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Hello everyone.

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Thank you for choosing me as
your final talk for today.

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(audience laughs)

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I'm extremely honored to be representing

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the Appalachian Club for one of the first,

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one of the first official
capacities in my tenure with them.

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And I would like to share

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with you some preliminary findings from a

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kind of a deep dive into our

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longstanding phonology data sets.

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And I want to emphasize
this is preliminary,

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so I think there's a lotta cool stuff

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that can come out of this.

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I'd be happy to talk with anyone

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after if they are curious
about anything I say.

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So before I get too much further,
I just want to acknowledge

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that all of the data collected here was

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brought to you by a multitude of people

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both within the AMC and
volunteers, interns,

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a full number of people that

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can't really fit all onto this list.

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So the purview of the AMC is primarily

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in this interest in alpine
research and conservation.

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We have done a lot of work
primarily in the Whites,

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but we are, you know, broadly interested

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in alpine areas across the northeast

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and not only alpine,

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but forests that occur
below tree line as well

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both along these high elevation areas

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and forests that we
actively manage and work on.

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And if we zoom into Mount
Washington for a second,

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this graph represents work done

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by my colleague Georgia Murray at the AMC.

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There seems to be
significant warming occurring

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at Pinkham Notch at low elevation

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near the base of Mount Washington.

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And also significant
warming on an annual basis,

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although not quite as dramatically

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at high elevation near the summit.

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If you zoom out for a second

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you can see sort of those
similar thing going on,

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not only on an annual
mean temperature basis

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but broken up by season.

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So particularly in winter and spring

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we're seeing this significant
warming trend through time.

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And these data were taken from

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locations of phenological
observations since 2004

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and are extracted from
Daymet gridded climate data.

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So given that plants are likely to respond

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to warming in some way

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and the mountain of
data which suggests that

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phenology will be impacted
by warming in some way,

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the AMC has been interested
in collecting phenology data

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and have been for quite a while now.

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The first flush of this effort was

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with the Mountain Watch Project

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which established permanent
alpine plots in 2004

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working with within the
AMC with partner groups.

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This was supplemented

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by opportunistic phenology
observations by hikers

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in the White mountains

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and woodland plots at lower elevation

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were established in 2007.

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We transitioned to following

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the National Phenology
Network protocols in 2014

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and which is in addition
of other permanent plots.

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And more recently we've launched a couple

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of iNaturalist projects including,

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let's see if I can remember the names,

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the Northeast Alpine Flower Watch

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and the Fauna and Flora

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along the Appalachian Trail Corridor.

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And I think up to this point

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we have over 33,000 observations.

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So pretty good so far.

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And this is all opportunistic sampling.

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This is just casual citizen,
citizen science derived data.

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And going a little bit into the protocol,

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we have an emphasis on
flowering pheno phases,

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although we you know, obviously
have a lot of information

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on vegetative pheno phases,
senescence, fruiting, et cetera.

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As I said, this is a combination

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of both permanent plots
and opportunistic sampling

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for both AMC staff and others, volunteers.

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And the data can be found
on, in a number of places.

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We have an Access database,

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it can be found within the NPN
framework and on iNaturalist.

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And I've been working pretty hard

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to synthesize all this to distill it down

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to one standardized format
and automate the process.

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It's been quite difficult
and time consuming,

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but I'm still working on that.

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So with this wealth of data

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we can ask some really cool ecological

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and conservation minded questions.

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And as sort of a case study for that,

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I'm thinking about this idea of

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this phenological window in the spring.

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So for those who love walking

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around the forest in the spring

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looking at all the cool spring ephemerals,

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you know that understory plants,

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ephemerals, seedlings either flower

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or leaf out much earlier
than the canopy closes.

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And it's a very critical time

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for these plants because
they're fixing a great deal

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of carbon that they're going to use

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throughout the year when
they have this relatively

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high light condition to work with.

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We know warming is likely
to advance the timing

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of both flowering and canopy leaf out.

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The rate relative to each other

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is going to matter a great deal

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for forest function in general.

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If spring flowering in the understory

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occurs at a rate that

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or advances us faster than canopy closure

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then we have this expanding window

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with more time of high

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for these understory
plants to take advantage

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of this high light condition.

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However, if this time is shrinking

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that could have a great deal of impact

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on the amount of carbon
being able to be fixed

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by understory plant seedlings

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and might have implications
of preceding mortality

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or other things that we care about.

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So this is just one possible avenue

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of research that can be
undertaken with this data set.

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And so coming at this, I developed
four main research points

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or questions I wanted to address.

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The first, what are the
patterns both spatial

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and temporal of understory
spring flowering or leaf out?

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And again, I'll focus mostly
on spring phenology here

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although we could ask questions

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of end of season phenology as well.

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What are the relationships between

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understory phenology and
climate that we can expand on?

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How does understory phenology
compare to canopy closure?

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This idea of the phenological
window and spread

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or to alpine plant phenology,

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which is also really interesting.

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And is there any evidence
for phenological advance

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of understory plants as a result
of particularly warm years

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that we can tease out through our data?

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And so I broke up our plant
species into the three groups.

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So we have woodland forbs, also shrubs,

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canopy trees, and alpine plants.

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The most abundant species

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for these woodland forbs and shrubs,

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there are 14 that I focus in on,

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four canopy trees, and
11 alpine plant species.

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And since I can't possibly
talk about all of them

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I'll choose one representative species

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for each group to key in on.

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So this map on the left
shows you observations taken

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with our Mountain Watch Project.

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There's NPN a little more restricted

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and then brace yourself, boom,

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-our iNaturalist observations.
-(audience laughs)

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And as you can see

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with our citizens
science derived data sets

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we greatly expand the spatial distribution

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and the amount of variation in climate

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that we can capture with this data set.

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So that has been really
great for us to have.

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And I'm only showing you
everything north of Mount Graylock.

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We also have observations galore

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in the Southern Appalachians as well.

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So take it all together,

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I synthesized, collated
nearly 800,000 observations

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taken since 2004, which is a lot.

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And then I did a little bit
of basic exploratory analysis.

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I looked at some correlations

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between the day of year
of flowering or leaf out

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and different climate
environmental variables.

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Calculated mean flowering
time for a number of species,

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just to have that baseline information

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and then use model selection
to kind of pick out

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what climate variables
explained more or less variation

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in the day of year.

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And I wrapped this up with
that phenological window idea

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using Bayesian regression
to compare shifts

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between spring flower
and that canopy closure.

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So looking at some summary results here,

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I'm showing you a median day

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of flowering for Canada
Mayflower over there on the left,

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which is a woodland forb species,

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a tree species Acer rubrum,
red maple over here on the top,

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and the much later flowering

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alpine plant species mountain cranberry.

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Since there was such a a
huge number of observations

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these come out to pretty
normally distributed,

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which is very convenient.

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Also keep in mind that this
could be further broken

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down into individual
years of median flowering.

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So we can see if there's
a particular warm year

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that is, has a earlier
median flowering time.

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Looking at my model comparison

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using multiple linear regression,

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the number of climate and
environmental variables

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that came out to be relatively
more important if you will,

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we've got elevation, latitude,
spring snow water equivalent,

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accumulating growing degree days,

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mean winter temp that's
preceding winter temp,

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which has a big influence on what happens

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in the following spring,
and then mean spring temp.

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And since these two explained

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most of the variation I
focused in on these two

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using more sophisticated
analysis moving forward.

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And plotting some climate relationships

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between that mean spring temperature

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and day of year of either
flowering or leaf out,

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we see that for our woodland
forb species here that

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this negative relationship
is nice and clean here

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and that pretty much breaks down

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with our alpine plant species.

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Then our tree species
is somewhere in between.

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It's negative relationship

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but not a lot of variance explained.

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And I wanna stress that
these are very representative

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of this, these species and
these groups in general.

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Generally the woodland,

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lower elevation woodland species
had this very nice clean,

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clean cut relationship,

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whereas for these others
it broke down a bit.

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As I said, you could pull
out median flowering time

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for individual years.

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And so if you do that
with Canada mayflower

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you see this declining
trend of earlier flowering.

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The color of these dots indicate

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average means spring temperature

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for the locations those
observations were taken

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and you you can kinda see that also tracks

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warming through time.

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I will also just wanna say this does,

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this relationship again
doesn't work as well

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for our trees and for our alpine species.

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So putting all together

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you can see that again our woodland,

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lower elevation woodland species

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has this nice relationship
with mean spring temperature

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and the day of flowering.

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Alpine, again pretty weak.

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And then this interesting thing happened

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with trees where it seems that
the time of canopy closure,

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the leaf out time of these tree species

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kind of went across this relationship

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with woodland understory plants,

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which made me think

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is that phenological window
that I was discussing earlier,

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is that shifting depending
on where you are,

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what temperature regime
you're in essentially?

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So looking at that, I calculated
the temperature sensitivity

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of these individual groups.

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I broke trees into either warm

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or cool areas using an 8.5 degree cutoff.

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And basically what this
y-axis is telling you is

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for every degree of warming,

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how many days early your
flowering is occurring

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or leaf out is occurring.

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And you can see there's
really no difference

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00:11:36,740 --> 00:11:40,730
between woodland understory
plants and this warm tree group.

264
00:11:40,730 --> 00:11:43,730
I also broke this understory
plants the two groups

265
00:11:43,730 --> 00:11:45,620
and they really didn't have any difference

266
00:11:45,620 --> 00:11:46,730
between the two either.

267
00:11:46,730 --> 00:11:51,500
But for this comparison
between woodland plants

268
00:11:51,500 --> 00:11:54,470
and trees leafing out in cool areas

269
00:11:54,470 --> 00:11:56,930
there was this much greater
sensitivity for these,

270
00:11:56,930 --> 00:11:58,910
for individuals in this group,

271
00:11:58,910 --> 00:12:02,960
which could indicate that at
least in these particular areas

272
00:12:02,960 --> 00:12:05,510
which are at higher
elevations and latitudes

273
00:12:05,510 --> 00:12:07,850
they could be experiencing a kind of a

274
00:12:07,850 --> 00:12:10,130
phenological window of contraction,

275
00:12:10,130 --> 00:12:12,470
which other people have seen

276
00:12:12,470 --> 00:12:15,320
and is an idea we should
probably expand on.

277
00:12:15,320 --> 00:12:16,850
And again our core alpine plant species

278
00:12:16,850 --> 00:12:18,470
aren't really doing much.

279
00:12:18,470 --> 00:12:20,210
They don't seem to be very responsive

280
00:12:20,210 --> 00:12:22,313
to warmer temperatures.

281
00:12:24,860 --> 00:12:26,273
So to wrap all this up,

282
00:12:27,470 --> 00:12:29,390
I just wanna impose upon you the idea

283
00:12:29,390 --> 00:12:31,040
that multiple data streams,

284
00:12:31,040 --> 00:12:34,850
multiple data sources
really enhances our spatial

285
00:12:34,850 --> 00:12:38,180
and temporal resolution of
these disparate data sets.

286
00:12:38,180 --> 00:12:39,487
It was really cool to
put all this together

287
00:12:39,487 --> 00:12:41,243
and see what came out.

288
00:12:42,740 --> 00:12:45,230
We now have a baseline understanding

289
00:12:45,230 --> 00:12:48,800
of median flowering times,
medium leaf out times

290
00:12:48,800 --> 00:12:50,540
for multiple understory species

291
00:12:50,540 --> 00:12:53,240
and trees in this region.

292
00:12:53,240 --> 00:12:55,220
And moving forward that
might be, you know,

293
00:12:55,220 --> 00:13:00,220
important to kind of compare to patterns

294
00:13:00,290 --> 00:13:02,330
that we would expect across latitude,

295
00:13:02,330 --> 00:13:05,300
at latitude and elevation were held.

296
00:13:05,300 --> 00:13:06,530
There was later flowering time

297
00:13:06,530 --> 00:13:08,333
at higher latitudes and elevations.

298
00:13:09,500 --> 00:13:11,360
Interestingly these alpine species did not

299
00:13:11,360 --> 00:13:12,590
seem to be as responsive

300
00:13:12,590 --> 00:13:14,490
or sensitive to warming
as woodland species.

301
00:13:14,490 --> 00:13:17,570
And I have many ideas
as to why that might be.

302
00:13:17,570 --> 00:13:18,830
I'm not gonna talk about now

303
00:13:18,830 --> 00:13:21,847
but be happy to address
any questions about that

304
00:13:21,847 --> 00:13:24,230
'cause I could talk about
this for a long time,

305
00:13:24,230 --> 00:13:28,043
and it's an area that we're
gonna be looking into for sure.

306
00:13:29,660 --> 00:13:30,650
It also begs the question

307
00:13:30,650 --> 00:13:32,750
are there any potential
alpine plant species

308
00:13:32,750 --> 00:13:34,310
that might be useful indicators

309
00:13:34,310 --> 00:13:36,530
of a shifting climate in any way?

310
00:13:36,530 --> 00:13:38,333
And there might be other,

311
00:13:39,200 --> 00:13:41,150
other climate variables
we're not exploring right now

312
00:13:41,150 --> 00:13:43,730
that could explain variation in flowering

313
00:13:43,730 --> 00:13:44,993
for alpine plant species.

314
00:13:46,010 --> 00:13:47,330
Differences in spring phenology

315
00:13:47,330 --> 00:13:49,880
between various vegetation
types might be important

316
00:13:49,880 --> 00:13:52,040
for things like forest regeneration.

317
00:13:52,040 --> 00:13:53,810
If you're thinking about shifting patterns

318
00:13:53,810 --> 00:13:55,820
of tree seasoning mortality for instance

319
00:13:55,820 --> 00:13:58,010
with this shifting phenological window,

320
00:13:58,010 --> 00:14:00,320
something that we could also look into.

321
00:14:00,320 --> 00:14:02,420
And like the other
presenters here have said,

322
00:14:02,420 --> 00:14:05,300
there will be climate
winners and losers likely,

323
00:14:05,300 --> 00:14:08,030
and it will not only depend
on the traits of those species

324
00:14:08,030 --> 00:14:09,830
but where you find them in space.

325
00:14:09,830 --> 00:14:12,083
So the spatial context
matters quite a lot.

326
00:14:13,610 --> 00:14:17,030
So moving on, there's a
few cool areas that we're,

327
00:14:17,030 --> 00:14:17,863
that we're diving into.

328
00:14:17,863 --> 00:14:21,153
So right now I'm working up pheno cam,

329
00:14:21,153 --> 00:14:23,490
helping to work up pheno cam images

330
00:14:24,380 --> 00:14:25,550
particularly at Pinkham Notch,

331
00:14:25,550 --> 00:14:27,432
but there could be other video cam images

332
00:14:27,432 --> 00:14:29,090
that we can take from,

333
00:14:29,090 --> 00:14:33,560
and pairing that with satellite
based vegetation indices

334
00:14:33,560 --> 00:14:38,180
that calculates start of
spring, end of spring metrics.

335
00:14:38,180 --> 00:14:40,610
And this is using both Landsat, MODIS,

336
00:14:40,610 --> 00:14:43,400
I've even started looking at Sentinel II,

337
00:14:43,400 --> 00:14:44,420
so I think it's pretty cool.

338
00:14:44,420 --> 00:14:47,600
So this I produced two days ago.

339
00:14:47,600 --> 00:14:49,714
So it's very, very cutting edge right now.

340
00:14:49,714 --> 00:14:50,547
(audience laughs)

341
00:14:50,547 --> 00:14:53,450
So stay tuned for cool
results to come with that.

342
00:14:53,450 --> 00:14:57,470
And there's going to
be a session after this

343
00:14:57,470 --> 00:15:02,470
about some gaps that we have
in our phenology monitoring.

344
00:15:02,600 --> 00:15:05,150
I would suggest to you that tree seedlings

345
00:15:05,150 --> 00:15:06,890
is perhaps one area where we're

346
00:15:06,890 --> 00:15:09,350
missing a lot of crucial information.

347
00:15:09,350 --> 00:15:13,010
In fact, with overstory trees
you can see those red dots,

348
00:15:13,010 --> 00:15:15,503
I lack a lot of that spatial,

349
00:15:17,090 --> 00:15:19,490
that spatial variation that
I had with other groups.

350
00:15:19,490 --> 00:15:23,270
So you know, trees in general,
if people could just like

351
00:15:23,270 --> 00:15:25,220
you know, with their iNat platform

352
00:15:25,220 --> 00:15:27,230
just take a picture of the
canopy when they're walking

353
00:15:27,230 --> 00:15:29,480
in the spring, that'd be really helpful.

354
00:15:29,480 --> 00:15:32,210
And then tree seedlings of
course, which was the subject

355
00:15:32,210 --> 00:15:35,810
of my dissertation and
something that I advocate for.

356
00:15:35,810 --> 00:15:37,430
So with all that

357
00:15:37,430 --> 00:15:40,172
I will take any questions and
thank you for your attention.

358
00:15:40,172 --> 00:15:43,255
(audience applauds)

359
00:15:51,253 --> 00:15:53,870
[Audience Member]
Thanks, that was awesome.

360
00:15:53,870 --> 00:15:55,520
I'm like a super data hungry person.

361
00:15:55,520 --> 00:15:57,733
I'm always like trying to
get my hands on more data

362
00:15:57,733 --> 00:15:59,630
and I'm interested to know like

363
00:15:59,630 --> 00:16:03,260
do you get more bang for
your buck with like the,

364
00:16:03,260 --> 00:16:06,600
your monitoring plots versus
the iNaturalist plots?

365
00:16:06,600 --> 00:16:08,090
-Yeah.
-And like how that-

366
00:16:08,090 --> 00:16:10,790
Yeah, in terms of sheer
number of observations

367
00:16:10,790 --> 00:16:13,520
far more come from our permanent plots.

368
00:16:13,520 --> 00:16:16,310
-Okay.
-But I will say

369
00:16:16,310 --> 00:16:19,640
having that nicely distributed data set

370
00:16:19,640 --> 00:16:21,770
in space is really handy.

371
00:16:21,770 --> 00:16:24,053
So you know, there's trade offs to both.

372
00:16:25,010 --> 00:16:28,190
We thought it was really cool
to bring them both together

373
00:16:28,190 --> 00:16:29,330
to answer these questions,

374
00:16:29,330 --> 00:16:33,230
kind of taking benefits
from both sides of things.

375
00:16:33,230 --> 00:16:35,690
So I advocate for doing both, but yeah.

376
00:16:38,930 --> 00:16:40,070
-Yeah.
-Following up on that

377
00:16:40,070 --> 00:16:42,050
the iNaturalist, so I assume that's

378
00:16:42,050 --> 00:16:44,347
like an opportunistic
thing depending on the,

379
00:16:44,347 --> 00:16:45,740
on the whatever's going out.

380
00:16:45,740 --> 00:16:47,120
-Yeah.
-Do you have any sense

381
00:16:47,120 --> 00:16:49,400
of whether that there's
any like bias in that,

382
00:16:49,400 --> 00:16:50,480
you know, 'cause obviously

383
00:16:50,480 --> 00:16:52,619
-it's gonna be dependent-
-Oh yeah.

384
00:16:52,619 --> 00:16:54,770
-On you know, everything.
-Yeah, it's a working

385
00:16:54,770 --> 00:16:56,352
assumption that it's biased.

386
00:16:56,352 --> 00:16:57,560
(audience laughs)

387
00:16:57,560 --> 00:16:58,720
I mean the reason being people go out

388
00:16:58,720 --> 00:17:00,680
in the woods and they see
beautiful flowering plants

389
00:17:00,680 --> 00:17:01,850
they're gonna take a picture of that,

390
00:17:01,850 --> 00:17:04,940
not the senescing plant or the, you know,

391
00:17:04,940 --> 00:17:05,870
plant with nothing on it,

392
00:17:05,870 --> 00:17:09,140
or a seedling that's just
leaves and that's not very cool.

393
00:17:09,140 --> 00:17:11,270
So yeah, there's probably
an inherent bias.

394
00:17:11,270 --> 00:17:13,190
That helps us at least in
the flowering pheno phase,

395
00:17:13,190 --> 00:17:16,160
but not help us for other pheno phases.

396
00:17:16,160 --> 00:17:18,590
And that definitely needs to be, you know,

397
00:17:18,590 --> 00:17:19,990
taken into account for sure.

398
00:17:21,050 --> 00:17:22,670
So yeah.

399
00:17:22,670 --> 00:17:23,690
Bob.

400
00:17:23,690 --> 00:17:25,670
[Bob] Yeah, thanks Jordan.

401
00:17:25,670 --> 00:17:28,280
You can take this as a lead
into a further discussion

402
00:17:28,280 --> 00:17:30,740
of the disconnect
between the alpine plants

403
00:17:30,740 --> 00:17:33,020
-and warming in the alpine.
-Yeah.

404
00:17:33,020 --> 00:17:37,100
[Bob] But might a confounding
factor be snow depth

405
00:17:37,100 --> 00:17:39,980
because if you get, you know, late snow,

406
00:17:39,980 --> 00:17:41,360
the plants are just gonna get a start

407
00:17:41,360 --> 00:17:43,100
that much later regardless
of the temperature.

408
00:17:43,100 --> 00:17:45,440
I mean the temperature will
obviously melt the snow sooner

409
00:17:45,440 --> 00:17:47,060
but if you have a big snow year,

410
00:17:47,060 --> 00:17:48,650
the plants are gonna get a late start

411
00:17:48,650 --> 00:17:50,690
regardless of what the
ambient temperatures are.

412
00:17:50,690 --> 00:17:52,490
Yeah, well I'm glad you
brought that up Bob,

413
00:17:52,490 --> 00:17:53,617
because we're currently...

414
00:17:53,617 --> 00:17:54,719
(audience laughs)

415
00:17:54,719 --> 00:17:57,920
-Planned
-We're currently working

416
00:17:57,920 --> 00:18:00,590
within a community snow
observation framework.

417
00:18:00,590 --> 00:18:01,430
So we're advocating

418
00:18:01,430 --> 00:18:04,910
for citizen science drive snow
depth measurements as well

419
00:18:04,910 --> 00:18:05,780
from these areas,

420
00:18:05,780 --> 00:18:08,600
in addition to our own
internal monitoring system

421
00:18:08,600 --> 00:18:11,000
where we're putting out snow
stakes all over the place.

422
00:18:11,000 --> 00:18:13,790
So that is something that
we're actively working on.

423
00:18:13,790 --> 00:18:17,000
I did include snow water
equivalent in these models.

424
00:18:17,000 --> 00:18:18,440
It's not the same obviously,

425
00:18:18,440 --> 00:18:20,360
but it was trying to get at that.

426
00:18:20,360 --> 00:18:22,400
I suspect the complex topography

427
00:18:22,400 --> 00:18:24,229
at high elevations probably
has something to do with

428
00:18:24,229 --> 00:18:25,643
the lack of response.

429
00:18:26,930 --> 00:18:27,950
I think you had one.

430
00:18:27,950 --> 00:18:29,810
[Participant] Yeah, I mean,
just as a follow up on that,

431
00:18:29,810 --> 00:18:32,570
I wanted you to expand on the
why for the alpine plants.

432
00:18:32,570 --> 00:18:35,210
But I was wondering if
you looked at models

433
00:18:35,210 --> 00:18:36,320
just for the alpine plants

434
00:18:36,320 --> 00:18:38,060
and if the winter temperatures,

435
00:18:38,060 --> 00:18:42,050
where if that was a more
important cue than warming.

436
00:18:42,050 --> 00:18:42,883
Forgot about snow,

437
00:18:42,883 --> 00:18:45,980
'cause I live in a place where
I don't have lots of snow.

438
00:18:45,980 --> 00:18:47,270
But then I'm also wondering if you think

439
00:18:47,270 --> 00:18:49,800
that these plants are
perhaps using photo period

440
00:18:50,750 --> 00:18:51,590
as their signal,

441
00:18:51,590 --> 00:18:54,170
because the growing season
is just relatively so short

442
00:18:54,170 --> 00:18:57,598
and just spring temperatures
are less reliable than-

443
00:18:57,598 --> 00:18:58,431
Yeah.

444
00:18:59,960 --> 00:19:02,690
Yeah, I agree, I agree
with everything you said.

445
00:19:02,690 --> 00:19:03,920
We are interested in looking at things

446
00:19:03,920 --> 00:19:06,320
like chilling degree days and yes,

447
00:19:06,320 --> 00:19:08,270
know that, as I said

448
00:19:08,270 --> 00:19:10,490
as possible co-variants that might explain

449
00:19:10,490 --> 00:19:13,433
this lack of observed response.

450
00:19:15,110 --> 00:19:17,390
You know talking about the
alpine plant species in general

451
00:19:17,390 --> 00:19:19,790
if I have an ecological theory

452
00:19:19,790 --> 00:19:21,350
and a data theory.

453
00:19:21,350 --> 00:19:24,680
One ecological, these
plants are very hardy.

454
00:19:24,680 --> 00:19:26,480
They've adapted to a
very harsh environment.

455
00:19:26,480 --> 00:19:29,810
So maybe a lack of change,

456
00:19:29,810 --> 00:19:32,930
a stability in flowering
time is adaptive in some way.

457
00:19:32,930 --> 00:19:33,890
That's one thing.

458
00:19:33,890 --> 00:19:36,530
Two, the data, this, the
climate data is extracted

459
00:19:36,530 --> 00:19:38,090
from Daymet which is relatively coarse

460
00:19:38,090 --> 00:19:40,550
as anyone has worked with it knows.

461
00:19:40,550 --> 00:19:43,730
I'm working on making fire
scale temperature maps

462
00:19:43,730 --> 00:19:47,570
across small region to see
if that might play a role.

463
00:19:47,570 --> 00:19:49,500
But we need a way to, you know

464
00:19:50,630 --> 00:19:54,680
have more localized fine
scale, find tune data sets,

465
00:19:54,680 --> 00:19:57,230
climate data sets to really
get at these questions.

466
00:19:58,820 --> 00:20:01,940
But if anyone has any
ideas about how to better

467
00:20:01,940 --> 00:20:04,190
approach these questions,
please let me know.

468
00:20:08,250 --> 00:20:10,132
(audience member talking faintly)

469
00:20:10,132 --> 00:20:13,215
(audience applauds)