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So yeah, thank you very much.

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Hey everybody. So my name's
Soren, and I've worked

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with a lot of different
people, including Tony, Aaron

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and myself from the University of Vermont,

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Chris Woodall, Jennifer Pontius,

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I'd like to say thank you
very much to all of them

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for supporting me in
this kind of long effort.

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Today I'm gonna talk to you about

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decadal changes in
northeastern adaptability

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and vulnerability to climate,
insects, and disease.

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I'm gonna go through a lot today,

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and try to go through it pretty quickly.

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If you guys see any kind of patterns

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sort of culturally that
I don't talk about,

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or want to talk shop
about the methodology,

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love to meet with you
after, but let's get to it.

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So I'm sure everyone's
seen a lot of these slides

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already today, but the
increased drought frequencies

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occurring in the northeast,
increased drought

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kind of intensity, but
also the recurrence.

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We're also seeing increases in temperature

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and increases in rainfall.

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So these kind of culminate in a lot

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of kind of changes that we
are going to be undergoing.

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There's also been fundamental
changes in stand dynamics,

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such as increased relative densities,

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and also kind of exposure.

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If you can look up in the northeast,

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we can see a lot of pink and red up there,

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right hand figure exposure
to insects and disease.

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And so this culminated in looking

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at some driving questions
for this research.

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Have changes in forest structure
and composition resulted

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in increased vulnerability to
climate, insects and disease?

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Are these changes consistent
both overstory and understory?

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And are there any specific regions

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in which the changes occur
that are not occurring?

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So are there any areas specifically

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where there's more
adaptability, less adaptability,

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more vulnerability, less vulnerability?

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So this is a sub-analysis

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of true meta-analysis
looking at the entire region

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for all states bordering
and east of the Mississippi.

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Of course, in this blue area

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where we're gonna be focusing on,

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the area has things like spruce
fir, and hemlock-hardwoods,

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maple-beech-birch, oak-pines, pitch pines.

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And you can really see that there's a lot

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of variability in this region,

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which is one of the reasons
why I love working up here.

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So this is really based
on three scoring systems.

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vulnerability to climate,

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vulnerability to insects and
disease, and adaptability.

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So vulnerability to climate.

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And all of these scores take
into consideration traits.

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So in kind of broader
shifts towards ecology,

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we're looking at, instead of
just species-level analysis,

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we're linking trait-level analysis.

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And so these scores, in
particular, take into consider

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all of these different traits
that allow for certain species

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to either thrive, reproduce,
and be productive in a region.

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So MODFACs being the fundamental drivers

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of modification factors for
the Iverson tree analysts.

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And then Potter's work
doing a bunch of other

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kind of scales where they
took into consideration

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climate vulnerability, insect
vulnerability and to compare

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them to these traits, to
predict whether or not

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scaling all of tree species

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in the relative exposure
rank to these threats.

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It goes through a lot
of different processes

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for the full scalarization,
but you end up with

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a species rank at the
end where more species

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with some scales are more
vulnerable than others.

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So you're thinking red
maple, highly adaptable,

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not very vulnerable,

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you're thinking highly vulnerable
species, like chestnut.

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individual species scores
is what really comes out.

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So these species scores end up

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kind of looking through
these distributions

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and to kind of give it a understanding

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of where these scores for certain species

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in the northeast land, you have red maple,

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yellow birch, white ash,
and eastern hemlock.

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The adaptability MODFACs
score for the Iverson models

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do kind of trend to be pretty harsh

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on the northeastern species,

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but we also took into
consideration the other scores'

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capture framework,
which looks specifically

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into the insects and disease.

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And you can see most species are not that

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actually vulnerable, but
you do have those outliers

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that pick up northeastern
species quite well

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as far as vulnerability. Capture
framework also for climate

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which takes into consideration pretty much

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all species that you would
find in the United States.

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And as you can see, each score

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fundamentally deals with
species differently.

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But the amalgamation of all
scores will hopefully result

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in a kind of holistic approach

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to look at general vulnerability

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and adaptability for the region.

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So the basic structure of this analysis

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went through FIA data,

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where we selected and
filtered a plot comparison.

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So it's a true one time
period, next time period

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we also maximize time.

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So they were hoping to
pick up on that change.

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As you know, force change turned slowly,

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so we're trying to maximize time

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to pick up any changes
that actually occur.

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You get out the data and you
apply the scoring frameworks

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to each individual species
to get out some type

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of vulnerability, and you
break it into two time series.

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So time one being 1998 to 2012,

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2013 to 2021 for the two time periods.

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This kind of gets around some

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of the temporal inconsistencies
fundamental in FIA.

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This methodology has been employed before,

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and works quite well.

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In the end, there's 25,000
plots for the northeast,

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with a meantime difference of 15 years,

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and a relatively low error score.

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The error score's coming from

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how well do those scores
actually apply to all

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of the species that you find
actually in the northeast?

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So it's very low.

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We had a very good actually
application of all the scores.

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So did things actually change?

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Yes, significance did pass value tests.

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So we we're able to figure out

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that the overall
distributions are shifting.

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So let's look at some things.

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Blue indicates on this side
is where you're losing species

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that are not as vulnerable
and are more adaptable, right?

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Red, in this case,

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is where you get a lot
of vulnerability shifts.

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So those species that
either underlying species

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within plots are shifting
to more vulnerable species.

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I also incorporated for
kind of ease of looking

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at these distributions,
quantile or percentile lines.

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And we can see that
the extremes are really

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where you're seeing differences.

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So the changes are the most
vulnerable, or at least

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in this case it's the
least vulnerable plots

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seem to be shifting the
most, and also the most

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vulnerable lots seem to
be shifting the most.

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Not a really good start
for that, but in general,

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we can say that the distributions,
there has been a change

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from less vulnerability
to more vulnerability.

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So let's look at some map space

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on each of these kind of distributions.

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So this is vulnerability to
climate, to capture score,

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and we're gonna focus in kind of on

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you can see this general spatial region,

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where you have high
vulnerability to climate.

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The northeast Adirondacks here

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do have low vulnerability climate.

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This is also for tree species.

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This is not for seedlings,
trees is the overstory.

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Moving on to the vulnerability
of insects and disease.

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We can kind of see how there's this

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kind of red values that
I see in Eastern Maine

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and the Acadian transition.

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And then northeastern Maine
again kind of pops out

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as a very vulnerable area,

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and as well as the central
coast area seems to be

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kind of more adaptable.

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So now that was, those previous
maps were to really look

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at like where things are already,

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this is the change that is occurring.

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So this is time one compared to time two.

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So what is actually on the landscape,

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based on species and size classes?

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How are things shifting?

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So I'd like to draw your attention

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to the Northeastern street hardwoods areas

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for both overstory and understory.

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Understory was considered
significant in its change,

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P value in the bottom left hand corner,

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as well as the distribution change.

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The one thing is that there does seem,

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it is quite sporadic in the
Hudson River Valley areas,

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where they saw a large increase
in less vulnerable species,

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as well as increases in
more vulnerable species.

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So there seems to be some kind
of complexity going on there.

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The largest changes in understory
seems to be down there.

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So I would always also
like to preface here

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that this is designed,

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the methodology is designed
for a large-scale analysis.

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I chose to kind of narrow
in on the Northeast

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just because of kind of the
designations of (indistinct)

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and seeding this kind of
occurs, so this demonstration.

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So changes in vulnerability to climate.

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Acadian transition,
again kind of popping up

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as areas of increased vulnerability.

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Northern Adirondacks
and seedlings seem to be

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another place where the
seedlings or understory

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seems to be coming actually
less vulnerable to climate.

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Likely due to sporadic increases
in understory diversity,

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there's a lot of fundamental civics

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that are driving these
patterns, and it's not,

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this analysis is not
really part of that level.

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So increase in vulnerability

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to insects and disease
is quite interesting.

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Where we found that the, again,

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the central Acadian transition

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seems to becoming more vulnerable,

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whether or not that's being
driven by an increase in hemlock

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and other kind of beech, other
species that are vulnerable

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to insects and disease wasn't
really fully ascertained.

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Again for the, just for the understory,

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we're able to see that
that pattern was held true.

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And again, the Northwestern
Adirondack seemed to come out

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as a place that the seedlings
were becoming less vulnerable

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to insects and disease.

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Large-scale average changes
in the overstory, really,

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was what this was able to pick up,

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which was quite nice to see.

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Again, the P value,
upper left hand corner.

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So that all kind of was fed

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into a cluster analysis higher cluster

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with like spatial constraints.

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Don't wanna have to talk shop,

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00:09:54,240 --> 00:09:58,290
but it came out with six
regions of similar change,

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and that found that there
were the eastern Adirondacks,

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the Adirondacks Catskills,

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the Green mountains and Whites, and then

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the southern like Acadian
transition where we saw

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a lot of those changes occurring.

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00:10:11,820 --> 00:10:15,123
And then this kind of Northern
Adirondacks region here.

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00:10:16,260 --> 00:10:18,510
And I'm gonna focus in on
these two, as these two were

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00:10:18,510 --> 00:10:21,933
the ones that had like the
biggest changes occurring.

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So these are broad biological
patterns that seem close

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to what we might expect,

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based on the species that occur there.

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And it'd require minimal
spatial constraints

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to actually get to these.

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So the fundamental, what patterns
seem to be held and true.

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So it went from, the worst
off in the region seemed to be

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00:10:42,750 --> 00:10:45,450
that kind of central Acadian
transition, with significance

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00:10:45,450 --> 00:10:49,200
both in overstory and
understory between all scores,

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and the change for the regions that seemed

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00:10:52,800 --> 00:10:56,490
to have like the best output
seemed to be the understory

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00:10:56,490 --> 00:10:59,430
specifically within the
north and Adirondacks, again,

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00:10:59,430 --> 00:11:02,553
significance for all variables
in overstory and understory.

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00:11:04,170 --> 00:11:06,600
So what species might be driving this?

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00:11:06,600 --> 00:11:09,783
I decided to look at the Adirondacks.

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This is a cumulative
frequency distribution plot,

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00:11:15,000 --> 00:11:18,603
but basically just means
that the species that end up

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the x axis right here is importance value.

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As it changes, it means
it's relatively the most

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important species within the region.

253
00:11:24,870 --> 00:11:26,940
So if you see any kind of movement here,

254
00:11:26,940 --> 00:11:29,880
it's gonna be more
proportionally influential

255
00:11:29,880 --> 00:11:31,724
on the score change.

256
00:11:31,724 --> 00:11:33,330
And as you can see, some dominant species

257
00:11:33,330 --> 00:11:35,160
are holding an increasing dominance.

258
00:11:35,160 --> 00:11:39,990
So we're looking at balsam,
fir, and red maple, again,

259
00:11:39,990 --> 00:11:43,500
red maple being highly
resilient and very adaptable.

260
00:11:43,500 --> 00:11:45,810
Probably any change there
can kind of mute those other

261
00:11:45,810 --> 00:11:47,332
scores so that it actually...

262
00:11:47,332 --> 00:11:48,900
-(audience member coughing)
-...More adaptable,

263
00:11:48,900 --> 00:11:51,060
and balsam fir being somewhat susceptible

264
00:11:51,060 --> 00:11:54,060
to climate and insect disease.

265
00:11:54,060 --> 00:11:56,160
That large shift can be maybe driving

266
00:11:56,160 --> 00:11:57,390
some of these patterns.

267
00:11:57,390 --> 00:11:58,980
There's also changes in species occurring

268
00:11:58,980 --> 00:12:01,140
in centers of the distribution.

269
00:12:01,140 --> 00:12:04,620
So changes between Tsuga
canadensis, eastern hemlock,

270
00:12:04,620 --> 00:12:07,020
and yellow birch.

271
00:12:07,020 --> 00:12:08,220
And there's also changes occurring

272
00:12:08,220 --> 00:12:09,150
throughout the distribution.

273
00:12:09,150 --> 00:12:11,100
So some shade-tolerant
species are increasing

274
00:12:11,100 --> 00:12:13,950
such as moose maple, striped maple.

275
00:12:13,950 --> 00:12:16,650
So it's quite interesting
to see what species

276
00:12:16,650 --> 00:12:19,923
might be driving these changes.

277
00:12:22,380 --> 00:12:25,350
So in conclusion, from the slides

278
00:12:25,350 --> 00:12:27,008
that I kind of just threw at you,

279
00:12:27,008 --> 00:12:28,170
(audience laughing)

280
00:12:28,170 --> 00:12:29,490
there have been changes in the understory

281
00:12:29,490 --> 00:12:32,130
and overstory based on
adaptability and the vulnerability

282
00:12:32,130 --> 00:12:33,450
to climate, insects, and disease.

283
00:12:33,450 --> 00:12:36,183
It has occurred in the last 10 years.

284
00:12:37,495 --> 00:12:39,045
And let me go through, I thought that...

285
00:12:39,045 --> 00:12:41,043
(audience laughing)

286
00:12:41,043 --> 00:12:42,005
Yeah, yeah.

287
00:12:42,005 --> 00:12:44,670
The spatial patterns, however,
differ under each score.

288
00:12:44,670 --> 00:12:46,980
So if you're interested
in insects and disease,

289
00:12:46,980 --> 00:12:50,370
you might be beneficial
to only use one score,

290
00:12:50,370 --> 00:12:52,170
or if you're interested in
climate and you will find

291
00:12:52,170 --> 00:12:55,530
that the Iverson models
kind of fit what you see

292
00:12:55,530 --> 00:12:56,363
on the ground,

293
00:12:56,363 --> 00:12:58,850
that might be the scoring
system you might want to use.

294
00:13:00,120 --> 00:13:02,730
The greatest observable
change, as you might assume,

295
00:13:02,730 --> 00:13:04,620
and it's not in the overstory,
it's in the understory,

296
00:13:04,620 --> 00:13:06,330
it's the regeneration
that seems to be driving

297
00:13:06,330 --> 00:13:08,220
a lot of these patterns.

298
00:13:08,220 --> 00:13:11,190
And then considering all of
the variables and the kind

299
00:13:11,190 --> 00:13:13,860
of meta-analysis, the
Southern Acadia transition

300
00:13:13,860 --> 00:13:16,500
and the Acadian boreal transition
were the most vulnerable,

301
00:13:16,500 --> 00:13:18,810
or saw the most change of
overstory and understory

302
00:13:18,810 --> 00:13:20,430
towards more vulnerable species.

303
00:13:20,430 --> 00:13:23,523
More dominance, meaning like
larger amounts of basal area.

304
00:13:25,560 --> 00:13:27,660
And species driving, these
trends appear to be driven

305
00:13:27,660 --> 00:13:29,490
by certain species becoming more dominant,

306
00:13:29,490 --> 00:13:32,010
like I just said, as well as kind of less

307
00:13:32,010 --> 00:13:35,193
vulnerable species replacing
other more vulnerable ones.

308
00:13:38,940 --> 00:13:39,773
Questions?

309
00:13:41,242 --> 00:13:44,409
(audience applauding)

310
00:13:50,010 --> 00:13:51,330
[Audience #1] Sorry if I missed this.

311
00:13:51,330 --> 00:13:56,330
On the scoring aspect,
especially for insects and pests,

312
00:13:56,730 --> 00:13:59,580
is that based on whether
there's a known insect

313
00:13:59,580 --> 00:14:01,890
or pest pathogen that infects,

314
00:14:01,890 --> 00:14:04,050
and then how do we take that information

315
00:14:04,050 --> 00:14:08,490
for future introductions of
future pests and pathogens?

316
00:14:08,490 --> 00:14:11,730
Or is just that scoring
system can't adapt to that?

317
00:14:11,730 --> 00:14:15,000
[Soren] So that scoring
system in particular

318
00:14:15,000 --> 00:14:18,600
takes into consideration
exposure to known pests

319
00:14:18,600 --> 00:14:21,750
and pathogens, and does
not take into consideration

320
00:14:21,750 --> 00:14:22,980
unknown pests and pathogens,

321
00:14:22,980 --> 00:14:25,020
but it does take into
consideration the traits

322
00:14:25,020 --> 00:14:26,820
that would allow them to be adaptable.

323
00:14:26,820 --> 00:14:28,890
So in some indirect way,

324
00:14:28,890 --> 00:14:32,460
that scoring system does account
for it, but not directly.

325
00:14:32,460 --> 00:14:34,290
[Audience #1] Yeah, I guess
so is American chestnut

326
00:14:34,290 --> 00:14:36,480
on the right side because it has

327
00:14:36,480 --> 00:14:37,920
a really well-known custom pathogen,

328
00:14:37,920 --> 00:14:41,370
or because it also
exhibits some set traits?

329
00:14:41,370 --> 00:14:43,170
[Soren] Primarily, the
score is gonna be based

330
00:14:43,170 --> 00:14:45,000
on known custom pathogens.

331
00:14:45,000 --> 00:14:45,833
Yes.

332
00:14:47,700 --> 00:14:48,533
Good question.

333
00:14:52,050 --> 00:14:53,790
And if you saw any kind
of spatial patterns

334
00:14:53,790 --> 00:14:55,020
that I didn't pick up as species,

335
00:14:55,020 --> 00:14:56,120
-please,
-(audience laughing)

336
00:14:56,120 --> 00:14:57,840
I love talking about this stuff. So...

337
00:14:57,840 --> 00:15:00,240
[Audience #2] I have a
really basic, basic question.

338
00:15:00,240 --> 00:15:01,482
[Soren] Yeah?

339
00:15:01,482 --> 00:15:02,550
[Audience #2] So I might have missed it.

340
00:15:02,550 --> 00:15:05,130
What was the timeframe of the study?

341
00:15:05,130 --> 00:15:06,450
Like what were the earliest inventories

342
00:15:06,450 --> 00:15:07,530
that you used, and the latest?

343
00:15:07,530 --> 00:15:10,950
[Soren] So 1998 was the
earliest, the latest was 2021.

344
00:15:10,950 --> 00:15:14,580
-Okay...
-So FIA is spatially fixed,

345
00:15:14,580 --> 00:15:16,468
but temporally varied.

346
00:15:16,468 --> 00:15:18,900
So we had, you have to come up

347
00:15:18,900 --> 00:15:21,390
with like two time samples
to really kind of consider

348
00:15:21,390 --> 00:15:23,310
all of that spatial variability,

349
00:15:23,310 --> 00:15:26,250
which has been used
before in other studies.

350
00:15:26,250 --> 00:15:28,470
[Audience #2] So then I guess
my really basic question,

351
00:15:28,470 --> 00:15:30,884
at the fear of sounding like an idiot,

352
00:15:30,884 --> 00:15:33,810
(Audience #2 and Soren laughing)

353
00:15:33,810 --> 00:15:36,240
is that enough time for
us to have seen any change

354
00:15:36,240 --> 00:15:39,660
that has already been driven
by these stress agents?

355
00:15:39,660 --> 00:15:43,500
So, you know, I would expect
that if the assumption is

356
00:15:43,500 --> 00:15:45,900
that they're already
undergoing climate stress,

357
00:15:45,900 --> 00:15:48,360
and they're already undergoing
insect and pathogen stress,

358
00:15:48,360 --> 00:15:51,810
wouldn't you expect the
more adaptable species

359
00:15:51,810 --> 00:15:55,680
to be taking over? And
why don't we see that?

360
00:15:55,680 --> 00:15:57,810
[Soren] I think that has to
do with kind of interactions

361
00:15:57,810 --> 00:16:01,020
between time, if it's too
short of a time window,

362
00:16:01,020 --> 00:16:03,240
you might be able to pick
up on change but adaptation

363
00:16:03,240 --> 00:16:07,140
I don't know if 15 year mean
is really long enough to see

364
00:16:07,140 --> 00:16:08,357
the forest really shift that kind of-

365
00:16:08,357 --> 00:16:10,293
-Right.
-future forest type.

366
00:16:11,400 --> 00:16:12,990
[Audience #2] So is it mostly
just successional patterns

367
00:16:12,990 --> 00:16:14,370
and language history that's still driving

368
00:16:14,370 --> 00:16:15,300
what you're seeing?

369
00:16:15,300 --> 00:16:16,133
[Soren] It could be.

370
00:16:16,133 --> 00:16:17,550
So this study who doesn't really drive

371
00:16:17,550 --> 00:16:19,860
towards that kind of
correlation or causation,

372
00:16:19,860 --> 00:16:21,300
this is really kind of more just, like,

373
00:16:21,300 --> 00:16:22,950
this is where the areas that are

374
00:16:22,950 --> 00:16:24,810
seem to be having more vulnerable species

375
00:16:24,810 --> 00:16:26,040
and less vulnerable people species.

376
00:16:26,040 --> 00:16:28,440
Yeah, what's actually driving
this is far more complex,

377
00:16:28,440 --> 00:16:30,450
as any kind of land use
manager probably knows,

378
00:16:30,450 --> 00:16:33,300
like, there's a lot of really moving parts

379
00:16:33,300 --> 00:16:34,920
that are driving these patterns.

380
00:16:34,920 --> 00:16:38,940
But yeah, this does not
answer that question.

381
00:16:38,940 --> 00:16:39,773
[Audience #2] Okay.

382
00:16:44,880 --> 00:16:48,720
-How well are we doing?
-(audience laughing)

383
00:16:48,720 --> 00:16:51,783
Awesome. And Andy, these are
my works and works cited.

384
00:16:53,160 --> 00:16:55,060
Yeah. Do you have any other questions?

385
00:16:56,573 --> 00:16:59,740
(audience applauding)