A reporting protocol for systematic conservation planning
Systematic conservation planning (SCP) typically uses decision-theoretic approaches to decide - given all available evidence - where, when and/or what to achieve the most beneficial for biodiversity conservation. SCP can be applied at the identification, planning, implementation and monitoring stages of a conservation project. It is usually an interdisciplinary approach integrating both qualitative and quantitative data and methodologies from a range of scientific disciplines.
Area-based and action-based conservation planning is crucial for achieving conservation policy objectives across scales. Yet despite decades of research and numerous scientific advances in Europe (Jung et al. 2024). and globally (McIntosh et al. 2017), it often remains hard to graps for those unfamiliar with the planning how decisions where made or what factors were considered influential in determining the identified outcomes.
The Overview and Design Protocol for Systematic Conservation Planning (ODPSCP) serves three main purposes. First, it provides a checklist for researchers and practitioners to outline all key steps in their planning work. Second, it introduces a standard approach to documentation that ensures transparency and reproducibility, thus facilitating peer review and expert evaluation of the conducted planning. Third, it helps study authors and decision makers to identify key strength, but also weaknesses of a systematic planning exercise.
This Shiny web application helps to implement the protocol through an easy understand user interface and allows to export the created protocols in a range of formats for further use. We encourage the scientific community, publishers and editors and policy makers to make use of this protocol.
Note: This online interface does not permanently collect or store any entered information. Please export your protocol after it has been entered.
Provide an overview of the conducted work
By default example popups are shown for text fields, which can be disabled through the questionmark at the top bar.
Study information
Provide a list of authors:
Add each author of the study to the table below. If a ORCID is not known or available, leave blank.
If the number of authors is extensive it might also be ok to simply add the lead author's name.(Doubleclick on an added row to change the input values)
Corresponding Author
(Optional) Link to study
Overview of scale and extent of a study
Spatial planning can be conducted at a range of different spatial and temporal scales, and realms. The fields below capture this information.
Study scale
Local refers to a study at any given single site, National to planning at a country level, Regional for studies beyond single countries (e.g. bioregions), Continental for entire continents (e.g. Europe, Africa) and global for truly global studies.
(Optional) Study region
Study location
Temporal coverage of the study
Define the temporal scale over which the planning and specifically the planning objective applies. This should not be interpreted as a period of data coverage. If outside the chosen scale, please provide details in the textbox.
Study realm
Data and code availability
Source: Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226-1227.
This box records whether a study makes available the data - both for input and outputs - as well as the software code or analytical to reproduce the analysis.
Are the used input data made available and if so where?
If applicable please enter a link to the data storage repository.
Are the created outputs made openly available and if so where?
Typical outputs include for example priority maps or performance indicators. Describe all outouts here and where they are stored.
Has the analytical code to reproduce the results been made available?
Preparing data for analysis and creating priority maps can be done with computer code. If such code was created, consider storing it somewhere and make it available.
What are the overall design criteria for the planning?
Aims and framing
Study aims
(Optional) Analytical Framework
Does the study follow an analytical framework, either explicitly defined within the study or through a reference to previous work? This could for example also be a specific planning protocol or established approaches such as structure decision making or adaptive management.
Example framework references:
Pressey, R. L., & Bottrill, M. C. (2009). Approaches to landscape-and seascape-scale conservation planning: convergence, contrasts and challenges. Oryx, 43(4), 464-475. DOI: https://doi.org/10.1017/S0030605309990500
Alvarez-Romero, J. G., Adams, V. M., Pressey, R. L., Douglas, M., Dale, A. P., Auge, A. A., ... & Perdrisat, I. (2015). Integrated cross-realm planning: A decision-makers' perspective. Biological Conservation, 191, 799-808. DOI: https://doi.org/10.1016/j.biocon.2015.07.003
Niemiec, R. M., Gruby, R., Quartuch, M., Cavaliere, C. T., Teel, T. L., Crooks, K., ... & Manfredo, M. (2021). Integrating social science into conservation planning. Biological Conservation, 262, 109298. DOI: https://doi.org/10.1016/j.biocon.2021.109298
(Optional) Theory of change
Most SCP applications are applied rather than curiosity driven. However an applied focus does not necessarily mean that the work will be implemented and the pathway to impact is clear. A theory of change is a apriori process that maps the relationship between a long-term goal of a planning objective and the necessary steps required to implement it.
Purpose of planning
Study purpose
As primary purpose we refer to the overall aim of a study such as the identification of areas to be placed under conservation management (e.g. Protected areas)
Multiple objectives
For a given purpose and objective function there can be often multiple, sometimes competing objectives involved in the planning. For example, if one would to identify management options that can consider both species and carbon storage as features by altering their weights.
Scenarios or planning variants
Often the output of a planning exercise is not a single prioritization, but multiple each with different assumptions, parameters or input data. Examples include planning approaches that account for various climate scenarios or assumptions regarding constraints. Please record whether there multiple scenarios or variants have been explored and how
What does not qualify here: 'Scenarios' that are essentially sensitivity checks or parameter calibrations. Only select Yes if multiple outputs are presented in the work.
Engagement of stakeholders
To facilitate sucessful implementation it can be considered important to involve stakeholders in the design and execution of the planning exercise. There are multiple ways of doing so and the fields below record these details.
Stakeholder engaged
Type of engagement
Stakeholders
Select or add broad groups of who was engaged. Note that co-authors are generally not considered stakeholders unless co-authorship is given due to co-design of the whole planning study.
Stakeholder engagement method
Monitoring and evaluation
Monitoring and evaluation is a key part of systematic conservation planning. Here we ask whether - following the implementation of the study - procedures are in place to monitor its outcome and what steps have been undertaken to evaluate the effectiveness of the planning.
Has a monitoring and evaluation plan be developed?
Such a plan could for example include the monitoring of the implementation and its effectiveness, or the mentioning of a specific methodology to evaluate the planning objectives and targets.
Specification
Under the protocol entry 'Specification' we list all the elements, features and datasets that are being used in the planning. Basic information on their broad categorization, type and origin are recorded. It records what type of information is included in the planning, not how individual information sources are used (for that see context).
Planning units and scale
The principal elements of a SCP application are generally called 'Planning units' (but see Glossary). They can be for example based on a gridded Raster layer or any spatial organization such as a polygon. A regular polygon has equal sides and angles.
Planning unit type
What was the spatial grain of planning?
Fill the text box below if the grain of the planning unit cannot be easily determined. For example if planning units are agricultural field sizes.
Planning unit costs or penalities
The decision where to allocate conservation efforts can to a large degree be determined by economic, biophysical or socio-economic constraints. One way of including those in planning studies is to treat them as a cost or penality, thus penalizing the selection of any outcomes with too high costs. Typical are for example the costs of land acquistion in area-based planning.
Zones and specific groups
Not all planning units or planning objectives might receive the same attention or are considered equally. Here we record whether certain area or themes were exclusively considered, included or excluded.
Was there any ecosystem specificity?
Planning can be conducted on all land or sea within a given region, but it can also be specific to certain ecosystems or land-use types, such as for example forests. Check No if all available land or sea within the study region was considered.
Where any zones used for the planning?
Planning can be structured by considering multiple management or land/water-use objectives through zones. Here we describe them if those are set.
Describe the zones used in the planning. Zones can be useful to prioritize for not a single, but a set of management decisions. For example, protected area managers might want to identify areas of minimal intervention ('core-areas') as well as sustainable use areas.
Reference: Watts, Matthew E., Ian R. Ball, Romola S. Stewart, Carissa J. Klein, Kerrie Wilson, Charles Steinback, Reinaldo Lourival, Lindsay Kircher, and Hugh P. Possingham. Marxan with Zones: Software for Optimal Conservation Based Land- and Sea-Use Zoning. Environmental Modelling & Software 24, no. 12 (December 2009): 1513-21. https://doi.org/10.1016/j.envsoft.2009.06.005.
(Doubleclick on an added row to change the input values)
Inclusion or exclusions?
Any areas or actions that were included or excluded by default?
Threats
Select any threats that were targeted in the planning or that, directly or indirectly, shape the planning outcome. The threat description broadly follows the IUCN Threat categorization system.
IUCN Threat classification: https://www.iucnredlist.org/resources/threat-classification-scheme
Threat types
How were threats considered in the planning?
There are multiple ways of including threats, for example by considering them as risk factor in the prioritization, as cost or penalty in selecting a solution. Here we record these various options.
Feature types and description
What types of features are included in the spatial planning? As features we do traditionally describe all (spatial) information that enters the prioritization with the aim of accruing benefits. Examples include estimates of the distribution of species or Nature Contributions to people (NCPs).
Feature types
Where any features types aggregated before use in the planning?
An common example is the use of 'stacked' distribution layers and subsequent inclusion of species richness in the prioritization.
Provide a summary of all features:
List all features used in the planning, their type and an approximate number. Where possible assign groupings based on the Feature types above.
(Doubleclick on an added row to change the input values)Alternatively upload a grouped feature table in csv or tsv format. Note that this table needs to have exactly 3 columns with the name | group | number
How were features created and what do they contain?
Commonly input features in the planning were created through separate processes, such as through qualitative data gathering or ecological modelling. Here we briefly describe how input features were created.
(Please be brief and where possible refer to existing text or other protocols)
Important contextual information with regards to the planning.
Selection criteria
Decision variable
Temporal considerations
For example, was the planning conducted in a way that considers future states or conditions?
Aspects of connectivity considered?
Connectivity planning
(Optional) Other connectivity details
Any other methodological detail - if any - on how connectivity was considered in the planning. For example, connectivity between PU could have been assessed using https://marxanconnect.ca/ or through other custom entries.
(Optional) Other constraints
Any other constraints used in the planning? For example (administrative) masks other areas indicating that some planning units are not to be selected?
Feature contexts
In many situations the features used in the planning might also have their own parametrizations and options. A common example is for instance the setting of targets as a specific constraint.
Feature targets
Feature weights
Were differential weights assigned to some features, such as for example giving threatened species a higher weight in the planning? If so, describe here:
Prioritization
With prioritization we usually refer to the process of taking the various datasets and parameters defined earlier and identifying 'solutions' that might be spatial, spatial-temporal or non-spatial in nature. This section elaborates on the process of prioritization and specifically the algorithms used to generate such solutions for the planning problem.
Used Software
There are multiple existing types of software that allow users to integrate various features, constraints and targets in a single prioritization. Some make use of mathematical optimization, others of heuristics or multi-criteria ranking approaches. Some of the most commonly used SCP software solutions are listed below. If the software for this study is not found in the list below, please select 'Other'.
Enter a version number of the used software. Also provide any other information related to software (for example if a specific solver was used for mathematical programming such as Gurobi or Symphony).
Outcome identification
In most prioritization exercises the way outcomes are identified is usually determined by the used algorithm approach (e.g. heuristic, optimization) and the question that is being asked for the decision variable.
In this field we record - if known - how the software makes decisions, e.g. what is being optimized or ranked and how. A typical example is the use of minimum set problems (in Marxan and others) to identify those areas where all feature targets are reached while minimizing a cost (area, opportunity cost, ...). While Zonation users should indicate the specific benefit function used for identifying value gain in the prioritization.
References:Arponen, A., Heikkinen, R. K., Thomas, C. D., & Moilanen, A. (2005). The value of biodiversity in reserve selection: representation, species weighting, and benefit functions. Conservation Biology, 19(6), 2009-2014.
Schuster, R., Hanson, J. O., Strimas-Mackey, M., & Bennett, J. R. (2020). Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems. PeerJ, 8, e9258.
(Optional) Key parameters
Identification of final priorities
Not always is there a single solution to the prioritization process and often more than one single prioritization is run. Besides factors directly included in the prioritization, it is also common to consider external or auxillary datasets, selection frequency across multiple iterations, or prioritization runs to identify a set of final 'priority' areas or actions. Here we record how the final priorities (those reported in the study) were obtained.
Performance evaluation
A performance evaluation determines the value or overall benefits of a given prioritization output in terms of chosen representative indicators or values. An example would be summarizing the average amount of a threatened species range covered, or overlap with other spatial layers.
(Doubleclick on an added row to change the input values)
Other performance evaluation
News and protocol updates
Version 0.5
- The design tab now includes a separate elements recording if, and what type of monitoring and evaluation exists.
- Added statement on data sharing.
Version 0.4
- Fixed several typos ✍️.
- Updated with further description and suggested edits by experts.
- Updated and shortened preface description page.
Version 0.3
- Incorporated Expert-Feedback and updated the protocol with additional fields and explanations.
- Added glossary, to be further expanded in future versions #4
- Added tooltip to the protocol for each fields (can be toggled off) #6
- Added option for enabling study region delineation #2
- Added option to export a word or pdf summary of the protocol #5
- Added a bookmark option to store the current setting of the protocol as server id (experimental)
Version 0.2
- Added option to support different planning unit types for regular and irregular polygons.
- Changed spatial grain of planning unit to provide input in case of a heterogeneous PU.
- Added a few further text examples to elements, ultimately to be replaced by popovers #6
Version 0.1
- Initial Publication of the protocol.
The protocol template
Glossary
Import a previously saved protocol
Here you can upload a previously saved protocol file. This file needs to be either in yaml or csv format. After uploading, the various entries in the protocol will be filled with the previous version.
> Importing a protocol overwrites all existing values!
> Re-exporting the protocol resets internally the version and date.
Output format
All protocol entries can be exported in a range of different formats for further use such as appending them to a manuscript. It should be noted that only 'csv' and 'yaml' are machine-readable formats and can be imported again by ODPSCP.
Protocol will be rendered as docx Word document, which can be easily modified as well. Suitabile for manuscript appendices or personal records.
Protocol will be rendered as document and then converted to a PDF format. This is the most convenient format for appending the protocol to any publications for example.
Protocol will be exported as a list in a R data object. RData objects can only be opened through R and the resulting file can be loaded again via load(file) in R.
Protocol will be exported as a comma-separated file (csv) that can be loaded in any conventional software that allows the modification of text or spreadsheets. The protocol can also be reloaded in R via read.csv(file).
Protocol will be exported in the YAML Ain't Markup Language, a human-readable data serialization language that is common among many programming languages. YAML files can be read and edited with any text editor and loaded as lists into R via yaml::read_yaml(file).