- So, to kick things off
in our discussion of ecological forecasting,
I want to start with the basic question, why forecast?
Why should ecologists care
about forecasting ecological processes and, in particular,
why might we be interested in making near-term projections?
So I'm gonna focus a lot over these lectures
on making forecasts that are both near-term and iterative.
And by near-term I mean over timescales from sub-daily
to daily, weekly, monthly, seasonal, interannual,
up to decadal, largely referring to timescales
that are relevant to decision making
and timescales where we can actually validate
or verify how well we are doing at making predictions.
One thing that's really important is that,
when we gain new information about how the world works,
then we want to incorporate that new information
to forecasts, so envisioning an idea where,
when we make projections and predictions,
we then gain new observations, and we want the ability
to incorporate those new observations
and new information into forecasts.
So, one of the main reasons that we are interested
in ecological forecasting is that there's a lot
of environmental decisions being made every day in society,
from individual citizens up through industries and agencies,
that are not always being made with the best available data
and best available information
about how ecological systems work and how they might respond
to different decision alternatives.
Fundamentally, decision making is about the future,
about how we think things will happen in response
to different decision choices, or if we make no decision.
Because decisions are fundamentally about the future,
often the best information that we can bring
to the decision-making process are explicit predictions
and projections of how we think things will change
in the future, which are fundamentally our forecasts.
That said, I don't think that ecological forecasting
is solely about decision making.
I think ecological forecasting
is fundamentally a win-win that has the potential
to both improve environmental decision making,
make ecology and environmental sciences
more relevant to society, while at the same time
accelerating and improving our basic science.
So one of the reasons that I think ecological forecasting
can improve basic science is I believe this forecast cycle,
this idea of iteratively improving our forecasts
as new information becomes available,
is in many ways a very strong expression
of how we think the basic scientific methods work.
So, in the scientific method, we start with a hypothesis
which, from a forecasting perspective,
is the models we use to make forecasts.
They are, models are an embodiment
of our current understanding, and current hypotheses
and theories about how systems work.
When we make projections with models,
those are essentially the predictions
that we make from our hypotheses
that we then want to test against the world.
When we confront those forecasts with new observations,
we are testing our hypotheses, and then,
when we bring that new information into the models
to improve our forecasts, we are updating our hypotheses
and updating our understanding of the world.
In many traditional ecological studies,
this cycle of making hypotheses
and testing them against the world
occurs on the timescale of a research grant.
So I might get a grant for two to five years
for the research, and I'll often go out
and collect data for most of that period,
and then analyze the data at the end of the project.
By contrast, in near-term ecological forecasting,
the idea is that we are continually
confronting our hypotheses with observations from reality,
rather than saving them all up to the end.
So this has the potential, we believe,
to really accelerate the pace at which we do basic science,
by iterating through this process of hypothesis testing
much more frequently and much more often.
By analogy, if we look at the field
of numerical weather forecasting,
I believe one of the reasons that that field
has gotten considerably better over time
is they are essentially making a global projection
of how they think the atmosphere works every six hours.
Because of that, they're able to confront their hypotheses
about how the world works four times a day,
and they've been doing this for 60 years.
And, with that kind of feedback, they've gained a lot
of understanding of how the system works.
Another benefit of ecological forecasting for basic science
is that forecasts require that you make projections
and predictions explicit before new observations are made.
So one of the challenges that faces a lot
of scientific fields right now is sometimes
called a crisis of reproducibility,
that we have a lot of results across all sciences,
and ecology is not exempt from this,
where we can fit models and test hypotheses
that often are challenged by new data.
And one of the reasons that that happens
is because it's very easy to overfit statistical models
when you have, when all you're doing is looking at data
from the past because you can keep trying different models
till you find the one that fits really well,
but in many cases you may be overfitting those models.
When you make predictions into the future, you can't,
you are less susceptible to that overfitting
because you can't continually retune the model
to things that haven't happened yet,
to observations you don't have yet.
You have to rely on more robust projections.
Another advantage of ecological forecasting
is it really forces us to do synthesis
because it forces us to take the information we have
and pull it together, assimilate it and use that
to make specific numerical projections that kind of,
in many ways, forces us to make our hypotheses
much more specific and much more quantitative.
So, if you have very vague hypotheses
and qualitative hypotheses, you can have a hypothesis
that, if I were to add nitrogen to a field of crops,
that the plants will grow faster.
Well, that's a very general hypothesis
because it doesn't say, are the crops gonna grow
5% faster or 25% faster or 50% faster?
By contrast, if I make a specific numerical projection,
then I can actually distinguish whether the observations
I see in any particular experiment are consistent
with what I understood about how the system works
from previous scientific work, rather than just relying
on a fairly vague null hypothesis
of just that plants do not respond to nitrogen.
So our traditional statistical testing just leaves us
with a very, I think, unsatisfying test of,
does this have an effect or not,
not necessarily is what we're seeing consistent
with what we understood previously.
Finally, one of the key parts of getting better ecology
and improving our science is something
that we have in common, I think,
with all scientific disciplines that have attempted
to make forecasts and attempted to mature to the point
that they have some predictive understanding
of the systems that they're working in,
which is, gaining that understanding requires feedback.
So, if you make predictions, but you don't learn
how well you are at making those predictions,
you don't really get better.
So I think ecology right now is,
a lot of what we've been focusing on as a field
has been projections over longer timescales,
for example, predicting how the carbon cycle
or a specific species or specific systems
might respond to climate change out to, say, 2100.
So, first, that is important and it's useful,
but one of the challenges about that
is that we don't actually learn very quickly
whether we're good at predicting out to 2100
because we've never gotten any data from that period
to see if do it any well.
By contrast, by making frequent short-term predictions,
we can actually learn a lot about our ability
to make predictions, to get this feedback
on how we are doing and use that to improve.