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Is Your Plan Is Working?

baseline cumulative effects land use performance indicators planning scenario planning Feb 20, 2017

Indicators are how you will be able to measure performance in scenario planning and implementation.  Think of it just like the dashboard in a car or truck where you have a number of indicators that inform you about the performance of your vehicle.  For example, you have a speedometer to tell you how fast the vehicle is moving, you have a fuel gauge so you know how much fuel is left before you hit a critical threshold of running out of gas, and you normally also have a temperature gauge so you know how hot the engine is and therefore how well your cooling system is working.  These are all indicators.

Defining an appropriate set of indicators at the earliest stages of the planning process is crucial because it influences many subsequent decisions including the study area boundary, how the landscape is stratified, and what data and information will be needed. 

What makes a good indicator?

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  • It fits with the interest of the target audience, is easy to interpret and invites us to action (read further, investigate, ask questions, do something)
  • It is representative of the issue or area being considered and can be tracked over time
  • It is understandable and interpretable allowing us to uncover the causes behind the trends
  • It is comparable with other indicators that describe similar areas, sectors or activities;
  • It is well-founded whether scientifically or in experience


How many indicators to measure is a delicate balance that provides enough detail to understand system dynamics but that avoids creating an onerous data set.  In my experience, I have found that somewhere between 6 and 20 is a good range to be in – I usually find comfort somewhere right in the middle.

Back in 2005 I was leading a very large cumulative effects project in the Oilsands Region of northern Alberta Canada.  I was leading a team of scientists and facilitators working with 47 stakeholders representing a spectrum of values including federal, provincial and municipal government, First Nations, Métis, industry including the big oil and major forest products companies, NGO’s and local interest groups.  We started with a list of 43 potential indicators.  From there we applied criteria adapted from the US EPA and the USDA that I have found very useful and used over and again.

Cultural, Economic or Ecological Relevance

  • Considered important (e.g. food, spiritual significance, quality of life)
  • Can be linked to plans and policy
  • Simple and understandable to the target audience

Response Variability

  • Predictable response to stressors
  • Anticipatory, sensitive, early warning
  • Low natural variability, high signal

Management Relevance

  • Stated in management goals etc.
  • Applicable to management decisions or thresholds

Feasibility of Implementation

  • Availability of affordable, existing data & not cost prohibitive to measure
  • Low impact of measurement
  • Easy to measure, repeatable

Interpretation and Utility

  • Stress repsonse distinguishable from natural variability
  • Can help to identify causes of ecological response
  • Historic data, baseline conditions known

 I apply scenario planning as a foundation of cumulative effects management planning using landscape simulation.  Over the past 20 years I have used at least 10 different GIS-based landscape simulation models, everything from excel spreadsheets to sequential simulation estate models, constraint impact mapping, simulated annealing near optimization, optimization using linear algebra – wow, a lot of different approaches & tools. Of these, my favourite is the ALCES model – its an acronym for “A Landscape Cumulative Effects Simulator” which I have been working with it for 12 years now.  

But my point here is that you are going to need some kind of tool or tools to at least forecast potential indicator performance so you can anticipate the outcomes of alternative strategies.  Ideally you’ll also be able to backcast so you can learn from historic evidence.  And what this means for your indicator selection is that the indicators you choose need to be able to be backcasted and forecasted using the analysis tools you choose.  You could have the best indicator in the world but if it can’t be used in your analysis tool – well it’s not going to be much value to you.

So, I always add a 6th criterion – compatibility with the analysis tool(s) you have.  As part of this, you should assess whether or not any of the indicators are already available in your tools or if they could be developed easily.  If not, you may find that the set of indicators you want determines the analysis tools you’ll need to acquire.

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Could these criteria help you with your planning? Leave a comment or question below.