Transcript Fish Species Diversity in the Ohoopee and Lower Oconee

Catlin Ames
Rachel Lindsey
James Waters
Tal Woodall
 Currently GCSU does not have a compiled interactive
database for study information in our own and
surrounding Basins. Ecological and Hydrological data
research and information is available but is not
accessible due to lack of a database.
 Access to information is one of the leading causes of
misinformation, and ignorance.
 Our goal is to create an interactive geodatabase for the
Altamaha River Basin that can be continuously
updated and viewed by anyone interested in the basin.
 Austin- Display mammalian data of the basin on the
database.
 Catlin-Display fish species and diversity of the
Altamaha River Basin.
 Tal- Display the bird data of the Altamaha River Basin
in the Geodatabase.
 Rachel- Display the hydrologic data on record of the
Altamaha River Basin.
Using collection site data compiled from Dr. Christopher
Skelton and Hank Forehand
 I took the collection site data and extracted the mean
diversity value to spatially represent fish diversity by
County, Physiographic, and HUC basins.
 To start I had to convert the collection points to raster.
 To make diversity values into a raster
 -conversion tools
 -to raster
 -point to raster
 I used Zonal Statistics under Spatial Analyst tools to
make a new table containing the raster values
extracted to the shapefile of choice (i.e. County).
 I then joined the new table to the shapefile (i.e.
County) and used categories to represent the mean
diversity value by theme
Fish Species Diversity By
Physiographic Region and
HUC 6
•The Washington
Slope physiographic
region covering
Baldwin, Hancock,
and Jones Counties
had the highest
overall mean species
diversity.
•HUC 10 basin
diversity shows that
the Ohoopee Basin is
higher in mean
diversity.
Mean Diversity
by HUC 12 basin
•The individual
basins show
scattered
diversity values.
•More
consistent
values are seen
in the Ohoopee
River Basins
•This spatial
representation
shows the lack
of collection
data for the
area.
 Fish diversity understanding is important as stated by
Stephen B. K. Sekiranda et al., ”High diversity
indicates low environmental impacts in the habitats
while low diversity is an indicator of high impacts of
environmental degradation.”
 The lack of correlation and contradiction between
physiographic and HUC basins is most likely due to
the lack of collection data from the area.
 Land use examination would also help to understand
the environmental impacts in the area, and also
knowledge of invasive species (i.e. predatorial fish,
river fauna) could contribute too.
By: Austin Waters
Summary
Sixteen different bat species have been documented in Georgia.
Baldwin County is located on the boundary of two differing faunal provinces,
which would lead many to believe that Baldwin would have a great species diversity.
Before Dr. Parmley and Bender’s research only 1 species of bat had been documented
in Baldwin County, despite the fact that most of the surrounding counties had at
least 7 species Already on record.
Their research consisted of fifteen different sampling sites around Baldwin County.
These collection points are located in a variety of settings including; buildings,
woods, and over water.
The majority of the collection was done during the summer months.
The researchers data helped fill in many distribution gaps that were located in
Central Georgia.
Their research has proven to be very useful for determining which species of bat live
in Baldwin County.
All of the data and information concerning bat species in Baldwin County
used in this project was collected by Michael J. Bender and Dennis Parmley.
Nycticeius humeralis
Lasiurus borealis
Eptesicus fuscus
Perimyotis subflavus
Myotis austroriparius
Tadarida brasiliensis
Lasiurus seminolius
Bat Sampling Sites in Baldwin County
1998 Landcover image from www.narsal.uga.edu
 Species diversity varies from
site to site.
 Some insight into why each
site’s diversity varies could be
determined by the surrounding
area’s (1000m) landcover.
 After creating the buffers around
each site in ArcMap I was able to
use ERDAS IMAGINE 9.2 to
determine the number of pixels of
each type within each buffer zone.
 Lastly, in Excel I was able to use pivot tables to create charts displaying
the percentages of each type of landcover within the buffer zones.
 Note: pixels equal 30x30 meter blocks
Legend
Metadata
GCSU Geographic Research Center
Cartographer: Austin Waters
Projection: NAD_1983_UTM_Zone_17N
Datum: D_North_American_1983
Creation Date: 04/23/09
 When you begin to look at the diversity of bats at different points
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


in Baldwin County and compare it to the landcover that
surrounds the sites much can be learned.
With long term data collection you might be able to determine
which species live in more urban landcover as opposed to rural.
Long term data collection would also provide insight into the
roosting habits of different species during different seasons.
Dr. Parmley and Bender’s research told a lot about which species
of bat live in Baldwin County, but more research is needed to
determine why these species choose to live where they do.
However, you can already see how the more urban areas exhibit
less diversity, while the site with the highest diversity has the
highest amount of agriculture and mixed forest type landcover.
Magnificent Frigate Bird
Tricolored Heron
Great Horned Owl
Red Tailed Hawk
American Oystercatcher
 Data compiled from,
Christmas Bird Count
Data, Breeding Bird
Surveys, the Georgia
Breeding Bird Atlas
project, and banding
records
 Species data for each
county entered into
excel table and joined
to the map of counties
within the basin
 Table of population data for
each county joined to species
count map
 Normalization tool used to
obtain ratio of total species
per county compared with
the population data for
that county
 Landcover raster image
obtianed from
X:DATA/Georgia intitled:
1998_30m_18class_12/17/01.
img
 Clipped the raster image
down to the counties
within the river basin
 Classified the landcover of
the area for analysis of why
the amount of species
occur where they do and
what species may be found
there
•Excel spreadsheet utilized to obtain total number of species
counted for each county and total of a certain species within the
basin
Discussion
By looking at the distribution of the species counts for each county one
may begin to see some patterns that relate to population, area and land cover.
From the first map it is apparent that some of the larger counties have the
highest species diversity. This may be due to the actual number of bird watchers
in the county or a greater concentration of birds of various species. When
comparing the maps some of the suspected patterns are validated and others
falsified. For instance, the counties on the northern rim of the basin had some
of the highest species diversity but when this data was compared to the county
populations they had the lowest ratios. This is probably due to this area being
heavily populated as part of Metro Atlanta and greatly outnumbering the
species counts. Moving towards the coast and looking at the southern end of the
basin, the distribution is much more diverse due to the area being much less
populated but still having moderate species counts. The last map of the
landcover of the basin can be used to create some inferences on what birds may
be causing the variance in the species distribution and the species to population
ratios.
 All data was gathered
from www.usgs.gov
 The information was
gathered from individual
gauge points within the
Altamaha Basin.
 There are two sets of data
for each gauge site:
 Low Flow Statistics
 High Flow dates
 The low flow data is represented as “30Q2”; which
means that every 30 years, the stream flows at the level
listed for 2 years.
 For example if Fishing Creek’s low flow was 30Q2: 1.2 cfs,
then every 30 years fishing creek would have a base flow
of only 1.2 cfs.
 There were a total of 80 points within the Altamaha
River Basin (including the Oconee, Ocmulgee and
Little Ocmulgee basins)
 Only about 60% of those sites have low flow data.
 The high flow data represents the highest level of
water in each stream over an interval of 20+ years.
Streams that did not have data for at least 20 years
(1980-2000) were not included.
 Only 40% of the gauge points had high flow data.
Most sites had no more than 5 years of data available.
Why is it important?
 Low flow data can be used
Low Flow against Area
4000
3500
3000
Low Flow (cfs)
in watershed management
by allowing you to predict
the course of that stream
in the future.
 You will know that the
water will be so low in 30
years, so you can predict
the effect it will have on
your stream/community
and prepare for it.
4500
2500
2000
1,900
1500
1000
500
0
-500
5,000.00
10,000.00
Drainage area (mi2)
15,000.00
Why is it important?
High Flow against Area
180,000
160,000
 High flow data can be
 This allows you to be
better prepared for
storms and flood
events.
98
120,000
High Flow (cfs)
used to predict stream
flow patterns during
specific precipitation
events.
98
140,000
94
100,000
94
94
98
80,000
94
60,000
98
94
94
94
94
40,000
94
94
94
94
20,000
90
0
-
5,000.00
10,000.00
Drainage area (mi2)
15,000.00
Graph of gaugepoints (highflow by year)
High Flow by year
39% are from 1994
23% are from 1998
19.3% are from 1990
Graph of gaugepoints (highflow by year)
(1980)-1984
67% from 80
33% from 84
2/1 of 3
1985-1990
86% from 90
14 from 89
6/1 of 7
1991-1995
92% from 94
8% from 95
12/1 of 13
1996-2005
88% from 98
13% from 05
7/1 of 8
 Overall there needs to be a better effort made to gather
data for all of the gauge points. As it stands, there is
very little long term data available. As previously
stated, high and low flow data are valuable to
watershed management in terms of predicting and
preparing for future events whether those events are
droughts or hurricanes.
 Catlin Ames

Victoria, Uganda." GIS-spatial comparisons of fish community structure in three bays with varying
catchments of land use, Lake Victoria, Uganda. Proc. of 2nd Eastern Africa ESRI User Conference
(EAUC), Kampala, Uganda. 13 Sept. 2007. National Fisheries Resources Research Institute. 26 Apr. 2009
<http://www.firi.go.ug/Publications/FIRRI%20Papers/GIS_psartial_compariosn_of_fish.pdf>
.
 Rachel Lindsey
 www.usgs.gov
 Austin Waters
 www.narsal.uga.edu
 Bender, Michael J., and Dennis Parmley. "Noteworthy
from Central Georgia." Southeastern
records of bats
Naturalist 7 (2008): 619-26.