- [Joshua] Good morning.
My name's Joshua Blouin
and I'm a master's student at the University of Vermont.
And this morning I'd like to discuss the first chapter
of my thesis:
Modeling Moose Habitat Suitability by Age, Sex
and Season in Vermont,
based on GPS Radio-collar Data and LIDAR Imagery.
So I'd like to start by just quickly introducing
some of the moose population trends over the years
here in the state.
So due to time constraints, I won't go into great detail
but just know that moose were excavated from Vermont
by the late 1800s.
But by the early 200s, they were exceeding
both social and biological carrying capacity,
particularly in the Northeast corner of Vermont.
And so a permitted hunt was put into place
by the Department of F and Wildlife
to purposefully drop that population down a bit.
This was successful, but managers were noticing
that there was continued decline of the population.
This continued decline initiated
Vermont's first research project
between 2017 and 2019.
126 moose were fixed with GPS radio collars
and wildlife management units E1 and E2
in the Northeast corner of Vermont.
And the purpose of this research was to really
examine mortality and productivity rates
for Vermont's largest moose herd.
So this study and the results from Jacob DeBow's thesis work
at UVM showed that heavy infestations of winter (silence)
we're a driving force for mortality in our study area.
And when I say heavy infestation 17 and 2019
126 moose were fixed with GPS radio collars
and wildlife management units E1 and E2
in the Northeast corner of Vermont.
And the purpose of this research
was to really examine mortality
and productivity rates for Vermont's largest moose herd.
And so with this, we're beginning to really
understand what mortality and reproductive rates
look like for Vermont's largest moose population
but an understanding of the relative suitability
of habitats in a landscape will really provide a foundation
for a population management here in the state
and is particularly important
for a species in decline, such as moose.
So what do we know about moose habitat in Vermont?
Well, we know that a habitat suitability index model
was developed in the 1980s by the
U.S. Fish and Wildlife Service
which was then applied to two wildlife management units
here in Vermont nearly 20 years ago.
And although this HSI really informed management
about habitat used by moose
and made use of the best available information at the time
it did have several limitations
and some of those limitations were
the fact that it was based on assumptions
from Northern Minnesota about habitat use
and the fact that there were no radio collar mooose here
in Vermont.
We proposed to create this upgraded HSI model
for Vermont moose based on really these three new sources
of information, GPS radio-collar locations of moose.
So actually having radio collared animals on the landscape
these contemporary maps of composition using NLCD
and these extremely fine scale examinations
of habitat structure using LIDAR
which I'll talk a little bit more about here in a second.
The overarching objectives of this paper were to
develop contemporary habitat, suitability models
by age, season, and sex for our moose,
as well as mapping that suitability across our study area.
Our overall process for creating
these resource utilization functions
and these utilization distributions
to really examine habitat use
within an individual's home range
is outlined by a (indistinct)
And the first step in the process
is to filter our GPS collar data by these two seasons.
So we examined habitat use annually,
but by the dormant
and the growth season,
as well as by age.
So young adult or mature adult and sex
and ultimately this filtering of GPS locations resulted
in the examination of 74 moose and over 40,000 GPS location.
So the next step is to calculate the home ranges
for each individual across the years and seasons
which we estimated using 95% Colonel home ranges.
we then determined each animal's utilization distribution
or UD within that home range.
So the UD is a probability map that is explaining
where the individual is most likely to occur
within their home range boundary.
So you think of this UD as a topographic map of sorts
where these peaks are identifying areas
of frequently used core areas
while these blue valleys are identifying less used areas.
So the next step is to create
our resource utilization functions for each animal
which in simple terms is really an equation
that will relate the height of the UDs.
So those peaks and valleys to resources
like habitat structure
and land cover characteristics at any given location.
But it's important to note that rather
than just describing the resources at exact GPS locations
we use the focal function in R
and created these mean values
for each resource across two different scales.
So the 400 meter scale another one kilometer scale.
The underlying resources included
that we thought were important
for determining habitat use by moose included
both it's composition and structure of habitats.
So LIDAR variables were describing
that fine scale structure
so the height characteristics of vegetation
across the landscape while the NLCD variables
were describing the overall composition.
So forest types that moose maybe using
during these different time periods.
So what is LIDAR and how can it potentially
improve the mapping of habitat here in Vermont
and particularly for moose purposes?
So LIDAR stands for Light Detection and Ranging
and it's an act of sensor technology.
So this means an airborne sensor directs a beam
of light towards earth surface.
And this light is reflected off of the ground surfaces
or vegetative structure back to the sensor.
So, the results of this is a very dense three-dimensional
point cloud that consists of not only an X Y coordinate
for each point describing where it is in space
but also the Z coordinate that's describing the height
of vegetation on earth surface.
So these point clouds are really cool to look at.
So they have great depth and texture,
and we can use this information to really filter out
and yield really specific components of that cloud.
So this map, for example, is a depiction of the point cloud
in our study area, it's really showing the height
of the vegetation.
So if you look at the yellow and orange color schemes
that's showing trees that are more mature or taller
canopy structures while the blues are really showing those
more shrubby young forest vegetation.
So if we draw a transect across this area,
we can see in the top left-hand corner there,
the returns of the points for that transect
and these points are showing
these more individual mature trees,
the yellow on the far left.
And as we progressed East into that more clear-cut
those points are becoming more concentrated
towards the forest floor.
So this really allows us to filter through
these point clouds to specific height classifications
that may be of importance to moose,
such as a forage classification for example,
which is identifying vegetation that is
less than three meters in height.
So back to our overall methods.
So after we've related the height of the UD
to the underlying lighter and NLCD structure
and composition variables,
we then wanted to examine combinations of these variables
to determine how they may influence the predictability
of habitat use by moose.
To do this
we developed nine resource utilization functions
Four (silence) each moose home range.
So (indistinct) predict the height of the UD
as a function of habitat variables at different scales.
So after we have created our model set for each moose
we repeat this process across all moose examined.
So 74 moos to create this population model
of habitat use by season, sex, and age.
So on to our key results.
This figure is showing the nine RUF models examined
on the Y axis and the average EIC ranking
on the X axis for mature, adult females, young adult females
and young adult males.
The shade of the bars is indicating the seasons
for dormant growth and weights
from a separate color study that we conducted.
We don't have time to really dive
into the color study and what that means.
So please just focus on the overall picture here.
The models with the lowest AIC ranking are the top models
for predicting habitat use.
And as you can see our two top models
across all seasons, sex, and ages
were these two models that were including both NLCD
composition variables
as well as these lighter structure variables.
Our top model was at the one kilometer scale.
The results from the top model
show the relative importance of habitat variables
on patterns of habitat used by moose age, sex, and season.
So you can see from this figure on the X axis
we have the habitat variables included in the top model.
And on the Y axis
we have the average standardized betas.
Again for the sake of the presentation
let's focus on the big picture here.
So the direction of the betas are indicating
whether the variable was used or not used.
And the height of the bar is indicating it's important.
So from this it's clear that moose really are differing
in their use of habitat structure
and composition by sex, season, and age.
However, there are some variables that are important
across all moose
primarily the forage vegetative structure
proved to be important for all moose across
both the dormant season and the growth season.
So the final step was the mapping
of our suitability across our study area.
So we used the coefficients
from our top ranked RUF model to create the suitability
across patches across our landscape.
So I think it's easiest to think
of this as kind of a recipe.
So different combinations of these habitat variables
determine the habitat suitability score
for each 10 meter squared patch on the landscape.
So some patches have just the right ingredients
and indicated by these coefficients
to make highly suitable areas for moose on the landscape.
While other areas may be lacking certain ingredients
which is really dropping those suitability scores
or pulling them down.
So these are two maps that are examples
of habitat suitability
for mature female moose across our study area.
So the map on the right is showing habitat suitability
across our study area, using NLCD composition variables only
while the map on the left is showing the suitability
for our top model, which was incorporating
both structure and composition variables.
And from the shade of these maps,
we can see that if we use the,
just the NLCD composition variables
the overall suitability tends to be a bit higher.
And for the top model on the left,
there appears to be more defined areas
of high suitability and low suitability.
So how can these HSI maps potentially help moose
in the region?
So, as I mentioned in the beginning,
declines and moose health survival
and fecundity due to the impacts of winter ticks
is really threatening their stability
and persistence both here in Vermont, but also regionally.
And the lack of information on the relative suitability
or quality of habitats is really limiting moose management
at a fairly critical time.
As the population may continue to show a future declines.
And although direct approaches
to managing winter tick abundance are limited
at this point in time
the manipulation of existing habitats may ultimately
promote moose population persistence on the landscape.
So generally speaking, our results
from these HSI maps showed
that female moose are actively using areas
with proportionally more regenerating forest
or that forge LIDAR structure
and canopy structure
while males are actively
using higher elevation mixed forest types.
And so these result in maps of habitat suitability
is really providing a means
of informing management activities
such as the restoration or alteration
of habitats to ultimately benefit moose
as well as policies around land use
that may contribute to population recovery.
So our findings from chapter one are very informative
but several questions still remain
such as, how does habitat selection relate
to this issue of winter tick abundance?
So tomorrow I'll be giving a talk
on the second chapter of my thesis
that is really focusing
on patterns of habitat selection
during two critical winter tick life stages
and how those patterns of selection may relate to fitness.
And so I hope to see you there tomorrow afternoon.
With that I would like to end my talk here today.
I just wanna give a shout out to some organizations
that made this study possible
such as the Silvio O. Conte Wildlife Refuge
and all of the kinds staff there
particularly refuge managers, Steve Agius.
I also want to thank VELCO for their continued support
that really made this project possible
especially during the capturing process.
There's a handful of people I wanna quickly thank
particularly my advisors, Jen Murdock
and Terry Donovan and folks from the
Vermont fish and wildlife,
such as Cedric Alexander, Scott darling,
Mark Scott and Tony Smith.
I also would like to thank the UVM spatial analysis lab
and Sean MacFadden
for all of his hard work dealing
with these large LIDAR datasets.
So thank you for tuning in
and I'll be happy to answer any questions folks might have.
Thank you.
- [Chairwoman] Alan Thompson asked,
"How does LIDAR differentiate
"between vegetation and any other substrate?"
- [Joshua] That's a really good question.
And to be perfectly honest
I don't know the exact details of how those pulses
or those reflections from that laser actually differentiates
between vegetation and other substrates.
I know there'll be returns
from LIDAR for example, for manmade structures.
And I think we can differentiate
between those structures and trees, for example,
given the amount of returns
from that actual reflection.
That's a question, I guess maybe
Sean MacFadden in the UVM Spatial Lab
would know a little bit more
about how LIDAR actually can differentiate between those.
Thank you.
- I was wondering, I probably missed the slide
where you showed the spatial configuration
of your captures,
but I was curious how you thought
or if you thought that your selection
of where you chose to capture moose drives the results
of your model in terms of what habitats are available
in the local landscape to those moose that you did capture.
- [Joshua] Yeah, I think that's a great question.
And it's one that is, it's a challenging one to address
because we can only really work
with what the capture crew was was able to do.
So they showed up in January
and over the course of three years, they captured 126 moose.
We told them during that capturing process,
"If you could really try to equally distribute
"where you capture moose, that would be ideal.
"So try to get an equal number
"of moose and E1 wildlife management unit as E2."
But of course there are limitations
and the capture crew had to use logging roads
and openings and just opportunistically color moose
when they could.
But given that, I think given our sample size
and how moose are fairly mobile on the landscape
I think it reflects fairly accurately
their habitat use during those towing periods.
But I think that's a really great question.
- So these weren't just helicoptered moose
- [Joshua] They weren't.
- They were.
- [Joshua] Yeah, so they were,
all of the capturing occurred strictly
with helicopter and hired out crew.