- So one of the reasons why ecological forecasting
and decision support go hand in hand is that decisions
are all about the future, and ecological forecasts
are designed to predict the future.
That's why ecological forecasting is so important
and has many opportunities to be included
as part of decision and management.
They help us to predict information that's
critically important for us to be able
to manage our environmental systems more effectively.
I think it's really useful to understand the components
of a decision so we can understand
how scientific information fits into them.
Decisions have three major components to it.
The first is uncertain scientific facts
and information or ecological forecasts.
The second are individual or stakeholder values.
What do we care about?
What are our goals and objectives?
And then the third are constraints.
These can be environmental constraints, social constraints,
and often times with environmental management decisions,
policy or regulatory constraints.
When we talk about decision support, we're not only talking
about tools like ecological forecasts and scenarios
and various modeling approaches that allow us
to integrate science into decision making,
we're also talking about processes.
These are processes that allow us to work together.
Scientists and decision makers to really understand
the information that needs to be brought
to bare to make better decisions.
That means that when we think about ecological forecasts,
I think it's really important to figure out ways
that we can co-produce ecological forecasts.
That means we want to figure out ways to work
with stakeholders so that scientists and stakeholders
are in the same room helping to think through and define
the problems and make sure that we're producing forecasts
that are relevant and useful.
Which means that we're really talking about a model
where we're not just trying to push out information.
Once we have a forecast that we think is relevant and useful
and hope that it's useful for other people and managers.
We're also not trying to design forecasts so that their only
responsive to a very narrow set of decisions, and that we're
defining them based only on stakeholder decision needs.
Ideally what we're doing is we're pushing the frontiers
and developing forecasts that might be useful for a range
of different decisions and applications.
We're also working with stakeholders to understand
the decisions and objectives that they care about
so as we're designing forecasts,
we're not just making interesting predictions,
we're making predictions that relate to specific types
of management decisions that are relevant.
And we're also thinking about how we make projections
that allow us to test different interventions,
different management options and understand
what might happen in the future.
That means one of the most powerful ways that
ecological forecasts can be used is as a laboratory world.
An experimental world where we can do anything we want to it
and understand what will happen.
This is what Holling talks about
in his book on adaptive management.
Is that we can make inferences.
We can understand what would happen and we can use models
as a way of developing large scale experiments
where it would be unethical to perturb various ecosystems
and instead we can perturb the model and understand
what may happen as a result of our actions.
So we've talked up to this point about some of
the components and opportunities for ecological forecasts,
but I think it's also useful to understand how we break down
and think about making really complex hard decisions,
like environmental management decisions.
And so one of the acronyms that might
be useful to remember is PrOACT.
What this stands for is Problem, Objective,
Alternatives, Consequences, and Trade-offs.
If you're using Mike Dietze's ecological forecasting book,
you'll see this in the decision support chapter.
In that chapter he has a conceptual model
that breaks down the PrOACTS steps.
One of the things that he highlights is that in addition
to alternatives, we're often times thinking about scenarios,
so that we can understand what might happen in the future
so we can constrain the boundary conditions that we need
for our models so that we can perturb them
based off of the same boundary conditions.
In addition when we talk about consequences,
I'm thinking about consequences for the full range
of objectives that we care about.
However, in ecological forecasting often times
the ecological forecaster is most interested
in what consequences we can forecast
that relate most specifically to those particular models
or ecological systems that they care about.
One of the things that's important to know
about the PrOACT approach is that it's not a linear process.
Ideally this employs adaptive management.
So even though you may see conceptual models
that really lay it out as problem, objective,
alternatives, consequences, and trade-offs.
If you're doing this approach, you would ideally
be thinking about adaptive monitoring,
improving your forecast intuitively over time
and how you use that information to feed in,
to understand whether or not the
management actions you took are actually working.
And if they're not, what actions you might want to take
and how you might be able to use the models
to help you access different approaches.
So let's break down each of the PrOACT steps.
A lot of this is described in much more detail in a book
called Structured Decision Making by Robin Gregory.
And for those that are interested in taking a deep dive
into how a decision analyst thinks about constructing
decision models that use ecological forecasts,
I recommend you look at that reference to really understand
the various components and how you can improve your
ecological forecast for those decision models.