Hello everyone.
Thank you for choosing me as your final talk for today.
(audience laughs)
I'm extremely honored to be representing
the Appalachian Club for one of the first,
one of the first official capacities in my tenure with them.
And I would like to share
with you some preliminary findings from a
kind of a deep dive into our
longstanding phonology data sets.
And I want to emphasize this is preliminary,
so I think there's a lotta cool stuff
that can come out of this.
I'd be happy to talk with anyone
after if they are curious about anything I say.
So before I get too much further, I just want to acknowledge
that all of the data collected here was
brought to you by a multitude of people
both within the AMC and volunteers, interns,
a full number of people that
can't really fit all onto this list.
So the purview of the AMC is primarily
in this interest in alpine research and conservation.
We have done a lot of work primarily in the Whites,
but we are, you know, broadly interested
in alpine areas across the northeast
and not only alpine,
but forests that occur below tree line as well
both along these high elevation areas
and forests that we actively manage and work on.
And if we zoom into Mount Washington for a second,
this graph represents work done
by my colleague Georgia Murray at the AMC.
There seems to be significant warming occurring
at Pinkham Notch at low elevation
near the base of Mount Washington.
And also significant warming on an annual basis,
although not quite as dramatically
at high elevation near the summit.
If you zoom out for a second
you can see sort of those similar thing going on,
not only on an annual mean temperature basis
but broken up by season.
So particularly in winter and spring
we're seeing this significant warming trend through time.
And these data were taken from
locations of phenological observations since 2004
and are extracted from Daymet gridded climate data.
So given that plants are likely to respond
to warming in some way
and the mountain of data which suggests that
phenology will be impacted by warming in some way,
the AMC has been interested in collecting phenology data
and have been for quite a while now.
The first flush of this effort was
with the Mountain Watch Project
which established permanent alpine plots in 2004
working with within the AMC with partner groups.
This was supplemented
by opportunistic phenology observations by hikers
in the White mountains
and woodland plots at lower elevation
were established in 2007.
We transitioned to following
the National Phenology Network protocols in 2014
and which is in addition of other permanent plots.
And more recently we've launched a couple
of iNaturalist projects including,
let's see if I can remember the names,
the Northeast Alpine Flower Watch
and the Fauna and Flora
along the Appalachian Trail Corridor.
And I think up to this point
we have over 33,000 observations.
So pretty good so far.
And this is all opportunistic sampling.
This is just casual citizen, citizen science derived data.
And going a little bit into the protocol,
we have an emphasis on flowering pheno phases,
although we you know, obviously have a lot of information
on vegetative pheno phases, senescence, fruiting, et cetera.
As I said, this is a combination
of both permanent plots and opportunistic sampling
for both AMC staff and others, volunteers.
And the data can be found on, in a number of places.
We have an Access database,
it can be found within the NPN framework and on iNaturalist.
And I've been working pretty hard
to synthesize all this to distill it down
to one standardized format and automate the process.
It's been quite difficult and time consuming,
but I'm still working on that.
So with this wealth of data
we can ask some really cool ecological
and conservation minded questions.
And as sort of a case study for that,
I'm thinking about this idea of
this phenological window in the spring.
So for those who love walking
around the forest in the spring
looking at all the cool spring ephemerals,
you know that understory plants,
ephemerals, seedlings either flower
or leaf out much earlier than the canopy closes.
And it's a very critical time
for these plants because they're fixing a great deal
of carbon that they're going to use
throughout the year when they have this relatively
high light condition to work with.
We know warming is likely to advance the timing
of both flowering and canopy leaf out.
The rate relative to each other
is going to matter a great deal
for forest function in general.
If spring flowering in the understory
occurs at a rate that
or advances us faster than canopy closure
then we have this expanding window
with more time of high
for these understory plants to take advantage
of this high light condition.
However, if this time is shrinking
that could have a great deal of impact
on the amount of carbon being able to be fixed
by understory plant seedlings
and might have implications of preceding mortality
or other things that we care about.
So this is just one possible avenue
of research that can be undertaken with this data set.
And so coming at this, I developed four main research points
or questions I wanted to address.
The first, what are the patterns both spatial
and temporal of understory spring flowering or leaf out?
And again, I'll focus mostly on spring phenology here
although we could ask questions
of end of season phenology as well.
What are the relationships between
understory phenology and climate that we can expand on?
How does understory phenology compare to canopy closure?
This idea of the phenological window and spread
or to alpine plant phenology,
which is also really interesting.
And is there any evidence for phenological advance
of understory plants as a result of particularly warm years
that we can tease out through our data?
And so I broke up our plant species into the three groups.
So we have woodland forbs, also shrubs,
canopy trees, and alpine plants.
The most abundant species
for these woodland forbs and shrubs,
there are 14 that I focus in on,
four canopy trees, and 11 alpine plant species.
And since I can't possibly talk about all of them
I'll choose one representative species
for each group to key in on.
So this map on the left shows you observations taken
with our Mountain Watch Project.
There's NPN a little more restricted
and then brace yourself, boom,
-our iNaturalist observations. -(audience laughs)
And as you can see
with our citizens science derived data sets
we greatly expand the spatial distribution
and the amount of variation in climate
that we can capture with this data set.
So that has been really great for us to have.
And I'm only showing you everything north of Mount Graylock.
We also have observations galore
in the Southern Appalachians as well.
So take it all together,
I synthesized, collated nearly 800,000 observations
taken since 2004, which is a lot.
And then I did a little bit of basic exploratory analysis.
I looked at some correlations
between the day of year of flowering or leaf out
and different climate environmental variables.
Calculated mean flowering time for a number of species,
just to have that baseline information
and then use model selection to kind of pick out
what climate variables explained more or less variation
in the day of year.
And I wrapped this up with that phenological window idea
using Bayesian regression to compare shifts
between spring flower and that canopy closure.
So looking at some summary results here,
I'm showing you a median day
of flowering for Canada Mayflower over there on the left,
which is a woodland forb species,
a tree species Acer rubrum, red maple over here on the top,
and the much later flowering
alpine plant species mountain cranberry.
Since there was such a a huge number of observations
these come out to pretty normally distributed,
which is very convenient.
Also keep in mind that this could be further broken
down into individual years of median flowering.
So we can see if there's a particular warm year
that is, has a earlier median flowering time.
Looking at my model comparison
using multiple linear regression,
the number of climate and environmental variables
that came out to be relatively more important if you will,
we've got elevation, latitude, spring snow water equivalent,
accumulating growing degree days,
mean winter temp that's preceding winter temp,
which has a big influence on what happens
in the following spring, and then mean spring temp.
And since these two explained
most of the variation I focused in on these two
using more sophisticated analysis moving forward.
And plotting some climate relationships
between that mean spring temperature
and day of year of either flowering or leaf out,
we see that for our woodland forb species here that
this negative relationship is nice and clean here
and that pretty much breaks down
with our alpine plant species.
Then our tree species is somewhere in between.
It's negative relationship
but not a lot of variance explained.
And I wanna stress that these are very representative
of this, these species and these groups in general.
Generally the woodland,
lower elevation woodland species had this very nice clean,
clean cut relationship,
whereas for these others it broke down a bit.
As I said, you could pull out median flowering time
for individual years.
And so if you do that with Canada mayflower
you see this declining trend of earlier flowering.
The color of these dots indicate
average means spring temperature
for the locations those observations were taken
and you you can kinda see that also tracks
warming through time.
I will also just wanna say this does,
this relationship again doesn't work as well
for our trees and for our alpine species.
So putting all together
you can see that again our woodland,
lower elevation woodland species
has this nice relationship with mean spring temperature
and the day of flowering.
Alpine, again pretty weak.
And then this interesting thing happened
with trees where it seems that the time of canopy closure,
the leaf out time of these tree species
kind of went across this relationship
with woodland understory plants,
which made me think
is that phenological window that I was discussing earlier,
is that shifting depending on where you are,
what temperature regime you're in essentially?
So looking at that, I calculated the temperature sensitivity
of these individual groups.
I broke trees into either warm
or cool areas using an 8.5 degree cutoff.
And basically what this y-axis is telling you is
for every degree of warming,
how many days early your flowering is occurring
or leaf out is occurring.
And you can see there's really no difference
between woodland understory plants and this warm tree group.
I also broke this understory plants the two groups
and they really didn't have any difference
between the two either.
But for this comparison between woodland plants
and trees leafing out in cool areas
there was this much greater sensitivity for these,
for individuals in this group,
which could indicate that at least in these particular areas
which are at higher elevations and latitudes
they could be experiencing a kind of a
phenological window of contraction,
which other people have seen
and is an idea we should probably expand on.
And again our core alpine plant species
aren't really doing much.
They don't seem to be very responsive
to warmer temperatures.
So to wrap all this up,
I just wanna impose upon you the idea
that multiple data streams,
multiple data sources really enhances our spatial
and temporal resolution of these disparate data sets.
It was really cool to put all this together
and see what came out.
We now have a baseline understanding
of median flowering times, medium leaf out times
for multiple understory species
and trees in this region.
And moving forward that might be, you know,
important to kind of compare to patterns
that we would expect across latitude,
at latitude and elevation were held.
There was later flowering time
at higher latitudes and elevations.
Interestingly these alpine species did not
seem to be as responsive
or sensitive to warming as woodland species.
And I have many ideas as to why that might be.
I'm not gonna talk about now
but be happy to address any questions about that
'cause I could talk about this for a long time,
and it's an area that we're gonna be looking into for sure.
It also begs the question
are there any potential alpine plant species
that might be useful indicators
of a shifting climate in any way?
And there might be other,
other climate variables we're not exploring right now
that could explain variation in flowering
for alpine plant species.
Differences in spring phenology
between various vegetation types might be important
for things like forest regeneration.
If you're thinking about shifting patterns
of tree seasoning mortality for instance
with this shifting phenological window,
something that we could also look into.
And like the other presenters here have said,
there will be climate winners and losers likely,
and it will not only depend on the traits of those species
but where you find them in space.
So the spatial context matters quite a lot.
So moving on, there's a few cool areas that we're,
that we're diving into.
So right now I'm working up pheno cam,
helping to work up pheno cam images
particularly at Pinkham Notch,
but there could be other video cam images
that we can take from,
and pairing that with satellite based vegetation indices
that calculates start of spring, end of spring metrics.
And this is using both Landsat, MODIS,
I've even started looking at Sentinel II,
so I think it's pretty cool.
So this I produced two days ago.
So it's very, very cutting edge right now.
(audience laughs)
So stay tuned for cool results to come with that.
And there's going to be a session after this
about some gaps that we have in our phenology monitoring.
I would suggest to you that tree seedlings
is perhaps one area where we're
missing a lot of crucial information.
In fact, with overstory trees you can see those red dots,
I lack a lot of that spatial,
that spatial variation that I had with other groups.
So you know, trees in general, if people could just like
you know, with their iNat platform
just take a picture of the canopy when they're walking
in the spring, that'd be really helpful.
And then tree seedlings of course, which was the subject
of my dissertation and something that I advocate for.
So with all that
I will take any questions and thank you for your attention.
(audience applauds)
[Audience Member] Thanks, that was awesome.
I'm like a super data hungry person.
I'm always like trying to get my hands on more data
and I'm interested to know like
do you get more bang for your buck with like the,
your monitoring plots versus the iNaturalist plots?
-Yeah. -And like how that-
Yeah, in terms of sheer number of observations
far more come from our permanent plots.
-Okay. -But I will say
having that nicely distributed data set
in space is really handy.
So you know, there's trade offs to both.
We thought it was really cool to bring them both together
to answer these questions,
kind of taking benefits from both sides of things.
So I advocate for doing both, but yeah.
-Yeah. -Following up on that
the iNaturalist, so I assume that's
like an opportunistic thing depending on the,
on the whatever's going out.
-Yeah. -Do you have any sense
of whether that there's any like bias in that,
you know, 'cause obviously
-it's gonna be dependent- -Oh yeah.
-On you know, everything. -Yeah, it's a working
assumption that it's biased.
(audience laughs)
I mean the reason being people go out
in the woods and they see beautiful flowering plants
they're gonna take a picture of that,
not the senescing plant or the, you know,
plant with nothing on it,
or a seedling that's just leaves and that's not very cool.
So yeah, there's probably an inherent bias.
That helps us at least in the flowering pheno phase,
but not help us for other pheno phases.
And that definitely needs to be, you know,
taken into account for sure.
So yeah.
Bob.
[Bob] Yeah, thanks Jordan.
You can take this as a lead into a further discussion
of the disconnect between the alpine plants
-and warming in the alpine. -Yeah.
[Bob] But might a confounding factor be snow depth
because if you get, you know, late snow,
the plants are just gonna get a start
that much later regardless of the temperature.
I mean the temperature will obviously melt the snow sooner
but if you have a big snow year,
the plants are gonna get a late start
regardless of what the ambient temperatures are.
Yeah, well I'm glad you brought that up Bob,
because we're currently...
(audience laughs)
-Planned -We're currently working
within a community snow observation framework.
So we're advocating
for citizen science drive snow depth measurements as well
from these areas,
in addition to our own internal monitoring system
where we're putting out snow stakes all over the place.
So that is something that we're actively working on.
I did include snow water equivalent in these models.
It's not the same obviously,
but it was trying to get at that.
I suspect the complex topography
at high elevations probably has something to do with
the lack of response.
I think you had one.
[Participant] Yeah, I mean, just as a follow up on that,
I wanted you to expand on the why for the alpine plants.
But I was wondering if you looked at models
just for the alpine plants
and if the winter temperatures,
where if that was a more important cue than warming.
Forgot about snow,
'cause I live in a place where I don't have lots of snow.
But then I'm also wondering if you think
that these plants are perhaps using photo period
as their signal,
because the growing season is just relatively so short
and just spring temperatures are less reliable than-
Yeah.
Yeah, I agree, I agree with everything you said.
We are interested in looking at things
like chilling degree days and yes,
know that, as I said
as possible co-variants that might explain
this lack of observed response.
You know talking about the alpine plant species in general
if I have an ecological theory
and a data theory.
One ecological, these plants are very hardy.
They've adapted to a very harsh environment.
So maybe a lack of change,
a stability in flowering time is adaptive in some way.
That's one thing.
Two, the data, this, the climate data is extracted
from Daymet which is relatively coarse
as anyone has worked with it knows.
I'm working on making fire scale temperature maps
across small region to see if that might play a role.
But we need a way to, you know
have more localized fine scale, find tune data sets,
climate data sets to really get at these questions.
But if anyone has any ideas about how to better
approach these questions, please let me know.
(audience member talking faintly)
(audience applauds)