DOI: 10.5061/dryad.5mkkwh7jk
title: Understanding fire conflict through stakeholder mapping in Madagascar's grassy Biomes

description: This dataset contains fuzzy cognitive mapping (FCM) data examining
 stakeholder perspectives on landscape fire drivers in Madagascar's
 Ambatofinandrahana district. The data were collected through 28 focus
 groups (n = 133 participants) conducted in April-May 2023 across five
 stakeholder groups: government officials, conservation practitioners,
 community leaders, rural farmers, and rural herders. Participants
 collectively identified variables influencing landscape fires and mapped
 causal relationships between them, assigning directional connections and
 strength ratings (1-4 scale). The repository includes: (1) an aggregated
 stakeholder matrix consolidating all 28 focus group maps; (2) five
 stakeholder-level aggregated matrices used for hierarchical clustering
 analysis; (3) a consolidated variable list documenting how 200+ original
 variables were condensed into 36 analytical categories;
 (4) network metric summary tables for variables including Katz
 centrality scores, in-degree, out-degree, OD/ID ratios;
 (5) variable-to-variable relationship data for each stakeholder
 group. These data support the findings reported in Convery-Fisher et al.,
 "Understanding Fire Conflict Through Stakeholder Mapping in
 Madagascar's Grassy Biomes," published in People and Nature.
description: # Understanding fire conflict through stakeholder mapping in
 Madagascar's grassy Biomes Dataset DOI:
 [10.5061/dryad.5mkkwh7jk](https://doi.org/10.5061/dryad.5mkkwh7jk) ## Data
 Description ### Research Context and Objectives These data were collected
 as part of a participatory research effort to understand and compare
 stakeholder perspectives on landscape fire drivers in Madagascar's
 grassy biomes. Fire management in Madagascar represents a long-standing
 conflict between rural communities who use fire as a traditional land
 management tool and government and conservation authorities who view fire
 as environmentally destructive. Despite decades of fire suppression
 policies, tensions persist, and management approaches remain ineffective.
 This research employed fuzzy cognitive mapping (FCM)—a semi-quantitative
 participatory method—to systematically capture how different stakeholder
 groups perceive the causal relationships driving landscape fires in the
 Ambatofinandrahana district, central Madagascar. The study was designed
 to: 1. Identify which variables stakeholders perceive as most important in
 influencing landscape fire dynamics 2. Compare how stakeholder perceptions
 of causal relationships vary across groups 3. Reveal areas of agreement
 and divergence that could inform collaborative fire management approaches
 ### Research Design Between April and May 2023, we conducted 28 focus
 group discussions with 133 participants representing five key stakeholder
 groups: government officials (n = 15), conservation practitioners (n =
 18), community leaders (n = 52), rural farmers (n = 25), and rural herders
 (n = 23). Focus groups were held across nine rural villages and two towns
 in the Ambatofinandrahana district. During structured 2-hour sessions,
 participants collectively identified social, economic, environmental, and
 political variables they perceived as influencing landscape fires. They
 then mapped causal relationships between these variables, creating visual
 cognitive maps that represent their shared understanding of fire system
 dynamics. Each relationship was assigned a direction (positive or
 negative) and strength rating (1-4 scale), providing semi-quantitative
 data suitable for network analysis. The resulting dataset captures diverse
 and sometimes conflicting mental models of fire dynamics, revealing not
 just differences in priorities but fundamental divergences in how
 stakeholders understand cause-and-effect relationships in fire management.
 These data provide empirical grounding for understanding why fire policies
 often fail and where opportunities for collaboration may exist. ### Study
 Significance This dataset represents one of the first systematic,
 comparative analyses of stakeholder perspectives on fire management in
 Madagascar using structured participatory methods. The fuzzy cognitive
 mapping approach bridges qualitative and quantitative traditions,
 producing data that preserve the contextual richness of local knowledge
 whilst enabling statistical comparison across stakeholder groups. The
 methods and analytical approaches demonstrated here are transferable to
 other natural resource conflicts globally where diverse perspectives
 complicate governance. --- **Related Publication:**\ Convery-Fisher, E.D.,
 Devenish, A., Staddon, S., Rafanomezantsoa, F.L., & Lehmann, C.E.R.
 Understanding Fire Conflict Through Stakeholder Mapping in
 Madagascar's Grassy Biomes. *People and Nature* [full citation upon
 publication]. **Funding:**\ This research was supported by the University
 of Edinburgh, Royal Botanic Garden Edinburgh, NERC E4 Doctoral Training
 Programme (Grant No. NE/S007407/1), Albert Reckitt Award (Royal
 Geographical Society with IBG), and UK International Development via the
 Biodiverse Landscapes Fund project 'Achieving Sustainable Forest
 Management through Community Protected Areas in Madagascar' (ecm
 62237). **Geographic Coverage:**\ Ambatofinandrahana district,
 Amoron'i Mania Region, central Madagascar **Temporal Coverage:**\
 April-May 2023 ### Files and variables ## File:
 aggregated_matrix_final.csv **Description:** Adjacency matrix representing
 the aggregated cognitive map across all 28 focus groups and five
 stakeholder categories. Each cell contains the weighted connection
 strength between variables (row to column). Positive values indicate
 positive causal relationships (increase in row variable causes increase in
 column variable), negative values indicate negative relationships
 (increase in row variable causes decrease in column variable). Values
 range from -4 to +4, with 0 indicating no perceived connection. This
 matrix was used to generate network metrics and visualizations in Figures
 3 and 5. **Matrix structure:** Variables as both rows (source) and columns
 (target), with cell values representing connection weights. **Missing
 values:** Represented as 0 (no connection perceived by any stakeholder
 group) ### Variables (n = 36): * **accidental_fire:** Unintentional fire
 escapes from cooking, land preparation, or waste burning *
 **advantage_fire:** The functional benefits of using fire as a land
 management tool (consolidated from multiple perceived benefits) *
 **agricultural_fire:** Use of fire to clear land for cultivation or
 prepare planting areas * **agricultural_inputs:** Availability of farming
 resources such as fertilisers, pesticides, tools, and seeds * **arson:**
 Intentional destructive fire use, typically for conflict, retaliation, or
 criminal purposes * **lack_of_basic_services:** Inadequate provision of
 education, healthcare, markets, and government services in rural areas *
 **cattle_ranching:** Livestock management practices and the economic
 importance of cattle herding * **cheap:** Fire as a low-cost or no-cost
 land management alternative (financial accessibility of fire use) *
 **climate_phenomena:** Seasonal weather patterns, rainfall timing, drought
 conditions, and temperature affecting fire behaviour *
 **community_conflict:** Social tensions within or between communities
 related to resource use and fire management * **community_organisations:**
 Local institutions such as *Vondron'Olona Ifotony* (VOI), fire
 management committees, and community-based natural resource management
 groups * **cultural_fire:** Traditional fire use practices embedded in
 cultural identity and customary knowledge systems * **dahalo_presence:**
 Cattle rustling activity; theft of livestock often linked with fire as a
 diversion tactic (from Malagasy term *dahalo* = cattle rustler) *
 **demographics:** Population size, density, growth, age structure, and
 migration patterns * **different_perspectives:** Divergent viewpoints and
 knowledge systems among stakeholders regarding fire use and management *
 **easily_available:** Accessibility and availability of fire as a tool
 (ease of ignition) * **enforce_rights:** Legal recognition and enforcement
 of land tenure, customary rights, and resource access rights *
 **expansion_of_agriculture:** Increasing agricultural land area through
 conversion of grasslands or forests * **fire_properties:** Fire behaviour
 characteristics including spread rate, intensity, timing, and extent *
 **forest_conservation:** Protection efforts targeting forest ecosystems,
 including protected area management * **forest_products:** Non-timber
 forest resources including firewood, charcoal, building materials, and
 medicinal plants * **grassland_fire:** Landscape fires burning in
 grassland ecosystems (outcome variable of interest; also called
 *dorotanety* in Malagasy) * **grazing_fire:** Deliberate use of fire to
 regenerate or manage grazing land; renew pastures and improve forage
 quality for livestock (also called pasture burning in manuscript) *
 **habit:** Habitual or customary fire use; the use of fire more out of
 tradition or routine than reasoned decision-making * **lack_of_concern:**
 Insufficient attention or care regarding fire impacts, fire risk, or fire
 management responsibilities * **law_enforcement:** Government efforts to
 implement and enforce fire regulations, including fines, arrests, and
 prosecutions * **land_degredation:** Environmental degradation including
 soil erosion, loss of vegetation cover, and declining ecosystem health
 (also called environmental degradation in manuscript) * **lightening:**
 Natural ignition source; lightning strikes causing fire ignition *
 **lighters:** Availability of ignition tools (matches, lighters, etc.) *
 **local_fire_management:** Village-level and community-led efforts to
 control or extinguish landscape fires (also called community fire
 suppression in manuscript) * **local_infrastructure:** Quality of roads,
 bridges, transport networks, communication systems, and market access in
 rural areas (also called inadequate rural infrastructure in manuscript) *
 **national_political_context:** Broader political dynamics, policy
 changes, and governance structures affecting fire management *
 **ngo_support:** Presence and activities of non-governmental organisations
 providing conservation, development, or capacity-building support *
 **people_passing:** Transient individuals traveling through the landscape
 who may inadvertently or deliberately start fires * **pests:**
 Agricultural and livestock pests targeted for control through burning *
 **pyrophilic_species_presence:** Presence of fire-adapted or
 fire-promoting plant species in the landscape *
 **qualities_of_the_natural_environment:** Landscape conditions including
 vegetation type, fuel load, topography, and ecosystem characteristics
 (also called landscape conditions in manuscript) * **social_cohesion:**
 Strength of social bonds, trust, and cooperation within communities *
 **social_status:** Prestige and social standing associated with cattle
 ownership and herd size (also called prestige of cattle ownership in
 manuscript) * **state_presence:** Government capacity including staffing,
 resources, and authority to implement fire management (also called limited
 government capacity in manuscript) * **time:** Seasonal timing of fire use
 and fire seasons (also called seasonal weather patterns in some contexts)
 **Note:** Some variables appear under alternative names in the published
 manuscript to improve clarity for readers; the mapping between dataset
 variable names and manuscript terminology is indicated in parentheses
 above. **Description:** Documentation showing how 200+ original variables
 identified by participants during focus groups were consolidated into 36
 analytical categories. This two-sheet workbook provides complete
 transparency in the variable consolidation process, showing both the
 consolidation decisions and the final variable definitions used in
 analysis. ## File: condensing_variables.xlsx **Description:**
 Documentation showing how 200+ original variables identified by
 participants during focus groups were consolidated into 36 analytical
 categories. This two-sheet workbook provides complete transparency in the
 variable consolidation process, showing both the consolidation decisions
 and the final variable definitions used in analysis. ### Sheet 1:
 Consolidation_Mapping This sheet documents the consolidation process,
 showing how each original variable identified by participants was grouped
 into broader analytical categories. **Variables:** *
 **original_variable:** Variable name or phrase as stated by participants
 during focus groups (in English, French, or translated from Malagasy).
 Examples include specific terms like "cow_farming",
 "firebreak", "road_condition", etc. *
 **consolidated_variable:** The final analytical category name assigned to
 this original variable (matches variable names in
 aggregated_matrix_final.csv and other datasets). Total of 36 consolidated
 categories. * **rationale_notes:** Explanation for the consolidation
 decision, describing why this original variable was grouped into its
 assigned category (e.g., "Climate/weather variable",
 "Government institutional capacity indicator", "Social
 issue reflecting inadequate community support systems") **Number of
 rows:** 200+ original variables consolidated into 36 categories
 **Purpose:** This sheet demonstrates the semantic and conceptual logic
 used to reduce the complexity of participant-generated variables whilst
 preserving the meaning and diversity of perspectives. Two researchers (ECF
 and LF) independently reviewed all consolidation decisions to ensure
 accuracy and minimize information loss. ### Sheet 2:
 Final_Variable_Definitions This sheet provides the complete list of
 consolidated variables with their manuscript names and detailed
 definitions used throughout the analysis and publication. **Variables:** *
 **consolidated_variable:** The analytical category name used in the
 dataset files (e.g., "accidental_fire",
 "cattle_ranching", "dahalo_presence"). These names
 match those used in all matrix files and network analysis outputs. * **New
 Variable Name:** The terminology used in the published manuscript for
 clarity and readability (e.g., "accidental fires", "cattle
 ranching", "cattle rustling"). Some variables retain the
 same name; others were adapted for a general scientific audience. *
 **Explanation:** Comprehensive definition of what this variable represents
 in the context of fire management in Madagascar. Definitions include the
 scope of the concept, relevant examples, and contextual information to aid
 interpretation. **Number of rows:** 36 consolidated variables **Purpose:**
 This sheet serves as the definitive reference for understanding what each
 variable means, bridging between the dataset terminology and the published
 manuscript terminology. Users of the dataset should refer to these
 definitions when interpreting results. --- **Note on terminology:** Some
 variables appear under different names in the dataset files versus the
 manuscript to balance database naming conventions (e.g., underscores,
 brevity) with reader accessibility (e.g., descriptive phrases). Sheet 2
 provides the authoritative mapping between these naming systems. ## File:
 network_metrics_summaries.xlsx **Description:** Summary tables of key
 network metrics for variables in the fire management cognitive mapping
 system. This three-sheet workbook presents the most influential variables
 identified through network analysis of the aggregated stakeholder matrix.
 These summaries support the identification of system drivers (transmitter
 variables), system outcomes (receiver variables), and overall variable
 importance reported in the Results section and Tables 2-3 of the
 manuscript. **Missing values:** Not applicable (only variables meeting
 specific thresholds are included in each sheet) ### Sheet 1:
 Katz_Centrality This sheet presents the top 10 most influential and
 connected variables in the aggregated network, ranked by Katz centrality
 scores. These variables represent the core components of the fire
 management system as perceived across all stakeholder groups.
 **Variables:** * **Absolute Rank:** Numerical ranking from 1 (highest
 centrality) to 10 (1-10) * **Variable:** Variable name as appears in the
 published manuscript (may differ slightly from dataset terminology; see
 condensing_variables.xlsx Sheet 2 for mapping) * **Type:** [Column appears
 in header but values not shown - likely variable classification as
 Transmitter/Receiver/Ordinary] * **Katz Centrality:** Katz centrality
 score calculated with attenuation factor α = 0.1. Higher scores indicate
 greater overall importance and connectivity within the network. Scores are
 unitless; range shown is 1.04-2.53. **Number of rows:** 10 variables
 (top-ranked by centrality) **Purpose:** Identifies the most structurally
 important variables in the fire system. "Uncontrolled Grassland
 Fires" (the outcome of interest) has the highest centrality, followed
 by direct fire practices (pasture burning, arson, agricultural fire) and
 key contextual factors (infrastructure, law enforcement, cattle rustling).
 **Note:** The manuscript refers to "landscape fires" or
 "grassland fires" rather than "Uncontrolled Grassland
 Fires," but these terms are synonymous in this context. --- ### Sheet
 2: OD_ID_RATIO This sheet identifies transmitter variables—those that
 primarily influence other components of the system rather than being
 influenced themselves. Transmitter variables can be thought of as upstream
 drivers or root causes in the causal network. **Variables:** * **Rank:**
 Numerical ranking based on classification category and out-degree to
 in-degree ratio * **Variable:** Variable name as appears in the manuscript
 * **Type:** Classification of transmitter type: * **'True'
 transmitters:** Variables with high out-degree and zero or near-zero
 in-degree (pure drivers with no incoming connections). Numbers in this
 column represent out-degree centrality scores. * **'Ordinary'
 transmitters:** Variables with non-zero in-degree but still functioning
 primarily as drivers (out-degree substantially exceeds in-degree). Listed
 with their rank within this category in parentheses. * **OD Centrality /
 OD/ID Ratio:** * For 'True' transmitters: Out-degree centrality
 score (cumulative strength of outgoing connections) * For
 'Ordinary' transmitters: Out-degree to in-degree ratio (how many
 times greater the out-degree is compared to in-degree) **Number of rows:**
 15 transmitter variables total (5 'True', 10
 'Ordinary') **Classification criteria:** Variables were
 classified as transmitters if they had zero in-degree with non-zero
 out-degree, or if their OD/ID ratio fell in the upper quartile of all
 variables. **Purpose:** Identifies root causes and external forcing
 factors in the fire system. 'True' transmitters represent
 fundamental drivers that are not influenced by other variables in the
 system. 'Ordinary' transmitters are influenced by other factors
 but still function primarily as causes rather than effects. **Key
 findings:** Socio-economic factors (community organisations, agricultural
 inputs, poor local services), institutional factors (political
 instability, NGO support), and biophysical conditions (fire properties,
 environmental conditions, climate) emerge as primary drivers. --- ###
 Sheet 3: ID_OD_RATIO This sheet identifies receiver variables—those that
 are primarily influenced by other components of the system rather than
 influencing others. Receiver variables can be thought of as outcomes or
 consequences in the causal network. **Variables:** * **Rank:** Numerical
 ranking based on classification category and in-degree to out-degree ratio
 * **Variable:** Variable name as appears in the manuscript * **Type:**
 Classification of receiver type: * **'True receivers':**
 Variables with high in-degree and zero or near-zero out-degree (pure
 outcomes). Listed with in-degree centrality score. * **[Ordinary
 receivers]:** Variables with non-zero out-degree but still functioning
 primarily as outcomes (in-degree exceeds out-degree). Rank within this
 category shown in parentheses. * **ID Centrality / ID/OD Ratio:** * For
 'True receivers': In-degree centrality score (cumulative
 strength of incoming connections) * For other receivers: In-degree to
 out-degree ratio (how many times greater the in-degree is compared to
 out-degree) **Number of rows:** 9 receiver variables (1 'True'
 receiver, 8 ordinary receivers) **Classification criteria:** Variables
 were classified as receivers if they had zero out-degree with non-zero
 in-degree, or if their ID/OD ratio fell in the upper quartile of all
 variables. **Purpose:** Identifies system outcomes and consequences. These
 variables represent the effects or results of other system components
 rather than causes. **Key findings:** "Uncontrolled Grassland
 Fires" is the primary 'True receiver' with the highest
 in-degree centrality (16.4), confirming it functions as the main system
 outcome. Other receivers include specific fire types (arson, pasture
 burning, agricultural fires, accidents) and management responses (law
 enforcement, local fire management), showing these are consequences of
 underlying drivers. --- **Interpretation notes:** 1. **Transmitter vs.
 Receiver classification:** A variable can appear in multiple sheets if it
 has moderate values for both in-degree and out-degree. For example,
 "Community Organisations" appears as both a transmitter (OD/ID
 ratio context) and receiver (ID/OD ratio context), indicating it both
 influences and is influenced by other system components. 2. **Terminology
 alignment:** Variable names in these sheets use manuscript terminology for
 readability. To find the corresponding dataset variable name (as used in
 matrix files), refer to condensing_variables.xlsx Sheet 2. 3. **Quartile
 thresholds:** The specific OD/ID and ID/OD ratio values that define the
 "upper quartile" cutoffs for transmitter/receiver classification
 are available in the full network analysis scripts. 4. **Centrality vs.
 Frequency:** High centrality indicates structural importance in the causal
 network (how strongly a variable connects to others), not how frequently
 participants mentioned the variable. ## File:
 stakeholder_variance_analysis.csv **Description:** Top 10 most important
 variables for each of the four stakeholder clusters identified through
 hierarchical clustering analysis. This file shows cluster-specific Katz
 centrality rankings used to generate Table 3 and Figure 5 in the
 manuscript, revealing how different clusters prioritize different fire
 drivers. **Missing values:** Not applicable (only top 10 variables per
 cluster are included) **File structure:** Long format with one row per
 variable per cluster (40 total rows: 4 clusters × 10 variables each) ###
 Variables: * **variable:** Variable name as it appears in the manuscript
 (e.g., "Habitual fire use", "Pasture burning",
 "Environmental degradation"). To find corresponding dataset
 variable names, refer to condensing_variables.xlsx Sheet 2. *
 **katz_centrality:** Katz centrality score for this variable within this
 specific cluster's aggregated cognitive map. This is not the overall
 centrality across all stakeholders, but rather the importance of this
 variable for this particular cluster. Scores range from approximately 1.03
 to 1.55. Higher scores indicate greater perceived importance and
 connectivity within that cluster's understanding of fire dynamics. *
 **cluster_number:** Cluster assignment (1-4) from hierarchical clustering
 analysis (Figure 4): * **Cluster 1** (n = 9 focus groups): Government
 officials (40%) and conservation practitioners (40%), with some community
 leaders (20%) * **Cluster 2** (n = 4 focus groups): Community leaders
 (75%) and conservation practitioners (25%) * **Cluster 3** (n = 5 focus
 groups): Exclusively community leaders (100%) * **Cluster 4** (n = 10
 focus groups): Rural farmers (50%) and herders (50%) **Number of rows:**
 40 (10 variables per cluster × 4 clusters) **Purpose:** This file
 demonstrates how stakeholder clusters differ in their perceptions of fire
 drivers. By comparing the top 10 variables across clusters, we can
 identify areas of agreement (variables that appear in multiple
 clusters' top 10) and divergence (variables highly ranked by one
 cluster but absent from others). For example: * "Habitual fire
 use" ranks #1 for Clusters 1 and 2 but doesn't appear in Cluster
 4's top 10 * "Pasture burning" appears in all four clusters
 but with varying centrality scores * "Cattle rustling" appears
 only in Cluster 4's top 10 **Relationship to figures/tables:** * Data
 used to create **Table 3** showing top variables per cluster * Used to
 generate **Figure 5** comparing variable importance across clusters *
 Cluster assignments reference **Figure 4** (hierarchical clustering
 dendrogram) **Note on variable terminology:** Variable names in this file
 use the manuscript terminology for readability (with spaces and capital
 letters). To find the corresponding variable name used in other dataset
 files (e.g., aggregated_matrix_final.csv), refer to the mapping in
 condensing_variables.xlsx Sheet 2. # Other Publicly Accessible Locations
 and Data Sources ## Other publicly accessible locations of the data: *
 **None.** This Dryad repository is the primary and sole public repository
 for these data. Upon publication of the associated manuscript in *People
 and Nature*, a link to this Dryad dataset will be included in the Data
 Availability Statement. ## Data was derived from the following sources: *
 **None.** These data represent original primary data collected through
 field research conducted in Madagascar in April-May 2023. The data were
 not derived from, extracted from, or based upon any existing datasets or
 secondary sources. **Methodological Framework:** Whilst the data
 collection methodology follows established fuzzy cognitive mapping
 protocols as described in the literature (particularly Özesmi & Özesmi
 2004; Devisscher et al. 2016; Tebbutt et al. 2021), all data values,
 variables, and relationships were generated directly by research
 participants during focus group discussions and represent their original
 perspectives and knowledge. ## Human subjects data All participants
 provided written or oral prior informed consent before participating in
 focus group discussions, in compliance with ethical approval from the
 University of Edinburgh School of GeoSciences' Research Ethics and
 Integrity Committee (approval #GEOS2022-585). Participants were informed
 that anonymised and aggregated data from the focus groups would be made
 publicly available to support scientific transparency and enable
 replication of research findings. Consent was recorded for all 133
 participants across 28 focus groups conducted in April-May 2023.