Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal' Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582 R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023 Principal Investigator: Graham W Prescott ([email protected]) Authors:
Biraj Adhikari ([email protected], ORCID: 0000-0002-4260-8706)
Davnah Urbach ([email protected], ORCID: 0000-0001-9170-7834)
Nakul Chettri ([email protected], ORCID: 0000-0002-3338-8879)
Eklabya Sharma ([email protected], ORCID:0000-0003-3089-8838)
Thomas Breu ([email protected], ORCID: 0000-0003-2348-504X)
Jonas Geschke ([email protected], ORCID: 0000-0002-5654-9313)
Markus Fischer ([email protected], ORCID: 0000-0002-5589-5900)
Graham W Prescott ([email protected], ORCID:0000-0001-5123-514X) Date of data collection: August 2021 - June 2022
Location of data collection: Kathmandu
Date of final release: Data Overview:
There are two excel files. Each excel sheet is also uploaded as a separate .csv file. 1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.
Sheet overview:
method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:
institution -> institutional affiliation of the participant
inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.
exp_years -> experience (in years) of the participant in the conservation sector of Nepal
sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively
outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)
conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score
text_out -> (optional) a description of why the participant gave that outgoing interaction score
inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)
conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score
text_in -> (optional) a description of why the participant gave that incoming interaction score method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:
Year -> Year when the indicator was measured
forest_cover -> Forest cover as a percent of total area (%)
kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)
kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)
redlist -> Red List Index
undernourishment -> Prevalence of undernourishment (%)
food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)
death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)
suicide -> Suicide mortality rate (deaths per 100,000 population)
uhc_index -> Universal health coverage (UHC) service coverage index
f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)
safewater -> Proportion of population using safely managed drinking water services (%)
electricity -> Proportion of population with access to electricity, by urban/rural (%)
cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)
renewable -> Renewable energy share in the total final energy consumption (%)
gdp_capita -> Annual growth rate of real GDP per capita (%)
manu_value -> Manufacturing value added as a proportion of GDP (%)
consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)
consumption_all -> Domestic material consumption (tonnes)
consumption_cap -> Domestic material consumption per capita (tonnes)
death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people
local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%) method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG. allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG. 2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.

Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal' Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582 R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023 Principal Investigator: Graham W Prescott ([email protected]) Authors:
Biraj Adhikari ([email protected], ORCID: 0000-0002-4260-8706)
Davnah Urbach ([email protected], ORCID: 0000-0001-9170-7834)
Nakul Chettri ([email protected], ORCID: 0000-0002-3338-8879)
Eklabya Sharma ([email protected], ORCID:0000-0003-3089-8838)
Thomas Breu ([email protected], ORCID: 0000-0003-2348-504X)
Jonas Geschke ([email protected], ORCID: 0000-0002-5654-9313)
Markus Fischer ([email protected], ORCID: 0000-0002-5589-5900)
Graham W Prescott ([email protected], ORCID:0000-0001-5123-514X) Date of data collection: August 2021 - June 2022
Location of data collection: Kathmandu
Date of final release: Data Overview:
There are two excel files. Each excel sheet is also uploaded as a separate .csv file. 1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.
Sheet overview:
method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:
institution -> institutional affiliation of the participant
inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.
exp_years -> experience (in years) of the participant in the conservation sector of Nepal
sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively
outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)
conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score
text_out -> (optional) a description of why the participant gave that outgoing interaction score
inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)
conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score
text_in -> (optional) a description of why the participant gave that incoming interaction score method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:
Year -> Year when the indicator was measured
forest_cover -> Forest cover as a percent of total area (%)
kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)
kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)
redlist -> Red List Index
undernourishment -> Prevalence of undernourishment (%)
food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)
death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)
suicide -> Suicide mortality rate (deaths per 100,000 population)
uhc_index -> Universal health coverage (UHC) service coverage index
f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)
safewater -> Proportion of population using safely managed drinking water services (%)
electricity -> Proportion of population with access to electricity, by urban/rural (%)
cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)
renewable -> Renewable energy share in the total final energy consumption (%)
gdp_capita -> Annual growth rate of real GDP per capita (%)
manu_value -> Manufacturing value added as a proportion of GDP (%)
consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)
consumption_all -> Domestic material consumption (tonnes)
consumption_cap -> Domestic material consumption per capita (tonnes)
death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people
local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%) method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG. allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG. 2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.

Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal' Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e Principal Investigator: Markus Fischer ([email protected]) Authors:
Biraj Adhikari ([email protected], ORCID: 0000-0002-4260-8706)
Graham W Prescott ([email protected], ORCID:0000-0001-5123-514X)
Davnah Urbach ([email protected], ORCID: 0000-0001-9170-7834)
Nakul Chettri ([email protected], ORCID: 0000-0002-3338-8879)
Markus Fischer ([email protected], ORCID: 0000-0002-5589-5900) Date of data collection: November 2020 - August 2021
Location of data collection: Kathmandu, Nepal
Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal' R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep Data Overview: 1. 'Literature_References.csv'
List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files.
2. 'Drivers_and_Trends.csv'
The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends.
The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP.
We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological).
If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1. 3. 'NCP_to_SDG.csv'
The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article.
The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)"
The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards.
The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from. Codes for NCPS:
HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options). 4. 'Data_consolidated.xlsx'
The above three data files combined into one xlsx document

Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal' Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e Principal Investigator: Markus Fischer ([email protected]) Authors:
Biraj Adhikari ([email protected], ORCID: 0000-0002-4260-8706)
Graham W Prescott ([email protected], ORCID:0000-0001-5123-514X)
Davnah Urbach ([email protected], ORCID: 0000-0001-9170-7834)
Nakul Chettri ([email protected], ORCID: 0000-0002-3338-8879)
Markus Fischer ([email protected], ORCID: 0000-0002-5589-5900) Date of data collection: November 2020 - August 2021
Location of data collection: Kathmandu, Nepal
Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal' R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep Data Overview: 1. 'Literature_References.csv'
List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files.
2. 'Drivers_and_Trends.csv'
The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends.
The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP.
We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological).
If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1. 3. 'NCP_to_SDG.csv'
The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article.
The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)"
The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards.
The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from. Codes for NCPS:
HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options). 4. 'Data_consolidated.xlsx'
The above three data files combined into one xlsx document

Signatories to the Minamata Convention on Mercury with ‘more than insignificant’ artisanal and small-scale gold mining (ASGM) sectors are required to develop and implement National Action Plans (NAPs) to reform their ASGM sectors in line with Annexe C of the Convention. We compiled the budgets of available NAPs for reducing mercury emissions from ASGM sectors. As of 2021-12-31, these were available for 16 countries from: www.mercuryconvention.org/en/parties/national-action-plans. We used these data to estimate the approximate costs of expanding such approaches globally.

Signatories to the Minamata Convention on Mercury with ‘more than insignificant’ artisanal and small-scale gold mining (ASGM) sectors are required to develop and implement National Action Plans (NAPs) to reform their ASGM sectors in line with Annexe C of the Convention. We compiled the budgets of available NAPs for reducing mercury emissions from ASGM sectors. As of 2021-12-31, these were available for 16 countries from: www.mercuryconvention.org/en/parties/national-action-plans. We used these data to estimate the approximate costs of expanding such approaches globally.

Title: Data for ‘Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro’ Recommended Citation: Masao CA, Prescott GW, Snethlage MA, Urbach D, Torre-Marin Rando A, Molina-Venegas R, Mollel NP, Hemp C, Hemp A, Fischer M (2022). People and Nature. Principal Investigator:
- Markus Fischer ([email protected]) Authors:
* joint first-author
- Catherine A. Masao ([email protected], ORCID: 0000-0002-1242-9117) *
- Graham W. Prescott ([email protected], ORCID: 0000-0001-5123-514X) *
- Mark A. Snethlage ([email protected], ORCID: 0000-0002-1398-8869) *
- Davnah Urbach ([email protected], ORCID: 0000-0001-9170-7834) *
- Amor Torre-Marin Rando ([email protected])
- Rafael Molina Venegas ([email protected], ORCID 0000-0001-5801-0736)
- Neduvoto P. Mollel ([email protected], ORCID: 0000-0002-4402-4667)
- Claudia Hemp ([email protected], ORCID: 0000-0002-5369-2122)
- Andreas Hemp ([email protected], ORCID: 0000-0001-9170-7113)
- Markus Fischer ([email protected], ORCID: 0000-0002-5589-5900) Date of data collection: 2018-09
Location of data collection: Moshi, Kilimanjaro Region, Tanzania
Date of final file release: 2022-01-13 Data Overview: We conducted a three-day stakeholder workshop in Moshi, Tanzania, in September 2018. The workshop was attended by 73 participants (16 women and 57 men), whom we invited to represent various sectors and local communities. We established the list of invitees through an extensive online search validated and complemented by key local informants. We divided registered participants into five groups based on their sectoral affiliation: 16 residents of local communities, including farmers (herein ‘Community’), 14 researchers and scientists (‘Research’), 16 professionals in conservation and management (‘Conservation’), 17 professionals in forestry, agriculture, and water management and governance (‘Resources’), and 10 other professionals mainly drawn from the tourism sector (‘Other’). We used two questionnaires—herein ‘habitat’ and ‘ecosystem services’— with open and closed questions. Closed questions were scored using a Likert-type scale. File overview: 1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv Data from the ‘habitat’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-09-22). Individual perceptions about the state of and trends in habitats and species diversity and about the direct and indirect factors driving these trends. We invited participants to fill out separate questionnaires for each habitat of importance to their sector or for which they had knowledge, starting with the most important one. 2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv Data from the ‘ecosystem services’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-01-09). The ‘ecosystem services’ questionnaire collected individual perceptions about the state of, trends in, and importance of NCP (Nature's Contributions to People), as well as about the factors driving observed changes in access and provision. With reference to the preliminary group discussion on NCP, we invited participants to fill out separate forms for each NCP they deemed important to their sector or had knowledge about and to indicate which habitat(s) provide(s) each of them. 3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv Adapted from the ecosytem services questionnaire data (kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv), coding the reasons for change in access to NCP. 4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv Tally of recommended measures towards recorded from the carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. See Table S7 for details.
Code used for analysis:
R code used for the statistical analysis and to create the figures available from: https://github.com/grahamprescott/kilimanjaro.ipbes.workshop.paper File details: 1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv 143 observations of 73 variables Key Variables:
- Group
(categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)
- Biome2
(categorical - standardised habitat categories used in the analysis, coded by Mark A. Snethlage)
- Habitat.area
(categorical - trends in habitat area over past 10 years (2008-2018); Decreased, Not Changed, Increased, No Answer)
- Habitat.condition
(categorical - trends in habitat condition over past 10 years (2008-2018); Deteriorated, Not Changed, Improved, No Answer)
- Habitat.area.will
(categorical - prediction for trend in habitat condition over next 10 years (2018-2028); Decrease Not Change, Increase, No Answer)
- Habitat.condition.will
(categorical - trends in habitat condition over past 10 years (2018-2028); Decrease, Not Change, Increase, No Answer)
Variables beginning with ES., DIR., IND., ACT. refer to ecosystem services (i.e. NCP), direct drivers, indirect drivers, and recommended actions associated with each habitat form. They are numerical and scored as 1 if that variable is mentioned (present) or 0 if not mentioned (absent). In a few cases where different ecosystem services listed by the participant are coded to the same variable the number is the number of times that ecosystem service is mentioned. Codes for ecosystem services (ES.): HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories. Codes for direct drivers (DIR.): ACT = ‘Human Activities’, CC = Climate Change, IAS = Invasive Alien Species, LUC = Land-Use Change, OVR = Overexploitation, POL = Pollution. Codes for indirect drivers (IND.): CLT = Cultural, DEM = Demographic, ECO = Economic, GOV = Governance, S.T = Science and Technology. Codes for recommended actions (ACT.): AWR = Awareness Raising, ECO = Livelihood, Economic & Moral Incentives, EDU = Education & Training, ENF = Law Enforcement & Prosecution, INS = Institutional Development, LAN = Land / Water Management, LAW = Legal & Policy Frameworks, PRT = Conservation Designation & Planning, RSR = Research & Monitoring, SPC = Species Management. 2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv 144 observations of 38 variables Key variables: - Group
(categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)
- Service.original (free text response to which ecosystem service the participant was filling out the form)
- ESCODE
(categorical - NCP category to which we assigned the free text response. Abbreviations: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories.)
- Biome
(categorical - which habitat provided the ecosystem service)
- Why.changed.provision
(free text response for why Provision changed)
- Why.changed.access
(free text response for why Access changed) [Note: although we theoretically expected a distinction between provision and access of each ecosystem service, we observed that this distinction was not strictly followed in practice and deemed the responses about access to be most accurate]
- Access
(categorical - changes in access to the ecosystem service over the last 10 years (2008-2018); Decreased, No Change, Increased, No Answer)
- Access.will
(categorical - predicted changes in access to the ecosystem service over the next 10 years (2018-2028); Deteriorate, Not Change, No Answer, Improve (note: no one responded ‘Improve’))
3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv 144 observations of 7 variables We took the following variables from the ecosystem services questionnaire:
- ESCODE
(categorical - NCP category to which we assigned the free text response)
- Access
(whether access to this NCP increased or decreased between 2008-2018)
- Why.changed.access
(free text response for why Access changed)
And created a new variable to synthesise the drivers of change in NCP access:
- Why.changed.access.code Note: a challenge with the ‘Why.changed.access’ variable is that many drivers are listed in the same response. To process this, we duplicated the rows with multiple drivers so that there would be one row per driver. We did this using Microsoft Excel for Mac. We did this so that each link from a driver to an increase or decrease in a given NCP could be visualised. The individual links are not standardised by individual respondent or response. They represent every instance of a reported link between a driver of change and a change in access to a given NCP. Responses or respondents who listed multiple instances of NCP access change and/or multiple drivers have therefore contributed more to the Sankey figure (Figure 4). We chose this approach because the aim in this case was to document the complex web of drivers leading to changes in NCP access, drawing upon the collective expertise of the respondents, not to test for individual differences between groups or respondents. Graham W. Prescott and Mark A. Snethlage independently coded each of the drivers and reached a consensus on any disagreements. Graham W. Prescott edited the final file. 4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv 11 observations of 6 variables

We also conducted a carousel session in which participants could suggest actions and actors that could contribute towards achieving a sustainable future for people and nature at Mt. Kilimanjaro. This file contains the tally of recommended measures arising from this carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. For full list of measures, see Table S7.

Title: Data for ‘Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro’ Recommended Citation: Masao CA, Prescott GW, Snethlage MA, Urbach D, Torre-Marin Rando A, Molina-Venegas R, Mollel NP, Hemp C, Hemp A, Fischer M (2022). People and Nature. Principal Investigator:
- Markus Fischer ([email protected]) Authors:
* joint first-author
- Catherine A. Masao ([email protected], ORCID: 0000-0002-1242-9117) *
- Graham W. Prescott ([email protected], ORCID: 0000-0001-5123-514X) *
- Mark A. Snethlage ([email protected], ORCID: 0000-0002-1398-8869) *
- Davnah Urbach ([email protected], ORCID: 0000-0001-9170-7834) *
- Amor Torre-Marin Rando ([email protected])
- Rafael Molina Venegas ([email protected], ORCID 0000-0001-5801-0736)
- Neduvoto P. Mollel ([email protected], ORCID: 0000-0002-4402-4667)
- Claudia Hemp ([email protected], ORCID: 0000-0002-5369-2122)
- Andreas Hemp ([email protected], ORCID: 0000-0001-9170-7113)
- Markus Fischer ([email protected], ORCID: 0000-0002-5589-5900) Date of data collection: 2018-09
Location of data collection: Moshi, Kilimanjaro Region, Tanzania
Date of final file release: 2022-01-13 Data Overview: We conducted a three-day stakeholder workshop in Moshi, Tanzania, in September 2018. The workshop was attended by 73 participants (16 women and 57 men), whom we invited to represent various sectors and local communities. We established the list of invitees through an extensive online search validated and complemented by key local informants. We divided registered participants into five groups based on their sectoral affiliation: 16 residents of local communities, including farmers (herein ‘Community’), 14 researchers and scientists (‘Research’), 16 professionals in conservation and management (‘Conservation’), 17 professionals in forestry, agriculture, and water management and governance (‘Resources’), and 10 other professionals mainly drawn from the tourism sector (‘Other’). We used two questionnaires—herein ‘habitat’ and ‘ecosystem services’— with open and closed questions. Closed questions were scored using a Likert-type scale. File overview: 1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv Data from the ‘habitat’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-09-22). Individual perceptions about the state of and trends in habitats and species diversity and about the direct and indirect factors driving these trends. We invited participants to fill out separate questionnaires for each habitat of importance to their sector or for which they had knowledge, starting with the most important one. 2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv Data from the ‘ecosystem services’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-01-09). The ‘ecosystem services’ questionnaire collected individual perceptions about the state of, trends in, and importance of NCP (Nature's Contributions to People), as well as about the factors driving observed changes in access and provision. With reference to the preliminary group discussion on NCP, we invited participants to fill out separate forms for each NCP they deemed important to their sector or had knowledge about and to indicate which habitat(s) provide(s) each of them. 3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv Adapted from the ecosytem services questionnaire data (kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv), coding the reasons for change in access to NCP. 4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv Tally of recommended measures towards recorded from the carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. See Table S7 for details.
Code used for analysis:
R code used for the statistical analysis and to create the figures available from: https://github.com/grahamprescott/kilimanjaro.ipbes.workshop.paper File details: 1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv 143 observations of 73 variables Key Variables:
- Group
(categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)
- Biome2
(categorical - standardised habitat categories used in the analysis, coded by Mark A. Snethlage)
- Habitat.area
(categorical - trends in habitat area over past 10 years (2008-2018); Decreased, Not Changed, Increased, No Answer)
- Habitat.condition
(categorical - trends in habitat condition over past 10 years (2008-2018); Deteriorated, Not Changed, Improved, No Answer)
- Habitat.area.will
(categorical - prediction for trend in habitat condition over next 10 years (2018-2028); Decrease Not Change, Increase, No Answer)
- Habitat.condition.will
(categorical - trends in habitat condition over past 10 years (2018-2028); Decrease, Not Change, Increase, No Answer)
Variables beginning with ES., DIR., IND., ACT. refer to ecosystem services (i.e. NCP), direct drivers, indirect drivers, and recommended actions associated with each habitat form. They are numerical and scored as 1 if that variable is mentioned (present) or 0 if not mentioned (absent). In a few cases where different ecosystem services listed by the participant are coded to the same variable the number is the number of times that ecosystem service is mentioned. Codes for ecosystem services (ES.): HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories. Codes for direct drivers (DIR.): ACT = ‘Human Activities’, CC = Climate Change, IAS = Invasive Alien Species, LUC = Land-Use Change, OVR = Overexploitation, POL = Pollution. Codes for indirect drivers (IND.): CLT = Cultural, DEM = Demographic, ECO = Economic, GOV = Governance, S.T = Science and Technology. Codes for recommended actions (ACT.): AWR = Awareness Raising, ECO = Livelihood, Economic & Moral Incentives, EDU = Education & Training, ENF = Law Enforcement & Prosecution, INS = Institutional Development, LAN = Land / Water Management, LAW = Legal & Policy Frameworks, PRT = Conservation Designation & Planning, RSR = Research & Monitoring, SPC = Species Management. 2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv 144 observations of 38 variables Key variables: - Group
(categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)
- Service.original (free text response to which ecosystem service the participant was filling out the form)
- ESCODE
(categorical - NCP category to which we assigned the free text response. Abbreviations: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories.)
- Biome
(categorical - which habitat provided the ecosystem service)
- Why.changed.provision
(free text response for why Provision changed)
- Why.changed.access
(free text response for why Access changed) [Note: although we theoretically expected a distinction between provision and access of each ecosystem service, we observed that this distinction was not strictly followed in practice and deemed the responses about access to be most accurate]
- Access
(categorical - changes in access to the ecosystem service over the last 10 years (2008-2018); Decreased, No Change, Increased, No Answer)
- Access.will
(categorical - predicted changes in access to the ecosystem service over the next 10 years (2018-2028); Deteriorate, Not Change, No Answer, Improve (note: no one responded ‘Improve’))
3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv 144 observations of 7 variables We took the following variables from the ecosystem services questionnaire:
- ESCODE
(categorical - NCP category to which we assigned the free text response)
- Access
(whether access to this NCP increased or decreased between 2008-2018)
- Why.changed.access
(free text response for why Access changed)
And created a new variable to synthesise the drivers of change in NCP access:
- Why.changed.access.code Note: a challenge with the ‘Why.changed.access’ variable is that many drivers are listed in the same response. To process this, we duplicated the rows with multiple drivers so that there would be one row per driver. We did this using Microsoft Excel for Mac. We did this so that each link from a driver to an increase or decrease in a given NCP could be visualised. The individual links are not standardised by individual respondent or response. They represent every instance of a reported link between a driver of change and a change in access to a given NCP. Responses or respondents who listed multiple instances of NCP access change and/or multiple drivers have therefore contributed more to the Sankey figure (Figure 4). We chose this approach because the aim in this case was to document the complex web of drivers leading to changes in NCP access, drawing upon the collective expertise of the respondents, not to test for individual differences between groups or respondents. Graham W. Prescott and Mark A. Snethlage independently coded each of the drivers and reached a consensus on any disagreements. Graham W. Prescott edited the final file. 4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv 11 observations of 6 variables

We also conducted a carousel session in which participants could suggest actions and actors that could contribute towards achieving a sustainable future for people and nature at Mt. Kilimanjaro. This file contains the tally of recommended measures arising from this carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. For full list of measures, see Table S7.

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