USGS Multimedia Gallery
This text will be replaced
To embed this video, click "menu" on the video player toolbar.
If no transcript and/or closed-caption is available, please notify us.
Song: All right Tom, now you're going to teach me fly fishing.
Tom: Yep. I'm gonna teach you a little bit
about fly fishing. This is a really fun
way to fish. It's a little different than
most forms because what you use for bait
are artificial lures called flies. They're
very light and usually for the kind of
fishing we're doing they're very small.
You usually have – there are two types:
there's a dry fly which floats on the
surface and that's always fun because you
see things coming up and grabbing it off the
surface. And then there are wet flies which
are weighted flies like this little one here.
And these you fish under the water. This
particular one is meant to look like a midge.
Then we have these other ones that
look like worms. Fish will pick those up under the water.
Song: I'm Song Qian with the USGS.
Tom: And I'm Tom Cuffney with the USGS.
And for the last 10 years now, we've been
studying the effects of urbanization
across the United States. We've looked now
in nine major metropolitan areas. The Raleigh
area, Boston, Birmingham, Milwaukee,
Green Bay, Dallas, Fort Worth, Denver,
Portland, and Salt Lake City.
Song: Since three years ago, I started working
with Tom and the USSS and colleagues on
this EUSE project and my role here is
mostly data analysis and modeling.
Tom: Yeah, the EUSE project that we're
working on is the effects of urbanization
on stream ecosystems which is where the
EUSE acronym comes from. We've really been
looking at the effects of urbanization
on fish, invertebrates, algae, habitat,
and chemistry. We've really been using
Song a lot. He's helped us in developing
models to help explain what is happening
in these streams and what helps us compare
among metropolitan areas and also to
predict possible changes in the effects
of possible mitigation procedures.
The flies themselves are so light that they
really can't be cast. So you have to use
the cast in a fly rod is the fly line itself.
The fly line is a colored line that you
have here and these are weight forward
fly lines. That means that most of the
weight is in the first section of the
fly line. You use this to lure the fly.
The clear mono filament part, this is
the leader and this is what connects
the fly to your fly line. You're
constantly replacing this and you use
different weights and leaders depending on
what type of fishing you're doing. If you're
fishing for very small trout, you may
use very light line. If you're fishing
for bass, you might use a much
heavier monofilament line.
Song: One particular thing we did was a
multi-level model and another thing we
did was Bayesian network modeling to
understand how the land-use changes
affect the stream ecosystems.
Tom: Now the multi-level models were
very important for me at least because it
helped us explain why we were seeing
different relationships between urbanization
and stream effects in different parts of
the country and that method really helped
us explain why we were seeing these different results.
The trick to fly fishing is that the fly
rod itself is a big spring and you need
to use that spring to cast a line.
The nice thing about the multi-level
models from the perspective of application
is that it showed that there were other
interferences in terms of factors that
were affecting urbanization like agriculture
in Milwaukee, Green Bay, and Denver,
and Dallas, Fort Worth which turned
out to be, were masking the effects we
saw of urbanization as well as the
ability to incorporate the climatic
factors of moisture and precipitation
which varied among the metropolitan area.
Song: These are examples about variables
operating at very different spatial-temporal
scales. We're looking at land use cover,
urban cover, agricultural coverage
which is something that varies at the
watershed scale but when we talk about
the background or antecedent agricultural
land use, that's in the regional scale.
Tom: The other thing that we did was looking
at the Bayesian Network models. Those are
very interesting models because they
employed a lot of prior knowledge and
that's something I think was rather
unique in that – was the ability to go
out and find experts and talk to them
about what should be happening in these
systems and what they expected to happen;
and then actually looking at the data.
So would you talk a little bit about how that process works?
Song: All right, so this is the one big
difference between Bayesian statistics
and classical statistical inference.
In the classical statistical inference
we based our inference purely on data
and all the other previous experience
and all this doesn't count. Essentially
every time when you go out and collect
data and try to develop a model, you are
trying to reinvent the wheel. With
Bayesian statistics, you can actually
logically combine information from various different sources.
Tom: There are other types of casts
where you can roll cast like this, which
is where you just don't have the line
behind you. You just pull it back and
roll it over. That can be used a lot
when you have something behind you that
prevents you from false casting like this.
All right, should be go upstream and give her a try?
Tom: All right. From an ecological standpoint
in terms of just trying to understand
ecology, we've also been working on a
number of models looking at understanding
specific elements of the biology.
Song: That's right so the statistics
fundamentally is a tool for learning.
Science is used in two different ways.
One is for helping management and helping
support decision making. The other is
learning and making inferences about
what's behind the natural phenomena.
From the statistical perspective,
statistics is always a tool for learning from data.
I could really get used to this!
Tom: Oh yeah man! You gotta get a rod!
So Song, over the last three years we've
done a lot of modeling that's helped us
both understand how urbanization changes
across the country, as well as to look
at possible things that we can do to
mitigate those effects. In a nutshell
what do you think are the three most
important things that we've come up
with in the last couple of years?
Song: I would say the first thing is
that complicated systems need a
little bit more complicated models.
Tom: And I think the fact that we're
seeing different responses to urbanization
in different regions of the country and
that the antecedent land use has a
strong effect on how urbanization
impacts systems is important.
Song: That's one thing that we feel
is very interesting, an interesting point;
the most interesting outcome from the multi-level analysis.
Tom: And then the third thing, I think,
that's been really important is the
Bayesian network models that have
allowed us to make relatively simple
models so managers can make adjustments
to kind of get some perspective on
what management decisions would do
in terms of changing the resources.
Song: That's right and the Bayesian
network model allows us to pull
information from various sources
and make a sound scientific judgment and management strategy.
Tom: So working with you in the last
few years, we've come a long way from
those simple regression models that I did initially.
Song: And to me working with you guys is,
I get to come out and actually look at
how the data was collected and
the real stream, the real ecosystem.
Tom: So it's been good talking to
you but let's get back to fishing.
Song: Yeah, let's go. This is just like learning statistics.
Tom: So what's the probability that you're gonna catch a fish?
Song: Well so far, zero!
Tom: That's the same as my probability. I think this is very classical.
[End of Audio]
Title: Bayesian Modeling, Fly Fishing, and Effects of Urbanization on Stream Ecology
Tom Cuffney and Song Qian describe their U.S. Geological Survey research on the effects of urbanization on stream ecology, while fly fishing.
Location: Eno River, NC, USA
Date Taken: 5/19/2011
Video Producer: Douglas A. Harned , National Water-Quality Assessment Program (NAWQA), USGS, North Carolina Water Science Center, Raleigh, NC
Note: This video has been released into the public domain by the U.S. Geological Survey for use in its entirety. Some videos may contain pieces of copyrighted material. If you wish to use a portion of the video for any purpose, other than for resharing/reposting the video in its entirety, please contact the Video Producer/Videographer listed with this video. Please refer to the USGS Copyright section for how to credit this video.
Additional Video Credits:
Douglas Harned: Producer, Video, Editor
Alan Cressler: Video
Brian Pointer: Video
Michelle Moorman: Video
Erik Staub: Video
Laura Nagy: Audio
Cari Cuffney: Audio
Tom Cuffney (USGS)
Song Qian (USGS)
Suggest an update to the information/tags?
* DOI and USGS link and privacy policies apply.