So yeah, thank you very much.
Hey everybody. So my name's Soren, and I've worked
with a lot of different people, including Tony, Aaron
and myself from the University of Vermont,
Chris Woodall, Jennifer Pontius,
I'd like to say thank you very much to all of them
for supporting me in this kind of long effort.
Today I'm gonna talk to you about
decadal changes in northeastern adaptability
and vulnerability to climate, insects, and disease.
I'm gonna go through a lot today,
and try to go through it pretty quickly.
If you guys see any kind of patterns
sort of culturally that I don't talk about,
or want to talk shop about the methodology,
love to meet with you after, but let's get to it.
So I'm sure everyone's seen a lot of these slides
already today, but the increased drought frequencies
occurring in the northeast, increased drought
kind of intensity, but also the recurrence.
We're also seeing increases in temperature
and increases in rainfall.
So these kind of culminate in a lot
of kind of changes that we are going to be undergoing.
There's also been fundamental changes in stand dynamics,
such as increased relative densities,
and also kind of exposure.
If you can look up in the northeast,
we can see a lot of pink and red up there,
right hand figure exposure to insects and disease.
And so this culminated in looking
at some driving questions for this research.
Have changes in forest structure and composition resulted
in increased vulnerability to climate, insects and disease?
Are these changes consistent both overstory and understory?
And are there any specific regions
in which the changes occur that are not occurring?
So are there any areas specifically
where there's more adaptability, less adaptability,
more vulnerability, less vulnerability?
So this is a sub-analysis
of true meta-analysis looking at the entire region
for all states bordering and east of the Mississippi.
Of course, in this blue area
where we're gonna be focusing on,
the area has things like spruce fir, and hemlock-hardwoods,
maple-beech-birch, oak-pines, pitch pines.
And you can really see that there's a lot
of variability in this region,
which is one of the reasons why I love working up here.
So this is really based on three scoring systems.
vulnerability to climate,
vulnerability to insects and disease, and adaptability.
So vulnerability to climate.
And all of these scores take into consideration traits.
So in kind of broader shifts towards ecology,
we're looking at, instead of just species-level analysis,
we're linking trait-level analysis.
And so these scores, in particular, take into consider
all of these different traits that allow for certain species
to either thrive, reproduce, and be productive in a region.
So MODFACs being the fundamental drivers
of modification factors for the Iverson tree analysts.
And then Potter's work doing a bunch of other
kind of scales where they took into consideration
climate vulnerability, insect vulnerability and to compare
them to these traits, to predict whether or not
scaling all of tree species
in the relative exposure rank to these threats.
It goes through a lot of different processes
for the full scalarization, but you end up with
a species rank at the end where more species
with some scales are more vulnerable than others.
So you're thinking red maple, highly adaptable,
not very vulnerable,
you're thinking highly vulnerable species, like chestnut.
individual species scores is what really comes out.
So these species scores end up
kind of looking through these distributions
and to kind of give it a understanding
of where these scores for certain species
in the northeast land, you have red maple,
yellow birch, white ash, and eastern hemlock.
The adaptability MODFACs score for the Iverson models
do kind of trend to be pretty harsh
on the northeastern species,
but we also took into consideration the other scores'
capture framework, which looks specifically
into the insects and disease.
And you can see most species are not that
actually vulnerable, but you do have those outliers
that pick up northeastern species quite well
as far as vulnerability. Capture framework also for climate
which takes into consideration pretty much
all species that you would find in the United States.
And as you can see, each score
fundamentally deals with species differently.
But the amalgamation of all scores will hopefully result
in a kind of holistic approach
to look at general vulnerability
and adaptability for the region.
So the basic structure of this analysis
went through FIA data,
where we selected and filtered a plot comparison.
So it's a true one time period, next time period
we also maximize time.
So they were hoping to pick up on that change.
As you know, force change turned slowly,
so we're trying to maximize time
to pick up any changes that actually occur.
You get out the data and you apply the scoring frameworks
to each individual species to get out some type
of vulnerability, and you break it into two time series.
So time one being 1998 to 2012,
2013 to 2021 for the two time periods.
This kind of gets around some
of the temporal inconsistencies fundamental in FIA.
This methodology has been employed before,
and works quite well.
In the end, there's 25,000 plots for the northeast,
with a meantime difference of 15 years,
and a relatively low error score.
The error score's coming from
how well do those scores actually apply to all
of the species that you find actually in the northeast?
So it's very low.
We had a very good actually application of all the scores.
So did things actually change?
Yes, significance did pass value tests.
So we we're able to figure out
that the overall distributions are shifting.
So let's look at some things.
Blue indicates on this side is where you're losing species
that are not as vulnerable and are more adaptable, right?
Red, in this case,
is where you get a lot of vulnerability shifts.
So those species that either underlying species
within plots are shifting to more vulnerable species.
I also incorporated for kind of ease of looking
at these distributions, quantile or percentile lines.
And we can see that the extremes are really
where you're seeing differences.
So the changes are the most vulnerable, or at least
in this case it's the least vulnerable plots
seem to be shifting the most, and also the most
vulnerable lots seem to be shifting the most.
Not a really good start for that, but in general,
we can say that the distributions, there has been a change
from less vulnerability to more vulnerability.
So let's look at some map space
on each of these kind of distributions.
So this is vulnerability to climate, to capture score,
and we're gonna focus in kind of on
you can see this general spatial region,
where you have high vulnerability to climate.
The northeast Adirondacks here
do have low vulnerability climate.
This is also for tree species.
This is not for seedlings, trees is the overstory.
Moving on to the vulnerability of insects and disease.
We can kind of see how there's this
kind of red values that I see in Eastern Maine
and the Acadian transition.
And then northeastern Maine again kind of pops out
as a very vulnerable area,
and as well as the central coast area seems to be
kind of more adaptable.
So now that was, those previous maps were to really look
at like where things are already,
this is the change that is occurring.
So this is time one compared to time two.
So what is actually on the landscape,
based on species and size classes?
How are things shifting?
So I'd like to draw your attention
to the Northeastern street hardwoods areas
for both overstory and understory.
Understory was considered significant in its change,
P value in the bottom left hand corner,
as well as the distribution change.
The one thing is that there does seem,
it is quite sporadic in the Hudson River Valley areas,
where they saw a large increase in less vulnerable species,
as well as increases in more vulnerable species.
So there seems to be some kind of complexity going on there.
The largest changes in understory seems to be down there.
So I would always also like to preface here
that this is designed,
the methodology is designed for a large-scale analysis.
I chose to kind of narrow in on the Northeast
just because of kind of the designations of (indistinct)
and seeding this kind of occurs, so this demonstration.
So changes in vulnerability to climate.
Acadian transition, again kind of popping up
as areas of increased vulnerability.
Northern Adirondacks and seedlings seem to be
another place where the seedlings or understory
seems to be coming actually less vulnerable to climate.
Likely due to sporadic increases in understory diversity,
there's a lot of fundamental civics
that are driving these patterns, and it's not,
this analysis is not really part of that level.
So increase in vulnerability
to insects and disease is quite interesting.
Where we found that the, again,
the central Acadian transition
seems to becoming more vulnerable,
whether or not that's being driven by an increase in hemlock
and other kind of beech, other species that are vulnerable
to insects and disease wasn't really fully ascertained.
Again for the, just for the understory,
we're able to see that that pattern was held true.
And again, the Northwestern Adirondack seemed to come out
as a place that the seedlings were becoming less vulnerable
to insects and disease.
Large-scale average changes in the overstory, really,
was what this was able to pick up,
which was quite nice to see.
Again, the P value, upper left hand corner.
So that all kind of was fed
into a cluster analysis higher cluster
with like spatial constraints.
Don't wanna have to talk shop,
but it came out with six regions of similar change,
and that found that there were the eastern Adirondacks,
the Adirondacks Catskills,
the Green mountains and Whites, and then
the southern like Acadian transition where we saw
a lot of those changes occurring.
And then this kind of Northern Adirondacks region here.
And I'm gonna focus in on these two, as these two were
the ones that had like the biggest changes occurring.
So these are broad biological patterns that seem close
to what we might expect,
based on the species that occur there.
And it'd require minimal spatial constraints
to actually get to these.
So the fundamental, what patterns seem to be held and true.
So it went from, the worst off in the region seemed to be
that kind of central Acadian transition, with significance
both in overstory and understory between all scores,
and the change for the regions that seemed
to have like the best output seemed to be the understory
specifically within the north and Adirondacks, again,
significance for all variables in overstory and understory.
So what species might be driving this?
I decided to look at the Adirondacks.
This is a cumulative frequency distribution plot,
but basically just means that the species that end up
the x axis right here is importance value.
As it changes, it means it's relatively the most
important species within the region.
So if you see any kind of movement here,
it's gonna be more proportionally influential
on the score change.
And as you can see, some dominant species
are holding an increasing dominance.
So we're looking at balsam, fir, and red maple, again,
red maple being highly resilient and very adaptable.
Probably any change there can kind of mute those other
scores so that it actually...
-(audience member coughing) -...More adaptable,
and balsam fir being somewhat susceptible
to climate and insect disease.
That large shift can be maybe driving
some of these patterns.
There's also changes in species occurring
in centers of the distribution.
So changes between Tsuga canadensis, eastern hemlock,
and yellow birch.
And there's also changes occurring
throughout the distribution.
So some shade-tolerant species are increasing
such as moose maple, striped maple.
So it's quite interesting to see what species
might be driving these changes.
So in conclusion, from the slides
that I kind of just threw at you,
(audience laughing)
there have been changes in the understory
and overstory based on adaptability and the vulnerability
to climate, insects, and disease.
It has occurred in the last 10 years.
And let me go through, I thought that...
(audience laughing)
Yeah, yeah.
The spatial patterns, however, differ under each score.
So if you're interested in insects and disease,
you might be beneficial to only use one score,
or if you're interested in climate and you will find
that the Iverson models kind of fit what you see
on the ground,
that might be the scoring system you might want to use.
The greatest observable change, as you might assume,
and it's not in the overstory, it's in the understory,
it's the regeneration that seems to be driving
a lot of these patterns.
And then considering all of the variables and the kind
of meta-analysis, the Southern Acadia transition
and the Acadian boreal transition were the most vulnerable,
or saw the most change of overstory and understory
towards more vulnerable species.
More dominance, meaning like larger amounts of basal area.
And species driving, these trends appear to be driven
by certain species becoming more dominant,
like I just said, as well as kind of less
vulnerable species replacing other more vulnerable ones.
Questions?
(audience applauding)
[Audience #1] Sorry if I missed this.
On the scoring aspect, especially for insects and pests,
is that based on whether there's a known insect
or pest pathogen that infects,
and then how do we take that information
for future introductions of future pests and pathogens?
Or is just that scoring system can't adapt to that?
[Soren] So that scoring system in particular
takes into consideration exposure to known pests
and pathogens, and does not take into consideration
unknown pests and pathogens,
but it does take into consideration the traits
that would allow them to be adaptable.
So in some indirect way,
that scoring system does account for it, but not directly.
[Audience #1] Yeah, I guess so is American chestnut
on the right side because it has
a really well-known custom pathogen,
or because it also exhibits some set traits?
[Soren] Primarily, the score is gonna be based
on known custom pathogens.
Yes.
Good question.
And if you saw any kind of spatial patterns
that I didn't pick up as species,
-please, -(audience laughing)
I love talking about this stuff. So...
[Audience #2] I have a really basic, basic question.
[Soren] Yeah?
[Audience #2] So I might have missed it.
What was the timeframe of the study?
Like what were the earliest inventories
that you used, and the latest?
[Soren] So 1998 was the earliest, the latest was 2021.
-Okay... -So FIA is spatially fixed,
but temporally varied.
So we had, you have to come up
with like two time samples to really kind of consider
all of that spatial variability,
which has been used before in other studies.
[Audience #2] So then I guess my really basic question,
at the fear of sounding like an idiot,
(Audience #2 and Soren laughing)
is that enough time for us to have seen any change
that has already been driven by these stress agents?
So, you know, I would expect that if the assumption is
that they're already undergoing climate stress,
and they're already undergoing insect and pathogen stress,
wouldn't you expect the more adaptable species
to be taking over? And why don't we see that?
[Soren] I think that has to do with kind of interactions
between time, if it's too short of a time window,
you might be able to pick up on change but adaptation
I don't know if 15 year mean is really long enough to see
the forest really shift that kind of-
-Right. -future forest type.
[Audience #2] So is it mostly just successional patterns
and language history that's still driving
what you're seeing?
[Soren] It could be.
So this study who doesn't really drive
towards that kind of correlation or causation,
this is really kind of more just, like,
this is where the areas that are
seem to be having more vulnerable species
and less vulnerable people species.
Yeah, what's actually driving this is far more complex,
as any kind of land use manager probably knows,
like, there's a lot of really moving parts
that are driving these patterns.
But yeah, this does not answer that question.
[Audience #2] Okay.
-How well are we doing? -(audience laughing)
Awesome. And Andy, these are my works and works cited.
Yeah. Do you have any other questions?
(audience applauding)