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Ashley Fortune: Hello, everyone, from the U.S. Fish and Wildlife Service's National
Conservation Training Center in Shepherdstown, West Virginia. My name is Ashley Fortune,
and I'd like to welcome you to today's broadcast of the NCCWSC's Climate Change Science and
Management Webinar Series. This series is held in partnership with the U.S. Geological
Survey's National Climate Change and Wildlife Science Center. Today's webinar is Part One
of a two part series being presented by Rob Klinger with the Western Ecological Research
Center for USGS. Everyone please join me in welcoming Dr. Shawn Carter, Senior Scientist
at the USGS National Climate Change and Wildlife Science Center in Reston, Virginia. Shawn,
would you please introduce our speaker? Shawn Carter: Sure. Thank you, Ashley. It's
my pleasure to introduce Rob Klinger today. He's a population and community ecologist
with the USGS at the Yosemite Field State and has a strong interest in animal plant
interactions and emergent properties that come from those interactions. He's been an
ecologist with the USGS since 1996 and prior to that he worked for both nongovernmental
organizations and governmental organizations, both in the states and also internationally,
primarily in Central and South America. So, without further ado, take it away Rob.
Robert Klinger: OK, thanks Shawn. Thanks for attending everybody. Before I jump into the
meat of this talk, I think it's very appropriate that I acknowledge and thank the many people
and agencies that have been able to keep this project going. First, of course, is the National
Climate Change and Wildlife Science Center, if it wasn't for the financial support that
they've given us, this project never would have been able to continue.
But, beyond just their financial support, what I've really appreciated with NCCWSC is
that, my perspective is that, they're taking the long view on these climate and wildlife
interactions. I think that that's going to bear a tremendous
amount of fruit in various ways, for various species and communities down the line. It's
been very refreshing to work with a group that has that longer view.
A lot of people have contributed ideas and data and you're going to see a number of these
names emerge strategically during this talk and just as we couldn't have gotten anything
done without the support of the NCCWSC, we couldn't have gotten anything done in the
field without the crew out of Bishop that I've been really privileged to work with over
the last six years or so. Really, you couldn't have asked for a better
group of people who went through a heck of a lot to collect a lot of the data that you're
going to be seeing in this presentation. So thank you to everybody who's been involved.
Where I'm going with this talk is we live in a very, very, very climo centric world,
if you will. We know darn good and well that our climate has changed and will continue
to change. But one of the things that has struck me in this particular environment,
it's almost as if 50 or 60 years of ecology seems to have been subsumed under this mantle
of climatic shifts. Just because the climate is shifting does not mean that the ecology
is going to just stop cold in its tracks. The big thing is that species' patterns and
processes are variable, and they respond in different ways to different types of forces.
That's why we have to think beyond just thermometers and rain gauges and packages of often coarse
data that fit neatly into these GIS layers. There's going to be some real functional consequences
to ecosystems if there are truly major reductions in abundance or major rain shifts in some
of these high elevation species. And that's really what we're trying to emphasize
in this. The modeling and forecasting that we're going to do are usually better when
there's a better understanding of the ecological context in which we're doing that modeling
and forecasting, and that context just wasn't there for the alpine zone of the Sierra.
I would argue that it's still not there, that we have a ways to go yet. What I'm going to
be doing with this talk is I'm going to spend a good deal of time, probably the first third
of it, really nailing down the conceptual foundation of the study.
Then I'll move into the data on the mammals, their abundance and habitat association patterns,
meadow composite, community composition, some of the temporal patterns that we're seeing
in meadow production, and then these plant/animal interaction experiments that we're doing.
Some real interesting results with some interesting implications that are coming out of those.
Then I'll try and tie things together and give a little bit of a preview on Part Two.
Now what do I mean by part one and part two of the presentation. I'm going to kick that
off with one of our closest collaborators, Dirk Van Vuren, out of UC Davis.
At the very get go of the project, Dirk gave us some very good advice. Dirk's been working
on high elevation squirrels for a long, long time.
He said, "We just don't know what's going on up there. We better start with the basics."
That's what this talk is about. It's about the basics.
I'm going to be focusing on contemporary ecological patterns for these species and one of their
critical habitat types. You'll hear me talk a lot about scale, a lot
about interactions, and getting at this notion of feedbacks between climate and ecological
processes. Part two of this talk, which I anticipate
being in about a year and a half, or maybe a little bit more than that down the road,
will be the modeling and the forecasting. While I'm going to be throwing a lot of data
at you, I'm going to be leaving out a lot of the meaty real technical stuff. That can
come up in the Q & A, or probably better yet, give me a phone call, shoot me an email. My
contact information will be at the end of the presentation.
We are working in the Sierra Nevada and the White Mountain ranges. There are a few interesting
differences between them but it's been a more limited effort in the White Mountains. I'm
going to be focusing on the patterns that are qualitatively similar between the ranges
on the Sierra Nevada. We're also looking at five species, one large
mammal and four smaller ones and what I'm going to be emphasizing today are the patterns
for the four smaller species. This project was born out of interest, need,
and a little bit of frustration on my part. Erik Beever was doing some very nice work
with Pica in the Inner Mountain West, showing that they were having good evidence of local
extrapatients contracting ranges. Erik, I think, would be the first to acknowledge
this that these results were being extrapolated to other ranges and other species. It was
getting pretty frustrating. There wasn't a lot of data to support this beyond what Eric
was doing. I was on the phone one day with Matt Brooks.
I was voicing my frustrations, to put it mildly. I said, "Matt, it's like everybody was throwing
up their hands and there's this foregone conclusion that these mammals were going to be disappearing
off these mountaintops in this rapturous descent into Heaven."
Matt just laughed and goes, "Well, there's your hypothesis, Rob." Matt and I, in a fairly
tongue in cheek way, coined this term of the Rapture Hypothesis. With apologies to a really
great former rock and roll band, REM, I think you know what the scenario is.
The planet heats up. The mammals get trapped up at the top and they start singing these
great rock and roll anthems of doom and gloom, but that may not necessarily be the case for
all of them. If you think of this in terms of first principles
of ecology and just using the part of science that is a body of knowledge, you have to step
back and say, "Well, how likely is this scenario?" The species differ in a lot of ways. The environment
varies tremendously. So does that speak for these consistent uniform predictable responses
or much more variable ones? To us, the issues were yes, climate is changing.
It's likely going to be unprecedented in recent times but we are not blind. What this figure
shows is a well known reconstruction of climate in the northern hemisphere over the last 2,000
years. It was a recent publication this spring that has pushed it back farther.
What it shows is that there's been large fluctuations in temperature extremes for millennia. That
means that species and ecosystems in the Sierra have continuously responded to these large
climatic fluctuations. The questions to us have been: 1) how have
vegetation communities responded? 2) How have the animals persisted? And really important,
3) how have the animals and the vegetation interacted through what is clearly not an
equilibrium system, if you look over ecological and evolutionary time scales.
So, setting the stage for all this, in terms of the data that's available, there's a pretty
fair amount on physical processes in the Sierra Nevada. But it wasn't always, or isn't always
that straightforward. We certainly know the temperature has increased. That pattern is
consistent. But the pattern for precipitation is a little bit more complex. It takes some
effort to wade through the data and try and figure out what's going on.
Well, we should have been so fortunate to have such frustrations with the biotic parts.
There was very, very little ecological data on animals or plants in the Sierra Nevada.
I would say that Connie Millar and collaborator Bob Westfall were the only ones that really
were consistently and had been consistently working in that zone. Everything else had
been spotty, short term, very localized. So we saw this as an opportunity to do good
science and collect some information that was badly needed in that zone, and that is
really the impetus that kicked off what we hoped would be a long term study. We're into
the sixth year of it. As I mentioned, it's a multi species study.
The large mammal is the Sierra Nevada bighorn sheep, which is a federally endangered subspecies.
Then the four smaller species that we're working on are the yellow bellied marmot, American
pika, and Belding's and golden mantled ground squirrel. You're going to be hearing me talk
a lot about scale today, and we've designed the project to look at things such as distribution,
abundance, habitat associations. We're not at the demographic stage yet, but
we're moving that direction. But we want to look at these different scales, and that's
going to allow us to do explanatory models, resource selection, predictive models of species
distributions all leading towards this notion of persistence. What is the likelihood of
persistence among these species within or across mountain ranges and along environmental
gradients? An area of particular focus for us are these
high elevation, herbaceous dominated plant communities. Broadly, I'm going to be using
the term "meadows" for these. It depends on where you draw your line, but these have disproportionate
importance to animals, not just mammals but animals in general, in these high elevation
zones relative to their total area. They make up somewhere between 8 and 14 percent of the
high elevation area of the Sierra Nevada. The concern that's been is that higher temperatures
are going to increase the likelihood of drying in these meadows, reduction in productivity
rates and overall biomass production, potentially making them more susceptible to colonization
by conifers and other *** species. If these herbaceous dominated communities transition
to *** dominated communities, this would represent or likely represent a real loss
in important high elevation habitat for many species.
The flip side of this though, something that often gets overlooked, is that higher temperatures
could increase productivity. Even through longer growing seasons, higher photosynthetic
rates. What this could mean is increased competition for these *** species to contend with, making
it harder for them to establish, harder for these transitions to occur.
This is what I call my "late night television slide": "But wait, there's more". There is
more. It's not just competition. It's interactions between animals and plants. Animals do modify
oftentimes the environment that they live in. Many examples of this in many parts of
the world. Functionally, in the high elevation zone these mammals likely play extremely important
roles as herbivores and granivores. We have a lot of data from here and Europe indicating
that is so. So that led us to pose the question, and this
is where the functional aspect of the study comes in, of: could the mammals decouple what
would be a potentially climate driven transition to these meadows to force patches through
herbivory and granivory? So putting this in a simple cartoonish but
with a little bit of animation sense, you see I call this the typical boring old climate
scenario. Rising temperatures and rising ranges in *** species from lower elevations result
in these transitions of the meadows to these stable, *** dominated communities.
Our alternative to that is a little bit more of a complex world, that there's going to
be alternative states and alternative pathways to get to these states that result either
from the individual or more likely the interactive effects of biotic processes and abiotic factors.
So that's the conceptual foundation of the project, so how are we getting at this?
OK, I'm going to probably have to do a quick shift on this slide so you can see the arrows.
They weren't translating well. But we're using remote sensing data to look at change in land
cover and condition, essentially meadow condition and meadow boundaries over a 40 year period.
We're doing some good old fashioned muddy your boot biology in collecting lots of data
from line transect, point counts, and habitat samples on mammal density, their ranges, their
distribution, occupancy, and habitat association patterns. To complement the observational
data we're doing these field experiments looking at the plant/animal interactions.
OK, here comes a little bit of shift, and then it'll go back to full screen. There are
all the arrows. Everything is back. OK, so the data from the remote sensing and the field
sewer base are flowing into different types of mammal distribution models, which we're
going to be comparing. Then the data from the remote sensing in the field surveys as
well as the output from the mammal distribution models is flowing into projected meadow conversion
models. Some of these models are going to be what
you often see pretty typically in the literature, ones that are unadjusted. They're being driven
by abiotic factors, but we're using the data from the field experiments to come up with
ways of adjusting this transition for these biotic interactions.
A little bit more specifically on the remote sensing data that I'm going to be emphasizing
in this presentation, I need to give a real shout out to a climate layer that was developed
as part of this project by Otto Alvarez and his major professor Qinghua Quo at UC Merced.
I don't have the time to go into the details of this other than to say that Otto avoided
a lot of the pitfalls that plagues downscaling efforts.
He basically started from scratch, and he did it in a real thoughtful, thorough way,
testing different types of code variants to increase the effectiveness in the interpolation,
especially for the precipitation layers. He did an enormous amount of quality control
of the data. This has actually been so successful that they're taking this global now.
In terms of measuring changes in meadow condition, we relied on some Normalized Difference Vegetation
Index, or NDVI, which is a way of measuring productivity or, in our case, production.
This was generated by Karl Rittger, who was then at UC Santa Barbara and is now at the
Jet Propulsion Lab in Pasadena, and coordinated by Tom Stephenson, one of our collaborators
on the project who heads up the Sierra Nevada Bighorn Sheep project.
Karl developed this layer for 4,700 meadows throughout the Sierra Nevada. We're using
data on 3,500 of those. These are bi monthly values going from April thru October from
1990 to 2010 at a 30 meter resolution. A very powerful, large data set.
In terms of the field survey data, the core of it were our land line transects. These
things here that look like intestinal parasites are the locations of the transects. We have
21 of them throughout the Sierra Nevada. This is very extensive sampling. It spans about
three degrees of latitude, captures about 90 to 95 percent of the Alpines in the Sierra,
spans an elevation gradient of about 4,500 feet.
These transects are 10 kilometers long. They've been sampled from 2008 to 2012 three to four
times a year. They were selected from a pool of a little over 60 potential routes and each
transect has 10 point count stations that are randomly located along it.
We also have done an equal amount of extensive and intensive habitat sampling, too, vegetation
sampling, if you will. Over 250 plots have been sampled between 2010 and 2012. As with
the mammal surveys, this is intensive data as well as extensive.
The field experiment, I'll get into the details of the design a little further on into the
talk, towards the end of the talk. Basically, this is your classic exclosure study where
we've done seeding as well as seedling plantings inside and outside of exclosures, so essentially
manipulating delivery as well as seed density and then measuring various factors and looking
at seed germination and survival of seedlings inside and outside those exclosures.
I've been talking about scale and I'm going to continue to talk about scale so I need
to define that a little more explicitly. When I talk about the range wide scale, we're talking
about the mean value of some variable across our study area. When I talk about the regional
scale, that's based on the transect on the order of 10 square kilometers. That's based
on the linear distance of the transect, 10 kilometers, and a one kilometer belt on either
side of that transect. At that scale, we have almost 10,000 observations that we've collected
over the five years of the study. When I talk about local scale, now we're at
the hectare scale. 20 hectares, 250 meter radius around each of our point count stations.
We've collected a little over 5,000 observations at that scale. Then the patch scales, which
are the geo referenced locations of the animals we observed in the field. It's about a half
hectare scale. We've got about 8,700 observations at the patch scale.
Let's get into the data now. If we were to see consistent responses among the species,
there were five general conditions we would expect to see met. Environmental variability
would likely have to be low. We would see some pronounced structuring geographically
in their distributions. Also, some correlation, spatial and temporal, in their abundance patterns,
both for species and assemblages. When I'm talking about an assemblage in this
presentation, I'm talking about the combination of species, so species identity and their
relative abundances, at a particular scale. Then the species would need to be restricted
in habitat breadth and have similar habitat use patterns. Let's start working through
these systematically, starting with environmental variability.
We have three general data sets, three classes of data, that we're using for these environmental
analyses, these relationships. We have a climatic data set, we have a land cover data set, and
a topographic data set. What we did, after looking at some correlations and pairing them
with variables, looked at these environmental conditions here at the regional scale, so
each of these points represents the environmental conditions for multiple variables for a transect.
Principle Components Analysis, quite a good one, explained almost 70 percent of the total
variation on the first three axes. The thing that jumped out of here is you don't see real
pronounced clustering. As a matter of fact, you see a lot of scatter throughout environmental
space. In other words, the environment is extremely variable. The gradients that we're
picking up are not simple ones. They're very complex. You have multiple variables from
each of those variable sets defining those gradients.
Interestingly enough, climate accounts for less variability overall than topography and
vegetation. You have this highly variable environment of which climate is not necessarily
driving the bulk of that variability. Shifting over, now, into mammal abundance.
Start off with a real simple diagram for each of the four species over time at the rangewide
scale. This is their density, the number of individuals per square kilometer. I show this
mainly just to show a very simple pattern. Because, if you parse down, if you go down
to the next scale and look at what's going on regionally, we're still at the number of
animals per square kilometer, you get a lot more dynamic view of what's happening with
the abundance patterns for these species. We see the same thing at the local scale.
A nice, neat, clean rangewide estimate, density per hectare, animals per hectare, that is
obscuring some very strong dynamics at the local scale. This is one of the things that
really jumped out at us is that these large scale patterns, here I've simply transformed
the inter annual rate of change, these large scale patterns are masking these incredibly
strong spatial and temporal dynamics. It's not just in magnitude, it's in direction
as well. So that between any two years, you can see increases going on, you can see decreases
going on, you can see stability. It's magnitude and direction. A lot of people would be tempted
to write this off as noise, but we're saying wait a minute, this might actually be the
ecologically relevant pattern. This is what we need to be paying attention to.
In terms of the geographic structure, here we've got the structure for each of the four
species. Here's latitude, here's longitude, this is their density, and this is their variation
in density. If we saw strong geographic structuring, you would expect clustering of points for
different levels of density or variation in density in different geographic regions.
But, what it looks like is more of a shotgun pattern. We see that, as well, at the more
local scale. We can test this statistically using some spatial statistics. We can actually
quantify how much structure there is. We have four species, we have five years of data on
each of these species. What we were looking for, if there was supposed to be some kind
of consistent response, is positive correlation of abundance with geographic distance and
that would be over a long geographic distance. What we see, instead, is this statistic, Moran's
I ranges between minus one and one, is all of the correlations were negative. They were
all less than zero. This held both regional and local scales. A little bit more of a complex
pattern for the assemblage, but we can, again, look at the composition and relative abundance
of those assemblages with geographic distance using a Mantel statistic. This gives the correlation
in species identities and relative abundances over geographic distance.
What we see are two positive correlations. One about one kilometer away from each other,
another about 18 kilometers away. We also see two negative correlations between 8 and
12 kilometers away. There's absolutely no correlation as you get farther away from 20
kilometers. What this is saying is that the assemblages
are similar within about a kilometer of each other and then maybe a watershed away that
has similar environmental conditions, but, also, there can be a lot of dissimilarity
between those assemblages within a few tens of kilometers of each other.
This is the big one. The correlations, be them positive or negative, are all occurring
within essentially 12 miles of each other, not beyond that. The message here is that
there is minimal geographic pattern in density or the variation in density. The correlations
in their abundance patterns are inconsistent and they're certainly not very extensive.
A lot of this is probably being due to these highly variable spatial patterns that we're
seeing in abundance and the temporal patterns. Now let's look at some of the habitat associations.
We're going to start among the species and we're going to look, again, at three scales:
regional, local, and patch. We have abundance by transect data, we have abundance by point
data, and then we have the incidence, presence/absence at geo reference location for which we've
got vegetation data for. Again, our three general environmental data sets and our goal
is to find the most parsimonious set combination from these that intersects variables that
explain the most variation and distribution abundance patterns.
Starting at the regional scale, each of these points represents a transect in a particular
year. The species composition, the relative abundances in that year. The first thing that
you notice is you see a lot of these mixings of transects in years and environmental space.
You don't get any discreet clustering either spatially or temporally in this.
For the most part, you see separation of species in environmental space, although the marmot
and the pica are fairly closely associated with each other at the regional scale. Keep
that in mind. The species are aligned with different environmental gradients and in this
parsimonious set of variables only one climate variable was retained. The rest came out of
the land cover and topographic data sets. Going down to the local scale, again, we see
a real mixing of the points in environmental space. The species continue to be separated,
for the most part, in environmental space, but now notice, at this scale, the associations
have changed. The marmot and the Belding's ground squirrel are more associated at this
scale than they were at the regional scale and the marmot and the pica are quite separated
in this scale where they were more closely related at the regional scale. Again, the
species are lining up on different environmental gradients. The climate variable is still completely
out of this set at this scale. Going down to the patch scale, now we see
complete separation of the species' environmental space. Of course, they continue to be related
to different environmental gradients and, again, only one climatic variable is retained
out of the set of the original 21. Now let's shift over a little bit and see
what their habitat use patterns are and how selected they are. Starting off with use,
we're going to have two indices of habitat selection that we're looking at. The first
one, which is abbreviated, the symbol is Bi, is the index of relative habitat selection.
It scales between zero and one. The closer to one, the higher selection for a particular
type. We have half a dozen general land cover classes.
What you see, not so much because of selection, of course we expect this, but each of the
species, each of the four species, is using several different of these land cover classes.
That indicates that, yes, they have their preferences, no surprise there, but they're
not necessarily that restricted in their habitat breadth.
Now let's shift over to the absolute index of habitat selection. If the 95 percent confidence
intervals for this index overlap one, that means that they're using this particular class
pretty much in relation and proportion to its availability on the landscape. If it's
above, that means they're selecting for it, it's used disproportionately more than its
currents. Below the line, that means that they're selecting against.
What we're seeing, and I'm using the marmot and the golden mammal ground squirrel as examples
here, is that there are these inter annual shifts in the magnitude of selection, even
in habitats that are clearly favored, land cover classes that are clearly favored by
these, you see these shifts in magnitude. Sometimes, for some of these classes, with
all four species, you would see disproportionately more use in one year, disproportionately less
use in another year, and then proportional use in another.
The notion here, the message is that they're not that stable in the magnitude of selection
over time. We can ask do we see the same thing spatially? Using the Belding's ground squirrel
and the pica examples, what you see is from transect to transect to transect, again, shifts
spatially in the magnitude of selection to the point, for example, here with the Belding's
ground squirrel, along some transects they use shrub dominated areas pretty much in proportion
to their availability. In other areas, they avoid them, but in others they're found disproportionately
more in the occurrence of shrub on the landscape. We see these patterns for all four species.
What seems to be going on, I'm using the marmot as an example of this but, again, it holds
for all four species, is that the proportion of different habitat types shifts from geographic
area to geographic area. They can adjust their selection behavior. I'm just showing two positive
relationships. Not all of the relationships are positive. Some are actually negative.
Habitat selection does seem to be varying with availability. They're shifting their
behavior. Now we can revisit these five conditions.
Is the environmental variability low? No, it's quite variable. The species aren't very
structured at all in their geographic distributions, they're very patchy distributions. There's
very inconsistent low and often in the opposite direction of what is expected in the geographic
correlation of abundance. The species don't seem that restrictive in
habitat breadth. They are, for the most part, facultative specialists. Use varies temporally,
and they can shift the selectivity among regions. They certainly are different in their habitat
use patterns, and that can also vary with scale.
Now let's shift over and look at the meadow structure and condition, this important habitat.
What I haven't mentioned up until now is look at their habitat associations. Out of those
half a dozen general land cover type of classes, meadows were the only ones that all four species
used at least in proportion to its availability on the landscape. A number of them, especially
the marmot and pica, consistently used it more than its availability on the landscape.
Needless to say, this is a very important habitat type, as we suspected, as we knew
anecdotally, for these species. It would be useful to know what is driving species composition
in these meadows so we can incorporate that kind of information into the modeling and
the forecasting that we're doing. This is something that Jen Chase and her office here
in Bishop has been leading. Her and I have been working on it. We've taken a metacommunity
approach. We're looking for evidence of the four processes
that are typically considered to be what maintains metacommunities. What we expected were very
strong species sorting along environmental grades. In other words, a lot of turnover
in species composition along gradients that we figured would be related to the climatic
variables. We also expected some dispersal effects.
Again, we had our environmental gradient data, we had the geographic distance among all pairs
of plots. We had 160 meadow plots that we were using in the analysis. We did two types
of multi varied analyses, and I won't plague you with their very long names, but these
are very efficient analyses because it lets you look simultaneously at turnover along
gradients and what the effect of dispersal is on composition.
Boy did we have some surprises coming. First of all, looking at an index and similarity
in species composition, this is a presence, absence one Sorensen similarity index scales
between zero and one. It's the same for other indices of similarity, as well. Even when
environmental distances were very close, very similar environmental conditions, you had
very, very dissimilar species compositions. Each one of these points represents the pair
wise comparison among all of those 160 meadow plots. We saw that geographically, as well.
A pair of plots that were a couple hundred meters apart from each other could be as dissimilar
in species composition, on average, as if they were 20 kilometers apart. We expected
about half of the variation in plant species composition in these meadows to be explained
by environmental gradients, but only about 10 percent was. An equally low amount of that
variation was being explained by these dispersal effects. There was a lot of residual variation.
Where we expected to compare the species abundance distributions to this neutral model, a null
model, just to show how different it was, this is where the biggest surprise came. The
pattern was consistent with expectations from neutral processes. If you looked at the observed
and the expected distribution of species, either by frequency of species in different
occurrence classes or as a cumulative probability, they were very, very, very similar.
This is indicating that local conditions and what are known as priority effects, essentially
who gets there first, is really responsible for a lot of the meadow composition. That
leads to the question if communities have assembled locally in these meadows would we
expect them to not reassemble the same way if there were changes? An open question.
Then, of course, we want to know how about the condition of these meadows? Are they getting
less production? This is where the NDVI data came in. The first thing we wanted to do is
make sure that NDVI was actually tracking herbaceous biomass. We did a simple regression
on that and we were very relieved to find that it was. This is the herbaceous biomass
data from our vegetation plots against different measures of NDVI and we got these very strong
correlations. We also wanted to make sure that these GIS
layers that were saying that a particular class was dominated by herbaceous vegetation
really was and so we did some confusion matrices of what we saw in the field, what the GIS
said. We were, again, relieved to see that, yes, our assumptions are being met. These
are being mapped pretty accurately. What is that temporal pattern? We can look
at the minimum NDVI, the mean NDVI, and the maximum mean NDVI per meadow per year. This
is using generalized additive models which were a very effective way of looking at variation
and time series data. We also had an interesting statistic that
we calculated, the coefficient of variation of NDVI. That's a measure of the spatial heterogeneity
within these meadows. If they were drawing, we expected that coefficient of variation
to be large and to increase. What we saw instead was that it was very small. There was very
little heterogeneity within these meadows with the NDVI data. For all four measures,
what we saw was a heck of a lot of variability but not much trend.
What trend we did see was with minimum NDVI and that was not indicative of drawing. It
actually appeared to be increasing. Another way of looking at that is simply to convert
these values into inter annual rates of change. What we saw was fluctuating in a highly variable
fashion around one. Pulling this together, production in meadows
over the last 20 years has been highly variable, but there's no evidence right now, that we
can detect, of a decreasing trend. For the animals, what this implies is, in terms of
potential forage amount available to them, it's been pretty stable. It might even be
improving in some ways. Now we'll get to the field experiments. We
had the assistance of hundreds of thousands, if not millions, of mosquitoes to set this
experiment up, of which, of course, we were eternally grateful for their help. We got
this set up in August of 2011. What this consists of, two sites, one in Yosemite National Park
and the other in King's Canyon National Park. At each site, we have three arrays. What the
arrays consist of are these combinations of seeded or unseeded within an exclosure or
outside of an exclosure and five different seed densities. We also jump to the next life
history stage and planted 84 seedlings. Now we're able to look at germination rates and
seedling survival inside and outside of these exclosures.
What we find is quite striking. In the first year after the initial seeding, this is within
exclosures, this is outside of exclosures. We see three to five times as much germination
within the exclosures that are protected from herbivory or granivory as we see on the outside.
Each of these lines represents one of those different seed densities. There is density
dependence, at least within the exclosures. The real story is across densities you see
higher levels of germination. Typically it's been said, I've heard this
over and over again, what matters for germination of *** species in these meadows is soil
moisture. Yes, that's true, but they have to get past the mammals first. There is a
soil moisture effect. There is also a competition effect, which I'm not going to get into.
We can jump to the seedling life history stage and we see the exact same thing. In the first
four months after planting the seedlings, there was 88 percent mortality, and that went
to 100 percent a year afterwards, outside the exclosure where there was only two and
a half percent mortality within the exclosures. Pictures here of a Belding's ground squirrel
and a marmot, each either in the process or after chowing down on some seedlings.
We said OK, this is interesting. That's great. That's experimental data. That's from two
sites, essentially. Is there evidence that this could be going on at a larger scale?
What we did is a decay analysis looking at the distance from the closest colonizing source,
a patch of krummholz, a patch of conifers, if you will. What we would expect, if herbivory
and granivory weren't important, is that these reverse J shaped curves for all pre life history
classes, but that's not what we see. What we see is low or no numbers of seedlings
and no decay of the counts over distance. That is implying that distance matters, but
so does granivory and herbivory. What we're seeing is a little bit of a disconnect over
time. A seed and a seedling escapes predation and they accumulate, typically, close to the
colonizing source over time. But, the seedlings themselves, probably the seeds and the seedlings,
are getting hit very hard close and far away. Jen Chase and I are calling this the "Gauntlet
Hypothesis". We're putting it in the vernacular, anthropomorphizing a conifer as asking, "What
the heck have I got to do to get established in these Alpine meadows?" Steven Ostoja likened
this to a sieve. It's an ecological filter. First you've got to get there. That's dispersing
from a colonizing source. The second probably biggest hit is you can't get munched by a
marmot, pica, or ground squirrel. The third hit is you can't dry out and the
fourth hit is you can't get beaten up by those herbaceous neighborhood bullies. It is tough
to get established in these meadows, at least in the Alpine zone.
Let's start pulling this together, now. Some early interpretations. As I said earlier,
we're very, very interested in what others consider noise. The mean response is probably
much less informative and meaningful. It's what's going on at the regional and local
and possibly even patch based scales. We need to integrate this type of data and this type
of information into the dynamics of the models in the forecast that we're developing.
The mammals don't appear to be simply waiting around to be victims. They can adjust their
habitat use and there is this evidence that they are managing their habitat, if you will,
through herbivory and granivory. This just does not speak very strongly to the likelihood
of uniform responses throughout the Alpine zone. There's a heck of a lot of heterogeneity
already in the environmental conditions, mammal distribution abundance, and the key habitat
type. This is implying to us that we might better
expect areas with a high and low probability of persistence. There's some caveats and some
implications that go along with these interpretations. One is that, simply, we have not reached a
climatic state yet where we're seeing large changes. These transitions could also be very
rapid. This notion of thresholds and tipping points rather than more declinal, gradual
change. The patterns that we're seeing in the Alpine
zones and the very upper part of the sub Alpine zone probably does not hold in other elevation
zones. Kaitlin Lubetkin, who is a PhD candidate at UC Merced, is doing some very nice experimental
and observational work in the sub-Alpine zone and she's getting very different patterns
than we are, but those patterns start falling apart as she pushes up towards a treeline
and our patterns start to fall apart as we push towards a treeline.
We're going to start working together on this to get a little bit more clarity on how far
we can extrapolate results in these different zones.
Change is happening, yes, but change is not necessarily synonymous with disaster. But,
if there are wholesale shifts from what we're seeing in our data, that has some very profound
functional implications for what is going to go on in the vegetation communities in
the Alpine zones. Right now, what we think is happening is that
these little windows of opportunity during a year or series of years when mammal abundance
is low, that conifers could, potentially, get established in the meadows. If there's
drastic changes in abundance, if there are big changes in range, those windows of opportunity
might open up to a full door. We plan on continuing our field experiments.
We're going to be doing a lot more analysis of the ecological patterns. One of the things
we're particularly interested in is comparing models with GIS data with data that is not
typically suited for GIS and see which is more informative. Of course, we're going to
continue working on changes in meadow structure and function.
With the modeling, one of the things that we plan on doing is making sure that each
of our models also has an uncertainty layer with this. Otto and Qinghua, UC Merced, and
I have talked about ways of doing this, way of spatially partitioning, temporally partitioning,
and spatio temporally partitioning the data to get at this notion of how to generate uncertainty
layers, quantify just how good these models are that we're building.
We're doing the same type of things with predictive RSF, Resource Selection Functions. We have
a very, very nice example of that that Alex Few is working with the Bighorn team, Tom
Stephenson's team, is doing with models being developed for the bighorn sheep. Then the
modeling of the meadow dynamics which, as I said before, is going to be really tricky.
It's going to take some time and some thought to do a good job on that.
Are the Alpine mammals doomed? Is Jacob's ladder going to descend out of the skies and
is there going to be a wholesale rush of these hairy, high elevation creatures getting away
from these hot temperatures and heading to a cooler but better place? Or are they going
to be singing a little bit different tune, adding some lyrics to REM's song of doom and
gloom? Yeah, it's the end of the world as we know it in some places, but there's trade
offs and some things are going to happen in some ways in some places and in other ways
in other places. With that, that's my contact information.
I think that we're now open for a Q and A session. Ashley, is that right?
Ashley: Yes. Thank you very much, Rob. Great presentation.
Robert: Thank you. Ashley: We are now open for questions. All
right. Our first one will be from Toni Lyn Morelli.
Toni Lyn Morelli: Hi, Rob. [laughs] Robert: Toni Lyn, how are you?
Toni Lyn: I'm doing [laughs] great. Good talk, really exciting to see, and it's just incredible
how many different aspects you've taken on with this research. It's really awesome, and
I'm looking forward to the years in the future, too. I have a question I'm thinking about.
You're talking about the species. You're looking at being speculative specialists. One thing
that strikes me is perhaps seasonally they are but most, I guess, probably all four that
you're thinking of. You're looking at and you're talking about today, have very important
habitat requirements in the winter. Some of the work that we've been doing or
been thinking about focuses on the impact of warming, in terms of snow pack and how
the hibernators might be starving to death during the winter and in the early spring.
You can imagine pica also have important habitat requirements in winter. So is that something
you're thinking about? Robert: Yeah, it is. When we're developing
the models, it's probably going to be fairly parallel to some of the work that you've been
doing is that issue of the insulating properties of snow pack but we also want to incorporate
the potential nutritional aspect in those types of models. And that's because yeah,
they might lose some insulating properties, but they might make it up in terms of increase
forage availability but yes, it's definitely something we're thinking of and it's something
that we hope to be incorporating into those models when we develop them.
Toni Lyn: Cool, thanks, looking forward to when more results come out.
Robert: Be patient. [laughs] Ashley: We have another question from Erik
Beever. Erik Beever: Excellent job, I echo, Toni Lyn's
comments. It was neat that you included a lot of different aspects. My question is about
scale, and could you maybe talk to, particularly relative to the habitat associations and the
resolutions at which we should think about those at the various scales of analysis, particularly
down at the lower scales? Can you talk about how those are meaningful in terms of what
distances these animals move and how you calculated those associations in terms of how fine they
were? Robert: OK, I think I see where you're going
with this. In other words, how did we come up with, say, a 10 square kilometer or a 20
hectare scale? Erik: Correct.
Robert: OK. So what those were based on were the sighting distributions of the animals
along the transect and at the point counts. We got into the literature, and particularly
with the marmot, they can have some pretty long dispersal distances. And so it seemed
like based on the sighting distributions for these species that these were the most meaningful
distances that we could come up with. Now, that being said, what we're going to be doing
with the modeling, and this is where that patch base and I think where you're going
with your question is really going to be important, is we have these precise geo reference locations.
And around each location, we're going to be developing models based at different distances
for species. So where pica is more limited you know, its home range is probably on the
scale of a few hectares; maybe a couple might have tens of hectares, although I doubt that
whereas a marmot might have a much larger home range. We're going to look and see what
the different predictions are as we vary that scale from those patch based locations.
Erik: Great. Excellent. Really quickly too, did you see the same kinds of patterns, in
terms of, most of the variance went to residual or unexplained variance, if you used a different
index of similarity? Robert: We used...
Erik: ...Sorenson's? Robert: Yeah, we did Sorenson's, Jaccard,
and Morisita Horn, and of course Morisita Horn takes into account the relative abundance,
whereas Jaccard and Sorenson's it's just based on presence/absence. And absolutely, yes.
We saw that same rapid drop off both environmentally and geographically in similarity.
Erik: Excellent, thanks. Great work. Ashley: Thanks Erik. We have another question
from Chris Hoving. Chris Hoving: Hi Rob, my question has to do
with if you have put any thinking towards carbon dioxide fertilization and the affects
that might have on productivity in these meadows? Robert: That's a really good question, Chris.
Yeah, we have thought about it but we haven't gone down that route yet. We doubt that we're
going to for now. We've got enough on our hands with the data that we've collected.
We want to get a handle on that. Then in the future, if we can start getting into those
kinds of questions. Yeah, nitrogen deposition, is another one that we've been thinking about.
We've broken our modeling scenarios into first generation, second generation, and third generation.
We see those kinds of questions probably being third generation questions for the modeling.
Chris: OK, thank you. Ashley: OK, thanks Chris. Do we have any more
questions out there? All right, Rob, do you have any closing remarks?
Robert: No, I think I've talked enough today. Ashley: [laughs] I would like to thank you
again for a great presentation.