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Hi, I'm Sam Puddicombe.

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I work with the Fish
and Wildlife Department.

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I was a part of this monitoring in 2023

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and also analyzed and
organized the data for it.

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Well Eldridge created
the study back in 2019.

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So over the past three years,

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we monitored 15 repairing tree
plantings on A and R lands.

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Specifically, we were
looking at mortality,

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natural regeneration as a function

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of some pre-site conditions

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or some conditions which
were pre-planting, planting

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and post planting variables.

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So let's talk about it.

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What button do I press to
make it go to the next slide?

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Press space bar?

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That works great, thank you.

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So why monitor?

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I think we can all agree
replanting is good.

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We've been doing it, we are
going to continue to do it.

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So why spend all of this time monitoring?

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And essentially we want
to know if the trees

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that we plant are surviving

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and if any of the natural
forest processes are starting

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to reoccur via natural regeneration.

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Essentially we wanna look
at those two indicators

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and see if they are related

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to any variables we could
potentially track so

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that we can do this work
better in the future.

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Tree planting is sort
of a bandaid solution

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to a really large challenge,
which is restoring all

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of the repairing areas and floodplains.

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So we're just trying to look
as to what variables can we try

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and recreate to create
more successful plantings

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and hopefully natural
regeneration in the future.

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So we've got 15 sites across the state.

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They range greatly in size
from way under an acre

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to over eight acres,
and they were all chosen

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as good repairing areas for tree planting.

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We did all of the planting
between 2018 and 2021.

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And in starting in 2019, we
started to have the concept

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of having a more in depth analysis.

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And so we put in some
more experimental plots

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with some independent variables
that we'll talk about later.

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We did the monitoring
in the past three years.

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So all of our data is zero

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through five years since
the original planting.

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Over the 15 plots or 15 sites,
we've got 81 plots per site.

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It ranges from one to 20 and
they also greatly range in size

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and they also greatly range in the number

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of trees that we planted there.

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So all is to say we
have a great variability

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within our data set and our sample.

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So what did we collect?

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Every tree had a unique
ID that we put on it

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and checked every single year.

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We also monitored what
species it was, its height

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and it's vigor and origin,

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which is the two most important indicators

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for what we're about to talk about.

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Essentially for us, for vigor,

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we're just looking at dead or alive.

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And then for origin we're
seeing if we planted that stem

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or if that stem was naturally occurring.

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There was also a place
for comments and photos.

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We also collected plot level data.

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This was mostly focused on
the ground cover, specifically

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how much thatch was there
and what was comprised of.

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This would be a very
interesting data set to look at.

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That is not what we're looking at today.

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We're just pretty much
looking at mortality

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and natural regeneration.

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So we were looking at the
three sort of categories

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of variables to measure
natural regeneration

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and mortality against.

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And these were pre-planting
conditions, planting conditions,

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and post planting conditions.

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Pre-planting conditions is
essentially what was the site

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before we got there

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and we categorize this in our
mind as high disturbance areas

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and low disturbance areas.

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So high disturbance areas is
somewhere where there was a lot

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of basically bare ground

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before we got there due to some work

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or similar event that occurred.

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So this would be
something like a cornfield

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or a scoured stream
bank from a flood event

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versus a low disturbance
area would be something

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that's pretty well maintained

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with being like a managed
grassland or a hay field.

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The pre-planning condition
can change within the site.

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What you see below in this
photo is one site south

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of Bethel, which is one of
our larger sites where all

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of the different colored
points are different plots

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that are also different
pre-planting conditions.

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So here's some photos

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of different high disturbance
pre-planting conditions.

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It's kind of hard to see,
but it's a bad photo,

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but there's a cow in the
background at the scoured bank one,

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so that's pretty high disturbance.

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And then here's the low disturbance
pre-planting conditions.

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You can see a lot of the grasses
are still intact and tall.

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We also have planting conditions,

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so that's like what occurred at planting.

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The most important one
that we were looking at

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is what we planted.

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So if it was like a livestake

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or a bareroot, a livestake
would be like a willow cutting,

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a bareroot would be something
you got at a nursery.

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We also looked at how we
planted those in terms

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of the spacing, who planted
them in terms of the crew

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and the density that we planted at.

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So stems per acre.

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And then finally we were looking

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at post planting conditions.

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So did we put exclusion
fencing around these trees?

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Did we treat them with herbicide?

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Those were pretty much the only two things

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that we were doing that affected it

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post the trees going in the ground.

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So our metrics.

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We're mostly looking at survival and,

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or rather mortality and
natural regeneration.

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And if we have time, we'll
look sort of at density,

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which completes the picture.

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So mortality, we're looking at
that purely of planted stems,

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so not the natural regenerating ones.

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So what we see here is our data

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that we collected from 2021 to 2023,

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and we can see an increase
of planted stem mortality

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about 10% each year

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with 2023 having a 25% average mortality.

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Two things of note is
I have no idea how good

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or bad 25% mortality is on
par with other tree plantings.

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And then you'll also see
that the standard deviation

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on this graph along with every
single other graph I'm about

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to show is huge and
there's a reason for that

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and we'll talk about it later,

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but basically the variability
within our data is very large,

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which is something important to note.

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So looking at it at our first variable,

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which is the pre-planting
condition, you can see

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that there is an effect

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or there is differences on

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what the pre-planting condition
was versus the mortality.

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Pointing out some outliers
there, untreated knotweed.

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That was one site.

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Unsurprising that there
was a high mortality

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in an area of untreated knotweed,

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but looking at those
factors of disturbance

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that we were talking about
before, things like hay

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and managed grasslands

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and athletic fields, those
are all low disturbance area

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and are kind of spread
across the mortality spectrum

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versus corn and berm cut

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and dirt road, those are
all high disturbance areas

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that are on the lower
end of the mortality.

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I did put those together,
amalgamated the low

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and the high disturbance
areas so that you can kind

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of better see the trend.

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And it's not a huge difference,
it's about 10%, again,

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with huge standard deviations.

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So it doesn't seem that
significant in the graph

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on the right, the left,
yeah, I guess your right.

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And but I separated it out
to two underlying variables,

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which were hay field

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and cornfield, which were sort
of our most robust data sets

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for pre-planting conditions.

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And there you can see a
much larger difference

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in that a hay field, which is a area

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with a low disturbance,

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had a 35% mortality versus a
corn field, which is an area

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of high disturbance, had only
around a 10% mortality, 11%.

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Other planting conditions or
going into planting conditions.

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We looked at the type planted.

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Here you can see

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that there is also not
a significant difference

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between livestakes and bareroots.

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Livestake, again, something
like a willow stake,

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only has about a 10% mortality
increase over bareroot,

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which is a pretty big
indicator of something

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that's successful for something
that's quite a bit cheaper

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and easier to maintain or to get ahold of.

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And then also I think the most fun thing

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that we see here is

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that the volunteer plantings
had a much lower mortality

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over all of the professional
and fish and wildlife crew.

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So if you can get 'em, get 'em

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because apparently they're better.

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So if anything else, not
a significant difference

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between these variables.

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Other planting conditions
we looked at were spacing

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and density.

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Oh, okay, great. Three minutes remaining.

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I can't see a great difference
between any of these.

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Okay, so we're gonna keep on going

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to post-planting conditions.

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Similar story for herbicide

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and exclosures, not a tremendous
difference that we can see.

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And okay, get a few points
on natural regeneration.

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Natural regeneration, which
we looked at as a function

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of stems per acre,

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naturally occurring as a function of time.

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We also saw increase over the three years

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that we were looking and the
most important thing in terms

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of a finding was definitely related

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to the pre-planting conditions.

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Here we see a much more segmented

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of the low disturbance areas

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having a low natural regeneration

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versus the high disturbance areas

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having a higher level
of natural regeneration.

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Here the differences between planting

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or between occurring average
stems an acre are much higher

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with the high disturbance areas having

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around 500 stems an acre
naturally regenerated

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versus the low disturbance
areas only having

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around a hundred.

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This is even more apparent
within the core corn hay example

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that I showed for mortality.

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In terms of planting
conditions, again, we didn't see

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anything that was too concerning
in terms of some variables.

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How much time did you say I have left?

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One minute. Okay.

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Bringing it over to the takeaways here,

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which I would like to
leave with was that yes,

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the higher levels of disturbance
we saw had higher levels

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of natural regeneration.

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That was the variable that
seemed to have the most effect.

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And then I really want to point out

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that this was an
incredibly patchy dataset.

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So natural regeneration
does seem to be patchy.

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There were huge standard
deviations and there was,

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because there was huge plot
by plot level variants.

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And even if you were like in
those plots, you would've seen

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that those natural generation
regenerated corners

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were pretty segmented to
one area or the other.

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Pre-planting condition
also did affect mortality,

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but it seemed less so
you could see that most

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within the hay corn example,

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which were our most like robust data sets.

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And then regarding planting

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and post-planting conditions,

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all of those variables did
not seem as significant.

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But there's still some pretty
big takeaways from there,

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which is that volunteer
planting does not seem

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to increase mortality.

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So they're a great option.

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Livestakes do not seem to have

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that much of a significant
increase in mortality,

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only 10% over bareroot.

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So they also should be considered
as a very viable option.

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And then herbicide and
enclosures, I don't think,

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I think there was too
many confounding variables

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to make many conclusions about that,

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but we did not find them
particularly helpful,

256
00:13:32,190 --> 00:13:33,750
at least within our study.

257
00:13:33,750 --> 00:13:36,000
So it's worth noting

258
00:13:36,000 --> 00:13:37,890
that maybe they shouldn't be considered

259
00:13:37,890 --> 00:13:42,423
like the most important aspect of success.

260
00:13:43,710 --> 00:13:47,160
Yeah, so there's other
graphs to talk about,

261
00:13:47,160 --> 00:13:48,903
but those are the main takeaways.

262
00:13:49,998 --> 00:13:52,998
(audience clapping)

263
00:13:56,760 --> 00:13:59,550
[Announcer] Well, does
anybody have any questions?

264
00:13:59,550 --> 00:14:01,300
Right, I see a question over there.

265
00:14:06,180 --> 00:14:07,013
[Audience Member] When you said yeah,

266
00:14:07,013 --> 00:14:08,250
the herbicide treatment,

267
00:14:08,250 --> 00:14:09,690
that was post-planting, right?

268
00:14:09,690 --> 00:14:11,680
I mean that was, you came in

269
00:14:12,539 --> 00:14:14,250
and herbicide around the seedlings?

270
00:14:14,250 --> 00:14:15,083
Yeah.

271
00:14:15,083 --> 00:14:17,790
Every two years a
glyophosphate application

272
00:14:17,790 --> 00:14:19,140
around the plant.

273
00:14:19,140 --> 00:14:20,190
Hopefully around the plant.

274
00:14:20,190 --> 00:14:22,200
I mean drift is a thing, but you know.

275
00:14:22,200 --> 00:14:23,880
No, I just wondered because you know,

276
00:14:23,880 --> 00:14:25,650
most of the corn fields probably

277
00:14:25,650 --> 00:14:28,440
were no-till herbicide treated beforehand.

278
00:14:28,440 --> 00:14:30,030
I wondered what it would do, you know,

279
00:14:30,030 --> 00:14:32,670
if you did like a site prep
where you treated, you know,

280
00:14:32,670 --> 00:14:36,030
even if there wasn't
corn there, herbicided it

281
00:14:36,030 --> 00:14:38,220
maybe the year before and
then went in and implanted it.

282
00:14:38,220 --> 00:14:41,070
Something like that might be helpful.

283
00:14:41,070 --> 00:14:42,990
Yes, you are certainly correct.

284
00:14:42,990 --> 00:14:45,000
Someone in the room has
already thought about that

285
00:14:45,000 --> 00:14:48,060
and a lot of this data
is in support of work

286
00:14:48,060 --> 00:14:51,120
which is already being done

287
00:14:51,120 --> 00:14:54,000
that I think we'll
speak about in a moment.

288
00:14:54,000 --> 00:14:57,360
But yeah, our glyophosphate
application was post,

289
00:14:57,360 --> 00:15:01,230
yeah, definitely there were
pre-glyphosate applications

290
00:15:01,230 --> 00:15:02,433
like in corn fields.

291
00:15:04,440 --> 00:15:06,140
[Announcer] Any other questions?

292
00:15:08,070 --> 00:15:09,963
First off, great presentation Sam.

293
00:15:10,830 --> 00:15:13,297
I had a question about deer herbivory,

294
00:15:13,297 --> 00:15:14,810
is there any data collected with that

295
00:15:14,810 --> 00:15:18,420
or can you speak
anecdotally if there isn't?

296
00:15:18,420 --> 00:15:19,864
Is that a big issue?

297
00:15:19,864 --> 00:15:22,380
Yeah, definitely at
certain sites we saw that.

298
00:15:22,380 --> 00:15:24,990
That was more put within comments so

299
00:15:24,990 --> 00:15:26,640
that if we did see something that was dead

300
00:15:26,640 --> 00:15:29,070
or not vigorous, we would
often put like beaver brows

301
00:15:29,070 --> 00:15:33,630
or deer brows or something
that we thought was the reason

302
00:15:33,630 --> 00:15:35,823
for its mortality.

303
00:15:36,810 --> 00:15:41,520
And that's why I think that
exclusion fencing dataset

304
00:15:41,520 --> 00:15:44,970
is not super helpful for
just looking at mortality

305
00:15:44,970 --> 00:15:46,140
or natural regeneration.

306
00:15:46,140 --> 00:15:49,140
I think it would be more
interesting to look at growth

307
00:15:49,140 --> 00:15:53,280
or other variables that we kept track of

308
00:15:53,280 --> 00:15:55,290
because I don't think it
tells the full story on yeah,

309
00:15:55,290 --> 00:15:57,450
how it prevents, you know,

310
00:15:57,450 --> 00:15:59,943
potential larger mammal herbivory.

311
00:16:01,106 --> 00:16:04,356
[Audience Member 2] Sweet, thank you.

312
00:16:10,483 --> 00:16:12,870
I just had a question about other factors

313
00:16:12,870 --> 00:16:14,370
that you may have looked at

314
00:16:14,370 --> 00:16:19,110
and weren't able to present on
things like the connectivity

315
00:16:19,110 --> 00:16:21,870
of the floodplain or the
incision ratio of the river.

316
00:16:21,870 --> 00:16:26,190
Did you look at any of those
factors in addition to the tree

317
00:16:26,190 --> 00:16:27,993
and treatment plots?

318
00:16:29,280 --> 00:16:30,480
Definitely anecdotally.

319
00:16:30,480 --> 00:16:34,410
And then I would say we had a couple sites

320
00:16:34,410 --> 00:16:36,390
that were restored floodplains, so areas

321
00:16:36,390 --> 00:16:39,180
that we intentionally would know

322
00:16:39,180 --> 00:16:41,340
that the water would be moving through.

323
00:16:41,340 --> 00:16:45,090
One, most of them were
through like berm removals,

324
00:16:45,090 --> 00:16:48,123
so that did change the
hydraulic connectivity.

325
00:16:49,020 --> 00:16:52,173
So we did track areas that
were going to be, you know,

326
00:16:53,040 --> 00:16:55,830
getting flooded quite
frequently by the river.

327
00:16:55,830 --> 00:16:59,670
How those did, that was a
pre-site condition, I believe.

328
00:16:59,670 --> 00:17:03,147
Just one that didn't have a level of a,

329
00:17:03,147 --> 00:17:05,973
like a disturbance
particularly assigned to it.

330
00:17:07,530 --> 00:17:11,310
And then also anecdotally,
some of the areas

331
00:17:11,310 --> 00:17:14,220
that got flooded, I mean
we know they got flooded

332
00:17:14,220 --> 00:17:17,070
because they just, part of
them weren't there anymore.

333
00:17:17,070 --> 00:17:19,260
We did see actually
like really high levels

334
00:17:19,260 --> 00:17:21,273
of regeneration there in particular.

335
00:17:22,320 --> 00:17:23,163
So yeah.

336
00:17:24,540 --> 00:17:26,370
[Announcer] This will be our last question

337
00:17:27,660 --> 00:17:32,660
Also other conditions like how
like the weather conditions

338
00:17:33,390 --> 00:17:35,010
during planting

339
00:17:35,010 --> 00:17:38,160
or like did you find particular
species that you found,

340
00:17:38,160 --> 00:17:43,160
you know, had higher
survivorship, any other conditions

341
00:17:43,380 --> 00:17:48,347
or you know, anything that you
found that stuck, you know,

342
00:17:48,347 --> 00:17:49,593
stood out?

343
00:17:50,490 --> 00:17:52,860
Yeah, I mean this data set is pretty huge

344
00:17:52,860 --> 00:17:55,563
and these were just a
few variables within it.

345
00:17:58,500 --> 00:18:01,200
I did not take the time to look
at it for this presentation,

346
00:18:01,200 --> 00:18:03,480
but that is something that
we're definitely curious

347
00:18:03,480 --> 00:18:04,313
in asking.

348
00:18:04,313 --> 00:18:06,255
And in terms of whether
at planting, I mean

349
00:18:06,255 --> 00:18:09,510
that would be definitely just
another thing to add on top

350
00:18:09,510 --> 00:18:12,840
of it that you could get from
looking at weather forecasts.

351
00:18:12,840 --> 00:18:14,783
But yeah, that would be very interesting.