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Lukas has really done all the
hard work for this project.

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I'm here just to provide context,

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because this project was part

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of a larger effort that was funded

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through the McIntire-Stennis
program led by Tony D'Amato.

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And it was really all about understanding

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how we can engage with landowners

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to actually implement
adaptive management strategies

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considering climate change.

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And so there were a couple of different

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big objectives to this project.

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And one was really just identifying

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what are some of the
opportunities and barriers

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to implement adaptive
management strategies.

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The second objective was actually testing

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some of these different
adaptive management

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silvicultural techniques in the field.

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And then where we came in was trying

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to figure out if we could develop

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some spatial maps to
quantify climate exposure.

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So if we know that adaptive
management is helpful,

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are there some places where it might

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be more necessary than others,

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and can we use that to sort
of prioritize our efforts.

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So I do just want to clarify

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what I mean by climate exposure.

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So when we think about climate
change impacts on forests,

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it really is a combination of two things.

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One is how much climate,

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environmental conditions have changed.

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So that's the exposure piece.

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And then the other piece is how

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sensitive various species are.

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We did not get into that.

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Other people are doing that
work and doing very well.

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We really just wanted
to look for our region

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at as fine a scale as we could get,

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can we quantify the relative exposure

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that's projected under
various climate scenarios.

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And so when we did
that, we wanted to think

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about data layers that
would get us information

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both on direct exposure,
so changes in temperature,

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precipitation, extreme
events, but we also want

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to incorporate any kinds
of secondary exposures

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that might come from these sort
of indirect climate impacts,

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the changes in the patterns or frequency

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or severity of pests and pathogens,

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different com competition
dynamics within the forest,

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and then also changes
in disturbance patterns.

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So our very first effort, way back when,

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was just trying to identify

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the data layers that are out there.

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Because again, this is meant
to be a spatially explicit map

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that can guide where management

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actions might be most helpful.

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And so that's where this
little figure comes in.

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So I know there's a lot of text here.

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Let's just focus on what we
wanted to get at to quantify

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this overall climate exposure.

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Well, the first one was
just climate changieness.

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That's the official
term I'm gonna give it,

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climate changieness.

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Can we just quantify how
much the climate conditions

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are going to vary from what
our forested ecosystems

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have an adaptive memory of, right,

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what have they been adapted to tolerate,

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and how far are we outside
of that adaptive memory?

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So we wanted to generate our own layer

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for climate changieness.

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There are other layers that already exist.

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So the FEMC Forest
Health Atlas has a great

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archive of disturbance patterns.

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So we can see where across the landscape

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disturbance is much more frequent,

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and think about that as
maybe identifying hotspots

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that might see additional
sort of secondary

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impacts of climate change.

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The Climate Change Tree Atlas
has also done amazing work

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in looking at suitable habitat
for various tree species

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and how they may shift under
various climate projections.

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And so we wanted to
incorporate that as well.

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And then we also wanted to incorporate

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some finer scale basal area coverage maps.

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Like you can't have a
species that's exposed

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if it doesn't exist in that location.

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So we wanted to incorporate
that and somehow aggregate

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it all into this one
metric of climate exposure.

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So I'm gonna just really
quickly go through

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some of these input layers
to give you an idea,

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and I'll start with climate changieness,

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because that's the one that we
actually developed ourselves.

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We decided to go with TerraClimate data,

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primarily because it has both historical

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norms and projections for not only

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sort of the standard climate metrics,

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but also for what I think of
as more ecologically relevant

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climate metrics that might actually impact

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tree function and competition.

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So evapotranspiration, soil moisture,

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snow water equivalent, so
thinking about snow pack.

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And so we really were
able to maybe do what some

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other climate modelers in terms of impacts

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to ecosystems have not been able to do.

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And so again, we had that at three

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different sort of timestamps, just,

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estimated a low and high emission scenario

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based on global mean, changes
in global mean temperature.

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But basically we have
the historical norms,

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a low emissions, and a
high emissions scenario.

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And so again, we're
interested in changieness,

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so it's really simple arithmetic.

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Take those historical norms for all 92

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of these unique climate variables

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that were in this TerraClimate dataset,

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and then subtract from that
what the projections are,

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so we can just get an idea
of how much this is changing.

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And again, this is spatially explicit.

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So we have this for
pixels across the region.

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But then we ended up with
these climate deviation

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raster layers, 92 of them.

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That is way too much data overload,

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likely auto-correlated with each other

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across months and different metrics.

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So we pooled these into a
principle component analysis

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to basically try to maintain
as much of the information

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about that climate
changieness as as we could,

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but smash that down into one metric.

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And so we did end up
with this one PCA layer

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that ended up accounting for about 50%

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of the total variability in
that climate changieness.

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And so we just wanted to go with that.

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It does turn out that what's interesting

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is that the variables that were most,

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provided the most
information to this first

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principal component were
not your traditional

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mean temperature, max temperature,

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it really was the evapotranspiration

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and soil water equivalent,
and then that potential

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evapotranspiration and
snow water equivalent.

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So really interesting,
kind of even just looking

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at which are sort of the climate variables

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that are gonna be changing the most.

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And it turns out that it
is the shoulder seasons

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and these sort of more
derived climate variables

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that are showing up as changing

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the most in these projections.

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So we end up with this one data layer.

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Again, it's unitless.

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It's like this metric that is just

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a principal component metrics,
but it quantifies how much

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those climate variables are changing

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from those historical norms
overall, right, in aggregate.

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And it is at a four
kilometer spatial scale.

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So each of these different data layers

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did have some scale
limitations in terms of working

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to aggregate those in the final model.

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And then we, again, back to the abundance,

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this was one that was already existing.

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David Gudex-Cross used sort
of some new kind of forced

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hyperspectral techniques with
lancet imagery and time series

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data to develop these percent
basal area species maps.

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Where that data was missing, we backfilled

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with FIA abundance, but the
idea is that really 14 species

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were the ones that we felt we had

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accurate basal area data for.

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So for 14 species we have
the percent basal area

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at a 30 meter resolution
across the region.

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So that was sort of are they there,

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are they gonna be exposed.

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Similarly, that disturbance layer

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coming from the FEMC Forest Health Atlas

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is an aggregate of the number of times

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that any disturbance has been mapped

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using those aerial detection surveys,

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systems run by the state,
and the type of disturbance

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is listed as a part of
that, of those polygons.

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And so we divided this into sort of three

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different disturbance scenarios.

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One is no disturbance,
let's just look at exposure

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if we didn't include disturbance at all.

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The other was limited
just to what we'd call

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climate related disturbances,
so wind disturbance, flooding,

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drought, frost, any
disturbances that had been

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sort of directly linked
to a climate event.

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And then we also ran a model
with all of those disturbances.

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So everything that had been recorded,

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regardless of the causal agent there.

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And then the last raster layer

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that we brought in for this
aggregate exposure model

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does come from the
Climate Change Tree Atlas,

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and we used their more
recently published shift map.

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So they're projecting how suitable habitat

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for each species is projected to change,

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and again, they had low and
high emission scenarios.

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And so again, we took that, which, really,

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the metric is relative importance
value for each species.

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That's sort of their way of quantifying

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how suitable the habitat might be.

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And we use that to see,
again, how it had changed

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from the current relative
importance value.

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So again, it's that change
in suitable habitat.

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And they do use climate variables

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to create these shift models.

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The reason why we decided
to keep this in here,

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even though we already had a
separate climate changieness,

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climate changieness raster,
is because the metrics

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that they use are actually not the metrics

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that ended up being
significant in our PCA.

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So this really is just
just based on temperature

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and precipitation norms
and minimum and maximum.

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And so this is really a different climate,

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set of climate information
than what we have

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in our climate changieness raster.

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So that, you know, those are the layers.

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Now how do you pull them all together?

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And that's where Lukas came in.

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We said, Lukas, you've
got great JS skills,

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how are we gonna pull this all together?

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So, as you can see we standardized

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all of these from zero to 100,

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with the exception of
PCSH, our projected change

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in suitable habitat, that
we had to allow for areas

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of climate advances for species like oak,

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that seem to be doing better
with higher emissions.

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So at the end, we came with
six different scenarios

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with species representing
different iterations

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of low and high emissions

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with the three disturbance scenarios.

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So you're looking at just a diagram

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of how different things are run.

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For this it's exclusively for sugar maple.

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And you can see that there's
six different scenarios,

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but it was also run for
13 different species.

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So there are 84 different
layers representing

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different low emissions and
high emissions scenarios,

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low emissions representing
the plus two degrees celsius

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scenario for climate and
the GCM 4.5 with PCSH,

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and then a plus four emissions scenario

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for the CLIM and the GCM 8.5.

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From there you can see, this
is an example of sugar maple,

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and it's different spreads
for all those scenarios.

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You may be looking at this

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and say are these all the same picture?

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-(audience laughs)
-The answer is no.

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It just happens to be
that with sugar maple

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there was very limited variation.

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And we'll talk about
that in the next slide.

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But the, it seems to be that sugar maple

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is already pretty exposed and
it's hit an exposure ceiling,

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I guess is a way of putting it.

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As you can see it's the second
highest of all exposure,

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but there's no significance difference.

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And if you can't really
see, I guess it's a little

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too grainy on the projection screen,

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but you can read that areas
such as Northern Vermont

259
00:10:32,220 --> 00:10:35,520
and New Hampshire are
seeming to be refugia

260
00:10:35,520 --> 00:10:37,740
in a lot of these scenarios,

261
00:10:37,740 --> 00:10:41,487
whereas areas like Northern,
or the Susquehanna Valley

262
00:10:41,487 --> 00:10:44,040
and the Hudson are all looking

263
00:10:44,040 --> 00:10:45,890
like they are gonna have a hard time.

264
00:10:46,860 --> 00:10:49,050
As I mentioned before,
this is not the case,

265
00:10:49,050 --> 00:10:52,640
if you can look at sugar maple down here,

266
00:10:52,640 --> 00:10:54,810
its variance is very different

267
00:10:54,810 --> 00:10:56,910
compared to a lot of these species.

268
00:10:56,910 --> 00:10:59,910
And we've really boiled this
down to three different trends.

269
00:10:59,910 --> 00:11:02,280
We have what we call our climate winners,

270
00:11:02,280 --> 00:11:04,530
our climate losers,
and then climate limbo,

271
00:11:04,530 --> 00:11:07,800
where it's just kind of like
on a platform right now,

272
00:11:07,800 --> 00:11:09,720
not looking to do too much.

273
00:11:09,720 --> 00:11:11,910
So in these trends you kind of see

274
00:11:11,910 --> 00:11:14,820
some systems based approaches too.

275
00:11:14,820 --> 00:11:18,480
You see montane species, such
as red spruce and balsam fir,

276
00:11:18,480 --> 00:11:21,480
that seem to be increasing
quite a bit in exposure

277
00:11:21,480 --> 00:11:23,280
with a high emissions scenario,

278
00:11:23,280 --> 00:11:26,250
whereas species like
the oaks and black birch

279
00:11:26,250 --> 00:11:29,536
tend to be making advances
and decreasing in exposure.

280
00:11:29,536 --> 00:11:32,670
From white pine and sugar maple,

281
00:11:32,670 --> 00:11:35,370
as I said, not much is happening,

282
00:11:35,370 --> 00:11:37,720
and it'd be interesting
to see where that goes.

283
00:11:38,940 --> 00:11:40,290
Beyond that, I guess I'll touch on this.

284
00:11:40,290 --> 00:11:42,060
You see all the variation here.

285
00:11:42,060 --> 00:11:43,590
There's, every species had its own

286
00:11:43,590 --> 00:11:45,540
different way of changing exposure.

287
00:11:45,540 --> 00:11:48,900
So we were very curious to
see how this would change

288
00:11:48,900 --> 00:11:50,820
if we just combined all the species

289
00:11:50,820 --> 00:11:53,248
for each of those six scenarios.

290
00:11:53,248 --> 00:11:57,810
And we wanted to see how these things

291
00:11:57,810 --> 00:11:58,860
would do as a weighted average

292
00:11:58,860 --> 00:12:00,870
with the weight being
the species abundance,

293
00:12:00,870 --> 00:12:03,780
so we are only accounting
for the percent basal area

294
00:12:03,780 --> 00:12:06,240
for that specific species at that time.

295
00:12:06,240 --> 00:12:08,990
So there's six different
scenarios represented by this.

296
00:12:10,050 --> 00:12:12,660
There's no need to like analyze
that, it's just an example.

297
00:12:12,660 --> 00:12:14,250
But we have a low emissions scenario,

298
00:12:14,250 --> 00:12:15,753
all disturbance included here.

299
00:12:17,190 --> 00:12:20,190
As you can see there's some
spatial trends unfolding.

300
00:12:20,190 --> 00:12:22,440
A lot of these areas,
such as the Berkshires

301
00:12:22,440 --> 00:12:26,580
and the Eastern Adirondacks
seem to be increasing exposure,

302
00:12:26,580 --> 00:12:29,160
whereas coastal Massachusetts
and Southern New York

303
00:12:29,160 --> 00:12:32,163
and Northern Maine are
areas of lower exposure.

304
00:12:33,540 --> 00:12:37,260
But when emissions increase,
it seems that a lot

305
00:12:37,260 --> 00:12:40,320
of this exposure, a lot of
this lower end exposure,

306
00:12:40,320 --> 00:12:41,760
is pushed upwards, as you can see

307
00:12:41,760 --> 00:12:44,400
in the histogram in the upper section.

308
00:12:44,400 --> 00:12:47,430
And a lot of mountainous
regions seem to be experiencing

309
00:12:47,430 --> 00:12:50,520
a lot more exposure, and
coastal areas of New England

310
00:12:50,520 --> 00:12:53,520
and Southern New York tend
to be making advances.

311
00:12:53,520 --> 00:12:54,990
I draw your attention to thoughts

312
00:12:54,990 --> 00:12:56,700
of what species grow where.

313
00:12:56,700 --> 00:13:00,840
As you saw in that graph,
spruce fir and sugar maple

314
00:13:00,840 --> 00:13:03,870
tend to dominate these
areas of the warmer colors,

315
00:13:03,870 --> 00:13:07,890
and the cooler colors are
dominated by birches and oaks.

316
00:13:07,890 --> 00:13:11,040
So if you think about it, the
kind of winners occupy areas

317
00:13:11,040 --> 00:13:14,730
where climate seems to be better off.

318
00:13:14,730 --> 00:13:16,290
Beyond that, we wanted to look at things

319
00:13:16,290 --> 00:13:20,610
from a regional perspective,
and we did that breaking things

320
00:13:20,610 --> 00:13:24,180
into ecoregions with
the L three designation.

321
00:13:24,180 --> 00:13:27,120
I'd like to point out
up in the upper right

322
00:13:27,120 --> 00:13:31,680
we have area designations
for the amount of cells

323
00:13:31,680 --> 00:13:34,140
that were using the
study, and that represents

324
00:13:34,140 --> 00:13:37,110
about 70% of the cells, those first two.

325
00:13:37,110 --> 00:13:38,370
And that represents areas

326
00:13:38,370 --> 00:13:40,590
of climate loss with increasing scenarios.

327
00:13:40,590 --> 00:13:43,770
Whereas a lot of these
smaller areas in the south

328
00:13:43,770 --> 00:13:48,770
seem to be making advances,
which is probably an artifact

329
00:13:48,900 --> 00:13:50,550
of the fact that there are a lot

330
00:13:50,550 --> 00:13:53,310
of oaks and birches in these ecoregions.

331
00:13:53,310 --> 00:13:55,113
So we came to some conclusions.

332
00:13:56,910 --> 00:13:58,950
It's pretty easy to say
that there will be impacts

333
00:13:58,950 --> 00:14:01,080
to conventional forest functionality

334
00:14:01,080 --> 00:14:03,660
when the emissions increase.

335
00:14:03,660 --> 00:14:07,110
And that is mainly driven,
as Jen had pointed out,

336
00:14:07,110 --> 00:14:09,720
in the fringes of the
winter, so shoulder season,

337
00:14:09,720 --> 00:14:13,050
and the projected changes
in suitable habitat.

338
00:14:13,050 --> 00:14:16,380
With our species, as we
mentioned, there are ways

339
00:14:16,380 --> 00:14:18,120
to think about how things are changing.

340
00:14:18,120 --> 00:14:19,710
You can think about it
from a species basis

341
00:14:19,710 --> 00:14:21,810
with the winners, losers and the limbo.

342
00:14:21,810 --> 00:14:23,940
Or you can think about it
from a regional perspective,

343
00:14:23,940 --> 00:14:25,260
where northeastern highlands

344
00:14:25,260 --> 00:14:26,820
and the Acadian plains and hills

345
00:14:26,820 --> 00:14:30,240
are set to have a harder
time or face more exposure.

346
00:14:30,240 --> 00:14:32,610
But you can also think about
it in a synthesis of ways,

347
00:14:32,610 --> 00:14:34,740
where you have these different ecosystems,

348
00:14:34,740 --> 00:14:36,780
like the mixed hardwood,
that tends to occupy

349
00:14:36,780 --> 00:14:38,850
the southern sections of the region.

350
00:14:38,850 --> 00:14:40,680
But then you have
spruce-fir systems that seem

351
00:14:40,680 --> 00:14:43,380
to really be facing higher exposure.

352
00:14:43,380 --> 00:14:46,140
And you have northern
hardwood systems that are not

353
00:14:46,140 --> 00:14:49,653
doing too well, but they're
not as bad as spruce and fir.

354
00:14:51,240 --> 00:14:53,490
The next steps for this project,

355
00:14:53,490 --> 00:14:56,220
one of the working sessions,
or one of the sessions earlier

356
00:14:56,220 --> 00:14:58,710
this day talked about ground truthing.

357
00:14:58,710 --> 00:15:02,160
And exposure model validation
is a huge part of this.

358
00:15:02,160 --> 00:15:05,430
This is just a a model, this is not truth,

359
00:15:05,430 --> 00:15:07,860
this is just suggestions and projections.

360
00:15:07,860 --> 00:15:11,160
So we need to go down to
continual forest inventory areas

361
00:15:11,160 --> 00:15:14,850
where we may expect areas
to be higher exposed

362
00:15:14,850 --> 00:15:17,750
or lower exposed, and see what
is on the ground right now.

363
00:15:19,020 --> 00:15:21,780
Past that, this is a lot of data,

364
00:15:21,780 --> 00:15:23,070
and you can draw a lot of conclusions

365
00:15:23,070 --> 00:15:24,720
from it just on first glance.

366
00:15:24,720 --> 00:15:27,540
But there are appropriate
ways to apply this data,

367
00:15:27,540 --> 00:15:30,690
and there are inappropriate
ways to apply this data.

368
00:15:30,690 --> 00:15:32,220
It is not an opportunity map.

369
00:15:32,220 --> 00:15:33,300
There's only 14 species,

370
00:15:33,300 --> 00:15:36,120
there's more than 14
species in this region.

371
00:15:36,120 --> 00:15:37,890
For example, you don't really think

372
00:15:37,890 --> 00:15:41,250
of Southern Massachusetts
without tulip poplar

373
00:15:41,250 --> 00:15:43,020
or other dominant species like bear oak

374
00:15:43,020 --> 00:15:45,580
or scrub oak and pitch pine.

375
00:15:45,580 --> 00:15:47,430
So you can't really consider
this opportunity mapping

376
00:15:47,430 --> 00:15:50,026
because you're not
watching all the species,

377
00:15:50,026 --> 00:15:52,316
to the whole species like palate

378
00:15:52,316 --> 00:15:53,730
I guess is a way of putting it.

379
00:15:53,730 --> 00:15:55,170
Beyond that, we want to
engage with stakeholders

380
00:15:55,170 --> 00:15:57,003
and wonder how this data is useful.

381
00:15:57,900 --> 00:16:00,920
Though we have it at a 100
meter scale at the final,

382
00:16:00,920 --> 00:16:02,700
we have to ask questions

383
00:16:02,700 --> 00:16:05,010
of at what scale this is appropriate.

384
00:16:05,010 --> 00:16:07,530
This is probably not a
landowner based tool,

385
00:16:07,530 --> 00:16:09,430
this is more of a regional based tool.

386
00:16:10,500 --> 00:16:13,140
And then we want to
find ways of leveraging

387
00:16:13,140 --> 00:16:16,500
it to help with climate restoration.

388
00:16:16,500 --> 00:16:19,380
There may be areas of
refugia that we can target

389
00:16:19,380 --> 00:16:22,620
for good adaptive management,
or there might be areas

390
00:16:22,620 --> 00:16:24,660
that are looking like
they're highly exposed,

391
00:16:24,660 --> 00:16:27,230
and we may have to go towards
the adaptive silviculture

392
00:16:27,230 --> 00:16:30,150
of climate change route
and enrich these areas

393
00:16:30,150 --> 00:16:32,850
with species that will fare better.

394
00:16:32,850 --> 00:16:35,310
Lastly, a step that we
have started to take

395
00:16:35,310 --> 00:16:38,373
steps with is data access
and application support.

396
00:16:39,510 --> 00:16:43,020
Right now we have a site
up on the FEMC website

397
00:16:43,020 --> 00:16:46,440
that outlays with JPEG
maps and raster layers

398
00:16:46,440 --> 00:16:48,450
the low and high emission scenarios

399
00:16:48,450 --> 00:16:51,840
for all disturbance as well
as all the base layers.

400
00:16:51,840 --> 00:16:53,790
More to come, there will be more added.

401
00:16:54,876 --> 00:16:57,884
You don't have to copy that
link if you don't want.

402
00:16:57,884 --> 00:17:00,503
We have a QR code if you want
to scan it with your phone.

403
00:17:02,160 --> 00:17:03,303
Yeah, any questions?

404
00:17:04,680 --> 00:17:06,808
A couple minutes for questions.

405
00:17:06,808 --> 00:17:09,891
(audience applauds)

406
00:17:17,160 --> 00:17:19,470
[Audience Member] I'll try
to speak through the mask.

407
00:17:19,470 --> 00:17:23,643
Seeing the seedling seed supply
presentation earlier today,

408
00:17:27,420 --> 00:17:29,610
and personally having
seen many tree planting,

409
00:17:29,610 --> 00:17:32,970
tree installation efforts
fail to the tune of thousands

410
00:17:32,970 --> 00:17:36,840
of trees die in a singular
planting location,

411
00:17:36,840 --> 00:17:41,070
what does it mean, or
let me ask you to comment

412
00:17:41,070 --> 00:17:45,360
on the shoulder season
changing drastically,

413
00:17:45,360 --> 00:17:47,580
it says a lot to me, because
it doesn't, is that not

414
00:17:47,580 --> 00:17:50,380
a very vulnerable time
for like newly germinated

415
00:17:51,390 --> 00:17:53,670
acorns and seedlngs of all species?

416
00:17:53,670 --> 00:17:56,040
Yeah, absolutely, so I was a field tech

417
00:17:56,040 --> 00:17:59,238
for the Adaptive Silviculture
for Climate Change effort.

418
00:17:59,238 --> 00:18:00,960
And my first, I think it was
late in May or something,

419
00:18:00,960 --> 00:18:03,837
I was working for Jess Whitepost,
she's in here somewhere.

420
00:18:03,837 --> 00:18:05,460
And there's a late frost event,

421
00:18:05,460 --> 00:18:08,700
and it destroyed a lot
of the first leaf out.

422
00:18:08,700 --> 00:18:10,920
And the fact that it's
getting warmer earlier

423
00:18:10,920 --> 00:18:13,593
but it's also up and down,
it's a lot of extremes.

424
00:18:14,820 --> 00:18:17,310
Just have to kind of fight through,

425
00:18:17,310 --> 00:18:18,780
and hope that some of them survive.

426
00:18:18,780 --> 00:18:21,180
I guess that's my, I
just graduated undergrad,

427
00:18:21,180 --> 00:18:22,568
I don't really know I'm talking about.

428
00:18:22,568 --> 00:18:24,090
(audience laughs)

429
00:18:24,090 --> 00:18:27,600
But I'd say you just have to keep at it,

430
00:18:27,600 --> 00:18:30,000
and there's really no
easy way to go about it.

431
00:18:30,000 --> 00:18:33,810
Maybe tubes will help by
keeping the local area warmer,

432
00:18:33,810 --> 00:18:35,613
but that's not my area of expertise.

433
00:18:35,613 --> 00:18:38,430
But, but that is why I
think having all of the data

434
00:18:38,430 --> 00:18:39,910
up there is important because maybe

435
00:18:39,910 --> 00:18:42,930
sort of this ecosystem
aggregate of the 14 species

436
00:18:42,930 --> 00:18:44,490
that we have is not
useful, if you're thinking

437
00:18:44,490 --> 00:18:46,350
about planting chestnut, oak or something

438
00:18:46,350 --> 00:18:48,390
that we haven't included in this model,

439
00:18:48,390 --> 00:18:50,550
but the input raster
for climate changieness,

440
00:18:50,550 --> 00:18:53,048
this might be of use to you.

441
00:18:53,048 --> 00:18:54,480
So if you want to see where
might we have a chance

442
00:18:54,480 --> 00:18:57,630
of not losing those tens
of thousands of seedlings

443
00:18:57,630 --> 00:18:59,670
that we plant, maybe look at this map,

444
00:18:59,670 --> 00:19:01,170
and then try to identify some places

445
00:19:01,170 --> 00:19:03,063
where it's not that changey.

446
00:19:04,643 --> 00:19:05,993
Thanks for your question.

447
00:19:07,843 --> 00:19:09,512
I think that's all our time for--

448
00:19:09,512 --> 00:19:14,512
-Good.
-(audience laughs)