Hi everyone.
Thank you for coming to my talk.
As Ben said, I'm Samantha Myers.
I'm a graduate student in the Applied Forest Ecology Lab
at UMass Amherst.
And today I'm going to be talking about my research,
looking at the effects of functional diversity
and structural complexity on forest carbon dynamics
in late-successional mixed hardwood forests.
We all know that forests provide this powerful
climate mitigation tool
because they can sequester and store carbon.
But forests in the Northeast are facing
a variety of compounding threats,
including invasive pests and pathogens
and compounding extreme weather events
that could fundamentally alter their ability
to continue to sequester and store carbon in the long term.
However, one way we can approach this is
through adaptive forest management practices
that can improve forest resilience
and protect these carbon stores
in these forests in the long term.
And traditional benchmarks for adaptive forest management
are typically to enhance the diversity of species
in the overstory or enhance the structural complexity
of the forest,
including increasing the diversity of tree sizes,
increasing the number of strata in the canopy,
or creating gaps and enhancing the amounts
of standing and downed deadwood.
However, when we're thinking about management
and specifically for forest carbon,
we want to assess are these the best measures
to maximize forest carbon benefits?
And one element we can bring in here
to assess as a potential benchmark
is looking at functional trait diversity.
Functional traits are these measurable traits
that contribute to the potential growth or mortality risk
of an individual and thereby impact the functioning
of the ecosystem that that individual is in.
And some examples of functional traits
include stem traits like wood density,
leaf traits like specific leaf area,
and reproductive traits like seed mass.
And specifically these traits are measured on individuals
and you can look at the individual level of these traits
in terms of the variability across individuals.
Typically these traits are aggregated to the species level,
looking at across all individuals of a species,
what is the mean trait value of that species.
And thereby the species level mean trait values
can be scaled up to an ecosystem level
based on the relative abundance of species in that ecosystem
and quantified the total functional diversity
in that system.
And though functional diversity is linked
to species diversity and forest structure,
it's not commonly considered in the context
of adaptive forest management.
But functional diversity could be a key driver of forest,
and when we're thinking about forest productivity
in forest carbon stores.
For example, functional diversity is known to relate
to ecosystem productivity just on a baseline level.
Looking at that, fundamental studies have looked
at how increases in functional diversity
translate to increases in plant biomass
on the community level.
Particularly in forests, a number of studies have found
that functional traits have that drive forest productivity
in both temperate and boreal forests.
However, the few studies that have looked
at functional diversity in forests
tend to use these commonly considered species level
mean trait values in their analyses.
However, using these species level trait mean values
doesn't account for the variability within a species.
Multiple individuals within that species
could have different trait values
based on their environment, their size, their age.
And these accounting for this variability
is really important if we're trying to make connections
between individual trait values and how they translate
to that individual's growth or other demographic processes.
In addition, a lot of these studies look at traits
independently of one another,
whereas we know ecologically that there is correlation
among different kinds of traits due to these evolutionary
physiological trade-offs that occur.
And one example of this is if we think about
there's an inverse relationship
between leaf nitrogen content and the lifespan of that leaf.
So it's important that we account for these correlations
between traits when we're trying to think about
the ecological meaning behind functional trait diversity.
However, to account for both these things,
we need data sets that have both local individual functional
trait information and long-term demographic information.
So the kinds of information you would get
from forest inventories measured
on the same individual trees.
And there are sparse data sets with both these elements.
We can think of one small example in the Northeast
is the Harvard Forest NEON site has some of this data.
But in our study, we are aiming to combine both
Massachusetts CFI continuous forest inventory data
with local individual functional trait observations
at these CFI plots to be able to predict
aboveground biomass in response to functional diversity.
And specifically we look to quantify the effects
of functional diversity, species diversity,
and structural complexity as drivers
of live aboveground biomass
and in the context of late-successional forests.
And why are we looking in late-successional forests?
Late-successional forests tend to be model study systems
when we're thinking about carbon storage.
They tend to have high carbon stores
and high structural complexity.
But they're also assumed to have lower carbon
sequestration rates.
However, our understanding of carbon dynamics
in northeastern hardwood forests and late-successional
hardwood forests is not very well understood.
And so what we can do is we come up with new information
and assess the data that we collect
against some more classical models of biomass development
across successional stages in forests.
For example, we can look at this Bormann and Likens model
looking at hardwood biomass accumulation.
I'm thinking about how late-successional forests
are predicted to be in this steady state equilibrium phase
when it comes to biomass accumulation
that has this equilibrium of biomass
with small scale fluctuations.
So what we can do is we can collect data in the field
and assess how it relates to classical models like this
and update our understanding.
So we went out and we utilized Massachusetts DCR
continuous forest inventory data from seven state forest
reserves that we identified plots in
that were late-successional or old growth.
And this is in western Massachusetts.
And in 26 of these plots that are shown here on the map
we sampled individuals in these plots
for five different functional traits
including specific leaf area, leaf nutrient content,
and wood density, which are known to affect
the photosynthetic capacity of these trees
and affect these growth processes and carbon storage
that we're most interested in.
We then calculated total live aboveground biomass
and structural complexity metrics at all the plots
in these forests from inventory years starting in 2000
to the present.
And one of our obstacles with this data
is that we wanted to figure out how to integrate
individual level functional trait observations
that we collected with stand-level structure
and biomass calculations to predict aboveground biomass
at that stand level.
And to do this, our model uses information both
from global trait databases where a lot of these studies
that that use species-level trait mean values
use these database mean values.
We combine that with the information we collected
in the field that includes the variability
within within a species, the individual trait variability,
to update our understanding of mean species-trait values.
And I'll give you one example here of how we did that.
So for example, we can see specific leaf area
for four different species.
And in blue we see the database mean value
and standard deviation.
And in orange we see the distribution of individual trait
observations that we collected in our CFI plots.
And with our model, we can take both of these pieces
of information and update our understanding
of the species-level mean trait value
for each species and trait combination that we have.
Our model also brings in this idea of the fact
that traits are not independent of one another.
So we are able to model dependence among these traits,
accounting for these evolutionary trade-offs,
physiological trade-offs between traits.
And then we use these updated species-mean trait
values to calculate functional diversity in each plot
and use that looking at how we wanted to predict live
aboveground biomass as a function of functional diversity,
structural complexity, and also accounting for density
and proportion soft wood.
So we know that density obviously has a large impact
on aboveground biomass forest density,
and we also know that these are mixed hardwood forests,
so it's important that we account for proportion soft wood,
thinking about how soft wood species
on the individual level have lower wood density
and store less carbon.
And we compared three different models.
Two models looked at functional diversity,
and one model included that local trait need update.
We found that integrating local, individual functional trait
information yielded the best predictions
of live aboveground carbon in our plots.
Also, as expected, we found strong effects of density
proportion of soft wood species and diameter diversity
on live aboveground biomass.
And when we see this positive effect of diameter diversity,
we think about how there are these large legacy trees
in a lot of our plots that are storing the bulk of biomass
and contributing to that positive relationship
with diameter diversity.
However, contrary to our initial beliefs,
we found that functional diversity
actually had a negative effect on aboveground biomass.
And considering that we thought the relationship
would be positive,
we looked into why this might be the case.
We found that in plots with the lowest functional diversity,
we looked at the relative abundance
of shade tolerance in each plot.
We can see here each plot is represented by one vertical bar
and we see that plots with the lowest functional diversity
were dominated by species
with mid to high shade tolerant hardwoods.
On the other hand, in our plots with the highest functional
diversity, we see a higher abundance
of shade intolerant species and mid tolerant species
as well as soft woods, which tell us that these plots
potentially had a more recent small scale disturbance
that effectively decreased the biomass stores
in these plots and allowed for the facilitation
of mid to of low shade tolerance species.
We also found that there were slightly positive rates
on average of biomass accrual in these plots.
So we can look at the change in total aboveground biomass
per year, and we see that on average
these are slightly positive rates of biomass accrual.
However, if we look at the scale of these,
it's pretty small, indicating that these plots
are generally around the biomass equilibrium.
We can also look at this change in aboveground biomass
compared to functional diversity.
And although the relationship isn't very strong,
we see a slight positive relationship
between functional diversity
and changes in aboveground biomass,
indicating that these plots with higher functional diversity
that have had a more recent small scale disturbance
have slightly higher rates of biomass accrual.
And we can take this information and compare it
to our understanding of classical models,
looking at how we believe that our plots are generally
at this study state equilibrium phase
in terms of having stable biomass over time.
However, we see these variations,
indicating the fluctuations in this graph,
and we can kind of map those two.
The fact that in our plots with lower functional diversity,
we see higher carbon stores that are placed
and can be kind of mapped to the peaks
of this steady state fluctuations,
whereas our low functional diversity plots
that have slightly lower carbon stores can be found
at these valleys of the steady state that have gone
through those more recent small scale disturbances.
However, given this, you see that the fluctuations
don't change the overall biomass
in these plots significantly,
and it remains around equilibrium over time.
So we found that forest exceptional dynamics
shift the effects of functional diversity on productivity.
While we've seen studies that have shown strong positive
relationships between diversity and productivity
in earlier to mid successional forests,
we find that this relationship can decrease
or even turn negative in later successional forests.
We also see our plots as an example of this classical model
of a dynamic steady state, where our biomass stores
are pretty much at equilibrium,
but we do see these small scale disturbances
that create these small trade-offs
between slight increases in functional diversity
and simultaneous decreases in aboveground biomass.
So what does this mean for forest carbon management?
So this, our results show that we should be looking
at forest carbon management from this landscape scale,
thinking about emulating a shifting gap mosaic
across the landscape with different stands
across the landscape in different successional stages.
Thinking about how in our later successional stands
we can preserve them knowing that they have relatively
stable high aboveground carbon stores
or aboveground biomass.
Whereas where small scale disturbances in these stands
won't significantly change the amounts of additional biomass
they're able to accrue.
Whereas elsewhere in the landscape in earlier
to mid successional stands, more active management
can be used to take advantage of this positive diversity
productivity relationship and the higher rates
of biomass accrual, if we think about that model
of biomass accumulation over time.
And with that, I'd like to thank my advisor, Malcolm Itter,
and my thesis committee as well as Bill Van Doren
at Massachusetts DCR and my funders.
And I'm over time, but...
(audience applauding)
[Chairperson] We've got about three minutes
for questions, so-
[Attendee] So I feel like I could come away from your talk
with two very different perspectives.
So I'm curious where you feel that your research fits.
Do you see this, your research, as being evidence
that managing for functional diversity and resilience
is antithetical to carbon management
or complimentary to carbon management?
[Samantha] I think it depends on the context.
I think that in later successional stands,
the functional diversity,
we didn't see that much variability
in the functional diversity in these stands.
So it might be more important to look in earlier
to mid successional stands when thinking about diversity
because later successional stands just tend to be,
at least for canopy dominant species,
tend to be dominated by more shade tolerant species.
So that variability is less than if we're looking
at early to mid successional stands when thinking
about diversity there.
[Attendee] I have a quick question.
It's sort of like related to that one actually.
In one of your slides that you gave a lot of statements
about the state of flux of carbon in older stands
or these successional stands,
and it was all followed with some scientific references,
except the statement is said, older late succession
forests are assumed to have a slower sequestration rate
than than younger forests did,
but no scientific literature to cite that.
Do you? Was that on purpose?
I mean, do you have some scientific-
[Samantha] Oh yeah, yeah, I'm sorry I didn't include that.
Yeah, I'll consider that for next time.
[Attendee] Yeah, that'd be awesome.
Because you know, this is kind of like
one of the big issues that we're dealing
with on public lands anyway
is the contention that to maximize carbon storage
preserve the older mature forest stands
and don't cut any trees.
So that's the science that is being thrown out there
from that perspective.
And there's some literature out there that contends
that that is the case.
Whereas, you know, as a public land manager,
we're still using the science that says that's not true.
You know, it's the counter science.
So I just wonder if you could comment on that.
[Samantha] Yeah, definitely.
I mean, like I said, a lot of the study
was based on the fact that the carbon dynamics
in these forests is not very well understood.
So we need to keep assessing some of our classical thoughts
about, you know, and over time consider also
how global change is affecting the carbon sequestration
as well.
[Attendee] Great.