- So one of the questions
that really interests me
about ecological forecasting
is thinking about,
as a discipline in ecology,
can we make ecology more predictive?
And kind of thinking about
can we forecast ecology
in a way similar to the way
we currently forecast weather.
One of the things that's
important in answering
that question is, having some
measure of predictability
to judge whether we can predict
ecological systems or not.
There are multiple possible
ways to judge predictability,
but one of the ones that
I've found very useful
is to think about the
uncertainty in our predictions
and how those grow through time.
If you made a graph
looking at the uncertainty
in your prediction versus time,
it should start at some level, you know,
what is our understanding of
the system right this moment?
And as I move into the future,
that uncertainty should increase in time.
In general, as uncertainties compound,
we're always going to see the pattern
that as we move out into the future
our uncertainty about
the state of the system
is going to grow.
So one measure of predictability would be,
what is the rate at which
that uncertainty is growing?
Another might be, when
does that uncertainty
get to some point where it's essentially
indistinguishable from our kind
of background understanding?
So it might converge
to kind of the average
understanding of the system.
In essence we're not doing any better
than random, just re-sampling
the data we already have.
We just are kind of
converging to the range
of natural variability in a system.
So how do we understand
predictability and what affects it?
One of the things that we can do
is think about the classes
of models that we use
to make predictions in ecology.
And one very common class of models,
we call dynamic models.
Models that try to predict
the future of the system
as a function of the
current state of the system.
And then possibly some
other sets of covariates.
And those models would then have
some sets of parameters
and then some process error
that captures the factors
that we don't understand
about the system.
What we can then do from
that general understanding
of dynamic models, is we can actually say,
well how does the variance
in a dynamic model
increase through time?
What we can do it
partition that uncertainty
in our forecast into five key terms.
And these correspond to
the key uncertainties
that go into the model,
and then think about
how those uncertainties that go in
translate into the predictions.
So first we have the uncertainty
about the initial condition.
So what is our uncertainty
about the current
state of the system?
We have uncertainties related
to the inputs, the drivers.
So this is the uncertainty
about how the future environment
is going to change and the sensitivity
of our system to those
environmental drivers.
We can then think about the
parameters in our model.
We can actually break that
down into two components.
We can think about what is the uncertainty
about the mean value of those parameters?
And then we can also think about
how do those parameters vary?
So one of the common characteristics
of ecological systems is there's often
unexplained heterogeneity and
unexplained variabilities.
We might have year to year variability
that we don't fully understand.
Or site to site variability
we don't understand.
Or variability among
individuals that we can
quantify but we don't yet know
the explanatory variables that describe
why we see that variability.
And then finally there's
this process error,
the unexplained components.
If we dive into each of these terms,
we can see that they follow a very common
repeating pattern which is,
the contribution of each uncertainty,
the overall predictive uncertainty,
can be thought of as the product
of the uncertainty itself, of the input,
and then how the system
responds to that uncertainty.
Mathematically that would be expressed as
the variance of one of these five inputs,
and then the sensitivity,
which is essentially
the slope of the relationship
between that input
and the output we're trying to predict.
So mathematically that would be expressed
in terms of the derivative,
which is again a measure of sensitivity.
This tells us, at a high level,
that if we want to understand systems
and the predictability of systems,
we need to understand the
uncertainties about the inputs
and we need to understand
the sensitivities.
One important problem facing
ecological forecasters
is determining the relative importance
of these five different uncertainties
for different classes of problems
we're trying to make forecasts for.
So to give an example, the first term
that we talk about it the uncertainty
about the initial conditions
and the sensitivity
of systems to their initial conditions.
That sensitivity to initial conditions
is what ecologists often think of as
the stability of the system.
And at a high level it might tell us
whether a system has stabilizing feedbacks
that cause it to go to
some sort of equilibrium,
or whether it does not
have stabilizing feedbacks
but instead is chaotic.
One important example of a chaotic system,
when it comes to forecasting,
is the atmosphere.
So back in the 50s and 60s when numerical
weather forecasters were first starting
to make predictions, they discovered that
their models were chaotic.
That discovery, which is
innate to the physical
equations that describe the atmosphere,
led them to realize that if they wanted to
make predictions that that specific term,
the uncertainty about
the initial conditions,
was going to dominate over
the other uncertainty terms
in any prediction they wanted to make
over non-trivial time scales.
Because of that understanding
that that specific terms,
the internal stability and
initial condition uncertainty,
was the dominant uncertainty,
over the 50 or 60 years
that weather forecasters
have been making predictions,
pretty much the entirety
of that approach, the
entirety of the workflow,
and the entirety of the system,
is largely optimized
around that understanding
of which uncertainty dominates
their prediction problem.
So we have billions of
dollars in weather satellites
and observational campaigns
and airplane measurements
and buoys and ground
measurements and radiosondes.
So all these observations that are made
on a continuous basis on the atmosphere.
Yes the data is interesting unto itself,
but one of the main reasons
that data is collected
is because atmospheric
scientists need to constrain
the initial conditions
of weather forecasts.
And they need to do this
on a continual basis.
So right now weather forecasts
are updated every six hours.
So every six hours they
need new information
about the current state of the system,
to keep the current
forecast's uncertainties
from blowing up due to the
chaotic nature of their system.
By analogy, ecological forecasters
do not know from first principles
which of these five terms
are going to dominate
the predictions that we're trying to make.
Understanding that, is
actually an empirical problem.
It's one that's going to require us
to attempt to forecast a large range
of different ecological
problems and then understand
and formally partition out
the different uncertainties
that contribute to those forecasts.
One of the things I hope to learn
by making ecological
forecasts is if there are
common patterns to the predictability
of different ecological systems.
So the null model here is
that there is no pattern.
That every time we
encounter a new ecological
forecast problem it's going
to be unique and different
than other ecological forecasting problem
we've ever seen.
I don't think that's what
we're actually going to see.
I expect that for certain
classes of problem,
we'll see that they're dominatedg by
different sources of uncertainty.
So there may be ecological
forecast problems
dominated by sensitivity
to the environment.
There may be ecological forecast problems
dominated by this chaotic
sensitivity to initial conditions.
There may be chronically data limited
ecological forecast problems
that are dominated by
the uncertainties in the parameters.
Or there may be certain
classes of ecological problems
that are dominated by the inherent
heterogeneity and variability
in ecological systems.
I feel that once we
have some understanding
of that variability
and what terms dominate
different ecological forecasts,
it's gonna have a real
impact on our field.
I think it tells us something broadly,
in a theoretical concept about how
ecological systems work,
about what sorts of processes
drive them, and I think
it does so in a way
that really excites me
because it allows us
to really take a broad
comparative approach.
It allows us to learn things, say,
about the predictability
of harmful algae blooms
and translate that understanding
to trying to predict disease dynamics.
Or at least it gives us a common language
that ecologists can use
across all sub-disciplines
to talk to each other
about predictability.
So in addition to providing us with some
theoretically understanding of what drives
ecological systems and the potential
for seeking generality in ecology,
and understanding different
processes across space,
there's very practical side
to trying to understand
which uncertainty dominates which is,
that if we want to make new predictions
for new problems in new systems,
if we can classify them as being similar
to other forecasting problems we know,
that tells us something
about how we should
approach that problem.
So if we have a new
emerging invasive species
or a new emerging infectious disease,
then we can say, well
that is going to be like
these other infectious disease
or invasive species problems
that were dominated by
certain types of uncertainty.
It helps us really focus in on what
we should be measuring
and how we should be
approaching that problem.
Because fundamentally, we
can't measure everything.
Knowing something about
where we should focus
our efforts can really make better use,
and more efficient use,
of the limited resources that we have.
The final reason I think understanding
which uncertainties dominate
ecological forecasts
is important is because I think it has
a real impact on the methods we use.
So what we measure should be impacted
by which uncertainties dominate.
How we build our models
are going to be reflected
in understanding what
uncertainties dominate.
And then how we build statistical tools
for bringing models and data together,
needs to be optimized around what sorts
of uncertainties dominate
ecological forecasts.
One thing we see right now is that
there's a lot of experience and tools
that we have the potential
to borrow from other fields,
like weather forecasts, for
developing ecological forecasts.
However, weather forecasts
are fundamentally
trying to tackle a different uncertainty
than ecological forecasts.
And I think there's real
value in stepping back
and asking, are those
tools and their assumptions
really the appropriate tools
for ecological forecasters?
Maybe we need to revisit where those tools
are derived from then make
slightly different assumptions
that are optimized for
the types of uncertainties
that dominate ecological forecasts.
