Lukas has really done all the hard work for this project.
I'm here just to provide context,
because this project was part
of a larger effort that was funded
through the McIntire-Stennis program led by Tony D'Amato.
And it was really all about understanding
how we can engage with landowners
to actually implement adaptive management strategies
considering climate change.
And so there were a couple of different
big objectives to this project.
And one was really just identifying
what are some of the opportunities and barriers
to implement adaptive management strategies.
The second objective was actually testing
some of these different adaptive management
silvicultural techniques in the field.
And then where we came in was trying
to figure out if we could develop
some spatial maps to quantify climate exposure.
So if we know that adaptive management is helpful,
are there some places where it might
be more necessary than others,
and can we use that to sort of prioritize our efforts.
So I do just want to clarify
what I mean by climate exposure.
So when we think about climate change impacts on forests,
it really is a combination of two things.
One is how much climate,
environmental conditions have changed.
So that's the exposure piece.
And then the other piece is how
sensitive various species are.
We did not get into that.
Other people are doing that work and doing very well.
We really just wanted to look for our region
at as fine a scale as we could get,
can we quantify the relative exposure
that's projected under various climate scenarios.
And so when we did that, we wanted to think
about data layers that would get us information
both on direct exposure, so changes in temperature,
precipitation, extreme events, but we also want
to incorporate any kinds of secondary exposures
that might come from these sort of indirect climate impacts,
the changes in the patterns or frequency
or severity of pests and pathogens,
different com competition dynamics within the forest,
and then also changes in disturbance patterns.
So our very first effort, way back when,
was just trying to identify
the data layers that are out there.
Because again, this is meant to be a spatially explicit map
that can guide where management
actions might be most helpful.
And so that's where this little figure comes in.
So I know there's a lot of text here.
Let's just focus on what we wanted to get at to quantify
this overall climate exposure.
Well, the first one was just climate changieness.
That's the official term I'm gonna give it,
climate changieness.
Can we just quantify how much the climate conditions
are going to vary from what our forested ecosystems
have an adaptive memory of, right,
what have they been adapted to tolerate,
and how far are we outside of that adaptive memory?
So we wanted to generate our own layer
for climate changieness.
There are other layers that already exist.
So the FEMC Forest Health Atlas has a great
archive of disturbance patterns.
So we can see where across the landscape
disturbance is much more frequent,
and think about that as maybe identifying hotspots
that might see additional sort of secondary
impacts of climate change.
The Climate Change Tree Atlas has also done amazing work
in looking at suitable habitat for various tree species
and how they may shift under various climate projections.
And so we wanted to incorporate that as well.
And then we also wanted to incorporate
some finer scale basal area coverage maps.
Like you can't have a species that's exposed
if it doesn't exist in that location.
So we wanted to incorporate that and somehow aggregate
it all into this one metric of climate exposure.
So I'm gonna just really quickly go through
some of these input layers to give you an idea,
and I'll start with climate changieness,
because that's the one that we actually developed ourselves.
We decided to go with TerraClimate data,
primarily because it has both historical
norms and projections for not only
sort of the standard climate metrics,
but also for what I think of as more ecologically relevant
climate metrics that might actually impact
tree function and competition.
So evapotranspiration, soil moisture,
snow water equivalent, so thinking about snow pack.
And so we really were able to maybe do what some
other climate modelers in terms of impacts
to ecosystems have not been able to do.
And so again, we had that at three
different sort of timestamps, just,
estimated a low and high emission scenario
based on global mean, changes in global mean temperature.
But basically we have the historical norms,
a low emissions, and a high emissions scenario.
And so again, we're interested in changieness,
so it's really simple arithmetic.
Take those historical norms for all 92
of these unique climate variables
that were in this TerraClimate dataset,
and then subtract from that what the projections are,
so we can just get an idea of how much this is changing.
And again, this is spatially explicit.
So we have this for pixels across the region.
But then we ended up with these climate deviation
raster layers, 92 of them.
That is way too much data overload,
likely auto-correlated with each other
across months and different metrics.
So we pooled these into a principle component analysis
to basically try to maintain as much of the information
about that climate changieness as as we could,
but smash that down into one metric.
And so we did end up with this one PCA layer
that ended up accounting for about 50%
of the total variability in that climate changieness.
And so we just wanted to go with that.
It does turn out that what's interesting
is that the variables that were most,
provided the most information to this first
principal component were not your traditional
mean temperature, max temperature,
it really was the evapotranspiration
and soil water equivalent, and then that potential
evapotranspiration and snow water equivalent.
So really interesting, kind of even just looking
at which are sort of the climate variables
that are gonna be changing the most.
And it turns out that it is the shoulder seasons
and these sort of more derived climate variables
that are showing up as changing
the most in these projections.
So we end up with this one data layer.
Again, it's unitless.
It's like this metric that is just
a principal component metrics, but it quantifies how much
those climate variables are changing
from those historical norms overall, right, in aggregate.
And it is at a four kilometer spatial scale.
So each of these different data layers
did have some scale limitations in terms of working
to aggregate those in the final model.
And then we, again, back to the abundance,
this was one that was already existing.
David Gudex-Cross used sort of some new kind of forced
hyperspectral techniques with lancet imagery and time series
data to develop these percent basal area species maps.
Where that data was missing, we backfilled
with FIA abundance, but the idea is that really 14 species
were the ones that we felt we had
accurate basal area data for.
So for 14 species we have the percent basal area
at a 30 meter resolution across the region.
So that was sort of are they there,
are they gonna be exposed.
Similarly, that disturbance layer
coming from the FEMC Forest Health Atlas
is an aggregate of the number of times
that any disturbance has been mapped
using those aerial detection surveys,
systems run by the state, and the type of disturbance
is listed as a part of that, of those polygons.
And so we divided this into sort of three
different disturbance scenarios.
One is no disturbance, let's just look at exposure
if we didn't include disturbance at all.
The other was limited just to what we'd call
climate related disturbances, so wind disturbance, flooding,
drought, frost, any disturbances that had been
sort of directly linked to a climate event.
And then we also ran a model with all of those disturbances.
So everything that had been recorded,
regardless of the causal agent there.
And then the last raster layer
that we brought in for this aggregate exposure model
does come from the Climate Change Tree Atlas,
and we used their more recently published shift map.
So they're projecting how suitable habitat
for each species is projected to change,
and again, they had low and high emission scenarios.
And so again, we took that, which, really,
the metric is relative importance value for each species.
That's sort of their way of quantifying
how suitable the habitat might be.
And we use that to see, again, how it had changed
from the current relative importance value.
So again, it's that change in suitable habitat.
And they do use climate variables
to create these shift models.
The reason why we decided to keep this in here,
even though we already had a separate climate changieness,
climate changieness raster, is because the metrics
that they use are actually not the metrics
that ended up being significant in our PCA.
So this really is just just based on temperature
and precipitation norms and minimum and maximum.
And so this is really a different climate,
set of climate information than what we have
in our climate changieness raster.
So that, you know, those are the layers.
Now how do you pull them all together?
And that's where Lukas came in.
We said, Lukas, you've got great JS skills,
how are we gonna pull this all together?
So, as you can see we standardized
all of these from zero to 100,
with the exception of PCSH, our projected change
in suitable habitat, that we had to allow for areas
of climate advances for species like oak,
that seem to be doing better with higher emissions.
So at the end, we came with six different scenarios
with species representing different iterations
of low and high emissions
with the three disturbance scenarios.
So you're looking at just a diagram
of how different things are run.
For this it's exclusively for sugar maple.
And you can see that there's six different scenarios,
but it was also run for 13 different species.
So there are 84 different layers representing
different low emissions and high emissions scenarios,
low emissions representing the plus two degrees celsius
scenario for climate and the GCM 4.5 with PCSH,
and then a plus four emissions scenario
for the CLIM and the GCM 8.5.
From there you can see, this is an example of sugar maple,
and it's different spreads for all those scenarios.
You may be looking at this
and say are these all the same picture?
-(audience laughs) -The answer is no.
It just happens to be that with sugar maple
there was very limited variation.
And we'll talk about that in the next slide.
But the, it seems to be that sugar maple
is already pretty exposed and it's hit an exposure ceiling,
I guess is a way of putting it.
As you can see it's the second highest of all exposure,
but there's no significance difference.
And if you can't really see, I guess it's a little
too grainy on the projection screen,
but you can read that areas such as Northern Vermont
and New Hampshire are seeming to be refugia
in a lot of these scenarios,
whereas areas like Northern, or the Susquehanna Valley
and the Hudson are all looking
like they are gonna have a hard time.
As I mentioned before, this is not the case,
if you can look at sugar maple down here,
its variance is very different
compared to a lot of these species.
And we've really boiled this down to three different trends.
We have what we call our climate winners,
our climate losers, and then climate limbo,
where it's just kind of like on a platform right now,
not looking to do too much.
So in these trends you kind of see
some systems based approaches too.
You see montane species, such as red spruce and balsam fir,
that seem to be increasing quite a bit in exposure
with a high emissions scenario,
whereas species like the oaks and black birch
tend to be making advances and decreasing in exposure.
From white pine and sugar maple,
as I said, not much is happening,
and it'd be interesting to see where that goes.
Beyond that, I guess I'll touch on this.
You see all the variation here.
There's, every species had its own
different way of changing exposure.
So we were very curious to see how this would change
if we just combined all the species
for each of those six scenarios.
And we wanted to see how these things
would do as a weighted average
with the weight being the species abundance,
so we are only accounting for the percent basal area
for that specific species at that time.
So there's six different scenarios represented by this.
There's no need to like analyze that, it's just an example.
But we have a low emissions scenario,
all disturbance included here.
As you can see there's some spatial trends unfolding.
A lot of these areas, such as the Berkshires
and the Eastern Adirondacks seem to be increasing exposure,
whereas coastal Massachusetts and Southern New York
and Northern Maine are areas of lower exposure.
But when emissions increase, it seems that a lot
of this exposure, a lot of this lower end exposure,
is pushed upwards, as you can see
in the histogram in the upper section.
And a lot of mountainous regions seem to be experiencing
a lot more exposure, and coastal areas of New England
and Southern New York tend to be making advances.
I draw your attention to thoughts
of what species grow where.
As you saw in that graph, spruce fir and sugar maple
tend to dominate these areas of the warmer colors,
and the cooler colors are dominated by birches and oaks.
So if you think about it, the kind of winners occupy areas
where climate seems to be better off.
Beyond that, we wanted to look at things
from a regional perspective, and we did that breaking things
into ecoregions with the L three designation.
I'd like to point out up in the upper right
we have area designations for the amount of cells
that were using the study, and that represents
about 70% of the cells, those first two.
And that represents areas
of climate loss with increasing scenarios.
Whereas a lot of these smaller areas in the south
seem to be making advances, which is probably an artifact
of the fact that there are a lot
of oaks and birches in these ecoregions.
So we came to some conclusions.
It's pretty easy to say that there will be impacts
to conventional forest functionality
when the emissions increase.
And that is mainly driven, as Jen had pointed out,
in the fringes of the winter, so shoulder season,
and the projected changes in suitable habitat.
With our species, as we mentioned, there are ways
to think about how things are changing.
You can think about it from a species basis
with the winners, losers and the limbo.
Or you can think about it from a regional perspective,
where northeastern highlands
and the Acadian plains and hills
are set to have a harder time or face more exposure.
But you can also think about it in a synthesis of ways,
where you have these different ecosystems,
like the mixed hardwood, that tends to occupy
the southern sections of the region.
But then you have spruce-fir systems that seem
to really be facing higher exposure.
And you have northern hardwood systems that are not
doing too well, but they're not as bad as spruce and fir.
The next steps for this project,
one of the working sessions, or one of the sessions earlier
this day talked about ground truthing.
And exposure model validation is a huge part of this.
This is just a a model, this is not truth,
this is just suggestions and projections.
So we need to go down to continual forest inventory areas
where we may expect areas to be higher exposed
or lower exposed, and see what is on the ground right now.
Past that, this is a lot of data,
and you can draw a lot of conclusions
from it just on first glance.
But there are appropriate ways to apply this data,
and there are inappropriate ways to apply this data.
It is not an opportunity map.
There's only 14 species,
there's more than 14 species in this region.
For example, you don't really think
of Southern Massachusetts without tulip poplar
or other dominant species like bear oak
or scrub oak and pitch pine.
So you can't really consider this opportunity mapping
because you're not watching all the species,
to the whole species like palate
I guess is a way of putting it.
Beyond that, we want to engage with stakeholders
and wonder how this data is useful.
Though we have it at a 100 meter scale at the final,
we have to ask questions
of at what scale this is appropriate.
This is probably not a landowner based tool,
this is more of a regional based tool.
And then we want to find ways of leveraging
it to help with climate restoration.
There may be areas of refugia that we can target
for good adaptive management, or there might be areas
that are looking like they're highly exposed,
and we may have to go towards the adaptive silviculture
of climate change route and enrich these areas
with species that will fare better.
Lastly, a step that we have started to take
steps with is data access and application support.
Right now we have a site up on the FEMC website
that outlays with JPEG maps and raster layers
the low and high emission scenarios
for all disturbance as well as all the base layers.
More to come, there will be more added.
You don't have to copy that link if you don't want.
We have a QR code if you want to scan it with your phone.
Yeah, any questions?
A couple minutes for questions.
(audience applauds)
[Audience Member] I'll try to speak through the mask.
Seeing the seedling seed supply presentation earlier today,
and personally having seen many tree planting,
tree installation efforts fail to the tune of thousands
of trees die in a singular planting location,
what does it mean, or let me ask you to comment
on the shoulder season changing drastically,
it says a lot to me, because it doesn't, is that not
a very vulnerable time for like newly germinated
acorns and seedlngs of all species?
Yeah, absolutely, so I was a field tech
for the Adaptive Silviculture for Climate Change effort.
And my first, I think it was late in May or something,
I was working for Jess Whitepost, she's in here somewhere.
And there's a late frost event,
and it destroyed a lot of the first leaf out.
And the fact that it's getting warmer earlier
but it's also up and down, it's a lot of extremes.
Just have to kind of fight through,
and hope that some of them survive.
I guess that's my, I just graduated undergrad,
I don't really know I'm talking about.
(audience laughs)
But I'd say you just have to keep at it,
and there's really no easy way to go about it.
Maybe tubes will help by keeping the local area warmer,
but that's not my area of expertise.
But, but that is why I think having all of the data
up there is important because maybe
sort of this ecosystem aggregate of the 14 species
that we have is not useful, if you're thinking
about planting chestnut, oak or something
that we haven't included in this model,
but the input raster for climate changieness,
this might be of use to you.
So if you want to see where might we have a chance
of not losing those tens of thousands of seedlings
that we plant, maybe look at this map,
and then try to identify some places
where it's not that changey.
Thanks for your question.
I think that's all our time for--
-Good. -(audience laughs)