ","),
  ) |>
  knitr::kable(format = "html", escape = FALSE)

term	count	count_excl
environment	6,833,509	4,913,689
species	4,751,451	2,918,991
nature	4,039,424	3,075,191
sustainable	3,442,154	2,374,580
soil	2,216,293	1,361,726
sea	1,450,752	794,057
forest	1,209,167	560,348
river	1,168,420	606,588
ecological	1,095,869	335,775
landscape	1,028,229	562,399
conservation	994,246	427,872
Earth	972,889	615,926
ecosystem	965,798	220,529
marine	881,662	368,791
ocean	762,961	334,620
habitat	656,908	136,469
coastal	575,714	181,793
lake	574,824	306,679
restoration	545,919	355,642
territories	542,695	330,802
mountain	502,009	244,257
coast	481,719	189,720
biodiversity	411,869	128,639
terrestrial	320,470	100,829
((protected OR conserved) AND areas)	304,378	106,052
((agricultural OR arable) AND land)	290,583	82,479
freshwater	254,644	76,852
planet	235,789	121,099
flora	234,317	116,580
fauna	212,001	58,114
maritime	182,472	104,186
plantation	176,844	77,142
desert	169,601	85,063
wildlife	166,431	46,816
wetland	165,876	54,185
grassland	139,600	32,674
estuary	112,667	30,843
bog	76,634	56,321
marshes	73,495	27,845
"natural resource"	69,697	25,425
meadow	59,903	16,627
woodland	59,176	14,186
biosphere	51,995	13,062
savanna	32,798	9,147
"coupled system"	32,262	26,300
dryland	28,935	10,149
peatland	23,295	8,628
tundra	20,690	4,786
mires	19,964	12,251
fjord	16,080	6,333
shrubland	12,716	1,167
bioeconomy	10,280	4,616
"resource system"	6,013	3,227
seascape	5,065	1,161
heathland	4,034	794
marshland	3,802	870
chaparral	3,057	1,193
"environmental resource"	2,538	817
agroforest	1,834	328
"agro-forest"	567	0

Keypaper in Corpus

No filtering, neither by type nor by publication year is done. Therefore, the pure search terms are evaluated. If a paper is included in this table, doex not mean it is included in the final SPC Corpus due to filtering by dates and types!

readRDS(file.path(params$keyworks, "kp_found_in.rds")) |>
  dplyr::mutate(
    id_display = sub("^.*/(W[0-9]+)$", "\\1", id),
    id = sprintf("<a href=\"%s\" target=\"_blank\">%s</a>", id, id_display),
    doi_display = sub("^https://doi.org/", "\\1", doi),
    doi = sprintf("<a href=\"%s\" target=\"_blank\">%s</a>", doi, doi_display)
  ) |>
  dplyr::arrange(
    in_spcc,
    in_spc,
    in_nature,
    in_openalex
  ) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("in_"),
      ~ dplyr::case_when(
        .x ~ '<b style="color:#008000;">☑</b>', # green bold checkbox
        !.x ~ '<b style="color:#cc0000;">☐</b>' # red bold empty checkbox
      )
    )
  ) |>
  dplyr::select(
    id,
    doi,
    citation,
    in_spcc,
    in_spc,
    in_nature,
    in_openalex
  ) |>
  knitr::kable(format = "html", escape = FALSE)

id	doi	citation	in_spcc	in_spc	in_nature	in_openalex
W2795786882	10.1007/s00267-018-1028-3	Ida N.S. Djenontin & Alison M. Meadow (2018)	☐	☐	☐	☑
W2128217744	10.1177/0306312713508669	David H. Guston (2013)	☐	☐	☐	☑
W3170399214	10.1016/j.mex.2021.101401	Daniel Beiderbeck et al. (2021)	☐	☐	☐	☑
W2964311550	10.1016/j.techfore.2019.07.002	Ian Belton et al. (2019)	☐	☐	☐	☑
W4233598570	10.1146/annurev-anthro-102218-011435	David Valentine & Amelia Hassoun (2019)	☐	☐	☐	☑
W2056591496	10.1016/s0016-3287(98)00101-3	Richard A. Slaughter (1998)	☐	☐	☐	☑
W2090312322	10.1016/j.futures.2007.11.010	Joseph Voros (2007)	☐	☐	☐	☑
W4414957974	10.1073/pnas.2501695122	Damaris Zurell et al. (2025)	☐	☐	☑	☑
W2910481941	10.1080/02513625.2018.1562795	Peter Schmitt & Thorsten Wiechmann (2018)	☐	☑	☐	☑
W4407294089	10.1080/02697459.2025.2459975	Romina Rodela (2025)	☐	☑	☐	☑
W2116090915	10.1016/j.futures.2012.10.003	Muhammad Amer et al. (2012)	☐	☑	☐	☑
W2057938345	10.1108/14636680810855991	Sohail Inayatullah (2008)	☐	☑	☐	☑
W1973628253	10.1016/j.enpol.2005.12.006	Will McDowall & Malcolm Eames (2006)	☐	☑	☐	☑
W4238224782	10.1016/j.futures.2015.08.007	I. Milojević & Sohail Inayatullah (2015)	☐	☑	☐	☑
W1992974326	10.1016/j.futures.2007.11.011	Richard A. Slaughter (2007)	☐	☑	☐	☑
W1990273831	10.1126/science.1242552	Silke Bauer & Bethany J. Hoye (2014)	☑	☑	☑	☑
W2784805535	10.1126/science.aam9712	Marlee A. Tucker et al. (2018)	☑	☑	☑	☑
W3085078608	10.1038/s41467-020-18457-x	Michelle Ward et al. (2020)	☑	☑	☑	☑
W4281717750	10.1126/science.abl8974	Angela Brennan et al. (2022)	☑	☑	☑	☑
W1880093763	10.1111/ecog.01507	Rafael A. Magris et al. (2015)	☑	☑	☑	☑
W2783683111	10.1016/j.biocon.2017.12.020	Santiago Saura et al. (2018)	☑	☑	☑	☑
W2791599583	10.1111/conl.12439	Rafael A. Magris et al. (2018)	☑	☑	☑	☑
W4297536966	10.1016/j.tree.2022.09.002	Maria Beger et al. (2022)	☑	☑	☑	☑
W2965201645	10.1016/j.biocon.2019.07.028	Santiago Saura et al. (2019)	☑	☑	☑	☑
W3132073286	10.1016/j.biocon.2021.109008	Annika T. H. Keeley et al. (2021)	☑	☑	☑	☑
W4390696637	10.1038/s41467-023-43832-9	Rachel Neugarten et al. (2024)	☑	☑	☑	☑
W4302293680	10.1111/csp2.12823	David M. Theobald et al. (2022)	☑	☑	☑	☑
W4387055605	10.1038/s44183-023-00022-w	Julie Reimer et al. (2023)	☑	☑	☑	☑
W4402144060	10.1002/ece3.70231	Amanda Liczner et al. (2024)	☑	☑	☑	☑
W4412704230	10.1073/pnas.2410937122	Robin Naidoo et al. (2025)	☑	☑	☑	☑
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SPC Corpus Measures and Numbers

These data is gathered from OpenAlex directly, not downloaded any works. The data is used to assess the quality of the TCA Corpus.

The query contains: - the search term (nature, spc, spcc) - the types selected (article, book, book-chapter, dissertation, editorial, preprint, report, review) - the date range (from 1992-01-01 to 2025-12-31)

Overall counts

readRDS(file.path(params$corpus, "st_hits.rds")) |>
  dplyr::mutate(
    count = format(count, big.mark = ",")
  ) |>
  knitr::kable(format = "html", escape = FALSE)

	count
nature	24,198,955
spc	5,636,809
spcc	3,012,239
openalex	204,925,445

corpus_counts <- readRDS(file.path(params$corpus, "st_hits.rds"))

render_mermaid(
  file.path("figures", "overview.mmd"),
  list(
    SPC_LIST = format_count(corpus_counts[["spc", "count"]]),
    NATURE_LIST = format_count(corpus_counts[["nature", "count"]]),
    BASE_QUERY = format_count(corpus_counts[["spcc", "count"]])
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart TD
    Start([Start literature search]) --> SPC["<u><span style='color:#1a0dab;'>spc_corpus.yaml<br/>Assemble base SPC corpus</span></u>"]
    click SPC "./input/search_terms/spc_corpus.yaml" "Open spc_corpus.yaml"
    SPC --> SPC_list["spc keyword set<br/>(planning & connectivity terms)<br/>(n = 5,636,809)"
]
    SPC --> NATURE_list["nature keyword set<br/>(environmental context terms)<br/>(n = 24,198,955)"
]
    SPC_list --> BaseQuery["Level 1 query<br/>spc terms AND nature dictionary<br/>(n = 3,012,239)"
]
    NATURE_list --> BaseQuery

    BaseQuery --> ChapterSelect{Apply chapter / theme refinements}

    ChapterSelect --> CH1["<u><span style='color:#1a0dab;'>Chapter 1<br/>chapter_1.yaml<br/>Governance & planning principles</span></u>"]
    click CH1 "./input/search_terms/chapter_1.yaml" "Open chapter_1.yaml"
    ChapterSelect --> CH2["<u><span style='color:#1a0dab;'>Chapter 2<br/>chapter_2.yaml<br/>GBF targets, nexus themes, SDGs</span></u>"]
    click CH2 "./input/search_terms/chapter_2.yaml" "Open chapter_2.yaml"
    ChapterSelect --> CH3["<u><span style='color:#1a0dab;'>Chapter 3<br/>chapter_3.yaml<br/>Restoration & conservation planning</span></u>"]
    click CH3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"
    ChapterSelect --> CH4["<u><span style='color:#1a0dab;'>Chapter 4<br/>chapter_4.yaml<br/>Connectivity evidence & metrics</span></u>"]
    click CH4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"
    ChapterSelect --> CH5["<u><span style='color:#1a0dab;'>Chapter 5<br/>chapter_5.yaml<br/>Foresight & futures (sections, themes, cross-cutting)</span></u>"]
    click CH5 "./input/search_terms/Chapter_5_1_2.yaml" "Open Chapter 5 search terms"
    ChapterSelect --> CH6["<u><span style='color:#1a0dab;'>Chapter 6<br/>chapter_6.yaml (+ chapter_6_r2.yaml optional)<br/>Enabling environment</span></u>"]
    click CH6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

Publication Years

readRDS(file.path(params$corpus, "st_years.rds")) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("count_"),
      ~ base::format(.x, big.mark = ",")
    )
  ) |>
  knitr::kable(format = "html", escape = FALSE)

year	count_openalex	count_spc	count_nature	count_spcc
2025	7,024,215	331,075	1,148,157	198,063
2024	8,672,251	400,237	1,433,604	226,457
2023	8,800,438	404,415	1,456,730	218,067
2022	7,945,924	355,335	1,329,258	189,721
2021	8,655,439	354,061	1,334,177	188,928
2020	9,467,779	340,077	1,314,812	179,764
2019	9,186,561	299,234	1,194,205	156,951
2018	8,960,894	275,594	1,113,328	144,676
2017	8,920,577	252,218	1,039,376	131,548
2016	9,108,688	240,796	1,006,504	126,257
2015	8,879,550	231,523	982,924	121,665
2014	8,761,131	224,144	973,759	118,497
2013	8,482,636	211,059	934,487	111,122
2012	8,020,462	192,702	875,804	102,068
2011	7,753,054	178,462	820,324	94,208
2010	7,241,883	162,139	765,335	85,762
2009	6,785,530	145,079	698,345	76,646
2008	6,276,250	128,869	624,309	67,795
2007	5,817,068	114,654	573,333	60,482
2006	5,481,662	104,924	531,430	54,936
2005	5,018,808	92,872	480,920	48,944
2004	4,620,470	81,646	434,493	42,851
2003	4,317,436	74,728	403,805	39,187
2002	4,105,570	71,375	389,172	36,511
2001	3,512,306	53,975	319,330	28,871
2000	3,528,569	50,249	305,546	26,879
1999	2,908,060	42,387	258,993	22,310
1998	2,811,560	39,149	245,375	20,324
1997	2,644,466	36,505	229,958	18,840
1996	2,544,070	34,986	223,725	17,814
1995	2,347,216	32,055	207,527	16,291
1994	2,216,839	28,771	192,955	14,327
1993	2,102,230	26,755	183,703	13,284
1992	2,005,853	24,759	173,252	12,193

This graph only shows the relative nuymber of publications per year to identify different trends.

readRDS(file.path(params$corpus, "st_years.rds")) |>
  tidyr::pivot_longer(
    cols = dplyr::starts_with("count_"),
    names_to = "source",
    values_to = "count",
    names_prefix = "count_"
  ) |>
  # scale each source so its total sum is 1
  dplyr::group_by(source) |>
  dplyr::mutate(
    total_count = base::sum(count, na.rm = TRUE),
    count = dplyr::if_else(total_count > 0, count / total_count, 0)
  ) |>
  dplyr::ungroup() |>
  dplyr::select(-total_count) |>
  ggplot2::ggplot(ggplot2::aes(x = year, y = count, color = source)) +
  ggplot2::geom_line(linewidth = 1) +
  ggplot2::geom_point(size = 1.5) +
  ggplot2::scale_y_continuous(
    labels = scales::label_percent(accuracy = 1),
    expand = ggplot2::expansion(mult = c(0.02, 0.06))
  ) +
  ggplot2::labs(
    x = "Year",
    y = "Share of total works (sum-scaled per source)",
    color = "Source",
    title = "Publications per year by source (each source sums to 1)"
  ) +
  ggplot2::theme_minimal(base_size = 14) +
  ggplot2::theme(
    legend.position = "bottom",
    panel.grid.minor = ggplot2::element_blank()
  )

Language

readRDS(file.path(params$corpus, "st_languages.rds")) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("count_"),
      ~ base::format(.x, big.mark = ",")
    )
  ) |>
  knitr::kable(format = "html", escape = FALSE)

language	count_openalex	count_spc	count_nature	count_spcc
English	148,846,144	5,288,696	22,525,939	2,845,795
Spanish	8,000,764	71,029	352,929	30,375
Japanese	7,787,128	1,701	14,813	847
French	5,980,155	38,307	269,824	20,131
German	5,957,848	13,895	63,583	5,261
Chinese	4,526,511	4,051	36,358	2,013
Portuguese	4,330,547	31,673	139,789	16,513
Indonesian	3,065,227	42,083	168,957	15,798
Korean	3,004,656	27,795	101,468	13,631
Russian	2,812,938	28,366	148,424	15,485
Italian	1,359,451	7,301	33,437	4,414
Polish	1,266,118	4,473	25,693	2,534
Turkish	884,257	5,415	26,426	2,932
Dutch	828,958	2,198	12,452	1,177
Arabic	734,494	3,589	18,948	1,541
Ukrainian	552,998	5,810	23,068	3,339
Swedish	512,579	5,564	18,269	2,606
Persian	489,990	2,521	7,940	1,539
Czech	486,942	4,093	18,998	1,659
Catalan	322,723	1,284	8,898	726
Croatian	228,056	3,082	13,735	1,586
Norwegian	196,452	1,068	4,788	505
Finnish	189,852	864	3,587	427
Latvian	188,932	2,863	13,130	606
Hungarian	175,778	1,582	7,548	820
Modern Greek (1453-)	161,551	1,083	4,300	571
Danish	160,609	334	2,692	147
Malay (macrolanguage)	153,050	2,710	10,498	1,346
Slovenian	107,001	830	3,894	448
Thai	106,295	2,476	5,129	875
Lithuanian	92,242	549	2,445	331
Vietnamese	80,039	243	1,512	177
Serbo-Croatian	79,403	1,241	4,513	606
Romanian	71,179	618	3,884	336
Serbian	65,154	552	2,600	285
Slovak	37,132	302	1,745	148
Estonian	32,041	181	1,407	109
Galician	31,127	125	683	41
Hebrew	26,528	227	2,565	128
Bulgarian	25,746	180	926	102
Uzbek	25,479	10	1,109	5
Basque	25,339	117	626	54
Afrikaans	24,413	305	1,668	189
Latin	21,507	71	553	43
Esperanto	20,983	116	703	52
Hindi	13,316	25	195	10
Kazakh	12,314	152	772	81
Icelandic	11,398	57	284	32
Azerbaijani	11,354	95	423	67
Norwegian Nynorsk	10,318	49	208	22
Macedonian	8,073	57	239	26
Bosnian	7,604	88	388	47
Albanian	7,220	24	154	17
Georgian	6,389	40	199	29
Tagalog	5,496	66	287	31
Belarusian	5,014	16	123	11
Armenian	4,707	86	483	54
Tamil	4,469	11	108	3
Welsh	4,229	9	86	5
Urdu	3,971	17	229	8
Tajik	3,820	7	30	5
Irish	3,787	17	173	14
Yiddish	3,734	20	426	11
Mongolian	3,565	16	122	7
Nepali (macrolanguage)	3,180	13	122	6
Swahili (macrolanguage)	3,038	16	74	13
Bengali	2,782	14	43	9
Malagasy	2,739	4	70	2
Occitan (post 1500)	2,417	10	43	5
Kannada	2,118	10	79	4
Marathi	2,084	7	51	6
Pushto	2,081	1	7	0
Western Frisian	2,075	12	59	7
Ido	2,007	20	162	15
Breton	1,932	10	61	6
Malayalam	1,502	9	109	6
Javanese	1,388	15	65	6
Kirghiz	1,212	10	65	3
Kurdish	1,171	4	24	3
Luxembourgish	1,107	4	46	3
Ndonga	1,037	138	504	97
Sundanese	1,017	6	49	2
Sanskrit	1,008	0	11	0
Maltese	962	1	13	0
Sinhala	793	3	10	1
Telugu	770	3	37	3
Interlingua (International Auxiliary Language Association)	764	5	17	3
Gujarati	693	1	6	1
Interlingue	674	11	29	4
Tatar	627	7	11	4
Walloon	580	3	25	1
Scottish Gaelic	570	1	10	1
Bashkir	524	2	9	0
Dhivehi	481	0	3	0
Volapük	344	4	7	1
Oriya (macrolanguage)	341	0	2	0
Burmese	339	3	16	1
Panjabi	333	0	11	0
Tibetan	311	0	4	0
Somali	311	2	4	1
Aragonese	304	1	5	0
Haitian	302	3	20	3
Cornish	236	0	5	0
Lao	223	2	11	1
Central Khmer	221	4	10	3
Quechua	184	0	7	0
Amharic	184	2	4	2
Chechen	143	0	1	0
Manx	141	2	6	0
Limburgan	138	0	2	0
Romansh	125	2	5	1
Yoruba	98	2	7	1
Guarani	97	0	1	0
Sindhi	97	0	0	0
Uighur	62	0	1	0
Turkmen	61	1	1	1
Norwegian Bokmål	52	1	1	0
Assamese	52	0	0	0
Faroese	50	8	28	8
Chuvash	42	0	2	0
Ossetian	35	0	0	0
Komi	23	6	4	4
Sardinian	14	0	1	0
Corsican	11	0	0	0
Avaric	9	0	0	0
South Ndebele	5	2	4	2
Zulu	3	0	0	0
Northern Sami	1	0	0	0
Tsonga	1	0	0	0
NA	0	321	3,062	209

This graph only shows the relative number of publications per year to identify different trends.

readRDS(file.path(params$corpus, "st_languages.rds")) |>
  # keep top 15 languages by total (before scaling)
  dplyr::mutate(
    total = count_openalex + count_spc + count_nature + count_spcc
  ) |>
  dplyr::slice_max(total, n = 15) |>
  dplyr::arrange(dplyr::desc(total)) |>
  # fix display order so largest stays on top
  dplyr::mutate(language = factor(language, levels = rev(language))) |>
  dplyr::select(-total) |>
  # reshape wide → long
  tidyr::pivot_longer(
    cols = dplyr::starts_with("count_"),
    names_to = "source",
    values_to = "count",
    names_prefix = "count_"
  ) |>
  # scale so each source sums to 1
  dplyr::group_by(source) |>
  dplyr::mutate(
    total_source = base::sum(count, na.rm = TRUE),
    count = dplyr::if_else(total_source > 0, count / total_source, 0)
  ) |>
  dplyr::ungroup() |>
  dplyr::select(-total_source) |>
  ggplot2::ggplot(ggplot2::aes(x = language, y = count, fill = source)) +
  ggplot2::geom_col(position = "dodge") +
  ggplot2::coord_flip() +
  ggplot2::scale_y_continuous(
    labels = scales::label_percent(accuracy = 1),
    expand = ggplot2::expansion(mult = c(0, 0.05))
  ) +
  ggplot2::labs(
    x = "Language",
    y = "Share of total works within each source",
    fill = "Source",
    title = "Publications by language (top 15), scaled so each source sums to 1"
  ) +
  ggplot2::theme_minimal(base_size = 14) +
  ggplot2::theme(
    legend.position = "bottom",
    panel.grid.minor = ggplot2::element_blank()
  )

Individual Chapter

Here we show the sub-searches for the individual chapters. In the tables indicating the number, an NA value indicates that the search string was to long (most likely) or any other error occured in the search. The same applies when an error message is given.

Chapter 1

ch1_hits <- readRDS(file.path(params$chapter_1, "st_hits.rds"))

ch1_counts <- setNames(ch1_hits$count, rownames(ch1_hits))

ch1_hits |>
  dplyr::mutate(count = format_count(count)) |>
  knitr::kable(
    col.names = c("Count"),
    align = "r"
  )

	Count
Set 1	4,190
Set 2.1	4,768
Set 2.2	88
Set 3	-4,209

render_mermaid(
  file.path("figures", "chapter1.mmd"),
  c(
    C1_1 = format_count(ch1_counts[["Set 1"]]),
    C1_2.1 = format_count(ch1_counts[["Set 2.1"]]),
    C1_2.2 = format_count(ch1_counts[["Set 2.2"]]),
    C1_3 = format_count(ch1_counts[["Set 3"]])
    # C1_4 = format_count(ch1_counts[["Set 4"]]),
    # C1_5 = format_count(ch1_counts[["Set 5"]]),
    # C1_6 = format_count(ch1_counts[["Set 6"]]),
    # C1_7 = format_count(ch1_counts[["Set 7"]])
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch1["<u><span style='color:#1a0dab;'>chapter_1.yaml<br/>Level 2 refinements</span></u>"]
    click Ch1 "./input/search_terms/chapter_1.yaml" "Open chapter_1.yaml"

    subgraph Chapter1Sets["Chapter 1 thematic searches"]
        direction TB
        C1_1["Set 1:<br/>land/spatial planning<br/>+ biodiversity goals<br/>+ societal needs/values<br/>(n = 4,190)"]
        C1_2.1["Set 2.1:<br/>???<br/>(n = 4,768)"]
        C1_2.2["Set 2.2:<br/>???<br/>(n = 88)"]
        C1_3["Set 3:<br/>evidence & precaution<br/>+ ILK knowledge base<br/>(n = -4,209)"]
        %% C1_4["Set 4:<br/>multilevel/transparent governance<br/>+ customary coherence<br/>(n = <<C1_4>>)"]
        %% C1_5["Set 5:<br/>participatory planning<br/>+ co-design / engagement<br/>(n = <<C1_5>>)"]
        %% C1_6["Set 6:<br/>equity / rights / tenure<br/>+ justice outcomes<br/>(n = <<C1_6>>)"]
        %% C1_7["Set 7:<br/>connectivity (land-sea/cross-scale)<br/>+ nexus & climate links<br/>(n = <<C1_7>>)"]
    end

    Ch1 --> C1_1
    Ch1 --> C1_2.1
    Ch1 --> C1_2.2
    Ch1 --> C1_3
    %% Ch1 --> C1_4
    %% Ch1 --> C1_5
    %% Ch1 --> C1_6
    %% Ch1 --> C1_7

Chapter 2

ch2_hits <- readRDS(file.path(params$chapter_2, "st_hits.rds"))

ch2_counts <- lapply(
  ch2_hits,
  function(df) {
    setNames(df$count, rownames(df))
  }
)

do.call(
  rbind,
  lapply(names(ch2_hits), function(sect_nm) {
    df <- ch2_hits[[sect_nm]]
    df$section <- sect_nm
    df$set <- rownames(df)
    rownames(df) <- NULL
    df[,
      c("section", "set", setdiff(names(df), c("section", "set"))),
      drop = FALSE
    ]
  })
) |>
  dplyr::mutate(
    count = format_count(count)
  ) |>
  knitr::kable(
    col.names = c("Section", "Set", "Count"),
    align = c("c", "c", "r")
  )

Section	Set	Count
GBF	GBF-1 - Urban	383,482
GBF	GBF-1 - Rural	556,382
GBF	GBF-1 - Freshwater	799,095
GBF	GBF-1 - Marine	423,677
GBF	GBF - 2 (Ecosystem restoration)	1,046,971
Targets	Target 3 (Protected areas / conservation coverage)	530,567
Targets	Target 4 (Species extinction & genetic diversity)	44,671
Targets	Target 5 (Wild species trade & use)	87,692
Targets	Target 6 (Invasive alien species)	15,151
Targets	Target 7 (Pollution)	214,601
Targets	Target 8 (Climate change & resilience)	239,276
Targets	Target 9 (Wild species management & use)	9,583
Targets	Target 10 (Sustainability in agriculture, aquaculture, forestry & fisheries)	286,570
Targets	Target 11 (Nature’s contributions to people / ecosystem services)	58,111
Targets	Target 12 (Green & blue spaces, urban planning)	66,948
Targets	Target 13 (Genetic resources & benefit sharing)	10,266
Targets	Target 14 (Biodiversity integration in decision-making)	178,602
Targets	Target 15 (Business & financial institutions disclosure & risk)	262,899
Targets	Target 16 (Sustainable consumption)	98,452
Targets	Target 17 (Biosafety)	9,339
Targets	Target 18 (Harmful incentives / subsidies)	47,459
Targets	Target 19 (Finance mobilisation)	89,981
Targets	Target 20 (Capacity building, technology & cooperation)	520,077
Targets	Target 21 (Data, knowledge & monitoring)	1,268,873
Targets	Target 22 (Participation & inclusion)	169,822
Targets	Target 23 (Gender equality)	74,306
Related to spatial planning process	Set Related	131,751
Nexus	Water	497,888
Nexus	Food	399,274
Nexus	Health	416,813
Nexus	Climate	627,545

ch2_gbf <- ch2_counts[["GBF"]]
ch2_targets <- ch2_counts[["Targets"]]
ch2_rel <- ch2_counts[["Related to spatial planning process"]]
ch2_nexus <- ch2_counts[["Nexus"]]

fmt_gbf <- function(name) format_count(ch2_gbf[[name]])
fmt_target <- function(name) format_count(ch2_targets[[name]])
fmt_rel <- function(name) format_count(ch2_rel[[name]])
fmt_nexus <- function(name) format_count(ch2_nexus[[name]])

render_mermaid(
  file.path("figures", "chapter2.mmd"),
  c(
    GBF_Urban = fmt_gbf("GBF-1 - Urban"),
    GBF_Rural = fmt_gbf("GBF-1 - Rural"),
    GBF_Fresh = fmt_gbf("GBF-1 - Freshwater"),
    GBF_Marine = fmt_gbf("GBF-1 - Marine"),
    GBF_Restore = fmt_gbf("GBF - 2 (Ecosystem restoration)"),
    T3 = fmt_target("Target 3 (Protected areas / conservation coverage)"),
    T4 = fmt_target("Target 4 (Species extinction & genetic diversity)"),
    T5 = fmt_target("Target 5 (Wild species trade & use)"),
    T6 = fmt_target("Target 6 (Invasive alien species)"),
    T7 = fmt_target("Target 7 (Pollution)"),
    T8 = fmt_target("Target 8 (Climate change & resilience)"),
    T9 = fmt_target("Target 9 (Wild species management & use)"),
    T10 = fmt_target(
      "Target 10 (Sustainability in agriculture, aquaculture, forestry & fisheries)"
    ),
    T11 = fmt_target(
      "Target 11 (Nature\u2019s contributions to people / ecosystem services)"
    ),
    T12 = fmt_target("Target 12 (Green & blue spaces, urban planning)"),
    T13 = fmt_target("Target 13 (Genetic resources & benefit sharing)"),
    T14 = fmt_target("Target 14 (Biodiversity integration in decision-making)"),
    T15 = fmt_target(
      "Target 15 (Business & financial institutions disclosure & risk)"
    ),
    T16 = fmt_target("Target 16 (Sustainable consumption)"),
    T17 = fmt_target("Target 17 (Biosafety)"),
    T18 = fmt_target("Target 18 (Harmful incentives / subsidies)"),
    T19 = fmt_target("Target 19 (Finance mobilisation)"),
    T20 = fmt_target("Target 20 (Capacity building, technology & cooperation)"),
    T21 = fmt_target("Target 21 (Data, knowledge & monitoring)"),
    T22 = fmt_target("Target 22 (Participation & inclusion)"),
    T23 = fmt_target("Target 23 (Gender equality)"),
    REL = fmt_rel("Set Related"),
    Nexus_Water = fmt_nexus("Water"),
    Nexus_Food = fmt_nexus("Food"),
    Nexus_Health = fmt_nexus("Health"),
    Nexus_Climate = fmt_nexus("Climate")
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch2L2["<u><span style='color:#1a0dab;'>chapter_2.yaml<br/>Level 2 GBF bundles</span></u>"]
    click Ch2L2 "../search_terms/chapter_2.yaml" "Open chapter_2.yaml"

    subgraph L1_GBF["GBF contexts & targets"]
        direction TB
        GBF_Urban["GBF-1 Urban<br/>(n = 383,482)"]
        GBF_Rural["GBF-1 Rural<br/>(n = 556,382)"]
        GBF_Fresh["GBF-1 Freshwater<br/>(n = 799,095)"]
        GBF_Marine["GBF-1 Marine<br/>(n = 423,677)"]
        GBF_Restore["GBF-2 Ecosystem restoration<br/>(n = 1,046,971)"]
    end
    Ch2L2 --> GBF_Urban
    Ch2L2 --> GBF_Rural
    Ch2L2 --> GBF_Fresh
    Ch2L2 --> GBF_Marine
    Ch2L2 --> GBF_Restore

    subgraph L1_TARGET["GBF contexts & targets"]
        T3["Target 3<br/>(n = 530,567)"]
        T4["Target 4<br/>(n = 44,671)"]
        T5["Target 5<br/>(n = 87,692)"]
        T6["Target 6<br/>(n = 15,151)"]
        T7["Target 7<br/>(n = 214,601)"]
        T8["Target 8<br/>(n = 239,276)"]
        T9["Target 9<br/>(n = 9,583)"]
        T10["Target 10<br/>(n = 286,570)"]
        T11["Target 11<br/>(n = 58,111)"]
        T12["Target 12<br/>(n = 66,948)"]
        T13["Target 13<br/>(n = 10,266)"]
        T14["Target 14<br/>(n = 178,602)"]
        T15["Target 15<br/>(n = 262,899)"]
        T16["Target 16<br/>(n = 98,452)"]
        T17["Target 17<br/>(n = 9,339)"]
        T18["Target 18<br/>(n = 47,459)"]
        T19["Target 19<br/>(n = 89,981)"]
        T20["Target 20<br/>(n = 520,077)"]
        T21["Target 21<br/>(n = 1,268,873)"]
        T22["Target 22<br/>(n = 169,822)"]
        T23["Target 23<br/>(n = 74,306)"]
    end
    Ch2L2 --> T3
    Ch2L2 --> T4
    Ch2L2 --> T5
    Ch2L2 --> T6
    Ch2L2 --> T7
    Ch2L2 --> T8
    Ch2L2 --> T9
    Ch2L2 --> T10
    Ch2L2 --> T11
    Ch2L2 --> T12
    Ch2L2 --> T13
    Ch2L2 --> T14
    Ch2L2 --> T15
    Ch2L2 --> T16
    Ch2L2 --> T17
    Ch2L2 --> T18
    Ch2L2 --> T19
    Ch2L2 --> T20
    Ch2L2 --> T21
    Ch2L2 --> T22
    Ch2L2 --> T23

    subgraph L1_REL["Spatial Planning Related"]
        REL["Spatial Planning Related<br/>(n = 131,751)"]
    end
    Ch2L2 --> REL

    subgraph L1_NexusSets["Nexus"]
        direction TB
        Nexus_Water["Water<br/>(n = 497,888)"]
        Nexus_Food["Food<br/>(n = 399,274)"]
        Nexus_Health["Health<br/>(n = 416,813)"]
        Nexus_Climate["Climate<br/>(n = 627,545)"]
    end
    Ch2L2 --> Nexus_Water
    Ch2L2 --> Nexus_Food
    Ch2L2 --> Nexus_Health
    Ch2L2 --> Nexus_Climate

    L1_GBF --> Ch2L4["<u><span style='color:#1a0dab;'>chapter_2_sdg.yaml<br/>Level 4 SDG filters</span></u>"]
    L1_NexusSets --> Ch2L4
    L1_REL --> Ch2L4
    click Ch2L4 "../search_terms/chapter_2_sdg.yaml" "Open chapter_2_sdg.yaml"
    subgraph SDGSets["SDG goal filters"]
        direction TB
        SDG1["SDG 1"]
        SDG2["SDG 2"]
        SDG3["SDG 3"]
        SDG4["SDG 4"]
        SDG5["SDG 5"]
        SDG6["SDG 6"]
        SDG7["SDG 7"]
        SDG8["SDG 8"]
        SDG9["SDG 9"]
        SDG10["SDG 10"]
        SDG11["SDG 11"]
        SDG12["SDG 12"]
        SDG13["SDG 13"]
        SDG14["SDG 14"]
        SDG15["SDG 15"]
        SDG16["SDG 16"]
        SDG17["SDG 17"]
    end
    Ch2L4 --> SDG1
    Ch2L4 --> SDG2
    Ch2L4 --> SDG3
    Ch2L4 --> SDG4
    Ch2L4 --> SDG5
    Ch2L4 --> SDG6
    Ch2L4 --> SDG7
    Ch2L4 --> SDG8
    Ch2L4 --> SDG9
    Ch2L4 --> SDG10
    Ch2L4 --> SDG11
    Ch2L4 --> SDG12
    Ch2L4 --> SDG13
    Ch2L4 --> SDG14
    Ch2L4 --> SDG15
    Ch2L4 --> SDG16
    Ch2L4 --> SDG17

Chapter 3

ch3_hits <- readRDS(file.path(params$chapter_3, "st_hits.rds"))

ch3_counts <- setNames(ch3_hits$count, rownames(ch3_hits))

ch3_hits |>
  dplyr::mutate(count = format_count(count)) |>
  knitr::kable(
    col.names = c("Count"),
    align = "r"
  )

	Count
Set 1	12,766
Set 3	3,598
Set 3.1A	964
Set 3.1BCD	311
Set 3.2CE	366
Set 3.2D	2,121
Set 3.2F	187
Set 3.3	4,065
Set 4	21,951
Set 4.1AB	1,858
Set 4.1CD	12,867
Set 4.2	11,921
Set 5.1	2,223
Set 5.2	1,071

render_mermaid(
  file.path("figures", "chapter3.mmd"),
  c(
    C3_1 = format_count(ch3_counts[["Set 1"]]),
    C3_3 = format_count(ch3_counts[["Set 3"]]),
    C3_3.1A = format_count(ch3_counts[["Set 3.1A"]]),
    C3_3.1BCD = format_count(ch3_counts[["Set 3.1BCD"]]),
    C3_3.2D = format_count(ch3_counts[["Set 3.2D"]]),
    C3_3.2F = format_count(ch3_counts[["Set 3.2F"]]),
    C3_3.3 = format_count(ch3_counts[["Set 3.3"]]),
    C3_4 = format_count(ch3_counts[["Set 4"]]),
    C3_4.1AB = format_count(ch3_counts[["Set 4.1AB"]]),
    C3_4.1CD = format_count(ch3_counts[["Set 4.1CD"]]),
    C3_4.2 = format_count(ch3_counts[["Set 4.2"]]),
    C3_5.1 = format_count(ch3_counts[["Set 5.1"]]),
    C3_5.2 = format_count(ch3_counts[["Set 5.2"]])
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch3["<u><span style='color:#1a0dab;'>chapter_3.yaml<br/>Level 2 refinements</span></u>"]
    click Ch3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"

    subgraph Chapter3Sets["Chapter 3 searches"]
        direction TB
        C3_1["Set 1:<br/>Protected/OECM + NBSAP + cases<br/>+ spatial prioritization + regional scales<br/>(n = 12,766)"]
        C3_3["Set 3:<br/>Restoration planning + connectivity + resilience<br/>(n = 3,598)"]
        C3_3.1A["Set 3.1 A:<br/>Objective<br/>(n = 964)"]
        C3_3.1BCD["Set 3.1 BCD:<br/>Concept<br/>(n = 311)"]
        C3_3.2D["Set 3.2 D:<br/>???<br/>(n = 2,121)"]
        C3_3.2F["Set 3.2 F:<br/>???<br/>(n = 187)"]
        C3_3.3["Set 3.3:<br/>???<br/>(n = 4,065)"]
        C3_4["Set 4:<br/>Systematic conservation planning / gap analysis<br/>(n = 21,951)"]
        C3_4.1AB["Set 4.1 AB:<br/>???<br/>(n = 1,858)"]
        C3_4.1CD["Set 4.1 CD:<br/>???<br/>(n = 12,867)"]
        C3_4.2["Set 4.2:<br/>???<br/>(n = 11,921)"]

        C3_5.1["Set 5.1:<br/>???<br/>(n = 2,223)"]
        C3_5.2["Set 5.1:<br/>???<br/>(n = 1,071)"]
    end
    Ch3 --> C3_1
    Ch3 --> C3_3
    Ch3 --> C3_3.1A
    Ch3 --> C3_3.1BCD
    Ch3 --> C3_3.2D
    Ch3 --> C3_3.2F
    Ch3 --> C3_3.3
    Ch3 --> C3_4
    Ch3 --> C3_4.1AB
    Ch3 --> C3_4.1CD
    Ch3 --> C3_4.2
    Ch3 --> C3_5.1
    Ch3 --> C3_5.2

Chapter 4

ch4_hits <- readRDS(file.path(params$chapter_4, "st_hits.rds"))

ch4_counts <- setNames(ch4_hits$count, rownames(ch4_hits))

ch4_hits |>
  dplyr::mutate(count = format_count(count)) |>
  knitr::kable(
    col.names = c("Count"),
    align = "r"
  )

	Count
Set 1	1,387
Set 2	241
Set 3	6,290
Set 4	29,809
Set 5	375
Set 6	2,676
Set 7	0
Set 8	89,971

render_mermaid(
  file.path("figures", "chapter4.mmd"),
  c(
    C4_1 = format_count(ch4_counts[["Set 1"]]),
    C4_2 = format_count(ch4_counts[["Set 2"]]),
    C4_3 = format_count(ch4_counts[["Set 3"]]),
    C4_4 = format_count(ch4_counts[["Set 4"]]),
    C4_5 = format_count(ch4_counts[["Set 5"]]),
    C4_6 = format_count(ch4_counts[["Set 6"]]),
    C4_7 = format_count(ch4_counts[["Set 7"]]),
    C4_8 = format_count(ch4_counts[["Set 8"]])
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch4["<u><span style='color:#1a0dab;'>chapter_4.yaml<br/>Level 2 refinements</span></u>"]
    click Ch4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"

    subgraph Chapter4Sets["Chapter 4 searches"]
        direction TB
        C4_1["Set 1:<br/>Connectivity review articles<br/>(n = 1,387)"]
        C4_2["Set 2:<br/>Connectivity benefits vs risks<br/>+ ecosystem services<br/>(n = 241)"]
        C4_3["Set 3:<br/>Structural vs functional connectivity<br/>(n = 6,290)"]
        C4_4["Set 4:<br/>Connectivity modelling toolkits<br/>(n = 29,809)"]
        C4_5["Set 5:<br/>Connectivity indicators & metrics<br/>(n = 375)"]
        C4_6["Set 6:<br/>Policy & governance integration<br/>(n = 2,676)"]
        C4_7["Set 7:<br/>Multilevel / transboundary governance<br/>+ IPLC inclusion + land tenure<br/>(n = 0)"]
        C4_8["Set 8:<br/>Movement ecology<br/>(dispersal / migration / permeability)<br/>(n = 89,971)"]
    end
    Ch4 --> C4_1
    Ch4 --> C4_2
    Ch4 --> C4_3
    Ch4 --> C4_4
    Ch4 --> C4_5
    Ch4 --> C4_6
    Ch4 --> C4_7
    Ch4 --> C4_8

Chapter 5

ch5_hits <- readRDS(file.path(params$chapter_5, "st_hits.rds"))

ch5_counts <- lapply(ch5_hits, function(df) {
  setNames(df$count, rownames(df))
})

do.call(
  rbind,
  lapply(names(ch5_hits), function(sect_nm) {
    df <- ch5_hits[[sect_nm]]
    df$section <- sect_nm
    df$set <- rownames(df)
    rownames(df) <- NULL
    df[,
      c("section", "set", setdiff(names(df), c("section", "set"))),
      drop = FALSE
    ]
  })
) |>
  dplyr::mutate(
    count = format_count(count)
  ) |>
  knitr::kable(
    col.names = c("Section", "Set", "Count"),
    align = c("c", "c", "r")
  )

Section	Set	Count
Sections 1 and 2	Set 1	203,060
Sections 1 and 2	Set 2	62,688
Sections 1 and 2	Set 3	61,473
Sections 1 and 2	Set 4	425,315
Sections 1 and 2	Set 5	267,697
Section 3	Set 1	2,919
Section 3	Set 2	15,626
Section 3	Set 3	117
Section 4	Set 1	63,322
Section 4	Set 2	21,664
Section 4	Set 3	14,256
Section 5	Set 1	15,193
Section 5	Set 2	84,675
Section 5	Set 3	53,802
Section 5	Set 4	29,203
Section 6	Set 1	180
Section 6	Set 2	4,258
Section 6	Set 3	3,121
Section 6	Set 4	2,521
Cross-cutting sets	Set 1	45,650
Cross-cutting sets	Set 2	232,690

fmt_ch5 <- function(section, set) {
  format_count(ch5_counts[[section]][[set]])
}

render_mermaid(
  file.path("figures", "chapter5.mmd"),
  c(
    C5_1 = fmt_ch5("Sections 1 and 2", "Set 1"),
    C5_2 = fmt_ch5("Sections 1 and 2", "Set 2"),
    C5_3 = fmt_ch5("Sections 1 and 2", "Set 3"),
    C5_4 = fmt_ch5("Sections 1 and 2", "Set 4"),
    C5_5 = fmt_ch5("Sections 1 and 2", "Set 5"),
    C5_3a = fmt_ch5("Section 3", "Set 1"),
    C5_3b = fmt_ch5("Section 3", "Set 2"),
    C5_3c = fmt_ch5("Section 3", "Set 3"),
    C5_4a = fmt_ch5("Section 4", "Set 1"),
    C5_4b = fmt_ch5("Section 4", "Set 2"),
    C5_4c = fmt_ch5("Section 4", "Set 3"),
    C5_5a = fmt_ch5("Section 5", "Set 1"),
    C5_5b = fmt_ch5("Section 5", "Set 2"),
    C5_5c = fmt_ch5("Section 5", "Set 3"),
    C5_5d = fmt_ch5("Section 5", "Set 4"),
    C5_6a = fmt_ch5("Section 6", "Set 1"),
    C5_6b = fmt_ch5("Section 6", "Set 2"),
    C5_6c = fmt_ch5("Section 6", "Set 3"),
    C5_6d = fmt_ch5("Section 6", "Set 4"),
    CC1 = fmt_ch5("Cross-cutting sets", "Set 1"),
    CC2 = fmt_ch5("Cross-cutting sets", "Set 2")
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch5L2["<u><span style='color:#1a0dab;'>chapter_5.yaml<br/>Level 2 foresight framing</span></u>"]
    click Ch5L2 "./input/search_terms/chapter_5.yaml" "Open chapter_5.yaml"

    subgraph L2Sets["Section 5.1–5.2 searches"]
        direction TB
        C5_1["Set 1:<br/>Future + project*/predict*/scenario<br/>(n = 203,060)"]
        C5_2["Set 2:<br/>Future + pathway*/narrative*/vision*<br/>(n = 62,688)"]
        C5_3["Set 3:<br/>Future-proof*/anticipat*/scenario planning<br/>(n = 61,473)"]
        C5_4["Set 4:<br/>Foresight*/backcasting/simulat*/trend*<br/>(n = 425,315)"]
        C5_5["Set 5:<br/>Model* AND scenario<br/>(n = 267,697)"]
    end
    Ch5L2 --> C5_1
    Ch5L2 --> C5_2
    Ch5L2 --> C5_3
    Ch5L2 --> C5_4
    Ch5L2 --> C5_5

    subgraph L3Sets["Section 5.3 searches"]
        direction TB
        C5_3a["Set 1:<br/>Drivers of change<br/>(n = 2,919)"]
        C5_3b["Set 2:<br/>Driver modelling approaches<br/>(n = 15,626)"]
        C5_3c["Set 3:<br/>Driver gaps<br/>(n = 117)"]
    end
    Ch5L2 --> C5_3a
    Ch5L2 --> C5_3b
    Ch5L2 --> C5_3c

    subgraph L4Sets["Section 5.4 searches"]
        direction TB
        C5_4a["Set 1:<br/>Interactions (synergy/trade-off/nexus)<br/>(n = 63,322)"]
        C5_4b["Set 2:<br/>Response options (integrated planning / NbS)<br/>(n = 21,664)"]
        C5_4c["Set 3:<br/>Cross-scale synergy & trade-off terms<br/>(n = 14,256)"]
    end
    Ch5L2 --> C5_4a
    Ch5L2 --> C5_4b
    Ch5L2 --> C5_4c

    subgraph L5Sets["Section 5.5 searches"]
        direction TB
        C5_5a["Set 1:<br/>Adaptive / transformative management<br/>(n = 15,193)"]
        C5_5b["Set 2:<br/>Uncertainty quantification<br/>(n = 84,675)"]
        C5_5c["Set 3:<br/>Tipping points & thresholds<br/>(n = 53,802)"]
        C5_5d["Set 4:<br/>Cascading risks & precaution<br/>(n = 29,203)"]
    end
    Ch5L2 --> C5_5a
    Ch5L2 --> C5_5b
    Ch5L2 --> C5_5c
    Ch5L2 --> C5_5d

    subgraph L6Sets["Section 5.6 searches"]
        direction TB
        C5_6a["Set 1:<br/>Science-policy-practice pathways<br/>(n = 180)"]
        C5_6b["Set 2:<br/>ILK integration & community planning<br/>(n = 4,258)"]
        C5_6c["Set 3:<br/>Enabling factors & coordination<br/>(n = 3,121)"]
        C5_6d["Set 4:<br/>Shocks, surprises, uncertainties<br/>(n = 2,521)"]
    end
    Ch5L2 --> C5_6a
    Ch5L2 --> C5_6b
    Ch5L2 --> C5_6c
    Ch5L2 --> C5_6d

    subgraph CCSets["Cross-cutting searches"]
        direction TB
        CC1["Set 1:<br/>Scales & telecoupling<br/>(n = 45,650)"]
        CC2["Set 2:<br/>Co-production & inclusion<br/>(n = 232,690)"]
    end
    Ch5L2 --> CC1
    Ch5L2 --> CC2

Chapter 6

ch6_hits <- readRDS(file.path(params$chapter_6, "st_hits.rds"))

ch6_counts <- setNames(ch6_hits$count, rownames(ch6_hits))

ch6_hits |>
  dplyr::mutate(count = format_count(count)) |>
  knitr::kable(
    col.names = c("Count"),
    align = "r"
  )

	Count
Set 1	12,626
Set 2	1,529
Set 3	84,873
Set 4	170,621
Set 5	3,624
Set 6	56,439
Set 7	255,581
Set 8.1	-4,128
Set 8.2	18,683
Set 9	60,445
Set 10	19,044

render_mermaid(
  file.path("figures", "chapter6.mmd"),
  c(
    C6_1 = format_count(ch6_counts[["Set 1"]]),
    C6_2 = format_count(ch6_counts[["Set 2"]]),
    C6_3 = format_count(ch6_counts[["Set 3"]]),
    C6_4 = format_count(ch6_counts[["Set 4"]]),
    C6_5 = format_count(ch6_counts[["Set 5"]]),
    C6_6 = format_count(ch6_counts[["Set 6"]]),
    C6_7 = format_count(ch6_counts[["Set 7"]]),
    C6_8 = format_count(sum(ch6_counts[c("Set 8.1", "Set 8.2")], na.rm = TRUE)),
    C6_9 = format_count(ch6_counts[["Set 9"]]),
    C6_10 = format_count(ch6_counts[["Set 10"]])
  )
)

%%{init: {'flowchart': {'htmlLabels': true}}}%%
flowchart LR
    Start([SPC Corpus]) --> Ch6["<u><span style='color:#1a0dab;'>chapter_6.yaml<br/>Level 2 refinements</span></u>"]
    click Ch6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

    subgraph Chapter6Sets["Chapter 6 searches"]
        direction TB
        C6_1["Set 1:<br/>Institutional & governance structures<br/>(n = 12,626)"]
        C6_2["Set 2:<br/>Political & strategic leadership<br/>(n = 1,529)"]
        C6_3["Set 3:<br/>Socio-cultural & stakeholder engagement<br/>(n = 84,873)"]
        C6_4["Set 4:<br/>Collaboration, trust & networks<br/>(n = 170,621)"]
        C6_5["Set 5:<br/>Financial & economic mechanisms<br/>(n = 3,624)"]
        C6_6["Set 6:<br/>Legal & policy frameworks<br/>(n = 56,439)"]
        C6_7["Set 7:<br/>Human & institutional capacity<br/>(n = 255,581)"]
        C6_8["Set 8:<br/>Data, knowledge & decision support (8.1 + 8.2)<br/>(n = 14,555)"]
        C6_9["Set 9:<br/>Ecological & spatial planning tools<br/>(n = 60,445)"]
        C6_10["Set 10:<br/>Cross-cutting process enablers<br/>(n = 19,044)"]
    end
    Ch6 --> C6_1
    Ch6 --> C6_2
    Ch6 --> C6_3
    Ch6 --> C6_4
    Ch6 --> C6_5
    Ch6 --> C6_6
    Ch6 --> C6_7
    Ch6 --> C6_8
    Ch6 --> C6_9
    Ch6 --> C6_10

Ch6R2["<u><span style='color:#1a0dab;'>chapter_6_r2.yaml<br/>Optional Level 3 filter</span></u>"]
Chapter6Sets --> Ch6R2
click Ch6R2 "./input/search_terms/chapter_6_r2.yaml" "Open chapter_6_r2.yaml"
subgraph Chapter6Case["Chapter 6 searches"]
    R2["Case-study keywords<br/>(case stud*, example*, initiative*, etc.)"]
end
Ch6R2 --> R2