Hi everyone. Thanks for being here.
I'm a postdoc here at UVM in Tony D'Amato's lab,
which is well represented at this conference.
And I've also been working closely with Chris Woodall,
long-term FIA researcher at the Forest Service.
And this is some ongoing work
that we've been working on and off on for most of the year.
And we're trying to get into wrap up mode
where we're taking a look at tree regeneration patterns
across the Midwest and northeastern US
and looking at what they imply
for future changes in carbon stocks.
So these days there's really widespread concern
about tree regeneration patterns,
really at a national scale,
both concerns about low abundance
and also undesirable species composition.
There's a lot of work coming out of the Western US
on, you know, regeneration failure
and will forests be able to, you know,
come back at all following disturbances?
There's also been some work at regional scales
in the Eastern US talking more
about undesirable species composition
and the tree regeneration layer
and how that could have bad outcomes down the road.
And to the extent that we are facing problems
with inadequate tree regeneration,
this might have implications
for carbon dynamics down the road.
And that's what we're investigating in this work
using FIA data.
So if we're trying to relate tree regeneration
to forest carbon stocks,
Chris came up with this idea of carbon replacement
during a brainstorming session and it stuck so far.
So if we're thinking about, you know, forest resilience,
that's often defined as, you know, the amount of change
or perturbation the system can withstand
and still bounce back to its previous state.
So if we're bringing that into, you know,
kind of a carbon perspective,
you imagine a given forest stand
that experiences a disturbance
and carbon stocks are reduced in that stand,
well then tree regeneration
will have really an important shaping influence
on carbon reaccumulation in that stand, how it recovers.
And there's a few different scenarios that are possible.
You know, down the road
you could have sort of a carbon replacement scenario
where you end up with about the same amount of carbon
as you had before,
or maybe you get a shift to a higher carbon state,
in which case, you know,
maybe there's opportunity to augment carbon stocks,
of course, to the extent
that we're not compromising all the other things
that we value our forests for here.
And then maybe you get a shift to a lower carbon state,
in which case you might wanna think
about targeting these stands to manage them
for improved tree regeneration.
So this is really the concept
that we're exploring in this work,
and the data set that we're using
is FIA's regeneration indicator.
This is a set of detailed tree regeneration protocols
that FIA began implementing back in 2012.
Plots are remeasured on five to seven year cycles.
So really plot remeasurement data
is just starting to come in, in numbers here.
And basically the idea is, you know, it's simple.
Basically seedlings are tallied out
by six different height classes,
instead of the traditional single height class
we used by FIA.
So it really provides a wealth of information
on the structure of tree regeneration.
And what we've been working on
is ways to effectively use these data
to make better predictions
about what direction our forest might be headed in
based on the regeneration layer.
And one approach that we've taken
is taking these seedling abundances
in different height classes
and developing statistical models
to predict sapling recruitment
on a plot that's remeasured five to seven years later.
The idea here is that it, you know,
it hardly matters how many seedlings you have
in a given stand.
What matters is, you know,
whether that eventually gets recruited
to larger size classes.
And so we're trying to, you know,
bridge the gap a little bit here
by translating seedling abundance
into later sapling recruitment.
This is some work that was published late last year.
I'm showing it here
because we're piggybacking on that in our current work.
And you know, thinking back to carbon,
if you're thinking about, you know,
predicting carbon trajectories,
our minds often jump to, you know,
process-based models, especially some
of these stand-based models like FVS that are widely used
to model changes in carbon stocks over time.
But it's worth noting that often in these models
regeneration is either represented very coarsely,
or it needs to be specified manually.
You know, case in point here is FVS where, you know,
across most of the country, including here,
you have to specify regeneration by seed manually.
And as a consequence,
regeneration is really a major uncertainty
when you're trying to predict long-term carbon trajectories.
And so there's really a lot of space
for empirical work here, I think,
looking to link regeneration to carbon stocks down the road.
And when you try to dive into the empirical work,
there's surprisingly little work
on how tree species composition
actually influences carbon stocks and sequestration rates.
And then less work still on what the species composition
of the regeneration layer implies
for future forest carbon stocks.
And a notable exception here is this work
that Chris was a co-author on.
This was published about a year ago.
They're using FIA data, trying to divide plots
into different forest communities
and looking at the potential carbon implications
of seedling patterns.
But we think there's a lot more room
for empirical work in this space.
And I'll show a different way of approaching the same issue.
So our overall goal here
was to explore this concept of carbon replacement
using the regeneration indicator
and hopefully get a picture
of what seedling pattern suggests
about future forest carbon dynamics across the northern US,
and then drilling down into what conditions
seedlings suggest greater versus lesser carbon stocks.
So our approach, a little bit complicated,
but I mentioned we're piggybacking on this method
we had developed to predict sapling recruitment
from seedling abundance by size class.
That, we use that
to generate sort of species composition scores by plot
that represents the probability of sapling recruitment.
We also generated composition scores
for live tree carbon by species
and then we fed that all into a big ordination
to represent species composition.
We then feed that into statistical models
that predict either live above ground tree carbon
and we also try to predict
what we're calling total tree carbon,
which is above ground live trees, to any dead trees,
and also downed woody material.
One of the advantages
of working with the regeneration indicator
is that these same plots,
they actually run transects
to quantify downed woody material.
Unlike the majority of FIA plots
where it's not actually measured.
We use model values.
Once we have these models,
we predicted carbon stocks using tree composition
and seedling composition
and compared the two over a sequence of stand ages
to essentially see what seedling composition implies
for future forest carbon stocks.
And if the two values were pretty close,
we call it a replacement.
If seedlings implied greater carbon stocks,
we call it a carbon gain.
If trees implied greater carbon stocks,
we call it a carbon loss.
So the models that we developed of carbon stocks,
unsurprisingly, stand age was the most important influence
on carbon stocks.
But we found that species composition
was also pretty important,
as indicated by the ordination axis scores.
And interestingly,
our models of live above ground tree carbon
and total above ground tree carbon
were highly similar in terms of variable importance
and also relationships
between independent variables and the response.
But the live carbon model was simply more accurate.
And, you know, this echoes previous work, you know,
downed woody material is important to carbon storage,
but it's really, really hard to predict at the plot level
unless you actually measure it.
Chris has done some work on this.
So I'll show results based on the live carbon model,
but just know the two are highly similar to each other.
If you look at the effect
of species composition on carbon stocks,
you get about the results that you'd expect.
Some of our, you know, late succession,
more shade tolerant, especially hardwoods,
are associated with higher carbon storage.
There are four axes in this ordination,
that the axes 3 and 4
basically show that if you have more oaks
in your your stand,
you tend to have greater carbon stocks.
Makes sense as well.
And here's the interesting part.
So if you compare carbon predictions
in seedlings versus trees,
you find quite a bit of difference
and also quite a bit of variability.
So in this map,
blues mean that seedlings apply greater carbon stocks
or carbon gains.
Reds suggest carbon loss
based on tree regeneration patterns.
And see there's a lot of spatial heterogeneity
and also quite a bit of difference
between the regeneration layer and the tree layer subplots.
And when you start drilling down
and looking at, you know, terrain, physiography,
forest type, you find some interesting correspondences.
If you look into, you know, FIA forest group,
basically forest type,
carbon replacement outlook is worse
in maple/beech/birch forests
or northern hardwood forest as well as oak/hickory,
and its best in spruce/fir.
We also found that carbon loss was more likely
in upland environments and hillslopes.
Some more results here.
Plots standing to lose carbon based on seedling composition
tended to be on steeper slopes.
They tended to be farther south.
And they also importantly tended to be in stands
that currently have greater carbon stocks.
So when you combine that with our stand age results,
what it really suggests
is that the most productive environments
are also at the greatest risk of losing carbon storage
based on tree regeneration patterns.
So to try to tie this all together here, you know,
we did find kind of poor carbon replacement prospects
in northern hardwood stands and oak/hickory forests.
This really, you know, echoes, you know, decades of work
showing that, you know,
these forest types have widespread problems
with tree regeneration for various reasons
being, you know, for browsing,
changes to disturbance regimes, in case of oak/hickory.
And we found in our work that, you know,
unsurprisingly shifts away from sugar maple
and oak species were associated with reduced carbon stocks.
I also think it's worth noting and emphasizing here
that of course there are often trade-offs
between carbon stocks and sequestration rates
and all the other things that we value our forests for.
So you know, we're not trying to advocate
for a reductionist approach, you know,
maximize the carbon stocks.
We just think this is one interesting metric
to try to make better sense
out of the regeneration layering,
what it means for the future.
So to sum up,
we think this kind of carbon replacement concept
is a useful way of looking at tree regeneration patterns.
And, you know,
our work is really kind of exploratory in nature
at this point.
But if, you know,
we adopted this approach,
made it more rigorous in the future,
I think there's a lot of room
for improved regeneration assessments
to help guide kind of regional scale management.
We're really facing, you know, big picture questions
like, you know, where and how do we manage
for improved natural regeneration?
Where might we, you know, want or need to plant trees
and where does that have the greatest benefit?
And I think there's a lot of space for,
you know, these empirical approaches
based on data sets like the regeneration indicator
to help guide these, you know,
really big regional scale questions that we face.
And, you know, in the context of limited resources.
I also wanna note the framework that we used
is not limited to assessing carbon stocks.
You know, we examined carbon
because it's an increasing focus in forest management.
But you could use the same framework to look at, say,
you know, resilience to climate change at the stand level,
functional traits, or more.
So I think there's also opportunity
to examine things other than carbon
and maybe start to look into trade-offs
between carbon and other attributes.
So thanks everyone for listening
and happy to take questions if there's time.
Yeah.
[Audience Member] How did you consider canopy disturbances
in this model?
So it really, really doesn't consider canopy disturbance.
I mean it's basically an advanced regeneration study.
I mean, we removed plots that were recently harvested
within the past five years from the analysis.
So, you know, limitations apply.
You know, obviously, when you, yeah,
when you open up the overstory you can get, you know,
additional, you know, things seed in, you know,
advanced regeneration is important,
but it's not everything.
In the kind of sapling recruitment models that get fed in,
we did find some pretty clear relationships
between like basal area and sapling recruitment probability.
Basically you need to remove a certain amount
of the overstory to increase, you know,
the likelihood of recruitment.
So it's to some extent baked in.
I think in the future, you know,
more and more data are rolling in
from the regeneration indicator,
like I mentioned of the plot measurement data
just coming in.
And I would love to do an analysis
that's more focused on recently disturbed
or recently harvested stands to get at that,
because that is like when a lot of the really important,
you know, recruitment takes place.
[Audience Member] Ah, so were they mostly closed canopy
-or? -Yes.
-Okay. -Yeah. Yeah.
So it's all plot measurements
in the regeneration indicator,
which is one eighth of all FIA plots,
like about 7,500 remeasured plots.
Which sounds like a lot until you realize, you know,
it's such a huge region.
[Audience Member] Where did you take this picture?
Oh yeah, it's just a random picture.
This is Adirondacks near Rocky Peak Ridge.