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Hi everyone. Thanks for being here.

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I'm a postdoc here at UVM
in Tony D'Amato's lab,

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which is well represented
at this conference.

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And I've also been working
closely with Chris Woodall,

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long-term FIA researcher
at the Forest Service.

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And this is some ongoing work

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that we've been working on and
off on for most of the year.

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And we're trying to get into wrap up mode

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where we're taking a look at
tree regeneration patterns

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across the Midwest and northeastern US

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and looking at what they imply

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for future changes in carbon stocks.

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So these days there's
really widespread concern

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about tree regeneration patterns,

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really at a national scale,

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both concerns about low abundance

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and also undesirable species composition.

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There's a lot of work
coming out of the Western US

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on, you know, regeneration failure

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and will forests be able to, you know,

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come back at all following disturbances?

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There's also been some
work at regional scales

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in the Eastern US talking more

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about undesirable species composition

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and the tree regeneration layer

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and how that could have
bad outcomes down the road.

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And to the extent that
we are facing problems

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with inadequate tree regeneration,

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this might have implications

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for carbon dynamics down the road.

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And that's what we're
investigating in this work

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using FIA data.

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So if we're trying to
relate tree regeneration

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to forest carbon stocks,

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Chris came up with this
idea of carbon replacement

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during a brainstorming
session and it stuck so far.

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So if we're thinking about,
you know, forest resilience,

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that's often defined as, you
know, the amount of change

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or perturbation the system can withstand

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and still bounce back
to its previous state.

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So if we're bringing that into, you know,

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kind of a carbon perspective,

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you imagine a given forest stand

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that experiences a disturbance

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and carbon stocks are
reduced in that stand,

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well then tree regeneration

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will have really an
important shaping influence

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on carbon reaccumulation in
that stand, how it recovers.

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And there's a few different
scenarios that are possible.

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You know, down the road

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you could have sort of a
carbon replacement scenario

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where you end up with about
the same amount of carbon

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as you had before,

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or maybe you get a shift
to a higher carbon state,

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in which case, you know,

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maybe there's opportunity
to augment carbon stocks,

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of course, to the extent

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that we're not compromising
all the other things

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that we value our forests for here.

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And then maybe you get a
shift to a lower carbon state,

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in which case you might wanna think

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about targeting these
stands to manage them

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for improved tree regeneration.

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So this is really the concept

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that we're exploring in this work,

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and the data set that we're using

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is FIA's regeneration indicator.

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This is a set of detailed
tree regeneration protocols

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that FIA began implementing back in 2012.

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Plots are remeasured on
five to seven year cycles.

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So really plot remeasurement data

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is just starting to come
in, in numbers here.

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And basically the idea
is, you know, it's simple.

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Basically seedlings are tallied out

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by six different height classes,

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instead of the traditional
single height class

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we used by FIA.

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So it really provides
a wealth of information

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on the structure of tree regeneration.

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And what we've been working on

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is ways to effectively use these data

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to make better predictions

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about what direction our
forest might be headed in

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based on the regeneration layer.

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And one approach that we've taken

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is taking these seedling abundances

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in different height classes

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and developing statistical models

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to predict sapling recruitment

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on a plot that's remeasured
five to seven years later.

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The idea here is that it, you know,

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it hardly matters how
many seedlings you have

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in a given stand.

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What matters is, you know,

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whether that eventually gets recruited

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to larger size classes.

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And so we're trying to, you know,

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bridge the gap a little bit here

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by translating seedling abundance

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into later sapling recruitment.

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This is some work that was
published late last year.

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I'm showing it here

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because we're piggybacking
on that in our current work.

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And you know, thinking back to carbon,

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if you're thinking about, you know,

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predicting carbon trajectories,

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our minds often jump to, you know,

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process-based models, especially some

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of these stand-based models
like FVS that are widely used

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to model changes in
carbon stocks over time.

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But it's worth noting
that often in these models

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regeneration is either
represented very coarsely,

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or it needs to be specified manually.

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You know, case in point
here is FVS where, you know,

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across most of the
country, including here,

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you have to specify
regeneration by seed manually.

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And as a consequence,

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regeneration is really a major uncertainty

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when you're trying to predict
long-term carbon trajectories.

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And so there's really a lot of space

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for empirical work here, I think,

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looking to link regeneration
to carbon stocks down the road.

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And when you try to dive
into the empirical work,

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there's surprisingly little work

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on how tree species composition

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actually influences carbon
stocks and sequestration rates.

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And then less work still on
what the species composition

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of the regeneration layer implies

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for future forest carbon stocks.

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And a notable exception here is this work

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that Chris was a co-author on.

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This was published about a year ago.

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They're using FIA data,
trying to divide plots

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into different forest communities

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and looking at the potential
carbon implications

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of seedling patterns.

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But we think there's a lot more room

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for empirical work in this space.

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And I'll show a different way
of approaching the same issue.

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So our overall goal here

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was to explore this concept
of carbon replacement

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using the regeneration indicator

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and hopefully get a picture

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of what seedling pattern suggests

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about future forest carbon
dynamics across the northern US,

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and then drilling down
into what conditions

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seedlings suggest greater
versus lesser carbon stocks.

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So our approach, a little bit complicated,

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but I mentioned we're
piggybacking on this method

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we had developed to
predict sapling recruitment

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from seedling abundance by size class.

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That, we use that

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to generate sort of species
composition scores by plot

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that represents the probability
of sapling recruitment.

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We also generated composition scores

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for live tree carbon by species

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and then we fed that all
into a big ordination

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to represent species composition.

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We then feed that into statistical models

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that predict either live
above ground tree carbon

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and we also try to predict

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what we're calling total tree carbon,

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which is above ground live
trees, to any dead trees,

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and also downed woody material.

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One of the advantages

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of working with the regeneration indicator

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is that these same plots,

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they actually run transects

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to quantify downed woody material.

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Unlike the majority of FIA plots

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where it's not actually measured.

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We use model values.

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Once we have these models,

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we predicted carbon stocks
using tree composition

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and seedling composition

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and compared the two over
a sequence of stand ages

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to essentially see what
seedling composition implies

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for future forest carbon stocks.

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And if the two values were pretty close,

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we call it a replacement.

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If seedlings implied
greater carbon stocks,

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we call it a carbon gain.

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If trees implied greater carbon stocks,

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we call it a carbon loss.

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So the models that we
developed of carbon stocks,

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unsurprisingly, stand age was
the most important influence

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on carbon stocks.

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But we found that species composition

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was also pretty important,

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as indicated by the
ordination axis scores.

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And interestingly,

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our models of live
above ground tree carbon

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and total above ground tree carbon

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were highly similar in
terms of variable importance

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and also relationships

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between independent
variables and the response.

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But the live carbon model
was simply more accurate.

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And, you know, this echoes
previous work, you know,

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downed woody material is
important to carbon storage,

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but it's really, really hard
to predict at the plot level

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unless you actually measure it.

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Chris has done some work on this.

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So I'll show results based
on the live carbon model,

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but just know the two are
highly similar to each other.

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If you look at the effect

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of species composition on carbon stocks,

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you get about the results
that you'd expect.

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Some of our, you know, late succession,

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more shade tolerant, especially hardwoods,

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are associated with higher carbon storage.

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There are four axes in this ordination,

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that the axes 3 and 4

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basically show that if you have more oaks

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in your your stand,

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you tend to have greater carbon stocks.

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Makes sense as well.

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And here's the interesting part.

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So if you compare carbon predictions

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in seedlings versus trees,

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you find quite a bit of difference

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and also quite a bit of variability.

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So in this map,

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blues mean that seedlings
apply greater carbon stocks

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or carbon gains.

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Reds suggest carbon loss

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based on tree regeneration patterns.

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And see there's a lot
of spatial heterogeneity

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and also quite a bit of difference

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between the regeneration layer
and the tree layer subplots.

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And when you start drilling down

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and looking at, you know,
terrain, physiography,

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forest type, you find some
interesting correspondences.

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If you look into, you
know, FIA forest group,

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basically forest type,

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carbon replacement outlook is worse

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in maple/beech/birch forests

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or northern hardwood forest
as well as oak/hickory,

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and its best in spruce/fir.

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We also found that carbon
loss was more likely

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in upland environments and hillslopes.

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Some more results here.

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Plots standing to lose carbon
based on seedling composition

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tended to be on steeper slopes.

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They tended to be farther south.

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And they also importantly
tended to be in stands

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that currently have greater carbon stocks.

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So when you combine that
with our stand age results,

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what it really suggests

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is that the most productive environments

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are also at the greatest
risk of losing carbon storage

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based on tree regeneration patterns.

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So to try to tie this all
together here, you know,

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we did find kind of poor
carbon replacement prospects

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in northern hardwood stands
and oak/hickory forests.

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This really, you know, echoes,
you know, decades of work

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showing that, you know,

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these forest types have
widespread problems

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with tree regeneration for various reasons

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being, you know, for browsing,

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changes to disturbance regimes,
in case of oak/hickory.

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And we found in our work that, you know,

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unsurprisingly shifts
away from sugar maple

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and oak species were associated
with reduced carbon stocks.

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I also think it's worth
noting and emphasizing here

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00:11:48,030 --> 00:11:50,250
that of course there are often trade-offs

266
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between carbon stocks
and sequestration rates

267
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and all the other things that
we value our forests for.

268
00:11:54,990 --> 00:11:57,210
So you know, we're not trying to advocate

269
00:11:57,210 --> 00:11:59,670
for a reductionist approach, you know,

270
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maximize the carbon stocks.

271
00:12:01,140 --> 00:12:05,370
We just think this is
one interesting metric

272
00:12:05,370 --> 00:12:07,110
to try to make better sense

273
00:12:07,110 --> 00:12:08,790
out of the regeneration layering,

274
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what it means for the future.

275
00:12:13,710 --> 00:12:14,700
So to sum up,

276
00:12:14,700 --> 00:12:16,790
we think this kind of
carbon replacement concept

277
00:12:16,790 --> 00:12:20,433
is a useful way of looking at
tree regeneration patterns.

278
00:12:22,890 --> 00:12:23,723
And, you know,

279
00:12:23,723 --> 00:12:26,250
our work is really kind
of exploratory in nature

280
00:12:26,250 --> 00:12:27,083
at this point.

281
00:12:27,083 --> 00:12:29,100
But if, you know,

282
00:12:29,100 --> 00:12:30,240
we adopted this approach,

283
00:12:30,240 --> 00:12:31,710
made it more rigorous in the future,

284
00:12:31,710 --> 00:12:32,880
I think there's a lot of room

285
00:12:32,880 --> 00:12:35,160
for improved regeneration assessments

286
00:12:35,160 --> 00:12:37,650
to help guide kind of
regional scale management.

287
00:12:37,650 --> 00:12:40,650
We're really facing, you
know, big picture questions

288
00:12:40,650 --> 00:12:42,990
like, you know, where and how do we manage

289
00:12:42,990 --> 00:12:45,000
for improved natural regeneration?

290
00:12:45,000 --> 00:12:47,820
Where might we, you know,
want or need to plant trees

291
00:12:47,820 --> 00:12:49,650
and where does that have
the greatest benefit?

292
00:12:49,650 --> 00:12:51,990
And I think there's a lot of space for,

293
00:12:51,990 --> 00:12:54,840
you know, these empirical approaches

294
00:12:54,840 --> 00:12:57,420
based on data sets like
the regeneration indicator

295
00:12:57,420 --> 00:12:59,760
to help guide these, you know,

296
00:12:59,760 --> 00:13:02,943
really big regional scale
questions that we face.

297
00:13:05,387 --> 00:13:09,363
And, you know, in the
context of limited resources.

298
00:13:10,770 --> 00:13:14,080
I also wanna note the
framework that we used

299
00:13:15,300 --> 00:13:19,260
is not limited to assessing carbon stocks.

300
00:13:19,260 --> 00:13:21,090
You know, we examined carbon

301
00:13:21,090 --> 00:13:24,030
because it's an increasing
focus in forest management.

302
00:13:24,030 --> 00:13:26,610
But you could use the same
framework to look at, say,

303
00:13:26,610 --> 00:13:30,090
you know, resilience to climate
change at the stand level,

304
00:13:30,090 --> 00:13:31,800
functional traits, or more.

305
00:13:31,800 --> 00:13:33,330
So I think there's also opportunity

306
00:13:33,330 --> 00:13:35,010
to examine things other than carbon

307
00:13:35,010 --> 00:13:37,830
and maybe start to look into trade-offs

308
00:13:37,830 --> 00:13:39,993
between carbon and other attributes.

309
00:13:43,080 --> 00:13:44,670
So thanks everyone for listening

310
00:13:44,670 --> 00:13:46,870
and happy to take
questions if there's time.

311
00:13:56,190 --> 00:13:57,570
Yeah.

312
00:13:57,570 --> 00:14:00,450
[Audience Member] How did you
consider canopy disturbances

313
00:14:00,450 --> 00:14:01,653
in this model?

314
00:14:02,670 --> 00:14:07,053
So it really, really doesn't
consider canopy disturbance.

315
00:14:08,850 --> 00:14:11,610
I mean it's basically an
advanced regeneration study.

316
00:14:11,610 --> 00:14:15,960
I mean, we removed plots
that were recently harvested

317
00:14:15,960 --> 00:14:19,503
within the past five
years from the analysis.

318
00:14:21,660 --> 00:14:24,810
So, you know, limitations apply.

319
00:14:24,810 --> 00:14:25,920
You know, obviously, when you, yeah,

320
00:14:25,920 --> 00:14:29,100
when you open up the overstory
you can get, you know,

321
00:14:29,100 --> 00:14:32,400
additional, you know,
things seed in, you know,

322
00:14:32,400 --> 00:14:33,900
advanced regeneration is important,

323
00:14:33,900 --> 00:14:35,163
but it's not everything.

324
00:14:36,390 --> 00:14:41,160
In the kind of sapling recruitment
models that get fed in,

325
00:14:41,160 --> 00:14:43,950
we did find some pretty
clear relationships

326
00:14:43,950 --> 00:14:47,850
between like basal area and
sapling recruitment probability.

327
00:14:47,850 --> 00:14:49,980
Basically you need to
remove a certain amount

328
00:14:49,980 --> 00:14:51,840
of the overstory to increase, you know,

329
00:14:51,840 --> 00:14:53,340
the likelihood of recruitment.

330
00:14:53,340 --> 00:14:55,650
So it's to some extent baked in.

331
00:14:55,650 --> 00:14:57,060
I think in the future, you know,

332
00:14:57,060 --> 00:14:59,370
more and more data are rolling in

333
00:14:59,370 --> 00:15:01,080
from the regeneration indicator,

334
00:15:01,080 --> 00:15:03,120
like I mentioned of the
plot measurement data

335
00:15:03,120 --> 00:15:04,113
just coming in.

336
00:15:05,597 --> 00:15:07,170
And I would love to do an analysis

337
00:15:07,170 --> 00:15:09,810
that's more focused on recently disturbed

338
00:15:09,810 --> 00:15:12,780
or recently harvested
stands to get at that,

339
00:15:12,780 --> 00:15:15,420
because that is like when a
lot of the really important,

340
00:15:15,420 --> 00:15:17,523
you know, recruitment takes place.

341
00:15:19,920 --> 00:15:22,500
[Audience Member] Ah, so were
they mostly closed canopy

342
00:15:22,500 --> 00:15:23,333
-or?
-Yes.

343
00:15:23,333 --> 00:15:24,240
-Okay.
-Yeah. Yeah.

344
00:15:24,240 --> 00:15:27,000
So it's all plot measurements

345
00:15:27,000 --> 00:15:28,320
in the regeneration indicator,

346
00:15:28,320 --> 00:15:31,260
which is one eighth of all FIA plots,

347
00:15:31,260 --> 00:15:34,623
like about 7,500 remeasured plots.

348
00:15:35,760 --> 00:15:37,800
Which sounds like a lot
until you realize, you know,

349
00:15:37,800 --> 00:15:39,800
it's such a huge region.

350
00:15:44,027 --> 00:15:45,750
[Audience Member] Where
did you take this picture?

351
00:15:45,750 --> 00:15:48,570
Oh yeah, it's just a random picture.

352
00:15:48,570 --> 00:15:51,903
This is Adirondacks near Rocky Peak Ridge.