- [Kate] Okay
Thanks to the Forest Ecosystem Monitoring Cooperative
for organizing this meeting and for giving me an opportunity
to talk about our program.
I, of course, wish we could do this in person,
but I'm glad we're at least able
to have this virtual meeting.
And so, I'm Kate Miller.
And today I'm gonna talk about
what we've learned over the last 14 years
of monitoring in eastern national parks,
as well as concerns we have for the future.
Before we get to that, I want to remind everyone
about the mission of the National Park Service,
which is to protect natural and cultural resources
unimpaired for future generations.
So parks really have the highest level of protection
of any federal land management agency.
And in all but a few cases,
it also includes protection from logging.
But as late as the 1990s, many parks
had very limited knowledge of their natural resources
and whether management actions were effective.
And it's hard to know
whether you're meeting the mission of the Parks Service
if you don't know what resources you have
and you don't know what condition they're in.
The Inventory and Monitoring Program
of the National Park Service
was established to fill this need.
Specifically, the I and M Program was established to assess
the status and trends of important ecosystems in parks
to help inform management decisions.
The I and M program started with baseline inventories,
like vegetation maps and small mammal inventories,
and has since transitioned into long-term monitoring.
I worked primarily for the Northeast Temperate Network,
which covers parks from Morristown National Historical Park
in New Jersey, up to Acadia National Park in Maine.
We also coordinate monitoring along the Appalachian Trail.
And we implement multiple long-term monitoring programs
in these parks, which I'm gonna walk through
in the next couple of slides.
We monitor water quality and quantity annually
in all but Boston Harbor Islands and Appalachian Trail,
and we've been doing that since 2006.
We've also been monitoring
breeding land birds annually since 2006.
And we mostly use volunteers to collect the data.
If you want to know more about this program,
I encourage you to check out
our program manager Aaron Weed's talk tomorrow,
where he's gonna be discussing some recent findings
from a trend analysis for this program.
And the info on his talk is below.
Okay, next we have a number of park-specific protocols
based on park resources and needs.
So we have freshwater wetland monitoring in Acadia,
and rocky intertidal habitat monitoring
in Acadia and Boston Harbor Islands,
and then coastal bird monitoring in Boston Harbor Islands.
And wherever possible, we've adapted our protocols
from existing national protocols.
So for wetland monitoring,
we use EPA's National Wetland Condition Assessment Protocol.
Our rocky intertidal protocol was adapted
from the Marine Protocol, which was developed
for rocky intertidal habitats on the West Coast.
And there really are a ton of benefits
to adapting existing and nationwide protocols,
which I'm gonna kind of talk about throughout this plenary.
Okay, finally, we have our forest health protocol.
So the parks with the green tree symbol are part
of our field-based forest monitoring program,
where we send crews out every year
to sample permanent forest plots.
Our protocol was adapted from the U.S. Forest Service's
Forest Inventory and Analysis Program,
which is a nationwide survey of permanent forest plots.
And we've been monitoring in these parks since 2006.
We sample on a four-year cycle.
And so we're midway through our fourth monitoring cycle.
And I'll note that the Appalachian Trail monitoring,
the reason why it's a different symbol,
is that we rely on summarizing data from FIA plots
within a specified buffer along the trail
instead of sending out field crews.
Okay, for those of you who are interested,
I'm gonna walk through our forest plot design.
So our plots are 20 by 20 meters,
except in Acadia where they're 15 by 15 meters.
So our plots are slightly larger than one FIA subplot.
And in each plot we monitor tree diameter at breast height
and condition of all trees that are
at least 10 centimeters diameter.
We also monitor seedlings and saplings
in three, two meter radius micro plots.
We periodically collect soil samples
for chemical analysis outside of our plots.
We measure coarse woody debris
along three, 15 meter long transects.
We estimate vascular percent cover for all species
within eight, one meter square quadrats.
And finally, we take photo points of sixteens of the plot.
Even though our plot diagram isn't the same as FIA,
wherever possible our methods match those used by FIA.
And major differences really are that we opted
for establishing many smaller plots versus fewer large plots
to better capture variability in forests across each park.
So our plots are smaller,
but we're sampling a lot of the same stuff as FIA.
In addition to any NETN,
there are over 90 parks at this point
that are monitoring forest health
using similar protocols adapted from the FIA program.
And particularly, in this northeastern cluster,
we've been monitoring forest health
using similar protocols for over 12 years.
And that's allowed us to do more powerful analyses
at the regional level and to compare patterns
in parks with surrounding forest using FIA data.
And I'll say that when the I and M networks
are first getting started, there was really no incentive
or pressure from above to develop compatible protocols.
Fortunately, there were a few Eastern folks with insight
to coordinate protocol development,
and we're in a much better place because of that.
And most of my plenary is gonna focus
on the things we've learned because we were able
to combine our data sets for regional analyses
and or compare our data with FIA.
One of the examples of how we've taken advantage
of having similar protocols adapted from FIA
includes a series of studies where we compared
forests and parks with surrounding forest using FIA plots.
And we did this by comparing forest data in parks
with FIA plots in the same ecological subsection
containing each park.
Ecological subsections, by definition,
share similar climate, topography, and geology,
such that you expect the same potential natural vegetation.
And so differences between park and surrounding forest
should be due to management differences.
So we looked at 10 metrics of forest structure.
We looked at tree growth and mortality rates,
five metrics of tree diversity,
and then we also assessed land use
and ownership patterns surrounding parks.
So today I'm just gonna talk about
the forest structure and diversity analyses,
but the papers are here if you're interested
in knowing more about the others.
First, I will cover the highlights
from our forest structure analysis.
So on this figure, the X axis has the 50 parks
that were in the analysis, sorted from low to high latitude.
An asterisk indicates a significance between the park
and surrounding forest, of P less than 0.05.
Each park here has a pair of points, with the green
representing the parks average and standard error,
and the yellow representing the surrounding forest average
for that metric of interest.
And what I want you to take away from this is that
in just about every case, the park has
a higher live basal area than surrounding forest.
So the green dots are above the yellow dots
in just about every case.
What's also interesting here is that, on average,
live basal area for eastern old growth forest
is 29 meters squared per hectare.
And while I wouldn't say that parks have old growth forest,
it does appear that many are at least approaching
old growth levels of basal area,
where surrounding forests are not.
For density of live trees over 30 centimeters DBH,
again, most parks' average density
is higher than the surrounding forest.
And ecological thresholds from two studies
on late-successional bird species indicate
that these differences are likely ecologically important,
as all but a few of the parks in the study meet
the lower threshold and many are above the higher threshold,
where surrounding forests are not.
Looking at coarse woody debris volume,
we find that our parks have considerably higher
coarse woody debris volume than their surrounding forests.
And based on ecological thresholds in the literature,
many of our parks are meeting at least a lower threshold,
some meeting the higher threshold,
where surrounding forests generally are not.
Okay, so now we're onto the results
from the tree diversity study.
So this figure is showing percent difference
between park and its surrounding forest
for the five trees closest to the center of each plot.
If the symbol is above the line,
the park average metric is higher.
If it's below the line, its surrounding forests were higher.
An asterisk indicates significant difference.
So first looking at species richness,
we found that 77% of parks had higher tree species richness
than surrounding forests.
For Shannon evenness, which is a measure
of how evenly distributed species abundances are,
61% of parks had higher evenness than surrounding forest,
so more diverse.
For McNaughton dominance, which is the measure
of the relative proportion
that the two most abundant species take up,
in this case lower McNaughton dominance means more diverse,
we had 74% of parks having lower dominance
than surrounding forests.
And then finally, looking at percent rare species,
in this case, rare species is defined as species
with low abundance, not rare, threatened, and endangered.
And for this study, we found that 61% of parks
had more rare species than surrounding forests.
Okay, so to put the results of those two studies together,
we found park forests were consistently different
from their surrounding forests
in having higher proportion of late successional forests,
higher live tree basal area,
higher density of large trees
considerably higher coarse woody debris volume,
and also higher stand-level tree diversity.
And based on ecological thresholds
that many of our parks met and surrounding forests did not,
the results are likely ecologically significant.
And so this was an important finding in our parks.
A lot of the eastern parks are cultural
or battlefield parks, where forests are generally
not the focus to managers or at least managers
have a hard time justifying resources
going towards forest management,
because they're not central to the parks' objectives.
And by showing that these parks are protecting
important forest habitat, managers have been able
to make a stronger case for protecting
and trying to improve the condition of their park forest.
That was the good news.
Now for the bad news.
There are some major issues facing many of our park forests.
The main ones being invasive plants, overabundant deer,
lacking or suboptimal regeneration,
and forest pests and canopy disturbances.
And many of these issues are also interrelated
and are together causing more severe impacts
than they would on their own.
The first example I'll give of issues
facing park forests is invasive plants.
So we just published an analysis of invasive plant trends
in ecological applications.
And this invasive trend analysis
included 39 parks from five networks.
So basically the parks you see in the map,
we looked at trends over 12 years,
which covered three cycles of monitoring,
and included over 1,400 forest plots.
For this analysis, we assessed trends in multiple metrics
of abundance and grouping levels to determine
how invasive plants are changing over time in our parks.
So first we looked at plot percent frequency,
which was the percent of plots within a park
where at least one invasive was found.
We looked at quadrat frequency,
which was the percent of quadrats
where at one invasive was found.
And so if you look at the NETN plot diagram on the right,
those are the little blue squares.
We also looked at quadrat percent cover,
which was the average cover
of invasive in the quadrats, again.
We looked at different grouping levels
and how they changed over time.
So that included total invasives,
which were all invasive species combined.
We also looked at guilds.
So we looked at trees, shrubs/vines,
graminoids, which are grasses and sedges,
and then herbaceous species,
which are non-woody species that aren't graminoids.
And then we also looked at species-level trends.
So we first ranked parks by level of invasion
based on the abundance metrics,
sorted first by average percent cover.
And we used the most recent four years of data,
so 2015 to 2018.
So the table here includes
the 10 most and 10 least invaded parks
out of the 39 in our analysis.
So this is not where you want to be number one.
So the two most invaded parks were
Monocacy and Antietam National Battlefields in Maryland.
And the 10 most invaded parks,
you can see that their average cover was
between 20 and 40% invasive cover.
That doesn't mean the other 60 to 80% was native cover.
More often, where deer are a problem,
it's bare grounds because of soil compaction.
And even looking at the 10 least invaded parks
in this study, a number of them still have
pretty high quadrat and plot frequency.
And it's just that their actual cover is pretty low.
So if you look at Marsh-Billings, for example,
it's the second one from the bottom,
the average percent cover is less than
1/10 of a percent in the quadrats, but yet we found
at least one invasive species in over 80% of the plots.
And Acadia is really the main exception here.
And they had low abundance across all metrics in Acadia.
And so Acadia was the least invaded park in this analysis.
We also ranks species based on how common they were
across parks and plots.
So Japanese stilt grass here was the big winner.
Japanese stilt grass was the most widespread species
found in over 80% of the plot parks, over 30% of the plots.
And the range of microstegium is still fairly limited
to Southern New England into the mid-Atlantic,
but it is moving its way forward
into the northern states in this analysis.
The next most common species
are primarily woody shrubs and vines.
So multiflora rose, Japanese honeysuckle,
Japanese barberry, Asian bittersweet.
These are all species that were common
throughout the range that we looked at in our study
and were pretty common throughout our parks.
Now we're gonna look at trends.
So in the figures I'm gonna show,
each park is gonna be represented by a line.
So there'll be 39 lines.
Red is a significant increase over time.
Blue is a significant decrease over time.
And gray is nonsignificant trends.
And here we're looking at how quadrat percent frequency
of total invasives changes over time.
And so you should notice there are
quite a few significant increases in quadrat frequency,
not a lot of declines.
And even the nonsignificant trends
were mostly trending upward.
The only significant decline we found in quadrat frequency
was Prince William Forest Park, that's the blue line.
And that park is in Virginia.
Now looking at average cover of total invasives,
we have even more significant increases
and steeper increases in quadrat percent cover.
And again, only one significant decline,
that was Roosevelt-Vanderbilt National Historical Sites
in New York.
Okay, so the next figures are gonna be
looking at species-specific trends.
So in this plot, the circle represents the slope
or percent change over time
in species-level quadrat frequency.
The error bars are 95% empirical confidence intervals.
Values that are to the right of the zero vertical line
indicate significant increases.
Values to the left are significant declines.
And this is sorted by species,
with the most number of significant trends.
And in case it helps, they're color-coded by guild.
So graminoids are yellow, herbs are green, trees are blue,
and then shrubs are shades of red and orange.
And looking at this figure,
you can see there are a lot more significant increases
in quadrat frequency at the species-level.
So many more on the right side of the vertical line.
Not so many on the left side.
And Japanese stilt grass, microstegium vinineum,
had the most number of increases among parks
and had the highest recorded increase.
And there's also an interesting interaction
that we caught in a couple of our parks
between herbaceous and woody species.
So in Morristown, for example,
we have garlic mustard, which is alliaria petiolata,
and cardamine impatiens, which is narrowly bittercress.
Both of those had a decline in quadrat frequency over time.
But it was countered by an increase in wineberry,
which is rubus phoenicolasius.
In Roosevelt-Vanderbilt, we had a similar pattern
where garlic mustard went down,
but Norway maple, acer platanoides, increased over time.
So there wasn't an actual decline
overall of invasives because of this interaction.
And in average cover, we find similar patterns.
So a lot more significant increases.
Japanese stilt grass, again,
has the most number of significant increases.
And again, we're seeing interaction
between herbaceous and woody species.
So in Morristown, for example, Japanese stilt grass
and garlic mustard declined significantly
in average cover over time.
But it was met with an almost equal increase
in Japanese barberry, which is berberis thunbergii.
And Valley Forge, which is in Pennsylvania,
we found a similar interaction,
where Japanese stilt grass went down,
but then we saw an increase in actually garlic mustard,
but then also a bunch of woody species.
So Asian bittersweet, wineberry, and tree of heaven.
so when we first started seeing these patterns in the data
we wanted to know whether this was some kind of artifact
of how we're collecting the data?
Or are we getting better at identifying invasive species?
Or if these reflected real patterns on the ground?
So we looked at our photo points
from our plots for evidence,
which this is an example of.
So this is a plot in Morristown, in New Jersey,
that we established in 2009.
This is what one of the scenes looked like
the first time we sampled this plot.
So you can see that there's Japanese barberry
in kind of the back half of the plot.
The foreground of the plot,
there's not a whole lot going on.
The little green sprigs are actually Japanese stilt grass,
kind of early in the season.
And then you do see a little bit
of barberry in the foreground,
and some wineberry in the right of the screen, bottom right.
Four years later, when we sampled this plot,
the barberry has filled in a lot in the foreground.
The wineberry in the bottom right
has more than doubled in size.
And there's really not a whole lot
of bare grounds in the picture.
And then when we last sampled this plot in 2017,
it's almost entirely covered
by Japanese barberry and wineberry.
And there's almost no Japanese stilt grass to be found
and very little bare ground.
And I should mention that this is a closed canopy forest.
So this is not like a forest gap was created.
And so the invasive shrubs responded to increased light.
This is just what happened in a closed canopy forest.
Moving onto Valley Forge.
This is a picture from a plot that we established in 2007.
If you remember in Valley Forge,
we saw a decline in Japanese stilt grass
and an increase of tree of heaven.
And so here, in 2007, the understory
is almost entirely dominated by Japanese stilt grass.
So if you've never seen Japanese stilt grass,
this is what it can look like.
So four years later, in 2011,
we see there's some seedlings coming in in the understory.
There's still a lot of Japanese stilt grass.
And I should mention that deer management started in 2010.
So in 2011, when we sampled this plot,
we're starting to see some response in the understory.
But unfortunately it's mostly tree of heaven.
Almost all of these seedlings are tree of heaven.
Four years later, the tree of heaven is much taller
and taking up a lot more space.
Four years later, which is the last time
we sampled this plot, in 2019
you can see that the tree of heaven is even bigger,
taking up a lot more space and there's not as much
Japanese stilt grass in the understory.
And so all of this is to show
that we have pretty high confidence
that the trends that we're seeing in the data
do in fact reflect real trends on the ground.
Okay, so just to summarize what we found here.
We found that invasive species, they're widespread
and consistently increasing in abundance
across our eastern parks.
35 out of 39 parks had invasives
in at least half of their plots.
30 parks had at least one significant increase
in invasive abundance.
Only two parks showed overall declines.
That was Prince William Forest Park in Virginia
and Marsh-Billings-Rockefeller
National Historical Park in Vermont.
I don't know a whole lot about the management history
of Prince William Forest Park,
but I do know that in Marsh-Billings, they have been
managing invasive species intensively for over 10 years.
And so I think that the decline we're seeing,
in Marsh-Billings at least,
is because of their management efforts to remove invasives.
There were also two parks that kept
relatively low invasive levels over time.
And that was Acadia National Park in Maine
and Saint-Gaudens National Historical Park in New Hampshire.
The most aggressive invaders were Japanese stilt grass
and shrubs and woody vines.
The likely drivers were overabundant deer, forest pests,
and other canopy disturbances,
exotic earthworms, fragmentation, and land use history.
And I highlight these three
because they seem to be contributing the most
to the invasive problems these days,
and are something that managers
may be able to do something about.
And so I'm gonna elaborate more on that next.
Deer are a major problem in many of our parks,
not in Acadia, but pretty much everywhere else.
And we're finding that the impacts
from chronic deer overabundance
continue to increase over time.
And deer eat native seedlings and herbs.
They facilitate invasive species
through seed dispersal and soil compaction.
And then also obviously eating the native species
and making room for the invasives.
And most parks do not allow deer hunting
as part of their enabling legislation.
And so they can't just open the park
to hunting to reduce the deer herd.
And so in most cases,
the parks that are actively managing deer
are hiring APHIS sharpshooters to reduce the herd.
And it's really expensive.
And pretty much as soon as a park decides
to go down this path, they basically have to do it forever
to keep deer numbers in check.
And even getting to the point where parks are allowed
to manage deer is a huge undertaking, requiring many years
to develop environmental impact statements.
And each park, at this point,
has to go through the same process.
And parks are often sued by hunters
who want to actually be in the parks hunting
and animal rights activists
that don't want the park to be killing animals.
And so that can really slow things down as well.
As more parks are going down this path,
our hope is that the approval process can happen faster
and management becomes easier.
But we've still got a long way to go.
So looking at the parks
that have the most impacts from deer,
we see this area from Saratoga up in New York,
down through Colonial in Southern Virginia.
This is where we see
the highest deer impacts in our parks.
And looking at the regeneration stocking index,
which was developed by the Northern Research Station,
with the Forest Service, we also find that the parks
that have the least percent of plots
that are stocked with regeneration
are also in the area with high deer browse impacts.
And in fact, there are five parks in this circle
that don't have a single plot
that has sufficient regeneration.
And even the parks that do have
an abundant regeneration layer,
like Gettysburg, pictured here,
a closer look at the composition
of the regeneration layer is also concerning.
So this map is of our forest plots in Gettysburg.
And each pie is a forest plot.
And the size of the plots is relative
to the total seedling density for that plot.
And the colors represent the species
making up the seedling layer.
And as you can see here, most of the regeneration
in Gettysburg is dominated by ash, which is brown,
or low canopy species, which are in pink.
And here, low canopy species are species
like flowering dogwoods, sassafras,
and ironwood, or red bud,
which they're species that have a tree-like form
but they don't get very tall
and they don't really reach into the canopy.
And both are not optimal species for maintaining
a closed canopy forest.
Forest pests, especially emerald ash borer now,
and gaps formed by intense storms,
are also contributing to the invasive
and regeneration problems in our parks.
So this is a photo of the canopy thinning, in Morristown,
as ash dieback from emerald ash borer.
But this could easily be from other parks in the region
where emerald ash borer has hit.
And the understory response to the canopy thinning
and the gaps, unfortunately, in these areas,
is dominated by increases in invasive species,
particularly invasive shrubs,
with little response in tree regeneration.
When deer and invasive problems go on for long enough,
the end result, which we're seeing more of,
is the formation of these invasive shrub thickets.
This is a photo from the gap in Morristown
that was formed from Hurricane Sandy in 2012.
We're also seeing gaps form like this
in ash stands in some of our parks.
And believe it or not,
there are actually four people in this photo.
This was taken in 2017 when we last sampled the plot.
And the only regeneration we found in this plot
was staghorn sumac,
which is the flagged sapling on the left.
No canopy forming tree species
were regenerating in this stand.
And it's really hard to imagine this being anything but
exotic shrub thicket, long-term.
And many of the forests in our eastern parks
are just one major disturbance away from becoming this.
And this is obviously bad for forest health
and for all the biodiversity
that depends on forest habitats.
But it's also bad for human health.
So studies have found that these thickets
can host more than double the density
of black-legged ticks than uninvaded forests,
and the ticks are more likely to carry Lyme disease.
So you've seen the issues that are facing many of our parks.
One question that we had as we were
starting to document these patterns,
was whether it's just in our parks,
or if this is part of a more widespread, regional issue?
And so that's what I'm gonna talk about next.
So to look at regional regeneration patterns
we used FIA data from 17 eastern U.S. states
to quantify regeneration debt in eastern forests.
We coined the term regeneration debt
to collectively describe issues
of either not enough regeneration
or mismatch in species composition
between the regeneration layer and the canopy species.
So a regeneration debt occurs if you either
don't have enough regeneration or if the composition
of the regeneration doesn't match the canopy.
And regeneration debt is not always a bad thing.
So if you think of a forest succeeding
from aspen and birch to spruce fir,
the regeneration debt here merely predicts a change
in the future composition of the canopy and is not bad.
However, regeneration debt can indicate a problem
where anthropogenic stressors are driving the patterns
and you have suboptimal species regenerating,
which I'll talk about in a minute.
And in addition to quantifying regeneration debt,
we related these findings to anthropogenic stressors
and metrics of climate change using model selection
to determine the likely drivers.
Okay, so these are some of the results from our analysis.
We're looking at seedling density across the region.
And again, this was using FIA data.
And so you'll notice that in Maine
and Northern New Hampshire,
seedling densities are really high.
But if you look in the coastal region,
from Massachusetts down to DC and Virginia,
there's some areas with really low seedling density.
So zooming in closer to this area
for Massachusetts down to Virginia,
there are areas, the darkest gray hatches,
where there's less than one seedling per 10 square meters.
And then the lighter gray hatches
are less than one seedling for four square meters.
That's really, really low.
And so next, I'm gonna walk you through the metrics
that best predicted these patterns
based on model selection.
So average invasive cover in these plots,
invasive plant cover, was an important predictor
for low seedling densities.
You can see strong overlap between the hatches
where there's low seedling density
and where there's high invasive cover.
Looking at deer browse impacts.
Unfortunately, this is only collected
in the Northern Research Station,
so I don't have data for Virginia.
But where we have low seedling densities,
we also have high deer browse impacts, in general.
And then finally looking at
degree of human modified land cover,
at the 300 meter square scale.
That was also an important predictor.
So where there's high modified land cover,
so basically non-forest land cover,
we have low seedling densities.
Next we looked at composition between
the regeneration and canopy using similarity metrics,
which are plotted on the map on the right.
So similarity is a measure of how similar
species composition is between two groups,
in this case, the seedling versus canopy strata.
So if you have all the same species
in the seedling and canopy layer,
then your similarity will be one.
If you have no species in common,
then similarity will be zero.
And in this case, we used Horn's similarity,
which also incorporates species abundance into the equation.
And the main takeaway here is that the same area
where we saw very low seedling abundance,
the seedlings that are present
are not the same as the species in the canopy.
And based on these patterns, we use model selection again
to determine which species and stressors
were most likely driving these patterns,
which we'll talk about next.
To determine which species or groups
are driving the low similarity,
we mapped relative abundance based on stem densities.
And so looking at oak first, which is on the top,
we see that oak dominates the canopy
across much of the region, that's the map on the right,
but is not as abundant throughout that area
in the seedling layer, which is on the left.
In contrast, beech is not very abundant in the canopy,
so that's the bottom right map, but it is pretty abundant
throughout the region in the seedling layer.
Invasive tree species and low canopy species
were also important predictors in our model.
And I think most importantly, looking at low canopy species,
on the left, there's very high relative abundance
of low canopy seedlings present throughout the study area,
but pretty low abundance in the tree layer.
And so again, low canopy species are species
like musclewood, ironwood, sassafras, pawpaw,
that have a tree-like growth form,
but they're not gonna be able
to form a tall closed canopy forest.
And some research has also shown
that many of those species are browse resilient.
So we finally, we combined the results
from the seedling and sapling densities
and the similarity between seedling and canopy
and sapling and canopy.
And so here, this was to look at the severity
of the regeneration debt.
And we can find in purple is the most severe
and then dark blue is high.
And that means that there was very low score
in three of the four metrics.
And overlaying our parks in the area
where we find the most severe regeneration debt
with the FIA data that also matches the parks
where we have the highest deer impacts
and the most regeneration problems.
Okay, so just to summarize the results
of the regeneration debt study.
We found severe regeneration debt in mid-Atlantic forests,
particularly in oak, hickory forests.
Anthropogenic stressors predicted the debt
better than climate change metrics.
And the most affected species were
oak, hickory, and southern pine species.
Okay, so now I'm going to shift
to talking about future concerns in eastern parks,
and this is primarily focused on climate change.
I'm sure many of you are familiar
with the Climate Change Tree Atlas predictions,
which predict changes in suitable habitat
by the turn of the century,
using species distribution models.
And species distribution models.
they essentially take environmental
and climate variables that best predict
the current distribution of a species,
and then predict the future suitable habitat
for that species using general circulation models
of future climate scenarios.
They're really helpful for predicting
which species are likely to be most vulnerable
or robust to climate change.
And as you can see in the maps here,
the Climate Change Tree Atlas predicts
a pretty major expansion of suitable habitat
for oak, hickory into the Northeastern U.S.
by the turn of the century.
And even though everyone involved
in the Climate Change Tree Atlas
is super clear about how to interpret these maps,
which are merely predictions of future suitable habitat,
but not future forest composition,
I've heard on many occasions,
a number of researchers or managers
misinterpret these maps as predictions
of what the forest will be in the next 50 to 100 years.
And interpreting the predictions this way,
even though it's wrong, isn't that alarming to managers.
They're generally okay with the idea
of northern hardwoods converting to oak, hickory.
And the research I'm gonna talk about next
was really largely in response to this.
I wanted to see how likely tree species
will be able to migrate into their newly suitable habitat.
Thinking about tree migration is important
because temperate tree species largely responded
to past climate change through migration,
rather than adapting to changes in place.
And tree migration depends on seed dispersal ability,
life history traits, like age to maturity,
habitat connectivity and landscape configuration,
between where a species is now and where it needs to be,
and then also regeneration.
So we already talked about regeneration debt
and regional patterns of regeneration.
Now I'm gonna talk about a dispersal simulation we did
to look into these three factors.
For this study, we simulated 100 years of dispersal
for 15 common eastern tree species that are predicted
to gain suitable habitat in the northeast.
And our objectives for the study were to assess
species-specific dispersal ability.
Determine influence of non-forest land use
on dispersal ability.
Identify if there are any major dispersal barriers.
And then also we compared dispersal rates
with rates of habitat shift
based on the Climate Change Tree Atlas.
Our simulations were informed by what we know
about species-specific dispersal rates,
including published dispersal kernels for each species,
life history traits, like age to maturity,
and we used a 90 meter grid size.
For our current range, we used the Climate Change Tree Atlas
current range for each species,
so that's in green on the map.
And for the range to migrate into,
we use future suitable habitat predicted
by the Climate Change Tree Atlas, using the average
of 3 GCMs and high emissions scenario, and that's in yellow.
And the map on the left simulates dispersal
with no dispersal barriers, so that's our null dispersal.
The map on the right simulates dispersal
with a dispersal barrier,
represented as non-forest land use, that's in gray.
And this is the start of the simulation
for southern red oak.
And next I'm gonna show you how the simulations play out
over 25 year time intervals.
So 25 years after our simulation starts,
we see that there's a pretty continuous band of dispersal
on the left, in the null model.
Not so much on the right,
where non-forest land use is effectively a barrier.
Here's 50 years later.
75 years later.
So at the end of the 100 year simulation,
in the null dispersal results on the left,
we found southern red oak only got
about 50 kilometers from its current range.
Considering that most oak species take 20 to 25 years
to reach maturity under ideal conditions,
this shouldn't really be a surprise.
There's only a maximum of four new dispersal events
for newly colonized cells.
I should also note that our null dispersal simulations
average roughly one to 10 kilometers per decade
for their dispersal rate, which is similar
to the rates of post-glacial migration
determined by the pollen record.
So we feel pretty confident that our simulations
are a decent representation of dispersal.
That's just not very far on the scale of our lifetimes.
And now looking at the simulation on the right
that incorporated non-forest land use,
we see that dispersal basically failed.
There was no effective dispersal between this area
from Northern Virginia up through central New Jersey.
And this area from Northern Virginia
up through central New Jersey
was a consistent dispersal barrier
for species whose ranges were south of or near this region.
So just to kind of summarize what we found in this study,
species dispersal rates were considerably slower
than their species habitat shifts,
as predicted by the Climate Change Tree Atlas,
even in our null models,
with 50 kilometers long distance dispersals.
So kind of maximum possible dispersal,
it was still slower than habitat shifts.
Oak species had the slowest dispersal rates
and also the fastest species habitat shifts.
No species filled the majority
of its predicted newly suitable habitat by 2100.
And we also identified these two major dispersal barriers
from Washington DC area up through central New Jersey.
And then there was an east-west barrier in central New York.
So putting this all together,
looking at the results from the dispersal simulation
and also our regeneration debt study, we find that
where the most extreme dispersal barrier occurred
overlaps almost exactly with where we found
the most severe regeneration debt.
And then, of course, overlaying where our parks are
in this map, we see that a number of our parks
are in the area that is both a severe regeneration debt
and a migration barrier, but also many of our parks
are north of this, and so they're likely to be affected
because of this migration barrier in the way.
Tying this all together, we found invasive species
and overabundant deer are impacting forest regeneration
in parks and throughout the region.
Many species predicted to gain suitable habitat
in the northeast have regeneration debts
in their current range.
And there's strong spatial overlap
of regeneration debt and dispersal barriers
that will likely impact tree migration.
And it's possible that these issues
are already impacting tree migration,
as there's some evidence of northern hardwood
and spruce fir species moving north.
Some evidence of oak hickory species moving west
because of increased moisture availability.
However, there's almost no evidence
of oak, hickory species moving north.
And it may just be lack of climate response,
or it may be that they can't migrate
through these dispersal and regeneration barriers.
And even though most of the issues I documented
are south of the region that many at this meeting work in,
this affects us too, because the oak, hickory species
that are predicted to gain suitable habitat in the northeast
are probably not gonna make it on their own.
Okay, so what should we do about these issues?
Management recommendations really are
that we need to start doing something
about these non-climate stressors
that are impacting tree regeneration,
particularly deer and invasive plants.
And I really think we need to do this at a regional level.
This obviously poses an enormous challenge,
considering how many states, jurisdictions,
and landowners are involved.
But if we don't start doing something big,
more and more of the eastern forest
is gonna look like the barberry thickets in Morristown.
We probably also need to consider range expansion
and assisted migration beyond the dispersal
and regeneration barriers that I talked about,
so that tree species are able to migrate
with their suitable habitat.
Okay, with the last minute or so I have left
I'm gonna completely shift gears
by ending on some advice for folks
who are starting up long-term monitoring projects.
And this is advice I would give myself
if I could go back in time
to when we were developing our protocols.
And so the biggest piece of advice I would give
is to be consistent and borrow from existing protocols.
So once you start using a method,
don't change it unless you have really good reason to,
so you don't lose data continuity.
And if you're monitoring forests,
do whatever the Forest Service FIA program is doing,
unless you have really good reason not to.
Also avoid complex sample designs.
So there were several networks, particularly some out west,
that use really complex panel designs.
And they had completely legitimate reasons
for doing so at the time.
But losing a year of monitoring to COVID
has created way more headaches for their analyses
than it has for those of us with simpler panel designs.
Quality control and quality assurance is very important.
So we re-sample 5% of our plots every year
with a different crew.
And this has helped us catch issues
from the field crew early on,
but it's also helped us improve
or drop methods that weren't very repeatable.
It also gives you confidence in your data
if you have very good precision across crews.
It's also helpful to try collecting
your data digitally wherever possible.
So for most of our monitoring protocols,
we record our data directly
into a database or app in the field.
And data quality has greatly improved as a result,
because we have built in checks
that check against previous surveys.
They check for missing data.
And they also require standardized values.
And as a result, within a day or two
of our crews leaving a park,
we're able to actually certify the data
and start analyzing it, instead of spending
the next month entering and checking data.
And finally look at your data as it's coming in.
So resolving errors is often possible if you find it
within a day or two of the data being collected.
This is also a lot easier to do
if you're collecting your data digitally.
Months or years later, there's really no way to know
whether a weird data point you found was real or an error.
So checking your data frequently is really helpful.
Okay, so that's all I have.
If there's time, I'm happy to answer any questions.
And for those of you looking for summer field work,
we're always looking for crew members for the summer.
And our job announcements usually go out in mid-February.
And if you are interested, feel free to contact me.
My email is included on this slide.
Thank you.