Awesome, thanks for the introduction.
Thank you everyone for coming to the talk,
here and virtually.
And first, yeah, I'm Larry, I'm with the Vermont Co-Op Unit.
I am an ecologist, and I am a computer scientist.
And I work with a lot of great people on this project
who are my co-authors on this who helped to collect data,
folks from Green Mountain National Forest,
Vermont Fish Wildlife Service,
as well as we received support from FEMC
and Ruffed Grouse Society
in pulling this project off.
And so the title
"Forest Monitoring for Early Successional Species,"
I could have said "Early Successional Bird Species,"
if you didn't figure out that was the direction
this was going already.
And this is an ongoing project.
So I'm gonna give you a bit of a progress update
on what we've done so far and where we're at,
and what our plans are for what comes next.
So first, just to place this in a particular bird species,
the ruffed grouse.
Anyone here ever see a ruffed grouse out in the woods?
-Usually you hear 'em first -(students laughing)
before you see 'em and they scare the heck out of ya.
They flush out from under your feet,
but maybe you don't see 'em as much as you used to.
This is could be considered an early successional species.
They really do well in habitats
that have a mixture of different forest ages.
Young forest as well as old forest together is
where you tend to get a lot of ruffed grouse.
And as we all know, the Northeast has become
much more forested over time.
And so, we might expect
that perhaps ruffed grouse would decline
as a result of that change in habitat.
This ruffed grouse I actually saw many years ago
in Garrison, New York, where I don't think they are
as common as they used to be, and it actually attacked me.
It tried to, it tried to take off my fingers,
-(students laughing) -which is a really cool thing
about grouse.
They can be really tame.
But if you look at data to see,
you know, how are ruffed grouse doing?
It tends to paint, at least in the northeast,
a not so great picture.
This is a graph of data from 1960 to present
from the Burlington Christmas Bird Count,
which is taking place for the 75th year this Sunday.
[man in audience] Oh, wow.
And what we see is that they were fairly regular,
not common, but regular up until about 10 years ago.
At which point they've become really, really hard to find.
And this is a trend.
I picked Burlington 'cause we're all here,
virtually or in person,
but this trend is consistent across
other Christmas bird counts throughout the region.
We look at other data streams,
such as the Breeding Bird Survey,
we see similar declines in ruffed grouse through time,
but as we try to get more nuanced,
this is a really new feature for eBird.
If you're not birders and not familiar with eBird,
it's a really cool resource.
But they've put together all their data,
I think from 2007 to 2020 to build these trend maps,
which really give you a fine scaled vision
of where these species are increasing
and where they're decreasing.
And it paints a much more nuanced picture for ruffed grouse.
We see that, yeah, in huge swaths of the Northeast,
they're declining, but in some parts farther to our north,
they're actually increasing, which, you know,
begs the question,
"Could change in climate be in play here too?"
So, as we try to get more fine-grained
about what is going on with ruffed grouse,
and we look more fine-grained in Vermont
at the evidence from the Breeding Bird Atlas,
we see that their decline in Vermont
actually isn't consistent throughout the state despite
what the previous slide made it seem like that.
And they've really declined, declined a lot
in the Champlain Valley and Connecticut River Valley,
but in other parts of the state they've held steady
or maybe even increased a teeny bit.
So, and, if we broaden our our scope
to other early successional species,
we see a really mixed bag.
Some species that have declined, you know, precipitously,
others of which have held fairly steady
and yet others of which have, have slightly increased.
And obviously there's lots of factors at play.
Forest type is one of those
and habitat type is one of those.
And these different species have all, you know,
all sorts of different micro type habitats that they,
that they can inhabit.
So, you know, it begs the question, you know,
what, what is the influence of forest,
of forest type, early successional habitat,
and in particular we look at it through the lens
of forest management, on the abundance
and the presence of these species.
So, that's the question that we're trying to answer.
For, you know, what is the impact of timber harvest
on the Avifauna, in particular,
these early successional species
that will come in after a cut?
And the way we went about trying to answer this question
is through an acoustic survey.
So, starting in the late spring and early summer
of this year, we deployed roughly 50 microphones.
They were Song Meter Micros from Wildlife Acoustics,
and we, we deployed them throughout the,
the Green Mountain National Forest,
some in the Southern Green Mountains,
some in the Northern Green Mountains.
And that map represents all of those locations.
And we left them out there
as long as their batteries lasted,
which was really through late fall.
We got, because of the timing of, of the start of the study,
we got a little bit of a late start for the,
the really vocal period for some species,
like ruffed grouse, but still within the window
for other species like mourning warblers for example.
But we left those units out
through basically November, December.
There's a couple of them still out there
that have yet to be collected.
And I'd say about two-thirds of these units are paired
with trail cameras that were part of a preexisting study.
So especially for these new sites,
we really tried to target early successional species.
So we get a mix of different habitat types,
some of these acoustic recorders
that are close to recent timber cuts,
others that are in more well-established forest.
And so we began collecting data, you know, just, you know,
over the last month really.
And this is sort of, well, let's just get a look from,
you know, from the field.
So this is the same site in the spring
when we set the camera and in the fall...
(or I say camera cause I do a lot of camera trap stuff too)
We set the, the acoustic recorders in the spring;
same site in the fall.
It's hard to see, but there it is
on the tree right there.
Oh, I have a laser pointer, cool.
-There's, -(students laughing)
there's the acoustic recorder
and there's the same recorder on the same tree.
And so we tried to set these as close as we could,
in most cases, to these recent cuts.
The cut is a little hard to make out,
but it's in the distance.
You see where the tall trees sort of stop right here.
This was the recent cut.
So, we tried to place them as close as we could
while also trying to find spots that were viable
for this paired camera recorder setup.
And, you know, in a lot of these regenerating stands,
the brush is too thick to put one
of these recorders right in the middle,
'cause our trail cams will be useless,
the vegetation is just too thick.
So, we often think, you know,
vocal repertoire is carried a little bit
into the surrounding forest.
So, so we placed them as close to the edges as we could,
in most cases and, and let them sit.
And so the, you know,
that was the first part is just get,
just get these devices out there
so we can start collecting data.
Oh, you gotta be kidding me.
(woman chuckles)
Okay, so we'll skip this one.
What this was gonna show is just a picture
of a recorder and a camera together on the same tree.
So pretend, pretend that's,
-what you're looking at. -(students laughing)
-[woman in audience] Wow. -(students laugh)
It worked on the test run, so I don't know.
But, but, so anyways, so here's a recorder on a tree
and then we had a data collection protocol
because we had these, you know,
this study's actually part
of a larger regional monitoring effort,
called the Northeast Wildlife Monitoring Network,
and so we've adopted a
standardized data collection protocol.
And this is a picture
of my buddy John Peckham collecting data.
So it's a bit hard to make out,
but on the tree here, here's a recorder
and in his hand is his phone.
We use Survey123, which a bunch
of people are probably familiar with,
is an Esri product that allows you to do digital forms
that can be filled out from the field.
And so John's filling out the survey form on Survey 123
to collect all of the site characteristics,
all of the site visit metadata,
including what the equipment was, where we were,
what the date was,
what are the surrounding conditions,
what's the condition of the equipment.
So, we collect a lot of different types
of metadata related to the site visit.
And then, after John clicks send on that survey form,
it, we use Webhooks,
which are a tool that you can use to automate workflows.
It's a Microsoft product, so it works well with SharePoint,
which is our cloud storage, as well as Survey123.
So when John clicks send on his survey form,
the first thing that happens is a folder is created
in the cloud, in SharePoint,
and the name of that folder, that directory,
is just a concatenation of the location name,
the equipment name, and the date.
So it makes it really easy for John,
when he gets back from the field,
to figure out where he uploads his data too.
The next thing that happens is John gets an email,
it says, you know, thank you for your field visit.
And that email includes a link to that SharePoint folder
where he can upload his data.
And then finally, John's able to upload his data
and into that folder is also copied this survey response,
which puts all the site metadata
from that data collection protocol in the field,
all of the media data that he's collected from the device,
all in one spot in a really neat organized way.
And so this, this procedure's been worked out through the,
through the Vermont co-op unit in,
in the project that I'm involved with called AMMonitor,
which is an R package that we are, it's been developed,
we're continuing to improve it.
And we're sort of branding it
as a data management ecosystem.
So it incorporates, you know, this, this first part,
the remote data collection, a piece of the workflow,
which is often neglected,
but an important part for us
to really make sure is well established
so that we can scale this and look
at a regional type model, where people, you know,
in really spread out, disparate locations,
can use the same protocol to collect data
under the same, yeah, under the same format.
And really be able to merge that data
and look at the big picture
and really alleviate some of those problems
of having data in different formats collected
by different people.
So that's sort of the first part of the wheel that we,
that we've just looked at,
is the remote data collection piece.
And then the cloud storage piece,
currently we've been using SharePoint,
but we're moving towards Amazon Web Services,
which affords us great opportunities
to apply models on our data,
which we'll look at in a minute.
Which brings us to the machine learning piece,
because we collect data at volumes
that's just far too vast for us to manually inspect,
you know, one by one,
so we do need to employ machine learning.
And so, my talk will take us through, you know,
these first three rungs of the wheel, but the,
the real concept behind AMMonitor is
that it's all these different aspects of the workflow
that you need in a remote wireless monitoring project
to take it from the, from the field to the file system,
to the decision tools that you need
in order to inform management decisions.
So we're measure conservation benchmarks.
So, so these later parts of the wheel are,
are still to come.
Right now we're still just collecting data,
but I didn't wanna leave it
just at the data collection part.
I guess one more thing that I sort of said this,
but to just visualize it,
everybody who uses this AMMonitor management system,
we have a number of different projects,
they have the same underlying database
which stores the data,
they're using the same Survey123 form.
So we have a bunch of independent projects
that on their own are collecting data,
but that data gets collected in the same format
and then stored in the same location
so that we can look at that data together
from a regional perspective
and start to build better models
because we have just, you know,
a greater wealth of data.
And yeah, a species that might be rare in one region,
might be more common in another.
So you end up with, you know, better sample sizes
for the species you're trying to model.
It's just this concept
of a regional network is really important.
But, the regional network, up until this point,
has been mostly trail cameras.
This was really a pilot for incorporating acoustic data.
And acoustic data comes with its own sets of challenges.
So, yeah, onto the data analysis.
So far, we've collected almost all of our 50 ish microphones
from the previous slide that we're each recording
about 37 minutes of audio per day, every day,
for months until the batteries died.
So if you add that up over the course of the season,
we could be looking at, you know,
upwards of 90 hours of recordings per site,
multiply that by, you know, roughly 50 sites.
It it would take you 130 days, 24 hours a day
to listen to all these audio recordings.
So obviously the, you know, that's not gonna work.
We need a different approach in order to deal
with this vast volume of data.
And so, our first crack at this was to use BirdNET.
Not many birders in, in the room,
-so I'm not feeling optimistic -(audience laughs)
about a lot of use going on,
but has anyone used the, the app called Merlin?
Wow, okay, so a lot
-of non-birders using Merlin. -(audience laughing)
Well that's all, well maybe that's your,
your ticket into, into the birding world.
It's a really cool app that basically,
you can hold up your phone to the world,
it'll record the, you know, whatever birds are singing
and then in real time tell you
what those birds are,
and it actually works really well.
I mean, occasionally it'll say you have,
like, a Bar-headed goose flying overhead.
-You know that's not right, -(laughing)
but a lot of times it gets it right.
And so, that was our first crack,
was to use BirdNET to try to,
you know, make this data manageable.
And so just, you know, really, over the last couple days,
we've gotten BirdNET running on a small sample
of the data, really just to showcase to you all here today
so that we can see what that looks like.
And still to be determined,
is how good are these BirdNET results?
Like, how much can we really bank on this?
And Angela really set it up well
in terms of talking about like the, the false negatives
or the, you know, the omissions
where, yeah, the species, the focal species was there
but you missed it.
And so we still have to assess
how well it's doing with that.
But just to see, you know, what it looks like
I ran BirdNET, which is, you know, the model
that we looked at, it sort of takes a subset
of all the species it can ID.
It narrowed it down to just 200 species that it can predict,
the species that are likely to be encountered in Vermont.
And so I ran it on all 87 hours of data
for one particular site and just looked at how it did
in predicting the presence of certain species.
It does give a confidence level
in terms of how likely it is
that it's detection is accurate.
And so, I just filtered out any detections
that had a confidence of 0.5 or above.
It's on a zero to one scale.
And so, you know, for woodcock, for example,
these were the detections we have for American woodcock,
which is another species that can tend to favor open areas.
And so, in the very first couple days it picked up a bunch
of woodcocks and I listened to these
and confirmed they were actually woodcocks.
And woodcock is a cool species
because it actually, they tend to continually do,
they continue doing their display,
well into the breeding season.
Way past the point where breeding is complete.
And so, we were actually picking up woodcocks periodically
throughout the season, which makes sense and,
and is sort of what we'd expect.
I haven't manually verified all of these yet
'cuz this data is pretty new,
and you'll notice there is a chunk
where the model hasn't been run yet.
So, we still have a few blanks, but so far,
you know, pretty promising for woodcock.
But how does it do with other species?
I tried mourning warbler,
another early successional 11th species
that really comes in and likes
that early regenerating forest.
And we have, like, a lot
of morning warbler detections,
like right during the peak of their breeding season,
and right around when they start getting quiet,
we stop detecting them.
Which, you know, it's a good sanity check.
It's sort of makes sense; we'd expect that.
I, again, I haven't verified all of these,
but I did verify a bunch of them and, and it was accurate;
they, they picked out the morning warblers.
But how does it do with ruffed grouse?
Grouse are species that, okay, do another hand raising.
How, how many folks have heard ruffed grouse
out in the woods?
The sound that they make.
I think of it as more, like, a,
it's a feeling than something
-that you hear. -(audience laughing)
It sort of reverberates through your body.
And so it was really uncertain.
How well are these microphones gonna pick up grouse?
How well are our algorithms gonna detect grouse?
We did a test, we brought the microphones out
before the study started to a forest
where we knew there's plenty of grouse
and recorded them and listening back on my headphones,
I could not detect the grouse,
but then when I looked at a spectrogram, there it was.
And they're so ultra low frequency
that I think sometimes you need
-like a subwoofer. -(audience laughs)
And our microphones, of course,
because of the timing of the study, went out a little bit
after, really, their peak drumming period,
in like, you know, really late March
through through late April.
So, so we missed that real drumming period.
So that was the question, is are we gonna get any grouse?
And so BirdNET actually detected two grouse
with accuracy over 0.5.
The second, was actually just wind,
like, wind can make a low pitch rumble.
So, it got it wrong once and then the second...
Oh, I have another species here.
We'll we'll come back to that.
The second one was a true detection.
So this is the spectrogram.
August 20th, 6:20 it picked out one ruffed grouse
out of 87 hours of recordings.
I'm gonna try to play it to see if we can hear it,
but just pointing at the screens, all these little,
little signals here are grouse.
And it was actually, it wasn't the drumming.
[man in audience] Hm.
It's not playing particularly well
through the speaker, is it?
[woman in audience] You hear it.
I think we need the subwoofer for this one too.
I actually can't even really hear it,
-but take my word. -(audience laughs)
I can send a link to this recording after.
And actually what you, what you can do is,
especially if you're at home on your computer already,
is go to Peterson Field Guide to bird sounds,
look up ruffed grouse and look up the bark vocalization.
Like this is, like, it's unusual.
This isn't the drumming, I've never heard this myself.
I haven't done a lot of birding,
but it actually manages to pick out a ruffed grouse.
So that's pretty cool.
I think it's a nice proof of concept
that BirdNET can detect our target species.
And then, of course, white-throated sparrow,
is just another sanity check.
These are a much more vocal species,
and they'll actually make noise.
They'll do a lot of the call notes,
which BirdNET's actually pretty good at identifying,
during the fall migration.
So, you see a boost for white-throated sparrow in the fall
and we do see presence earlier in the season,
which makes sense given the habitat.
So, the sanity checks are working out,
it's still a little bit to be determined how,
how good these models are gonna be,
but the next steps are really to run this BirdNET model
on all of our data,
all 130 days worth of audio recordings
and they, these microphones will go out again next season
so we can get that early golden period for ruffed grouse.
And with that...
With that, I think I'm just gonna wrap up
and we'll have plenty of time for questions.
So, just thank you to our cooperators on this project;
Vermont Fish and Wildlife,
Green Mountain National Forest,
received support from Ruffed Grouse Society
and FEMC and also to the USGS Cloud Hosting Solutions,
which provided a lot of the,
the computing resources and technical support to,
to get BirdNET up and running.
And I'm sure I forgot somebody, but well, you all.
-I forgot you all. -(audience laughing)
Thank you all.
Yeah.
(audience applauding)
Mm hm?
[man in audience] What's recent?
Excuse me?
[man in audience] What is recent?
Recent cuts?
Oh, what, okay, what is defined as a recent cut?
Yeah, so that's a good question.
One of the harder parts
of this was actually finding good places
where we could place these devices.
Some of the, you know, when we say recent,
some of these just from looking
at the size of the stems could be 10 years ago.
[man in audience] Oh yeah.
So, this is still part of what we need to work out,
is, step one is what is actually in these recordings.
Step two is how do we better define the different,
the different experimental and control groups
that we're trying to assess
because it's not like, like recent,
like unless we all, you're right,
unless they're all cut at the same exact time, you know,
it's apples to oranges.
[man in audience #2] So different species,
like different phases of their regrowth.
Yeah. And in some cases they're,
and actually the one of the other talks from,
from this morning's session,
looking at using tools to assess different kinds of cuts.
Like, you know, did they leave a couple large trees
and or or was it, was it a clear cut?
So, these are all, these are all the different types
of co-variants that we're gonna need to work into.
We're gonna also, like Angela,
we're gonna use an occupancy framework
to try to measure what these impacts are.
And so we need to, we need to find a way
to incorporate these co-variants.
It's still to be determined, but you know,
the other thing is that when you look at it on the map,
sometimes once you get down there in the field,
it gives you a much better impression.
And so, one of the informative things
about setting all these microphones is
that we might need to be more diligent
when we go back out to these same locations
to redeploy them next spring,
to also be taking some sort of forest measurement
so that we can accurately depict
what those co-variants are.
Because to your point, "recent";
it should have the air quotes on it.
-Right. Sure. Right. -"Recent' cuts.
Yeah.
Yup?
So, when you mentioned, like,
with the wind being tagged as a ruffed grouse,
if there's noise that wasn't birds,
like that it's getting picked up by the microphones,
is BirdNET going to give you a prediction no matter what?
Like, will it refuse to say "No bird", or...
So, it's, BirdNET itself is a,
it's like a global model that's trained
on, I think like, 3000 bird species
and supposedly, I think, there might be
some non-avian classes that it can do as well.
I mean from our perspective, if there's nothing there,
then we just get in all data.
So like, we have these recorders going
at night sometimes too, so that we could pick up,
you know, things like woodcocks or frogs
or other things like that.
So, BirdNET's not gonna do the frogs;
we have to find another tool to do that.
But if it's just a,
like, a minute minute of silence
or a minute of heavy rain,
it's, it's just gonna have no detections.
Rain and wind happen to be pretty easy to,
to figure out when it comes to recordings
because rain just has a really distinct form
on the spectrogram and wind, it just looks
like a steady noise throughout.
Those are actually gonna be pretty easy
without sophisticated,
you know, convolutional neural networks
for us to, to tease out.
But it remains to be done and I think
that definitely has, has an impact.
Like, we can't, we can't consider like,
like, if it's a windy day, we're our,
our detection probability is gonna be lower too, right?
That might be what you were trying to get at there.
So, that does need to be taken into account as well.
Oh, a lot of questions.
Yeah, go ahead.
[man in audience #3] Were there any species
that you didn't expect to see that were detected?
-Ruffed grouse. -(audience laughs)
[man in audience #3] Okay.
I mean, I knew that they were there.
I just was skeptical whether BirdNET would be able
to pick one out, and I was especially surprised
that it new the bark vocalization;
just 'cuz, you know, these algorithms tend to do better
on species that they have a lot of training data
that they are exposed to.
This is not, it's not something people record a lot.
So, I was really pleasantly surprised with that.
But I've only just scratched the surface.
We haven't run it on the full gamut of species
that we expect and I'm anticipating
that it'll do better on some species than others.
The easy, the easy things are,
you know, it's a true positive, that's easy.
It detected a species,
we confirm that it was right about that species.
That's easy.
False positives are easy too.
We can know that it said the species was there,
but then we listened to it and, and it wasn't accurate.
But yeah, the false negatives are harder.
Knowing what it missed means going through audio
and just listening to it, where we don't know anything,
we don't have any preconception
about what BirdNET detected or not
and how, especially for species that may be infrequent
that are expected to be infrequent, you know,
that is a real challenge.
How, how do you know,
how do you get an impression
for how many false negatives there are
that are getting through,
which is important in your modeling framework
if you wanna be able to take that into account.
So, there's still a lot of work to be done.
This is very pretty preliminary,
but the early results seem promising.
Yup?
[man in audience #4] It looks like you're,
maybe, for like a species and you're monitoring,
like how are you dealing
with different preference towards,
like, distance from that cut...
you might find a couple of grouse
to, compared to,
are you just putting a seat down or up
and seeing what you get or...
Yeah, so I think that, you know,
each species is going to, basically,
have its own occupancy model.
So, I think right now we're, we're looking
at that in that framework where, again, we're,
I'm a little bit ahead of it here because,
and my role is more so in the machine, you know,
in the machine learning and,
and work, data workflow pieces working.
But I mean, to try to anticipate
what we might do going forward is that, you know,
each species could be looked at totally independently.
So, we have one model for woodcock
where we might find their responses
to different co-variants differ from something
like a ruffed grouse, which is sort of what we expect.
I don't know if, looking at,
it doesn't sound like I answered your question all the way.
[man in audience #4] Well just...
Like, the monitor says the location
-of a single site, -Yes.
[man in audience #4] But each species can vary
and you can use that depending on how far or close they are.
Yes. Yeah.
So I, I mean I think the initial assumption,
the simple assumption is
that each species is acting independent
from other species.
So yeah, we would expect those behaviors to vary.
It's not gonna be, I would be very surprised
if our outcome or if our, yeah,
if the outcomes from this, were that, you know,
ruffed grouse behaved the same way
to different forest disturbances as woodcock.
[host] Thank you Larry.
I think that's all we have time for.
Okay. We can...
I'm the last, so we can catch ourselves afterwards.
Thank, thanks again everyone.
(audience applauding)