now that we're all here um
so i want to welcome everyone to the
2020 REU presentation
uh this is the the final
six week of of research for five
students
normally our reu program is
held at stone laboratory and the reu
students are conducting
their their their own hands-on data
collection or running experiments
however that was not possible this year
due to covid and
the campus closures but we still wanted
to
give the reu students an opportunity to
do research this year um
you'll hear from five students um
off uh um you'll you'll hear from five
students today
all students conducted a data analysis
using previously collected data
however you know a lot of science
is done through analyzing other people's
data
or downloading data from instruments
that are deployed
that are deployed far away so
you know you know this type of research
that
the reu students did this summer is not
really new to science it's quite common
you know to do
data analysis of other people you know
however what is new to science
since covid is these virtual meetings
these virtual conferences
and meeting with collaborators
collaborators over zoom
or webex or all these other forms
um so so you know just want to remind
you know the students and the presenter
or the attendees that
you know doing a virtual reu doesn't
you know it's you're not doing the
hands-on experience but you're still
gaining the valuable experience of
analyzing data
finding data that you're not collecting
and and generating a powerpoint
presentation
um we do not have a guest speaker or i'm
sorry a
guest presenter this week but we do have
a guest vip
uh i want to introduce
um introduce dr jan weisenberger who is
the senior
associate vice president of
the office of research and she's also
stone lab's biggest supporter
down on the columbus campus
thanks justin um yeah so i am i always
call myself i'm the cheerleader for
stone lab down on the columbus campus
and most summers it is a highlight of my
summer
to come up to the island to the lab and
hear
the student reu presentations and of
course we're doing that a different way
this summer
as justin pointed out and and i'd like
to sort of riff off this a little bit
something that has become increasingly
clear to me
in all the various groups that i work
with both within the university and
outside the university is that
we need to be thinking very hard about
the fact that what we're doing right now
while it's different
and it's a pivot we want to not think of
it
as simply a poor substitute for what we
would be doing otherwise
we need to be thinking about what are
the positive
aspects of this more virtual experience
and what are the the lessons that we're
going to take with us
even when we emerge out the other side
so
as i talk to our partners in industry
many of them have said you know the
ability to connect virtually
has expanded our network so much and
we're getting to know lots of people
that we
otherwise wouldn't have gotten to know
so that's a positive
the technology that we can put to bear
that allows us to do our work under
these current circumstances
pieces of that are going to go with us
as we go forward
even after the pandemic's over and
as justin pointed out and i'm going to
riff on him for a little bit
there are massive treasure troves of
data
out there that have been collected and
in a conversation i was in recently with
some of our microscopy people
they were saying when we image things
using an electron microscope
and we give the results to the uh
researcher we are maybe using
20 of the available information
that we have gotten from that image and
imagine what new discoveries we could
make
if we analyzed the other 80 of those
data
and so in thinking about the experiences
that all of you have had this summer
this is exactly in that realm it's
perhaps an unexpected treasure
that we're getting from this current
experience
where we might have ignored some of the
existing data
so it was a good experience i think for
all of you
even if it wasn't the experience you
thought you were going to have
and it's important going forward to
think what did i learn from this
either about myself about the research
process
or about technology that i really do
want to take with me
as i go forward and so i want to say
congratulations to all of you
everybody has had to become incredibly
flexible
and incredibly good at operating in the
presence of not complete information
about how the world is going to be
and hopefully we'll come out the other
side of this
better and stronger i look forward to
hearing everybody's presentations
tonight
all right thank you jan for those uh
those nice words
uh as i as i've said before dr winslow
will be joining us later he had
a uh an important meeting that was just
just scheduled so he's going to join
hopefully around 4 30
he said but hopefully sooner uh
so the the order of presentations
will follow the tradition of of
that we have at stone lab for the ru
presentations following
the order of the food web
before each student presents i'll ask
the supervisors to unmute themselves
and introduce the student the
presentations will be about
should be about 12 minutes long with
about about three minutes for questions
and answers
uh for questions please type the
questions into the chat
and i will serve as the moderator
and i will read the questions uh
following the presentation
um uh christina could you
give any last minute webex
questions that we may need absolutely
um if you as an attendee have questions
like justin said
you can put those in the chat box which
should be on the right hand side of your
screen
if it's not there should be a row of
buttons at the bottom of your screen
one of those is a speech bubble and if
you click that it should pop up the chat
um you can send questions either to all
panelists
or directly to dr chapman either will be
fine
and if you have any technical questions
or issues
feel free to send those also via the
chat to
again either all panelists or to me um
or to the host which is also me
but yeah i think that should cover
everything
all right thanks christina all right
let's go ahead and get started um
our first presenter is is my student
um so the our first speaker is peter
zameron who's a
rising junior at oberlin college
peter peter one of his
supervisors at oberlin college and
another student
visited stone lab to measure primary
productivity
january 2020 after working with peter
for three weeks during oberlin college's
winter
term or winter break i encouraged peter
to apply for the ru program
i had a couple of projects in mind
uh for him to do that involved you know
that about
algae and running experiments but we
couldn't do that
so i you know i thought about how can we
use the data that peter collected during
the winter
so an obvious question we had was
well how does the data we collected
during the winter compared to
summer so peter is going to talk about
comparisons
between winter and summer nutrient
concentrations
in lake erie's western basin so that
peter you can go ahead and get started
uh i'll meet myself and turn off my
screen
so go ahead peter okay thank you for the
introduction justin
yeah so um i'm sorry
let's see so justin said
i'm comparing the nutrient
concentrations between the winter and
the summer
so there has been a lot of research
in the summer not just about
not just for nutrients but about the
harmful algal blooms
hypoxia events and nutrient
transformation and cycling
um but with the winter there isn't as
much
of the re the other research i did read
about
we know there's spring diatoms and that
there's generally low
uh biological productivity so um why is
there uh that gap
so there's several reasons um first
being that uh because there's so much
going on this summer
um it's often referred to as the growing
season um
researchers are just focused on that uh
instead of the winter
additionally there's a lot of challenges
that come with uh winter field work on
lake
erie um you know there's a lot of
shifting ice conditions which
uh can make getting to the sampling
sites very difficult um it can be very
dangerous
um you can see here from 1940
david chandler who worked at stone lab
you can see how he
tackled that certainly not the safest
thing
so and you can also have freezing of
sampling equipment
that was the challenge that we had as
when we were collecting data in the
winter how do we keep
our intake or our sampling equipment
from freezing
so i hope to fill in that data gap
as one of my objectives and to see if i
really could
quantify any differences between the
seasonal and nutrient concentrations
so as to what nutrients and parameters i
was focusing on
um specifically uh ammonium is one so
that's
your uh the first nutrient really that's
absorbed by
are used um nitrate uh
most abundant but least favorite form
nitrite which is an intermediate of
nitrate
dissolves reactive phosphorus or drp as
the favorite form of phosphorus
as well as silicate and that's really
important to diatoms
as they use it to construct their cell
walls
other things that i measured were
chlorophyll a which is a good indicator
of algal biomass
and turbidity and specific conductivity
turbidity is essentially a measure of
water clarity
um and then specific conductivity um
measures the connectivity of the water
but both can be used to differentiate
between water masses
which i'll explain later how that is
helpful to me
so for uh the sample locations
um here is a map showing it is in the
western basin
just on the border between the western
and central basin um
on south bass island just here's the lab
right here as film as
their wadding water quality lab and
that's where the winter
selecting site was but for the summer
data
it came from a two meter integrated
tube sampling adjacent to the buoy
um and you can see that here
for our winter setup we had a direct
intake going into the lab here as you
can see is the pump
and that ran into a five gallon bucket
with a ysi sonde
and we directly took samples from the
hose
so once we had the samples once we
collected the sample
they then needed to be analyzed so for
chlorophyll we used a fluoroprobe
um and for the nutrients we used a seal
nutrient analyzer which is
it's really great you can do a lot uh do
pretty much all the nutrients at once
um it's just a lot of work to set up
um so as to which summers we ended up
using
um uh we wanted to get a good range
of different summers um they're pretty
variable so
we used 2015 which was a large broom
bloom
2016 was a small bloom and then 2019
just as
the most recent summer so once we had
all the data collected
we then had to perform uh statistical
analyses on it so i
did a anova statistical test um
which help to calculate the p-value
which determines
variance just as a whole um and then
what's really important uh in
determining uh differences
is the two key tests um and you can
determine variance between really any
two groups
so the next thing that i did
was um i had to plot all of that
so i did box and whisker plots for all
the parameters
as well as um time series to show
temporal patterns
um if there were no temporal patterns i
just excluded it um
because i there wouldn't be enough time
to go over that but um
so here's an example of the box and
whisker plot
on the y-axis we have the specific
parameter so this is ammonium
and its concentration here we have the
label
for the different winter different
years and seasons these are
these are the same for everything so uh
summer 2015 is blue
2016 is red 19 is green
and 20 20 winter is purple and so these
box and the script plots you can see
they show the range of data um so the
top and bottom
uh here if you need to read that
but what's important is the means is the
x
here you can see there um in there and
then
uh well i guess i know the pointer but
um
the line in the middle is the median so
looking at ammonia we can see there is a
definitely a higher ammonium
concentration now one thing i i just
forgot to mention but
the results of the two key tests um we
did i did plot
on the whisker plots and you can see
these different letters
um similar letters um
are associated with similar groups so
you can see a is different from b
so in this case 20 20 winter is
different
with a being higher um so
ammonia didn't really have a temporal
pattern um
phosphorus uh dissolves reactive
phosphorus and the winter also had a
higher
concentration as you can see here
um likewise silicate was higher
much higher actually nearly three times
the average is nearly three times
that of when compared to the summers um
nitrate concentrations were also greater
except when you compare it to 2015
um if you recall that was the
large bloom year um so
a lot of there were a lot of excess
nutrients in like during that time from
all the rain and the runoff um
so nitrate showed no temporal pie in the
winter but it is worth noting that
summer there is an overall decreasing
trend to show
the winter nitrate concentrations they
were not significantly different
um compared to the early summers and the
temporal trend for nitrite closely falls
out of nitrate
given that it's an intermediate
so uh chlorophyll a um
it was lower in the winter except when
you compare to 2016
which was the drought year so there
really wasn't much
algae at least that we could quantify
using chlorophyll a
and here you can see that in the summer
when you have these blooms in late
summer it really you can
see that here um on the left graph
which shows uh the summer of chlorophyll
concentrations
in the winter you can see on the right
really not much going on
so another i mentioned earlier about um
specific kind of connectivity and
turbidity
so it's really useful when trying to
differentiate between different
water masses um
so if you see on the bottom graph um
this is from the winter and it shows uh
various peaks and specific connectivity
um
so if you look at the blue arrow
that correlates or corresponds to
february 2nd
and if you look at the blue arrow on the
upper graph this is just nitrate as an
example i could
picked another nutrient um but
if you see there's a spike on that day
for those
um specific connectivity and nitrate so
um and here's uh the picture taken the
aerial
picture that's a even more striking way
to see
that um from there
um so as
for how how did my data really compare
to
other studies um encamped in it all
likewise they found
that dissolved dissolved nitrogen
concentrations were typically higher
um and chlorophyll was lower when
compared to summer
and one thing about uh
this winter is there really wasn't much
ice um blank at all found
that mild winters affected nitrogen
silica concentration positively
the phosphorus concentration negatively
so that brings up the question
of how do different winters compare
based on ice cover
um and how does that affect nutrients
and nutrient concentrations um
so that's something that could
definitely be looked into
um here you can see 2020
on the very end here um
much much lower there was practically
none um
it was it never really fully iced over
so that's uh pretty much all i have
uh is for my acknowledgements i would
like to thank
the friends of film lab for funding the
ru
and the great lakes commission has
collaborative for funding
the project that i did in january
with my professor from oberlin um
uh i'd like to thank dr chaffin for
being my mentor and helping with the
research in the winter
as well as uh dr rachel evelets
my professor uh kyra stanislawczyk
who pretty much trained me and the other
student
eliza goodell to help us with all
processing and collecting all this data
from the winter
so with that um i guess
opening up questions
all right thank you peter uh we have a
question from doug kane
uh dr kane asked where the
number of sampling days used for summer
versus
winter to minimize differences between
them
so the actual the duration then
let me go back
so for summer we
um we defined it as june
uh june 1st through september 30th
and then for winter we actually started
we started
uh really late december through march
30th
um yeah so i guess that's something i
could refine or
consider is making it so that that the
range
is the same i know that
uh we definitely had
more data from the winter um
when compared to the summer
we were sampling every day in the winter
for the most part at least as
long as we could so
okay dr kane says thanks
um i don't see any other questions
so again i'm not sure how to think peter
if you all can clap um
at home that would be great we have one
more question that just came in
so uh dr brian ford who's a the new
assistant director
at stone lab who uh spoke during one of
our meetings
uh he asked what is the fate of winter
ammonia
and silica in lake erie
does it remain in the water column so
peter what do you think happens to
those nutrients that we measure in the
winter
yeah so ammonia um as i mentioned
previously in the
in my presentation is the most favored
form
or it's it's it's the fastest
nutrient that is uh
to be taken up by um
algae and bacteria um and so that it's
as long as there's
no algae in the water at least
um it's going to accumulate so that's
why we see those
higher concentrations um
that uh once you see the space icon
blooms
and once they start uh growing that the
silicate will
most likely uh start to decrease as you
all right all right thank you peter
again let's all thank peter and uh
the best way we can and we'll move on
so our next presenter is alexis
and i'll ask dr beatty to
introduce alexis so if it's
alexis you can change the powerpoint to
yours
and we'll let darren introduce you
okay i think hopefully you can hear me
um
i had the uh pleasure of working with
alexis brown
this uh summer to
work on a project that before the
coronavirus outbreak
was originally planned on being quite
intense in this sort of data analysis of
existing data that that we actually did
do here but i had hoped to to include
some amount of lab work just for
a well-rounded project so it wasn't too
big of a challenge i think for us to
switch into
this um this style of a presentation
and so alexis is a senior at
cleveland state university and
one of the probably the main reason that
i chose her for this ru is that
she's majoring in
biology but also is minoring
in math and statistics so a
i would say a perfect and somewhat rare
combination in biology students
that made her
sort of an excellent choice for this
particular project
so i think we were pretty successful
this summer in doing what we set out to
do
and so i will let alexis
show you her excellent work
all right thank you dr beatty so our
project was developing an early alert
system for harmful algal blooms in lake
erie
and we used sensors that are stationed
on buoys
in lake erie in order to do our analysis
so harmful algal blooms are
characterized by the explosive growth of
a single species of algae
and some of these blooms though not all
produce toxins and these can be very
harmful to the environment
and to humans so one of the most
infamous cases around lake erie was the
2014 toledo water crisis
where over 400 000 people were without
clean water
so the other costs to algal blooms
include
the death of wildlife toxins that cause
illness and hospitalization in humans
and economic costs to the fishing
industry
tourism and even water treatment plants
so the ultimate goal of our project is
to develop
an automated early warning system so
we're hoping to analyze data from
sensors near the intake valves of water
treatment plants
and develop a notification system
that sends text alerts directly to plant
operators when an impending bloom may
occur
so they can begin dispensing those
treatment chemicals
to eliminate the toxins in the water
so in order to understand how our method
works you'll need to understand
alternate states of an ecosystem so for
our purposes we have
two main states the low algal abundance
state where there's a diverse
algal community and no bloom present
this will be referred to as our baseline
state
then our alternate state is the high
algal abundant state
where one species of algae in our case
in lake erie it'll be one species of
cyanobacterial algae
dominates the algal population and this
is when a bloom can occur
then you also need to know about the
transition state
which is an unstable state that occurs
between the
baseline and alternate states and
the theory of regime shifts between
alternate states of an ecosystem
states that you can expect to see an
increase in variance of some key
ecosystem parameter
before that shift occurs
so to explain increasing variance uh
when we're in
our baseline state or our alternate
state we will expect to see
some amount of variance in the amount of
phycocyanin which is the pigment
specifically found in cyanobacterial
algae
but during that transition state the
amount of variance
in that phycocyanin reading is expected
to increase
dramatically and that's the signal we're
looking for in order to predict
when this alternate state will occur
so the mathematical principle you need
to understand is the likelihood ratio
which type of data point or a set of
data is more likely to fall
within one distribution or another
so our two distributions are the
baseline state and
the transition state we will be using
a specific ratio called the quickest
detection or
qd ratio which compares the likelihood
of falling in
the transition distribution versus the
baseline distribution
and if that ratio is greater than one we
know it falls somewhere in that
transition distribution
and there's potentially an impending
bloom if it's less than one it falls
within that base distribution and we can
expect there is no impending bloom
and finally you'll need to understand
the concept of a rolling window
so in order to explain i chose the seven
day rolling window as an example
so how this works is as soon as we get
seven days of data phycocyanin data
we can run our analysis one time using
those seven data points so we'll take
the standard deviation
of that phycocyanin reading calculate
the qd ratio
and our alarm statistic and we'll see
that in this case the qd ratio is less
than one
so it falls within that baseline state
and we can conclude that there's no
alarm going to be triggered
if we shift that window forward a couple
days
our qd ratio is now greater than one and
we can expect it to fall within the
transition state
so that means that we can expect
a possible alarm to be triggered but if
you notice
that plot does not fall solidly within
the transition state it's kind of
between
the overlap of the baseline and
transition states
so that's where we need to set a minimum
threshold that guarantees
with within a reasonable amount that it
will be
in the transition state and we can
solidly
conclude that there may be an impending
blow
and so with the seven day window it will
shift forward
let me go back one slide so it will
shift forward one day at a time
but the amount of data we're analyzing
will always be seven days of data
so that's really important to note
so our method is called the quickest
detection method
during our procedure we analyze three
different rolling window lengths
7 14 and 21 days we also define
our distribution using three different
buoys in lake erie
so we ran our analysis one time with the
stone lab gibraltar buoy as our baseline
and we used that to define that baseline
distribution
and then we ran the analysis a second
time using the cleveland
buoy as our baseline and then our
transition distribution was based on
the baseline buoy we chose
and the toledo data is where we're
trying to predict whether there is a
bloom or not that's our target site
during our analysis we also set a
minimum alarm threshold
at 300 and we kept that constant and the
reason we kept it constant is when i did
some analysis
of changing that alarm threshold it did
not alter the date of the first alarm
very much
so we just kept it constant for all of
our analysis
and that minimum alarm threshold is
in order to ensure that we don't have
too many false alarms
in our final analysis we also
assumed that alarms before august were
considered false alarms
since we have so many years of analyzing
blooms in lake erie
we know that the blooms are going to
occur usually in august or september
so the alarms before august we consider
to be too early and
we consider them to be false
so here's a spread of the four years
that we analyze the four years of data
from 2015 to 2018.
the blue curve is our target site which
is the toledo buoy
the orange curve is the stone lab
gibraltar buoy which was our first
baseline
and then our second baseline was the
cleveland data
so if you notice with the cleveland data
we only have
the full season's worth of data for 2017
and 2018
so that's why we couldn't run the
analysis for
2015 and 2016 because we did not have
that complete data
another important note is in 2015
our baseline experienced a bloom before
our target site so because of that
our baseline was not a reliable or
stable baseline
and we expected some error for this
analysis
if you notice in the other years the
baseline stayed much lower and much more
consistent
so those years we expected some more
reliable results
then if you look at the severity of the
bloom in terms of
the psychocyanin readings in 2015 and
2017
they were extremely large blooms and the
2016 and 2018 had very small blooms
so we're expecting very
small or no alarms for those two low
bloom years
because it's not enough to trigger that
alarm
and i also included the satellite images
of the maximum
extent of each of the blooms for the
four years at the bottom
so here's our data for the four years
with
the 21 rolling day window and gibraltar
as our baseline so the
red and orange circles at the top
represent when each alarm occurred
the hollow ones are the ones that
occurred before august that we are
considering to be
invalid in some respects and the ones
that are filled in
are the ones that occurred after august
so
if you take a look at 2016 and 2018
there are no filled in circles so in
essentially that means we had no valid
alarms for those years which is kind of
what we would expect
because those blooms were so small in
our target region
if you look at 2015 the filled in
circles
occurred right around the time and
slightly after
that major bloom event so those alarms
were considered
late uh and you can based on
our data from the previous slide on our
baseline
blooming first that's why we
thought maybe those alarms were late
in 2017 you can see the filled in alarms
occurred well before that bloom
event and some of them appeared early so
those first three ones were about a
month in advance
the next three were about a week in
advance of that major bloom event
so that is important to note for our
analysis
so if we take into account the error in
the 2015 baseline buoy
the method worked in all of our cases so
with the gibraltar baseline
it correctly predicted the 2017 bloom
for all three rolling windows
and correctly omitted the 2016-2018
blooms
with all three rolling windows however
we aren't sure that the
timing for that 2017 alarm
was actually appropriate so
essentially this method is a work in
progress
we will need to calibrate our model and
test it out many many more times in
order to see if it actually is working
properly for
lake erie
and future things that we could try out
are alternate methods of determining the
distributions
in our methodology we used a dynamic
distribution which
updates every single day throughout the
season
in a previous version of this study they
used a
static distribution which was defined at
the beginning of the season
and kept the same throughout the season
and that model is a little bit
more robust against gaps in the data
because if you have gaps during the
dynamic distribution method
you can't recalculate it on a rolling
basis
so that's kind of one of the issues with
the dynamic however it does
capture some of the weather
variabilities so that's one advantage to
the dynamic
we could also try a longer rolling
window which might not
be as susceptible to blips in that
variance
of the phycocyanin and might give us
more accurate alarms
we also need to analyze more existing
buoys and make sure
this method works across the lake in
different locations
and we'd also like to survey water
treatment plant managers
to see exactly how much time they want
before a bloom event
so they can dispense those
chemicals in order to treat the water
and i'd like to thank stone laboratory
for
hosting this remote reu this was my last
chance for an undergraduate reu since i
am going to be a senior so i very much
appreciate
having this remote opportunity and thank
you to darren beatty for taking
me on this summer i really appreciate it
um
we got all of our data from the great
lakes observing system website
and thank you to noaa ohio sea grant and
friends of stone laboratory for making
this reu possible thank you alexis
uh any questions
yeah i don't see any questions coming in
chat um
i i have a question alexis um
would in your minds kind of your opinion
would the water plants be more concerned
about
toxins the blooms produce
yes absolutely from my knowledge of
water treatment in bloom events they do
have to
filter out the cells which would be
a reflection of the biomass of the algal
bloom
but i think the toxins are more of a
concern
because they can harm human health
right so these sensors cannot measure
toxins
so would you envision this being a a
two-part
like a an early warning for them to
either take a sample or an early warning
for them to
bump up treatment just in case if there
are toxins then they don't have time to
do
analysis that's a very good question
i think it wouldn't be a bad idea to use
this alarm
as a way of notifying them to take that
toxin reading and we actually did
briefly look at kind of the correlation
between algal biomass
and that toxicity um
but we'd have to do a bit more animals
just to see if there is some kind of
correlation between that
but yeah i agree that this alarm might
be used
for testing for that toxin
rather than maybe jumping right into
dispensing treatment for a toxin
okay great um
so i i'm not sure if i'm having
technical difficulties
but my computer is webex seems to be
frozen
so i don't know if there are any other
questions in the chat function or not
i am not seeing any other chat questions
at least none that were sent um to
me or to all participants okay
all right so i'm just going to assume
that maybe
webex will start working again for me
do you see any questions yet any other
questions
oh okay there we go um let's see
one person is asking whether a false
positive
or a false negative alarm would be worse
for
a water treatment plant operator
so if it were theory
i feel like that might be more
detrimental to have a false
negative because
it wouldn't be
multiple methods of checking for blooms
so that
we're very secure in it so i'm going to
assume they'd have other methods
as a background going on at the same
time so
if there were a false negative meaning
we didn't have an alignment we should
i'm assuming that other methods of
monitoring would catch that
if it were a false positive it would
certainly be
less detrimental because
they'd be looking for things when they
don't really need to be
so to answer your question the false
negative will probably be more time
all right thank you
question have one more question if you'd
like
okay um duck kane is asking
how your math background um helped you
with this are you
yeah so my stats background
really helped me understand the standard
deviation and that
rolling window standard deviation
without my stats background i probably
would not have grasped this project very
well
so i really really i'm really glad i
have that math background
okay um just to do some troubleshooting
while we're at it
um justin if your computer is being
cleared it might be easiest to just
sign out of webex completely and then
come back
that's what my plan was so i'm going to
our next speaker is elaine
and i'm going to ask dr kane to
introduce introducer and i am going to
close out and hopefully resign in as
quick as i can
thank you
hi this is doug kane uh
formerly of defiance college well i
guess for another week it defines
college
and then um then i will be at heidelberg
university
um i had the pleasure of working with
alina hess
who is a student at florida southern
college in lakeland florida
which is where the detroit tigers have
their spring training
baseball's starting back up so that made
me think of that
cleveland used to i think you have their
spring training there
but she is a native of airport harbor
and has worked with the
odnr division of wildlife
lake erie fisheries research unit as
a high schooler and that made her the
perfect
person to work on this project
this project i did not actually think of
it
um those of you know that know john
hageman
the former lab manager at stone lab he
came to me and asked
if i could find a student to work on
this and he was gonna work
more on the um
actual looking at the fish diets
um with her but of course then covered
19 came but we
we had a plan b which was to use the
data
that odnr had been collecting uh for
this project we focused on the western
basin
uh but maybe next year we can have the
central basin two and look
work on that um
i don't think uh i think that's about it
and
i i i
yield the presentation to alina
thank you dr kane so i looked at the
diet composition of the yellow perch and
white perch in the western basin
and we were primarily focused on whether
bithotrophy's longemanus were becoming
more important
in their diets
so what are bethotrophies so they are a
large predatory cladocerin so they can
get up to about 150 millimeters in
length
and they feed on other copepods and
rotifers and other platocerines now
they're commonly referred to as the
spiny water flea
so they have these really long caudal
spines
that are really hard material that you
can see over here
and those can actually make up about 70
to 80 percent of their total body length
and they use those as a defense against
predators
and they tend to be found in temperate
to cooler waters
and they're originally native to
northern europe and asia
but they have found their way into our
great lakes
they're originally thought to have been
brought over through the ship ballast
waters
and then once the first lakes were
contaminated they made their way through
the rest of the waterways
probably through contaminated fishing
gear so they first appeared in lake
huron in december in 1984
then later in superior in september of
1985
and they were first documented being in
lake erie in october in 1995
but it is predicted that they were
within the lake much earlier before we
even knew about them
so why do we care why do we want to look
at the spiny water fleas within our fish
so the reason is that many of the
predatory fish such as the yellow perch
and the white perch
are fish that we eat so we care about
them
in general for our fishing industries so
they have begun feeding on the
pithotrappies
and they found that the larger fish are
eating the larger brethotropis
so they have the larger longer spines
and the issue with these spines is that
when they consume
that the spines can be retained in the
fish's stomach
for a long amount of time they're not
easily or very quickly digested
so as the spines are retained in their
stomachs they can give them a false
sense of fullness so they're not going
to continue eating
and then they can also receive internal
puncture wounds from them
so it can actually harm their insides
and cause them to die
and it's also been found that they're
still that their growth rates are being
slowed down
so ultimately this means that the bigger
fish are since they are eating them
the bigger fish are the ones that are
dying off so that means if we're going
out to catch the fish
that we're going to have to end up
catching the smaller fish who aren't
as likely to be dying from the bittle
trophies
which in return means that your dinner
plate that you're eating of your
yellow perch is going to be smaller
so some of the items that the yellow
perch and white perch eat
are shown here they have a large variety
of different prey items that they feed
on
but some of the big ones are the
daphnias the leptadora kinty
the hexagenias the bosmias the amphipods
and the cyclopoints which are all
pictured here
so for this study we looked at the white
perch data from 1990 to 2005
and then yellow perch data from 1989 to
2004
and this data was from the sandusky fish
unit from odnr
and each year they sampled fish from may
through september
the sample size varied depending on the
day but over here on this map you can
see all those dots for sections where
they were
sampled and they collected the fish by
using trolls
and once they were collected each fish
was given a unique fish id
and then as they were taken back to the
lab their lengths and weights were
recorded
as well as their sex and then their
stomach content fullness whether it was
full empty or partially full
and then once they were taken back to
the lab a diet analysis was
performed on each one of them to find
their diet composition
so here's what the average total length
for both the white perch and yellow
perch
through the years this was just the
average of the sample size majority of
them range from about 150
to 180 millimeters in length
so for our analysis and statistics we
looked at the percentage of misotrophies
found in each sample
for each year for both species as well
as across the 15-year span
and we also looked at the number of
individual bishoprics so the count
that was in their diet each year as well
we performed some chi-square tests as
well as regression tests
and for those we looked at the length
versus the year and the percent and
number of methyltrophies
as well as the amount of chironomids
they were feeding on
with against the epithelias they were
feeding on and we found the p-value for
each one of those
and like i said we looked at that for
each individual year as well as a
15-year time span across both species
so getting into the yellow perch results
we found that there were five
years where there were no pithotrophies
present in the diets
from the sample those used were 1991
1995
1996 2002 and 2004
and within the 15-year time span
collectively there were 10
686 prey items found that were analyzed
and within the 15 years out of those 10
000 prey items the top three groups with
the chironomids
the hexagenias and the bithotrepeas so
the bifu trophies made it into
the third highest at 10.5 percent the
hexagenias were 15 percent and the
chironomids were 15.6
the majority of the individual years for
the yellow perch the bits of trophies
made up about zero to 5.1 percent of
their diet
which isn't a whole lot but they are
still there but however three years
had pretty high increases in the amount
of ethotrophies that were there
in these three years 1989 1998 and 2003
so over here on the left is the overall
diet composition through the 15-year
time span
so you can see the biggest group was
over here the hexagenias and the
chironomids
and with the trepies was third with 1
123 individuals found in their diets
and then over here on the right was our
first year where there was a pretty big
increase
so compared to the zero to five percent
that this atrophies normally made up
and this year they made up about 19.2
percent
in 1989 and then they continued to
increase over the next two spikes
so our next one was 1998 when they made
up about 32.9 percent
of the overall diet and in 2003 was the
largest amount of bitotrophies
which was 399 individuals
or 53.1 percent of their overall diet
composition that year
so here were the counts as well as the
percentage of ipo trophies across all
the years
so you can see there were the pretty
high spikes in these three years
compared to the zero to five percent
that we were normally finding
across the board
and then here were the results from our
amount of chironomids and the amount of
mythotrophies
so moving on to the white perch we found
very similar results but not an
identical so we also had about four
years that there were no obstacles at
all in the diet composition
that was 1995 1996 2004 and 2005
and overall they're about 8 389
collective prey items
so a little bit less than the yellow
perch but their top three
were very similar with the chironomids
at 22.7 percent
the bethel trophies here were second at
12.9 percent
and the amphipods at 10.8 percent so
here the bits of trophies made up about
zero to seven point seven percent which
is a little bit more than the yellow
perch
but they as well had about three pretty
high increase
years which were 1998 2001
and 2003
so over here on the left was their
overall diet composition
the bits of trophies made up about 13.1
percent which was the second most preyed
upon
item across the 15-year time span and in
1998 was their first big spike compared
to their usual zero to seven percent
which was 367 individuals at
38 of their diet so a pretty big
increase
there and then the next two years were
2001
where they were the second highest with
119 individuals
and this year we had a pretty big
increase of the chironomids as well they
made up almost 50 percent of their
overall diet
and then 2003 was the biggest increase
of bittotrophy
making up 44 which was 263 individuals
that were counted
so here are the percent versus the
counts so we did have some other smaller
peaked years
in both but the highest ones you can
still see were pretty significant in
increase compared to the bottom years
where it was about the zero to seven
percent
and there were the chironomids versus
the bits of trophies
so overall between both the white perch
and yellow perch we found that
the consistency the three consistent
years between both species where there
were none were 1995
1996 and 2004 and then both of them
had a high increase in bithot trophies
in 1998 and 2003
so there are some theories about why
this may have happened
so the bethel trophies are more abundant
when
the lake temperatures are cooler so
their availability may be due to
different lake temperatures throughout
the year if they were cooler years
they may have been more abundant another
theory is that it could be due to their
prey availability in the area at the
time
so one of their big prey items were the
amphipods
they made up a pretty big group they
were in the top three for both years
overall
but if you look at the yellow perch for
1989 the amphipods are about 15.4
percent
but they decreased pretty sharply in 98
in 2003
where we had those really high increase
in bithot trophies they were only at 1.2
percent and 0.5 percent
and the same trend followed in the white
perch 98 was 5.7 percent
and they took those pretty hard declines
in 2001 and 2003
about 0.1 and 0.3 percent
so another thing with that prey
availability is they found in a study
previously by hayward
that the growth rates are higher in the
central basin
compared to the western basin where we
looked at that
so the growth changes of the yellow
perch they found that were due
directly to the food supply so their
prey availability was different compared
to the central basin
making it harder for them to find food
and their food consumption was
suppressed so the growth rates were
suppressed as well
and then another study done by schaefer
in 1986
found that the competition between the
species could also influence the growth
weights
so we found that the white perch and
yellow perch had a lot of overlap in
their diets
they weren't completely identical but
they do feed on many of the same prey
items
so they found that their study was in 81
and 82
their main prey items for both groups
are those cryonimids and clydosarins
so if we have both species competing for
the same amount
of food they will have to adjust what
they're eating if they're not able to
find that so it looks like that the
yellow perch were not
able to access the same amount of food
as the white birch were resulting in
their slower growth rates in the western
basin
so overall in conclusion we found that
the mythotrophies have made their way
into the great lakes as an established
member of the community
and they have shown that they are an
alternative food source to our predatory
fish like the white perch and yellow
perch
however they do still have negative
impacts on the fish that can cause the
growth rates to slow
and cause death in general but overall
they have proven to be an alternate
member of the prey
their abundance has been fluctuating
throughout the years
which could be due to the prey
availability as well as lake
temperatures
so for future research we could look at
more recent years
and keep a consistent sample size for
the amount of yellow perch and white
perch collected each day
which might help to ensure that the data
is
more accurate and then we can also look
at zooplankton abundance
in the same years to look at in
correlation with
the bislo trophies and the yellow birch
diet to see if there was a change a
large change in prey available
availability for those years
so i'd like to thank everyone at stone
lab for helping me make this
reu possible as well as my supervisor dr
doug
kane and the odnr sandusky fish station
for giving us our
research data and here are my works
cited
and that is all so thank you very much
if you guys have any questions i'll be
happy to answer them
thank you elena a question from dr
beatty um he wants to know were
bethesdriffies
present in fish diets before they were
officially recognized
in the lake yes i believe they were
they were being noticed they just
weren't being studied so there wasn't a
huge draw for all the diets
within those recent early years but as
they started showing up more often they
did start doing more diet studies on
them
huh that's interesting um
uh christina asks
are white perch invasive
i believe they were introduced i'm not a
hundred percent sure
but they did follow after the yellow
perch
yeah yeah white perch are an invasive
species
um another question do these diets have
negative impact on fish reproduction
that i haven't looked into yet so that
could be a future research study that we
could look
looked at as well those studies
previously have really just focused on
their growth rates
okay i i have a question um
this year the western basin we're seeing
a lot of low oxygen
near the bottom so like the bottom meter
is running out of oxygen uh
knowing these the fish's diets
would you expect this year
would you expect more bifid trophies or
less bifid trophies in
perch diet i would probably expect there
to be more
since they do tend to live in the upper
to middle water column
so they might be more available for them
to consume
okay great i'm not seeing any
other questions so let's all thank alina
and i will ask dr susan gray to
introduce our next speaker elizabeth
hi everyone this is ann gray um so
elizabeth t ford worked for me worked
with me this summer
um she is a forestry fisheries and
wildlife
major in the school of environment and
oh my goodness
i'm tired um school of environment
natural resources at osu
um i actually met elizabeth last summer
she took my fish taxonomy course up at
stone lab so
um it was great that she got that
experience and i got to see how hard she
was willing to work
in four weeks for extreme heat and
weather and such things so i was really
excited when i saw her application for
an reu
to do a fish project and
um we had this idea of a fish project
that would have involved um
some fun fishing for walleye on lake
erie
but because of kovid she ended up
doing an exciting project looking at the
data of
other people getting to fish on lake
erie
so i was really thrilled that she was
still willing to
work with me on this data set that you
know i've had for a few years and just
never
taken the opportunity to work with the
data set
because we're always you know out on the
lake so with that i will hand it over to
elizabeth
thank you dr gray for that introduction
um like she said we are looking at
other people's data that they collected
and kind of comparing it to um
the algal bloom surveys as well as the
krill surveys
so what we're going to be looking at is
the effects of harmful algal blooms on
lake erie's recreational walleye fishery
so as many of you know and is kind of a
lexus
very elegantly uh
introduced lake erie has a history with
harmful algal blooms
and most recently they have been closely
monitored
um as you can see in this photo in front
of you
this was the harmful algal bloom in 2015
which had a
severity index of 10.5 which
is the highest that we have seen to date
so far and as she stated
that um harmful algal blooms can
affect a lot of the wildlife that is
around
lake erie as well as within it and that
includes the fish
and that leads us to the walleye so
as many of you have probably seen and
heard they have had
successful recruitment years here
recently for the walleye fishery
and a lot of fishermen are out on
lake erie right now fishing for these um
walleye
and um we wanted to look at and see
if the um
sever if the increased severity of the
harmful algal blooms
is infecting is affecting the
recreational walleye fishery within
lake erie's waters
so the research question that we
proposed was
how do harmful algal blooms affect the
recreational wildlife fishery on lake
erie
so to do that we kind of came up with an
objective
and that was to access angler fishing
behavior
and success across years with different
levels of harmful algal blooms
and we kind of came up with three
predictions with three different
variables so the first prediction that
we had
was that angler hours would decrease
as algal bloom's severity increased
because
a lot of fishermen perceive harmful
algal blooms as
a um they
a lot of times they like to go out onto
the water and
just like to view the clear waters it's
an intrinsic value
and with the um algal blooms split on
the water they're green
and that's just something that fishermen
perceive
is um not very pleasing to the eye
so we figured that that would end up
decreasing the angle hours over the
years
um our second prediction had to do with
catch per unit effort of the walleye
and we figured that that would also
decrease
as uh algal bloom severity increased
and then prediction number three that we
had was the mean total seasonal catch
of walleye would end up decreasing as
algal bloom's severity increased as well
so that was our three main predictions
that we had throughout this
study so throughout our method
we were able to we were given a summary
of krill survey data from the years of
1989 to 2017
and what we did we focused on the data
from the months
of july through september during the
years of 2002-2017
and the main reason why we focused on
july through september was because
that's when
um that's when the harmful algal blooms
and any aloe bloom is
at its highest peaks is during those
months and that's whenever they
become very apparent and the reason why
we focused to the years 2002
to 2017 was for the simple matter of
that's
the data that we had available through
the
um uh um
lost my words the harmful algal bloom
severity index that's the data that we
had available for those
so the variables that we looked at as i
explained before
was angler hours catch per unit effort
of walleye and total seasonal catch of
walleye
so as you can see over here is the
interview form on the left that they
um that cruel surveyors actually give
the fishermen
and what they do is they put their start
time and their finish time and we were
able to calculate
how many hours that each fisherman was
out on the water fishing
so catch per unit effort of walleye is
how many
fish were caught during a set period of
time
so with this crow survey data i was able
to look at how many fish was harvested
and how many fish were released
and how many hours they fished during
that time
so what i did was i would
add the harvested amount plus the
released amount
and i would divide that by how many
hours that the fishermen were fishing
during that time and that's how i would
get
the catch per unit effort of walleye and
then the total seasonal catch
of walleye i just added the um
the caught and released and i added all
that together
and got the mean for that whole
season
so the other methods that we used was
looking at
the harmful algal bloom severity index
and as you can see in this table from
noaa this is the
severity index from all the previous
years
and the predicted for this year which
i believe they just had a conference
over here recently and
um basically what a severity index is
it's the biomass
of algae within the water over a period
of time
and the biomass is kind of just an
estimated
algal estimated amount of algae within
the water at that time so we used those
severity index
we used the severity index in relation
to the other variables to see
if there were relationships between
so kind of how we analyzed our data we
used
general linear regressions to test for
relationships between the harmful algal
bloom severity indexes
during the years of 2002 through 2017.
so on the on the the um this slide in
the
next coming slide you will see on the
left there is july
through september data and august only
data
and we used um the july through
september data as a whole
study and then we kind of last minute
did an august only study that since
that's usually whenever um
the algal blooms would peak is in august
so we kind of looked at both of those
and to see if there was any
relationships between
the harmful algal bloom severity index
and
the other variables so here you can see
the total angular hours data
and you can see that there was no
significance with the july through
september data but in the august only
data on the right
you can see that there was a positive
trend in the total angler hours
this one shows the mean angler hours
through
july through september and august only
and august and in the august only month
of all the all the years through 2002 to
2015
uh well through 2017 i mean um
there was a significance within that
data so you could see that there were
um the mean angler hours was slightly
affected
by um the severity of
the harmful algal bloom
and this one shows the mean catch per
unit effort of walleye
and you can see that there is no um
significance between
either the july through september data
or the august only data
and then the mean total catch there was
also no significance within the data
as you can see in both graphs
so to summarize what we found is there
was a positive trend
between harmful algal blooms severity
index and the total angler hours during
august but not across the whole season
um there was a positive significant
relationship between the harford bloom
severity index
and the mean angler hours in august but
not throughout the whole
season and then there was no
relationship between
the have severity index and catch per
unit effort or the mean total catch
of walleye and we noticed after looking
at some of the recruitment data that
there could have possibly been
a relationship between
fishermen basing their fishing
throughout the years on the recruitment
and the number of
walleye and the lake
during those years which was predicted
each year by odnr
so that kind of leads us very nicely
into the future directions
so if this study was to be done again
next summer
it would be very beneficial to
incorporate the recruitment and walleye
population data to test for
relationships between the harmful algal
blooms
the population and angling behavior to
see if
um there was any relationships there
another thing that would be a good
direction is to be
using monthly queerful data from buoys
in lake
erie because the harmful algal bloom
severity index
is a very coarse measurement that is a
collection of algal bloom data
throughout the whole season
so that kind of narrowed down our
data a little bit so there is data
available that
um that you can look at for this this is
the hobbs
data data portal through glg los
and this is from the um
buoy right off of gibraltar island and
this is from
2015. so this data is very well
accessible you can download it
as an excel file and you can look at the
data and compare it to
the other variables that we had
previously looked at
in our study so this would be something
that would be very beneficial to
incorporate within the study to see
if there was any relationships there
so i would like to acknowledge quite a
few people that have helped me
throughout
this research so travis hartman from
odnr
i would like to thank him for supplying
the krill survey data
um dr chelsea nieman who i was able to
meet last year during my class with dr
gray
she did a very awesome job at
summarizing this cruel survey data so i
did not have to go through
and um go and summarize
all of it because there was over a
hundred thousand rows of data so
i'm very um very thankful for her for
summarizing a lot of it for me
in the past years next i'd like to thank
dr susan gray for continuing to advise
my project
even through this virtual forum i know
it was
very trying for the both of us and it
was very different
um i'd really like to thank dr
justin chaffin for making this
experience possible for all the reus
when most of the other ru programs were
canceled um as we talked about at the
beginning there was
only a few reu programs that were still
up in the air for students like us to be
able to learn from
and i'd like to thank all of the donors
who were able to support this research
experience including
john kreitz former chair of eeob
thomas langlois former director of stone
laboratory and
jewel julius and kate stone
so i'd like to thank all of them so um
that's all i have if anyone has any
question i would be willing to answer
them
i think it was a question from dr kane
uh dr kane says so the take-home message
is that anglers will put up with bad
algal blooms as long as their walleye
out there to be caught
he continues this is important because
it goes against what we have heard from
charter boat captains
in the past um
to go further on some of the target
captains we work with
say they will move their boat or cancel
trips
when there are bad algae blooms
so how so how do you take that question
or comment
that uh anglers will put up with a bad
boom as long as they're walleye
out there so i do agree with that
statement
i do know going through a lot of
research papers i found
that a lot of fishermen who were
fishing on charter boats they found that
they were willing to
pay more to the charter to
drive them further away from an alga
bloom to be able to catch
more walleyes so that is something to
incorporate as well but yes
i think that fishermen are more
more willing to go out and go fishing
and tolerate the
kind of the ugliness of the algal bloom
if there
is walleye to be caught
there's a two questions
from that i think are similar
quickly reading them um
did the anglers fish in the area
of the lake were there blooms you know
that is could the anglers
be fishing in the bloom or could they be
fishing on the edge of the bloom
or outside of the blue so through the
krill survey data
they had different um
areas where they were fishing so from
the data from what i've seen
the fishermen were fishing within the
algal bloom as well as outside of
so it was a nice distribution between
okay uh
yeah that's okay i think that addresses
the other questions
too um okay thank you
all right thank you elizabeth all right
our last
and final speaker i'll ask
dr james marshall to introduce rhodes
everybody uh as my ornithologist friends
would agree
birds are best and therefore that's why
they're last
uh you may not agree sorry my reu
students are usually studying
calves although we talk about them as
harmful angry birds
uh which usually for up there's at least
one red wing blackbird that attacks us
while we're there so that's a
usually out but we couldn't get out on
this year so instead we took a look at
some questions that
we've had data for for a long time but
never have an opportunity to look at
so rose wetzel comes to us from
pittsburgh pennsylvania where she's a
rising senior at susquehanna university
he's an ecology major which i appreciate
um and she has an interest long-term
restoration ecology
but this summer though she's been
looking at our bird data over the last
nine years or so so i'll let rose take
it away
thank you dr marshall um i am rose
wessel and
we this summer have been looking at body
condition and survivorship
of breeding birds on the lake erie
islands
as you can see from the map here on the
left
um the lake erie islands and great lakes
region
is in the path of two major migration
flyways
and so this area is really important to
migratory birds
they can stop on the islands and either
rest or
collect food or they can wait for
the appropriate winds whether that's a
tailwind or a headwind to take them
north or south depending on the season
and with animals such as these migratory
birds in mind
um we have two preserves that were
transformed from
more human-dominated habitat um into
preserves
so a lot of human structures were taken
out and
uh succession of plants was allowed to
occur
what we wanted to look at this summer
was whether
the work on these preserves has paid off
in terms of providing additional benefit
for breeding birds
one of the ways we looked at this was
looking at their survivorship
which is their survival from year to
year
and another way was looking at their
body condition index
so we know that if preserves are
providing valuable habitat
birds will have better condition in
these areas
and lastly we wanted to look at whether
our future data will still be valuable
and usable even though no data was
collected this summer
and that will probably be an important
question for any researcher
who has a similar similar situation
where they were unable to get data this
summer for the covid19
so for our study sites we had three
different human dominated habitats
and one of these was on gibraltar island
we basically hang up miss nets from one
side of the island to the other
and this is considered human dominated
because we have things like
the stone lab cottages and dining hall
in other buildings
we also have a site in the northwest
island vineyards
and we have a site around the stone lab
bay view office
like i mentioned we also have the two
preserves which are on
middle bath island and southwest island
our data collection began in the year
2011
and the last year of data we have for
this summer was the 2019 data
since this summer no one was at the
islands to collect it
each summer we visit the same five
locations and we visit once
we visit each location location once
each each week
uh we usually hang up these miss nets
which you can see in the picture
from about 7 30 a.m until noon
and this allows us to grab the birds
that fly in
and get caught in these pockets like the
tree swallows on the left
and we can then mark the birds with a
band that
shows us if we catch it another year in
the future it shows us we've already
caught it
or it can show other birders that
someone else has caught it
and we can also collect measurements
from these birds such as wing length
weight
age or sex it's important to note here
that for two species the northern
cardinal and the red-winged blackbird
we use tarsus data which um
is the part of the bird's foot
that the band is attached to and it's
just a better indication of body
condition
because it's not subject to change as
much as wing length is
and that data was collected by the stone
lab evolution course
one of the ways we study these birds is
using a program called mark and mark
uses our recapture rates to estimate the
yearly survivorship
what that means is that in an ideal
world we would be able to recapture
every single bird that survived but
because this is not an ideal world
we capture about 10 of the birds that
survive um and so mark just uses that
10 or whatever it happens to be for a
particular year
to estimate how many birds have actually
survived
we used the red wing blackbird and
american robin data for
our survivorship estimates they were the
only two survivorship estimates we did
because we have the most data on those
species and therefore it's enough to
provide us reliable estimates
for these simulations we looked at the
short term
by removing the year 2018
um you can think about it like
next year we won't have 20 20 so
um it shows us what 2021
data might look like because we do have
the year 2019
but for the simulation we removed the
year 2018.
and for a longer term look at things we
removed the year
2014.
to begin our body condition index we had
to disregard any species that
um didn't have more than 25 recaptures
because it just is not enough data
and we also removed from any species we
removed the hatchier aged birds
which are very young birds that would
skew our
body condition index positively because
they tend to have a lot of body fat
since they're still being cared for
and to create this body condition index
we
did a linear regression using wing
length versus weight
and it's also important to note here
that for the red wing blackbird and
northern cardinal we did use that tarsus
data that i was talking about earlier
um and the data
here the body condition index rather is
actually just the residuals of that
regression that we created so you can
see here from the graph
um any uh bird that has
a dot above the line would be a bird
that's in better than average condition
and one below the line would be
representing a bird with below average
condition
after we formatted all our data we used
a t-test
to determine whether there was any
significant difference between
human-dominated habitat and preserved
going back to our survivorship estimates
for a minute
these are our graphs for the american
robin as you can see
the blue bars representing 2019 are
pretty close to
the orange bars representing our 2020
simulation and most of these numbers are
pretty similar
to what our real expected range of
survivorship would be
which would be about fifty percent
the same holds true for our red wings
blackbird survived
survivorship simulations and the reason
this graph here on the left looks a
little bit different
is because for um
this for this survivorship estimate mark
estimated it using a model that did not
take into account the different habitat
types so it was actually cons
survivorship was constant over habitat
types and was not dependent on that
it was dependent on time
moving along to our body condition index
um
i'm going to start with several species
who did not have a
significant difference and that was most
of our species
one of these is the barn swallow which
um did not have a significant difference
between
human-dominated habitat and preserves
but it did show
um kind of negative condition in both of
these areas
and the house bench showed the same
trend
and was still not statistically
significantly different between these
habitat types
again the redwing blackbird not
statistically significant
but it did do a little bit better in
human dominated areas
and it's important to note here you can
see our error bars are pretty large
um that is because of our recapture rate
which is very low
the american robin is another bird with
um
with no statistically significant
difference but it's doing a little bit
better in human dominated areas
as is the tree swallow which is still
not a statistically significant
difference
the american goldfinch still not a
significant difference
but it is doing better in preserve which
was very uncommon for our species
and the northern cardinal again not
significant
but it does have a better condition in
human dominated areas
the first of our birds that did have a
significant difference was the
brown-headed cowbird
and it is doing better again in
human-dominated areas
which we don't consider necessarily a
bad thing for this species because it's
a brood parasite um so it might be
sort of risking other birds nesting
success
and the baltimore oriole also was
statistically significant
the difference between habitat types and
it did show a better body condition
in the human dominated areas
the yellow warbler was our last species
and did show a significant difference
and it was doing a lot better in human
dominated areas
to go back to our initial question of
whether
preserves are providing good breeding
habitat we unfortunately did not see any
evidence of this
in um in our study because when we
looked at survivorship and body
condition index most of them did not
have any sort of significant difference
between these two habitat types
but when we did see any any difference
the birds were often doing better in
human dominated areas
so therefore our data suggests that the
preserves are not quite as good nesting
habitat
and reasons we theorize this might be
true are because
these preserves are extremely small so
bigger preserves might actually provide
better nesting habitat but with the way
they are
density of birds that are actually using
these areas could
provide a stressor on nesting birds as
could
predation which might not be as frequent
in
human-dominated areas because of the
abundance of predators
however nesting success study would be
recommended to
truly gauge the value of these preserves
to birds and the reason
we haven't been able to collect that
data is because the reu students don't
come up
to stone lab in the right part of the
summer for that
the bottom line is that even though we
haven't seen a clear
value for nesting birds these preserves
are still valuable
um they're valuable for many other
species such as the lake erie water
snake which is endemic to the area
and other birds um such as these which
are considered
either endangered or threatened by the
state of ohio
have been cited on the islands
even though we haven't evidenced them in
our study
those could be birds that are also
benefiting from these areas
i would like to thank stone lab for
having this opportunity
like alexis i am a rising senior so it
would be my last chance to
to be an reu student so i'm really
grateful i got that opportunity and that
everything was carried out
even with this tough virtual situation
so thank you to dr marshall for being my
advisor
um and i'd also like to extend my thanks
to
lisa brol who helped me with a lot of um
either curious questions i had or
questions on the islands and the
preserves in general
because i am interested in restoration
ecology so she gave me
some knowledge into the plants and these
other bird species
thank you
all right thank you rose uh we have we
have some questions
um they need to slow down so i can read
them
um you mentioned the mist nets are
checked
once a week so our local predators
and every time someone asks a question
that the words jump on me
so i think the clarifying question for
your methods um
are your miss nets are checked once a
week are there local predators i may get
to
the the birds uh get get to the captured
birds before you do
um that's a bit more of a question of
how i explained it
so um we actually hang up the miss nets
and take them down
and so the birds would just be flying in
there from
when we are there from 7 30 a.m until
noon and then we can just grab
any bird that flies into there and we
can um
mark them and that way they're not at
in danger to predators for a very long
time
great a question from dr
kane uh the cowbirds are an egg species
and thus would
probably expect them to do well in
humans
we're with humans who make the edges do
you know if the baltimore orioles are
considered
in head species
i don't um i know they're a little bit
more of a
habit sorry less of a habitat generalist
than say the american robin
or the red-winged blackbird which can
pretty much do well anywhere
um dr marshall was saying he's seen them
in his neighborhood which would
certainly be
probably human-dominated habitat um
so i haven't seen them in my
neighborhood but i know that
um they're a little bit less they're a
little bit more sensitive to
habitat type
was there an existing habitat
suitability index for any of these
species
and if so do you did you compare the
test sites
to see if they were within the range of
the index um i did not look into that
but that would definitely be an
interesting
thing to do for a future study
a question i have um
kind of a two-part question when
uh when any of these birds are are
feeding
uh um hey how far away will they fly to
find food
and b could they be could they be making
their home
in the preserve but flying to the human
dominated system
to find food and then that's where you
catch them
so i think for some of our islands they
could be
flying two different sites so
like south bass island where we have a
lot going on we have the
preserves and we have a lot of human
dominated areas
um i could certainly see them flying
across south bass island but
they're not going to hop from island to
island a lot
um so some of our smaller islands they
probably wouldn't cross
between those human dominated habitats
and preserves as much
okay great um that was i don't see any
more questions for rose
so uh
dr chris winslow our our director joined
and if chris would you like to
make any comments yeah i apologize that
i couldn't uh make it on for all of the
presentations but the ones i was able to
hear
as usual just impressive um so this has
been phenomenal for me to
to wrap up my work day to day listen to
these great talks
and i did want to take uh time to echo
some of the thanks from the
from the reu's to to justin and our
supervisors
uh i just love the creativity that uh
justin and that team put forward
so dr kane and dr marshall dr baby dr
gray just to
to open up your your schedules to allow
for this to occur virtually so thanks to
the
the supervisors and and wow just amazing
work from our
ru's and i want to just give a shout out
to dr jan weisenberg our senior social
bpp
she loves this event she has not missed
many and for her to join virtually this
is exciting to see too so thanks
everybody for all your work
um with these presentations today
all right thank you everyone for joining
i want to thank the
the panelists or i guess the students
and the supervisors um
i want to thank the attendees i've been
keeping a rough count on the attendees
but there was quite a few
who've been outside our our normal
weekly meetings i want to thank them for
for joining and and contributing to the
other questions
uh with that i would uh
you know just like to ask are there any
last
parting comments or questions
while we're all while all the ru's and
supervisors are
are together justin started interrupt
again i would just love to say remember
if any of this is to the level where the
supervisors think this would be a great
presentation
you know even if it's at the ohio
academy of sciences or something like
that remember come back to justin and
myself uh
we can cover some of the registration
and a little bit of the travel for that
if not all depending on if it's local or
hawaii
um so let us know on that that would be
uh great and if you're
on the scovit is really we would
normally be packing up to get on a boat
and go over get an ice cream cone so
if you're ever in columbus or up on the
islands uh you have my contact
information i'd love to
take you out and celebrate
all right thank you chris for the ice
cream uh
offer i'll definitely take you up next
time i see you
all right again so um so thank you again
everyone for joining uh i want to thank
the reu's for
for working hard in these challenging
difficult but i i believe it was
rewarding for you
um it was definitely rewarding for me uh
you know as a
more of a a scientist
you know i don't teach so i didn't have
to go through
the quick learning process of all this
virtual stuff
um well i did have to go through just
not at the same time that
the the other supervisor the other
supervisor did when they had to teach so
so this was new
new to me i found it extremely valuable
and and i know and i hope you you all
did too
so with that i'll say goodbye and um i
hope
to see you hope we all can meet sometime
if you're ever hit out at south fast
and we're open you know feel free to
stop by and say hello
all right thank you everyone
congratulations everybody
