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. Add a new author row Remove last author row

(Doubleclick on an added row to change the input values)

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.

---

Update View Clear map

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.

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

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.

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)

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.

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.

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.

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.

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.

Planning can be structured by considering multiple management or land/water-use objectives through zones. Here we describe them if those are set.

Any areas or actions that were included or excluded by default?

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.

An common example is the use of 'stacked' distribution layers and subsequent inclusion of species richness in the prioritization.

List all features used in the planning, their type and an approximate number. Where possible assign groupings based on the Feature types above.

Add a new feature row Remove last feature row (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

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)

For example, was the planning conducted in a way that considers future states or conditions?

Any other constraints used in the planning? For example (administrative) masks other areas indicating that some planning units are not to be selected?

Were differential weights assigned to some features, such as for example giving threatened species a higher weight in the planning? If so, describe here:

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.

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.