Humans kill approximately 500 million to 1 billion

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kill approximately 500 million to 1 billion birds a year through direct and
indirect contact (Erickson et al. 2005).   Bird mortality associated with vehicle
collisions is just one example of a negative direct interaction between humans
and birds, and it has been documented that a taxonomically diverse group of
birds are susceptible to mortality from contact with vehicles (Case 1978,
Decker 1987, Ashley and Robinson 1996). 
One particular family of birds, Anatidae, or commonly known as
waterfowl, are susceptible to these incidents when their habitat is in close
proximity to high traffic areas.  Like
their name suggests, these birds primarily reside in areas with water, most
commonly in wetlands.  Wetlands are
highly productive, and for waterfowl, are an important food resource, source of
nesting habitat, and habitat in general. 

The prairie
pothole region in the Central United States has extremely high numbers of
waterfowl due to a high density of wetlands and small lakes.  These birds use this region as a migration
route where they may stop to rest and replenish energy stores, as well as a
nesting area.  One study by Sargeant
(1981) focused on mortality associated with vehicles in North and South Dakota,
an area in the prairie pothole region, estimating mortality in this region due
to roads at 4,500 ducks per 4 months, or 13,500 per year (Sargeant 1981).  This prairie pothole region is a very
productive region for the recruitment of waterfowl in the Central flyway, when
compared to another flyway, the Atlantic flyway.  However, the population of humans in this
region is substantially lower than of another region, the Atlantic flyway.  The Atlantic flyway encompasses the East
Coast of the United States and is used by both sea ducks as well as land ducks
and species of geese and swan.  With this
area being high in human population and a main route for waterfowl, vehicle
strikes, while not studied, should be a high priority for research.  While this study could not approximate how
many waterfowl will be killed, it can be used to inform a specific area of one
way they can protect these waterfowl from being killed from vehicles.  This area is of high interest as it is
located within 5 miles of the Delaware River, an important migratory route on
this flyway.  Also noteworthy is this
area is subjected to high traffic from large vehicles due to a landfill
operated by Waste Management within this study region.  This landfill receives waste not only from
the surrounding area, but also as far away as New York City, approximately 75
miles away. 


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question will investigate areas deemed as high priority habitat for both
resident waterfowl and for those birds stopping along their migration route.  This area will then be related to how close
it is to roads to determine which areas of food are of high concern for vehicle
strikes.  Our analysis will attempt to
answer the following question:  Where are high priority waterfowl habitats,
and what would be the associated cost of protecting these areas with fencing?

Data and Approach:

first piece of data we used was an image of Tullytown from the data set NAIP
Geotiff.  All it contains is an image of
the area of interest.  The metadata
is:  Vintage- 8/24/17, Resolution-1m, Coordinate System-WGS_1984_Web_Mercator_Auxiliary_Sphere
, and Datum-NAD83.  The next data we used
was the NHD of Pennsylvania Shapefile. 
This data contains many various files, but the one we used was the
NHDWaterbody which contained the necessary waterbodies we wanted to use.  The metadata is:  Vintage- 9/14/17, Resolution-1:24,000-scale
data, Coordinate System- GCS_North_American 1983, and Datum- NAD83.  The final data we used was the USGS NTD for
the roads layer.  This also contains many
different files, but we used the RoadSegment file for our analysis.  The metadata is: Vintage- 8/19/17,
Resolution-1:24,000-scale data, Coordinate System- GCS_North_American 1983, and
Datum- NAD83.  We began our analysis by
reprojecting everything into WGS_1984_Web_Mercator_Auxiliary_Sphere.  We then clipped both the NHD data and NTD
data to the extent of our image.  On our
hydrography data, we had to do a -10m buffer, 10MBufferInside, to get a lake
with 10m around the edge removed, and then we created a 10m buffer on the -10m
buffer layer to get the shoreline created, Shorelinebuffer.  What also was done before any other analysis
was that aquatic vegetation was digitized from the Tullytown image file.  In this digitization step, you could clearly
see water lilies as well as submerged aquatic vegetation, which were both
digitized, creating a new shapefile called Food, and calculating area in square
meters and hectares.  The roads layer was
also buffered 100m, creating a new layer called RoadBuffer.  Next came the critical intersects, beginning
with the food and shoreline layers, creating a layer that highlights where the
food is within 10m of the shoreline, Food_Shoreline_Intersect.  This layer was then intersected with the road
buffered layer to get a high priority area close to the roads, called
HighFood_Intersect, again calculating area in square meters and hectares.  From this layer, we digitized a fence, unless
the area was sticking into the water as an edge, because the birds could be
limited in their food access.  This final
layer was known as the High Risk Fence, with us calculating length, in meters
for our cost analysis, in meters.  We
created another fence from our Food layer and our Road layer.  This layer was called Food_Road_Intersect,
and we calculated area in square meters and hectares, creating a layer with
food close to roads, but not necessarily on the shoreline.  From this intersected layer, we again
digitized a fence, naming it Medium Risk Fence, again calculating length, in
meters, for our cost analysis, in meters.


results can be seen in Table 1, as well as our final map, where it highlights
that there is a smaller area of food that is both near roads and shorelines,
when compared compared to the food just near the roads.  We also provided a cost to two types of
fences, Medium Risk and High Risk, based on their location to both the Food near
the Roads layers, or MediumFood_Intersect, and the HighFood_Intersect.  This data can be seen in Table 2, with the
High Risk Fence being smaller and costing less, with the cost for both fences
being associated with the Critterfence 700 fence.


on our analysis of where waterfowl feed and their proximity to roads, we have provided
the town of Tullytown, and Waste Management, as they are the primary traffic
issue, two fences that can be constructed to protect these waterfowl from being
hit by vehicles.  Due to the high volume
of traffic this area sees, we believe these fences, while birds may fly over
them, will help reduce mortality from vehicle strikes in these areas due to the
nature of the birds not walking up the shoreline to rest.  We also believe this model, or one similar to
it, can be fitted to any other areas of interest in determining where wildlife
can be protected from vehicle strikes, whether it be waterfowl or other
animals, using data on their optimal habitat. 
This analysis provides not only the locations, but also the cost
associated with erecting these fences to protect the waterfowl, and thus gives
the town and company enough information to decide where these fences might be
optimally constructed.

Categories: Management


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