**DOI Implementing Adaptive Management: Set-Up Phase**

**Thursday, September 27, 2007**

**Originating from: National Conservation Training Center**

**Step 4: Models - Ken Williams**

**\>\> B. Frost:**

Next on the agenda is step 4, and we\'re going to turn that over to Ken
again and he is going to walk us through the modeling section. Ken?

\>\> K. Williams: Okay. First, I\'ll take issue with Fred, who said that
he thought he got the short straw. I really think that the person who
got the short straw was Mike who was the guy who was slated to give this
presentation but Mike couldn\'t make it, so the short straw got handed
off to me. I think it is for sure that many practitioners of Adaptive
Management see that modeling is one of the largest hurdles that needs to
be overcome in the application of Adaptive Management. I want to
understanding, though, that it doesn\'t ‑‑ it\'s not necessary, it need
not be that. I want to make a few points to make that case. Point number
one is this, that every scientific investigation has implicit in it an
underlying model, one that embeds the investigator\'s assumptions and
highlights the hypotheses to be investigated. Were it not the case there
would be no way to focus the investigation on questions of interest, and
in fact, the whole field of he can spear mental design can be seen as a
highly structured exercise in scientific modeling. If it\'s true for
science, it\'s true for management as well. Every management strategy
has implicit in it and 81 lying model, one that embeds the manager\'s
understanding of the resource being managed and were it not so there
would be no way to determine what to manage or how to manage it. In fact
the selection of management actions is always guide bide a model in the
manager\'s head about what outcomes to expect. That model may not be
explicit, but it\'s always there. Otherwise management would be nothing
more than a random coin toss and it\'s not. Point number three, models
can take many different forms. Sometimes just a simple diagram will
suffice. Sometimes a verbal description of the resource itself. Maybe
nothing more than one\'s intuitive concept of the resource. More
formally, perhaps a mathematical model. But in any and all cases,
whatever it\'s form, the model is used to capture assumptions and to
express one\'s understanding about the system under investigation. Point
number four, there is no right model of a resource system. For some
applications, models may be simple. Some may be complicated. Some may be
general. Others may be specific. Some may require a great deal of
technical detail. Others may require little. Which kind of model to use
and what its attributes need to be come down to the nature of the
management problem that\'s being addressed, come down to the scale of
the management problem being addressed, come down to the available or
needed knowledge about what the management situation is. Now, with that
as background, let\'s get a bit ‑‑ well, let me go one more step that I
wanted to mention here. The point number five, the fact is that if you
think about modeling broadly, you\'ll see that you use models almost
every day. You use them to help organize your thoughts about resource
problems. You use them to focus your attention on what needs to be done.
You use them to identify the results of your management actions. You use
them to recognize what\'s known and what\'s not known and is needed. The
trick, of course, is to use models smartly, intelligently, to help you
with your decision making and not hinder it. Now, with all that having
been said, let\'s get on with some comments about the process of
modeling in adaptive management. Start by recognizing that in an
adaptive context the role of models is to link management actions with
management outcomes so as to promote both management and learning.
Models play that role by making one\'s underlying assumptions about the
natural resource system explicit and generating predictions of
management impacts, thereby, allowing those assumptions to be tested
with data. In order to do that, the models in Adaptive Management must
have certain features. One is that they need to describe resource status
as changing through time in response to both management and
environmental conditions. That is, a dynamic model is needed to
characterize a dynamic resource system. Another is that time specific
management actions are included in the model as drivers that influence
the resource. Actions like harvest or stocking or translocation or
seeding or any number of habitat alterations. A third feature, evolving
environmental conditions are incorporated as needed as drivers of
resource dynamics. Conditions like annual precipitation or temperature
fluctuations or amount of snow cover or water turbidity or carbon
dioxide concentration. This figure illustrates the points that I just
made. What it shows across the top are management actions taken through
time that influence the resource system. Across the bottom it shows the
resource ‑‑ it shows environmental conditions evolving through time and
influencing the resource system. In the middle it shows the resource
actually changing through time in response to environmental conditions a
and management actions. The issue here is to capture this structure with
models so as to make decisions in a way that promotes learning through
time while pursuing management objectives. Not only do models in
Adaptive Management link management actions to management outcomes, they
also express uncertainty about those outcomes. We\'re often unsure about
what the effects of management actions are because we\'re not sure about
how the system actually works or different people have different ideas
about how the system actually works. Alternative models can help to
capture these hypotheses and express uncertainty about them. And it\'s a
central focus of Adaptive Management to reduce this uncertainty and
thereby improve management by its reduction. Now, to do that,
alternative models must meet certain requirements. They must differ in
their predictions, that is, for a given management action, different
models need to behave differently. And they must be testable in that
monitoring data can be used to discriminate amongst them. Here\'s the
basic idea about the use of models to reduce uncertainty. The
alternative models in Adaptive Management are based on different
hypotheses about how the system ‑‑ the resource system works. And,
therefore, they predict different outcomes from management. With the
predicted outcomes compared and contrasted against monitoring data,
which, in turn, allows us to learn which hypotheses are most important
for ‑‑ are most appropriate for the resource system under investigation.
That learning about which hypotheses work well and which don\'t can be
used then to improve future decision making. Now, if all that sounds a
lot like scientific testing of hypotheses, it should. In fact, the use
of management to help discrimination amongst hypotheses in this way is
the main reason that Adaptive Management is so often described as
science‑based management. A couple of comments as I come to close on
this\... One that is often voiced is that there simply is not enough
data to build a model. My response is that Adaptive Management is most
useful under exactly those circumstances, because not enough data
translates into significant uncertainty. My second response is that it
is always possible to build models based on ecological understanding,
absent the collection of new data, and in fact, we do it all the time.
Second comment that\'s often voiced is to ask: what is the alternative
to building and using explicit models? The response I have is that
alternative is to let the models implicit in management that are always
implicit in management to remain unexpressed an untested, and, of
course, I argue that that is surely a less than optimal way to proceed
with resource management. Final comments that I\'ll make about this have
to do with the modeling process itself. I would suggest that a starting
point would be to identify the ecosystem processes that link management
actions to desired outcomes and to the objectives that capture those
desired outcomes. Secondly, to identify sources of uncertainty that
impede management and express that uncertainty, then, with simplified
models. Then to develop the quantitative details of those models and
build them out as the need requires and as the data allow. And then
lastly I would comment that it is helpful to work with an individual who
is actually adept at guiding the model development process because in
that way the model ‑‑ the modeling process can be accelerated
considerably. Bert ‑‑ I think that\'s enough for sort of an overview on
malting. I\'ll hand it back to you.

**\>\> B. Frost:**

Great, Ken. Thanks a lot.