Code and data for 'Enabling conservation Theories of Change'

Citation: Buelow et al. (2023) 'Enabling conservation theories of change'

An interactive web application and associated code can be found here

About | Abstract | Scripts | Data sources | License

About {#about}

This repository provides the code and data to reproduce figures in Buelow et al. (2023)

cbrown5 is a contributor to this code base

Abstract {#abstract}

Global Theories of Change (ToCs) can provide broad, overarching guidance for conservation and sustainable use of Earth's ecosystems. However, broad guidance alone cannot inform how conservation actions will lead to desired socio-ecological outcomes. Here, we develop a framework for translating a global-scale ToC into focussed, ecosystem-specific ToCs that consider feasibility of actions, as determined by national socioeconomic and political context (i.e., enabling conditions). We used coastal wetlands as a case study for developing the framework and identified six distinct multinational profiles of enabling conditions ('enabling profiles') for their conservation. For countries belonging to profiles with high internal capacity to enable conservation, we described plausible ToCs that involved strengthening policy and regulation. Alternatively, for profiles with low internal enabling capacity, plausible ToCs typically required formalising community-led conservation. Our 'enabling profile' framework can be applied to other ecosystems to help operationalise the Kunming-Montreal Global Biodiversity Framework and meet Sustainable Development Goals.

Scripts {#scripts}

Run a Bayesian latent variable model (LVM) on raw indicator data to gap-fill missing indicator values by country

1. 01_lvm-model.R
   • The full model
2. 01_lvm-model-no-rams-manage.R
   • The model without the Ramsar Management indicator (to assess robustness to violation of the 'missing at random' assumption)
3. 01_lvm-model-no-cons-spend.R
   • The model without the Conservation Spending indicator (to assess robustness to violation of the 'missing at random' assumption)

Calculate the median predicted standard normal indicator values from the posterior distribution of the LVM, for each indicator and country

1. 02_predict-indicator-vals.R
   • Obtain the posterior distributions of latent variable scores and coefficients for each indicator and country (i.e. MCMC samples) to calculate the entire range of predicted standard normal indicator values and extract the median value. Repeat for each lvm model listed above.
   • Also extract every 96th predicted value from the entire range of predicted values for each indicator and country to assess Enabling Profile robustness (see 09_cluster-robustness.R)

Check that the raw and predicted standard normal indicator values are strongly positively correlated

1. 03_raw-vs-predicted.R

Evaluate robustness to violation of 'missing at random' assumption for Ramsar Management and Conservation Spending indicators

1. 04_evaluate-MAR-consspend.R
   • Assess robustness of model parameter estimation to violation of the 'missing at random' assumption for the Conservation Spending indicator (Fig S3)
2. 04_evaluate-MAR-ramsmanage.R
   • Assess robustness of model parameter estimation to violation of the 'missing at random' assumption for the Ramsar Management indicator (Fig S4)

Run a cluster analysis on the predicted standard normal indicator values to classify countries into Enabling Profiles and produce a map

1. 05_cluster.R
   • Fig 2A

Determine the importance of indicators for classifying countries into Enabling Profiles with classification trees

1. 06_trees.R
   • Fig 2B and C

Identify and map the main drivers of mangrove and seagrass loss in each Enabling Profile

1. 07_drivers-loss.R
   • Fig 3 and 4

Produce an ordination of standard normal indicator values for each country, and group by Enabling Profile

1. 08_ordination.R
   • Fig S1

Assess the robustness of Enabling Profiles

1. 09_cluster-robustness.R
   • Fig S6 and S7

Validate missing value interpolation

1. 10_imputation-validation.R

Simulate missing not at random and assess interpolative capacity

1. 11_simulate-mnar.R
2. 12_plot-sims.R
   • Fig S5

Compare country pairwise clustering differences when variables are removed

1. 13_compare-clusters.R
   • Fig S8

Data sources {#data-sources}

Data category	Indicator/Driver	Description	Source
Policy indicator	Nationally Determined Contribution (NDC) commitment	The commitment of a country to reducing green-house gas emissions, pledged as Nationally Determined Contributions (NDC)	Watson, R., McCarthy, J.J., Canziani P., Nakicenovic, N., Hisas, L. 2019. The truth behind the climate pledges. The Universal Ecological Fund (FEU-US). Retrieved from https://feu-us.org/
Policy indicator	Nationally Determined Contribution (NDC) mitigation	Whether coastal wetlands are included in a country's Nationally Determined Contribution (NDC) mitigation strategy	Herr, D., & Landis, E. 2016. Coastal blue carbon ecosystems Opportunities for Nationally Determined Contributions. Policy brief. Gland, Switzerland: IUCN and Washington, DC, USA: TNC. Retrieved from www.bluecsolutions.org
Policy indicator	Nationally Determined Contribution (NDC) adaptation	Whether coastal wetlands are included in a country's Nationally Determined Contribution (NDC) adaptation strategy	Herr, D., & Landis, E. 2016. Coastal blue carbon ecosystems Opportunities for Nationally Determined Contributions. Policy brief. Gland, Switzerland: IUCN and Washington, DC, USA: TNC. Retrieved from www.bluecsolutions.org
Policy indicator	Multilateral environmental agreements (MEAs)	Cumulative count of a country's multilateral environmental agreements (MEAs) since the year of its first MEA membership until 2019	Ronald B. Mitchell. 2020. IEA Membership Count Dataset from the International Environmental Agreements Database Project (Version 20200214). Eugene: IEADB Project. Retrieved from https://iea.uoregon.edu/. Dataset generated on: 14 February 2020
Policy indicator	Bilateral environmental agreements (BEAs)	Cumulative count of a country's bilateral environmental agreements (BEAs) since the year of its first BEA membership until 2019	Ronald B. Mitchell. 2020. IEA Membership Count Dataset from the International Environmental Agreements Database Project (Version 20200214). Eugene: IEADB Project. Retrieved from https://iea.uoregon.edu/. Dataset generated on: 14 February 2020
Policy indicator	Governance effectiveness	Composite indicator of the quality of a country's public services, civil service, degree of its independence from political pressures, quality of policy formulation and implementation, and credibility of its government's commitment to such policies	Worldwide Governance Indicators, The World Bank. 2019. www.govindicators.org, accessed on [24/11/2020]
Policy indicator	Marine protected areas (MPA) target	The percent of a country's Economic Exclusion Zone (EEZ) set aside as a marine protected area	Wendling, Z., Emerson, J., de Sherbinin, A., and Esty, D. 2020. Environmental Performance Index 2020. New Haven, CT: Yale Center for Environmental Law & Policy. Retrieved from epi.yale.edu
Policy indicator	Ramsar protection	The area of coastal wetlands under Ramsar jurisdiction in a country standardised by coastline length	The Ramsar Convention Secretariat. 2014. https://rsis.ramsar.org/, accessed on [04/04/2021]
Policy indicator	Ramsar management	Whether a country has Ramsar-listed coastal wetland sites (i.e., intertidal marshes, intertidal forested wetlands, and marine subtidal aquatic beds) with management plans implemented (0 = no, 1 = yes)	The Ramsar Convention Secretariat. 2014. https://rsis.ramsar.org/, accessed on [04/04/2021]
Regulation indicator	Ramsar management	Whether a country has Ramsar-listed coastal wetland sites (i.e., intertidal marshes, intertidal forested wetlands, and marine subtidal aquatic beds) with management plans implemented (0 = no, 1 = yes)	The Ramsar Convention Secretariat. 2014. https://rsis.ramsar.org/, accessed on [04/04/2021]
Regulation indicator	Regulatory quality	A composite indicator of regulatory quality that represent the ability of a country's government to formulate and implement sound policies and regulations that permit and promote private sector development	Worldwide Governance Indicators, The World Bank. 2019. www.govindicators.org, accessed on [24/11/2020]
Regulation indicator	Wastewater treatment	The percentage of wastewater that undergoes at least primary treatment in each country, normalised by the proportion of the population connected to a municipal wastewater collection system	Wendling, Z., Emerson, J., de Sherbinin, A., & Esty, D. 2020. Environmental Performance Index 2020. New Haven, CT: Yale Center for Environmental Law & Policy. Retrieved from epi.yale.edu
Regulation indicator	Wastewater treatment	The percentage of wastewater that undergoes at least primary treatment in each country, normalised by the proportion of the population connected to a municipal wastewater collection system	Wendling, Z., Emerson, J., de Sherbinin, A., & Esty, D. 2020. Environmental Performance Index 2020. New Haven, CT: Yale Center for Environmental Law & Policy. Retrieved from epi.yale.edu
Regulation indicator	Sustainable nitrogen use	How far a country is from achieving sustainable nitrogen use for crop fertilisation	Wendling, Z., Emerson, J., de Sherbinin, A., & Esty, D. 2020. Environmental Performance Index 2020. New Haven, CT: Yale Center for Environmental Law & Policy. Retrieved from epi.yale.edu
Regulation indicator	Sulphur dioxide emission reduction	The average annual rate of increase or decrease in SO2	Wendling, Z., Emerson, J., de Sherbinin, A., & Esty, D. 2020. Environmental Performance Index 2020. New Haven, CT: Yale Center for Environmental Law & Policy. Retrieved from epi.yale.edu
Regulation indicator	Nitrous oxide emission reduction	The average annual rate of increase or decrease in N2O	Wendling, Z., Emerson, J., de Sherbinin, A., & Esty, D. 2020. Environmental Performance Index 2020. New Haven, CT: Yale Center for Environmental Law & Policy. Retrieved from epi.yale.edu
Regulation indicator	Environmental taxes	A measure of the number of taxes, charges, or fees for the following categories: 1) environmental management, 2) air pollution, 3) energy, 4) noise pollution, 5) fossil fuels, 6) transport management, 7) waste management, and 8) water management	OECD, Policy Instruments for the Environment (PINE) Database, http://oe.cd/pine , accessed on [24/11/2020]
Engagement indicator	Conservation spending	Average annual conservation investment (I$m) for countries that are signatories to the Convention on Biological Diversity (CBD) and Sustainable Development Goals (SDGs)	Waldron, A., Miller, D. C., Redding, D., Mooers, A., Kuhn, T. S., Nibbelink, N., Roberts, J. T., Tobias, J. A., & Gittleman, J. L. 2017. Reductions in global biodiversity loss predicted from conservation spending. Nature, 551, 364--367. https://doi.org/10.1038/nature24295
Engagement indicator	Biodiversity projects	The number of biodiversity-related conservation projects in a developing country that are receiving funding from OECD (Organisation for Economic Cooperation and Development) countries, standardised by country area (km2)	http://www.oecd.org/dac/stats/biodiversity.htm, accessed on [24/11/2020]
Engagement indicator	Environmental NGO projects	The number of environmental NGO aid projects in a country standardised by country area (km2). Note that data is provided on a voluntary basis and so is not fully comprehensive	https://ngoaidmap.org/, accessed on [24/11/2020]
Engagement indicator	Biodiversity engagement	A score based on the number of times biodiversity key words have been sourced from twitter, online newspapers and google in a country (per month)	http://biodiversityengagementindicator.com/, accessed on [24/11/2020]
Drivers of wetland loss	Mangrove loss drivers	Human settlement, commodities (agri/aquaculture), erosion, non-productive conversion (clearing), extreme weather events	Goldberg, L., Lagomasino, D., Thomas, N., and Fatoyinbo, T. (2020). Global declines in human-driven mangrove loss. Global Change Biology 26, 5844--5855. https://doi.org/10.1111/gcb.15275
Drivers of wetland loss	Seagrass drivers	Aquaculture/fishing, boating, catchment processes, disease, climate and storms	Dunic, J. C., Brown, C. J., Connolly, R. M., Turschwell, M. P., and Côté, I. M. (2021). Long-term declines and recovery of meadow area across the world's seagrass bioregions. Global Change Biology 27, 4096--4109. doi:10.1111/gcb.15684