# Presentation: 083323

## Modeling and Visualizing Species MovementPresented at: NASA Joint Science Workshop onBiodiversity, Terrestrial Ecology, and Applied ScienceCollege Park, Maryland, August 21-25, 2006

**Funding: NASA NCC2-1186 & NCC13-03009, NSF DEB 0074444 & DEB-0413570, and NPS**

- Fred Watson1
-   Simon Cornish1
-   Bob Garrott2
-   PJ White3
-   Rick Wallen3
-   Susan Alexander1
-   Wendi Newman1
-   Thor Anderson1
-   Jon Detka1
-   Jason Bruggeman2

- 1California State University Monterey Bay
- 2Montana State University Bozeman3National Park Service

**Notes:**

Thanks Ed.

Before I begin I’d like to acknowledge my Co-PIs and Co-Authors as listed here.

My primary role here today is as the PI of a REASoN project.

We’re a three-way collaboration between PJ White and Rick Wallen with the National Parks Service at Yellowstone National Park;
Bob Garrott’s large-mammal wildlife ecology group at Montana State University Bozeman;
and my group, which specializes in remote sensing, landscape modeling, and visualization.

## National Parks Servicebison monitoring & management activities

**Notes:**
Our mission is to Research and Develop ways in which NASA capabilities can contribute to wildlife ecology, specifically with the National Parks Service at Yellowstone National Park.

My goal today is to
- seek feedback,
- to inspire new activities along similar lines,
- and to illustrate some of the things that ‘have worked’ and are working with respect to our particular collaboration between NASA, the wildlife science community, and the Parks Service.

## A selection of productsin the pipeline

- Landscape visualization kiosk
- Canyon Visitor Education Center
- Real-time snowpack modeling
- Information that supports bison management at the boundary
- Landscape inputs to wildlife studies
- The hard science upon which policy is based
- A model for predicting species movement
- Potential contributions to any application where species move:
- biodiversity
- invasive species
- ecological forecasting
- disease vectors

**Notes:**
We have lots of things in the pipeline,
and I’m going to show you four of them:
- a Park kiosk visualization system,
an application of a dynamic landscape model to real-time Park management support,
- an application of landscape mapping & modeling to investigations of species-habitat relationships,
and finally, on perhaps the longest pipeline of the four, the development of a new theory of species movement with the potential to enhance any application where the underlying interest is in the distribution of species, and where those species achieve their distribution by dispersal within heterogeneous environments.

## Landscape visualization kiosk

**Notes:**
So to get started,
and to get familiar with the study area we’ve been working in,
lets look at the interpretive kiosk.

<Show kiosk DVD in Windows Media Player>
<Point out:
Its been deployed this week in new VEC
A few hundred thousand About half a million people will walk past it each year
 It is designed as a metaphor for a virtual interpretive trail
 It uses several NASA technologies:
 remote sensing data for vegetation & geothermals;
 and landscape modeling for snowpack;
 visualization software developed in-house.

## Slide 5

**Notes:**
Before I continue, I should briefly point out the importance of bison.

In simple terms,
they were once abundant in the millions,
then they nearly went extinct,
and in the past 100 years the Parks Service has successfully restored the only continually wild herd,
to the point where they now have to co-manage them with their neighbor agencies,
in sometimes controversial ways that often reach the national newspapers.

## Real-time snowpack modeling

**Notes:**
Now, lets look at another way that the Parks Service is using our products.

Each year, the Yellowstone bison move down from the central high country toward the lower boundary areas.
In big snow years, some of them attempt to cross the boundary.
I say ‘attempt’ because the policy is to haze them back into the Park in order to minimize conflicts with surrounding land uses.
If needed, the Park will capture them in pens, and then release them as soon as landscape conditions have ameliorated.
So when is that?
That’s where our snowpack model comes in. In 2004 and 2006, the Parks Service asked us to provide real-time snowpack maps during this critical period.
so that they could have more objective information upon which to base the pen-releasing decisions.
This is a pretty important decision, because if happens to lead to adverse consequences, they need to be able to stand by its objective basis.
(Note that iIn 2005, there was hardly any snow, and no captures were necessary
– this unpredictability of climate is one of the reasons its hard to benchmark these things!)

## Landscape model (snowpack)helps inform decision on when to release bison

- Landscape model (snowpack)helps inform decision on when to release bison

- Optimal time for release?

**Notes:**
This graph illustrates the temporal dynamics of the real-time snowpack modeling application a little better.
Conceptually, its pretty simple:
- wait until the snow has melted enough,
relative to co-predicted meltouts and movements in previous years,
until making the decision to release any captured bison.

So this application speaks to the workshop goal,
to find out “What has worked” with respect to applications of NASA capabilities within partner agencies.

## The cold face of collaboration

**Notes:**
Its worth noting that one of the reasons worked because of the strong one-on-one relationships we’ve built with collaborating researchers and agency staff through experiences like joint fieldwork.
This particular example arose not specifically as part of our initial planning for the project,
but more by virtue of us developing and advertising a general spatial dynamics analysis capability for the Park,
and then seeing which specific applications are called for by the Park
– in this particular case, on time scales far, far shorter than funding cycles!

Notice here that the prediction of species movement is still done by humans (i.e. subjectively). It would be better of we could get to the point where this kind of decision could also draw on credible predictive models of species movement. By credible, I mean journal-published.

## Landscape inputs to wildlife studies

- Pr( Use of location by species )= *f *( Landscape covariates,,,Temporal covariates,,, )

**Notes:**
The major decisions and policies get their science as much as possible from the peer-reviewed literature
<i.e. as opposed to real-time management; that uses real-time information).
So a core part of our work is working – as landscape scientists <gesture to self> – with wildlife ecologists to attribute statistical models of wildlife response with the appropriate landscape data –
i.e. vegetation cover, phenology, geothermal heat flux, or snowpack.

The most recent example of that type of collaboration was lead by Jason Bruggeman for his dissertation.
Interested in where bison choose to located their movement corridors,
Jason compared statistical models predicting these corridors from a number of covariates.

## Landscape inputs to wildlife studies

- Probability of bison corridor travel(Bruggeman et al., submitted)

**Notes:**
The above map is the results of the best model from that comparison.
Snowpack was one of the key explanatory variables, which underscores the importance of understanding the snowpack in order to truly understand the movements of bison.

Its this kind of information that managers – such as interagency panels - use to set policies about how, if it all, to respond to things like dramatic changes in bison population, or bison use of locations on the other side of the Park Boundary.

## Slide 11

**Notes:**
So I’m speaking in the invasive species session. What do bison have to do with invasive species?
Well, the prosperity and management of both of these important groups of organisms is very often dependent on how they move through heterogeneous landscapes.
This is especially so if the ability to move is constrained by the landscape.

Consider for example Tamarisk. It is an invasive species that did not instantly realize its ultimate niche. Rather it is moving through the landscape in a way that is highly constrained by landscape attributes.

To get at these dynamics, there’s much more that we can do with the underlying models that we use to describe and predict both species movement, and its outcome, the distribution of species.

This is the final topic that I’ll look at here today.

## Some false positives

- Probability of bison corridor travel(Bruggeman et al., submitted)

**Notes:**
Lets return to that analysis of where bison travel corridors are.

It turns out that there’s a few places on this map where no bison has ever been recorded.
These are places that might be great habitat,
but the bison don’t seem to be able to get there.
They are isolated by tracts of apparently unsuitable habitat.

This is not uncommon. Back at this meeting two years ago in Asilomar,
I recall a number of presenters showing maps of species distributions predicted usually for biodiversity analyses.
And if you listened carefully,
they noted false-positives in places that were spatially disjunct from the actual distribution of the species.

This morning, there was a talk that mentioned geographic barriers in the context of niche models.

So we’re working on a method that accounts for this,
by going back to the basics of how you can re-express movement within a probabilistic, statistically testable framework.

## Bison distribution & landscape covariates

- Vegetation

- Slope

- Snowpack

**Notes:**
Continuing with the bison case study,
we looked at predicting the overall distribution of bison given spatial covariates,
and some data on prior bison locations
- withholding the rest of the data for independent testing later.

## Bison utilization distribution:Habitat-selection analysisusing standard Resource Selection Functions (RSFs)

**Notes:**
First, we produced a habitat-selection analysis using current methods (not unlike the Bruggeman analysis earlier).

We ran one variant with a wide study area, which predicted obvious false positives in places like the Idaho potato farming region (where bison are not welcome!).

We then ran a more customary variant involving a tighter study area (delineated using a kernel home range). It also produced false-positives, although much more subtly. Again, these were (as expected) in places where the habitat is good, but the access is poor.

## Two paradigms

- Pr( Use ) =		*f* ( Landscape covariates )
- Pr( Use ) =		*f* (Distance to previous locations)

**Notes:**
Those analyses represent one of two existing paradigms,
where the probability of use is a function of landscape covariates.

The other relevant one is exemplified by kernel density estimation. This is basically a smoothing of data comprising the point locations of actual bison observations.

So whereas habit-selection / niche-modeling is usually a function of landscape covariates and other variables that do not include actual observations;
the kernel-smoothing approach is a function of the distance to actual observations, without reference to the habitat characteristics.

## Two paradigms

**Notes:**
Here’s how the two paradigms compare visually.

On the left is the habitat-selection result from before;
and on the right is the kernel density estimate.

The kernel estimate is faithful to the regions of past observations, but of course has no habitat component.

## A new approach:Selective Computational Diffusion (SCD)

- Pr( Use here ) =
- 	Pr( Use next door ) * *f* ( Landscape here )			Iterated many times (computationally demanding)

**Notes:**
Its conceptually quite simple to combine these.

We simply assert that the probability of a location is
the probability of use of any adjacent location,
multiplied by a local habitat-selection function representing the probability of using some habitat conditional on use of an adjacent location.

i.e. we represents how species must move through habitat in order to get to habitat.

Further, unlike conventional individual-based or agent-based models that you might be thinking of, it does this probabilistically in terms of PDFs,
so it can be tested and compared using statistical metrics,
such as the likelihood function.

## Comparison of approaches

**Notes:**
In this table, we compared various variants (!) of each approach, at various resolutions, using various degrees of independence between model-fitting data and model-testing data.
The new method, SCD, performed better (in this particular case study) than the existing methods.
For convention, we expressed the strength of this comparison in information theoretic terms using Akaike’s Information Criterion (AIC) – for those of you who are familiar with this.

## Taking SCD out on the road(so to speak!)--- Observed movement--- Predicted movement

- Taking SCD out on the road(so to speak!)--- Observed movement--- Predicted movement

**Notes:**
This is really just the first of many steps and applications, that we hope will lead to the availability of the method to be incorporated into any of the toolkits that spatial ecologists anywhere might choose to address problems like this in fields of biodiversity, ecological forecasting, invasive species, and disease vectors.
For example, its procedurally trivial to apply SCD to the question of predicting actual movement pathways, by specifying it to find the most probable route away from a given location (based on a prior calibration against distribution data in a similar landscape).
We have briefly tried this with the Yellowstone bison.
This figure shows the landscape from the earlier application on real-time modeling of snowpack at the bison-boundary interface. But this time we’ve made a preliminary prediction of the actual movement of the bison.
Here, the blue lines represent actual movement, observed using GPS collars.The red lines are predicted movements.
You can see that there’s a satisfying correspondence between the two.
Now, I admit, this is a new algorithm. But it was primarily funded not by our REASoN project, but through our funding within Intelligent Data Understanding program managed by Joe Coughlan. But its entree was granted by virtue of the relationships built through the REASoN project.
The challenge now for the REASoN project is to keep maturing the various things like this that our in our pipeline toward more applications in the Parks Service, and beyond.