In my last blog post, I analyzed spatial autocorrelation in the whale movement parameters swimming speed and turning angles between consecutive segments of the whale’s trajectory for a single whale. In this update, I am expanding on this analysis by analyzing over a range of distances the spatial autocorrelation in swimming speed and turning angles in the trajectories of three foraging whales in the Stellwagen Bank National Marine Sanctuary. Positive autocorrelation in either parameter would mean that, when comparing two trajectory segments, the values for this parameter are similar between the two segments, and negative autocorrelation would mean that they are not similar. A correlogram shows the values of the autocorrelation coefficient for a range of distances between the trajectory segments. Here, I am presenting results from the analysis of the whale trajectories using the R CRAN adehabitatLT package (Calenge 2011).

The correlogram below shows the Moran’s I autocorrelation coefficient for the swimming speeds of three whales. Two whales show significant autocorrelation in swimming speed over short distances (<1000 m) (p<0.05, indicated by red circles). This means that during segments of the whale’s trajectories that are within 1000 m of each other, the whales maintained similar speeds. This is not surprising because generally it does not seem likely that the whales would abruptly change their swimming speed over such short distances.

After converting the turning angles to radians, the analysis of autocorrelation in turning angles (Moran’s I) revealed that the turning angles of only one trajectory were significantly positively autocorrelated at distances of 1000 and 2000 m (p<0.05, indicated by red circles).

 

 

Next, I used the R CRAN package adehabitatLT (Calenge 2011) to calculate first-passage time (Fauchald & Tveraa 2003) as metric for the search effort along each whale’s trajectory, and used linear regression to relate first-passage time to the environmental variables water depth and seafloor slope. The image below shows the three trajectories (in turquoise: 195b, purple: 188b_f, red: 188a) on a slope chart of the Stellwagen Bank National Marine Sanctuary area (USGS/NOAA).

 

 

Basend on the description by Fauchald & Tveraa (2003), for each trajectory, first-passage time quantifys the spatial scale of the animal’s foraging effort. The values of first-passage time at this spatial scale distinguish areas with high foraging effort (long first-passage time) from areas with low foraging effort (short first-passage time). The color-coded figure below shows first-passage time for whale 188b_f relative to seafloor slope (red: long first-passage time, green: short first-passage time).

Simple linear regression revealed that depth explained 17.2% of the variance in first-passage time for the trajectory of whale 188a (p=0.001). Separately, depth and slope explained 14.2 and 29.2 %, respectively, of the variance in first-passage time for the trajectory of whale 188b_f (each p<0.0005) (see figures below). For the trajectory of whale 195b, neither depth nor slope were significant predictors of first-passage time (each p>0.2).

Some authors (see Calenge 2011 for details) have suggested the analysis of autocorrelation of movement parameters of an animal’s trajectory following the standardization of the segment lengths. I will investigate this method in a follow-up analysis. Furthermore, I will re-analyze autocorrelation in turning angles using the absolute values of the turning angles instead of radians to facilitate the interpretation of the results.

 

Literature cited:

Calenge, C. 2011. “Analysis of Animal Movements in R: The adehabitatLT Package.” Saint Benoist, Auffargis, France: Office Nationale de La Chasse. http://cran.gis-lab.info/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf.

Fauchald, P. & T. Tveraa. 2003. “Using First-Passage Time in the Analysis of Area-Restricted Search and Habitat Selection.” Ecology 84 (2): 282–88.

The goal of this exercise was to investigate spatial autocorrelation in the movement parameters of a foraging humpback whale. I used location points of sightings of the whale at the water surface between consecutive dives to infer the path of the whale (Friedlaender et al., 2009). To facilitate the analysis, I assumed linear travel of the whale below the surface between consecutive surfacings. I projected the location points and used the adehabitatLT package in R CRAN (Calenge, 2011) to plot the location points of the whale as well as the linear travel segments between these points (see graph below).

The blue triangle indicates the first, the red square the last observation of the whale at the surface.

Using projected data, the adehabitatLT package calculates the distance traveled by the whale between consecutive observations, the turning angle between consecutive linear segments of the whale’s path as well as the duration between the observations. Using the distance and duration data I calculated the swimming speed of the whale.

Then I calculated the spatial autocorrelation in swimming speed and turning angles using Moran’s I for the entire path, and also for a small section of the path which I assumed to be a foraging area of the whale (cluster of points southeast of the blue triangle). In this small area, the whale spent a comparatively large amount of its time and swam shorter distances between consecutive surfacings, possibly indicating foraging activity.

A small p-value in one of the parameters would provide convincing evidence for the hypothesis that the movement of the whale is autocorrelated in the respective parameter, i.e. that neighboring locations have more similar values than locations that are further apart.

The results from the current analysis (see table below) provide moderate evidence for spatial autocorrelation in swimming speed for the analysis of the entire path, indicating that the whale swam slower in certain parts of its path and faster in other parts (Calenge 2011). However there was no evidence to suggest that travel speed in the small foraging area was autocorrelated. This could be explained by the fact that in the foraging area, the whale swam at a constant, slow speed to the probability of prey detection or encounter (Benhamou, 1992). When leaving this foraging area, the whale is likely to increase its speed, resulting in separate areas of the whale’s path with lower and higher swimming speeds, which would explain the autocorrelation in swimming speed observed for the entire path.

 

	Entire path		Foraging area
	Speed	Angle	Speed	Angle
p-value	0.017	0.841	0.702	0.651

 

 

Benhamou, S. (1992). Efficiency of area-concentrated searching behaviour in a continuous patchy environment. Journal of Theoretical Biology – J THEOR BIOL, 159(1), 67–81. http://doi.org/10.1016/S0022-5193(05)80768-4

Calenge, C. (2011). Analysis of Animal Movements in R: the adehabitatLT Package. Saint Benoist, Auffargis, France: Office Nationale de La Chasse. Retrieved from http://cran.gis-lab.info/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf

Friedlaender, A. S., Hazen, E. L., Nowacek, D. P., Halpin, P. N., Ware, C., Weinrich, M. T., Hurst, T., Wiley, D. (2009). Diel changes in humpback whale Megaptera novaeangliae feeding behavior in response to sand lance Ammodytes spp. behavior and distribution. Marine Ecology Progress Series, 395, 91–100. http://doi.org/10.3354/meps08003

 

 

Analysis of flood levels at 10, 50, 100 and 500 year events and their impact on potable, waste, and storm water systems in Alsea, Oregon, USA. 

 

The objective of this research is to:

• Investigate whether the existing Geographic Information Systems (GIS) information is complete, or needs to have more data added to represent the current condition of the water systems.
• Work with other individuals to update the GIS information so that aspects of the systems which are lacking can be repaired.
• Determine the cause of the sinkhole that has developed next to the Alsea School District’s playground, and under the sidewalk that accesses the school grounds along the western edge next to Route 34.
• Survey the historical flood effects and where the impacts would be detrimental to the town.
• Create boundaries of the 10, 50, 100 and 500 year floods to determine properties and systems potentially impacted.
• Develop plans to help the Alsea Emergency Preparedness Committee visualize the areas of greatest concern.
• Distribute the findings in the Alsea Valley Voice.  Upon final grading and review by the Geographic Information Systems & Science (GIScience) Department at Oregon State University (OSU).

The Alsea Watershed and Benton County Department of Public Works:

Most studies investigating the spatial scale of the watershed have focused on the town and the local roads. Multiple types of storm-drain pipes from concrete to poly (vinyl chloride), also called PVC, have been used over the years. Connecting to these makes repairs difficult. The alternative is to replace the entire line.

Analyzing what is needed to ensure proper water distribution and drainage will contribute to the town’s knowledge of where the water will travel, and how the storm water system may conflict with the existing man-made pathways.

The existing data for this analysis is sourced from public records through Doug Sachinger, the GIS Coordinator from Benton County Public Works.  He provided all of Benton County’s existing data for Alsea.  This is because the town of Alsea does not have a public works department, and is unincorporated. GIS data was missing throughout the town. Stub-outs for water meters appear on the map without any connecting system to feed them, and no water meter data exists yet to verify that any particular stub out is providing potable water to a location.

 

Alsea Potable, Sewer and Storm Water Systems

 

This is an example of the incomplete GIS data that the town of Alsea currently has on file with Benton County. There are stub-out locations that do not appear to be connected to the potable water system, and houses that are currently being serviced by the public water system which do not have their stub-outs shown.

 

Alsea Stub out not connected to the water system

The size of homes and the tax evaluation to run a hot spot analysis were shaded darker red, indicating the property locations made the lots more valuable.

Regional Hotspot data

 

It is interesting to note that the areas of highest value are located between the inlet of the potable water from the Alsea River and the outlet of the waste water treatment facility downstream into the Alsea River.

 

Alsea Hot Spot for property values

EXISTING SYSTEMS IN ALSEA

Since the town of Alsea is an unincorporated township, Benton County is the authority on Public Works.  In March, 2015 the old town storm water overflow was permeating into town members’ crawlspaces.  To remedy this, the county added three surface catchments and rerouted the water to a storm water ditch.  This added to the volume of water exiting through the school’s playground.

Temporary Stabilization Concern

When evaluating the potential cause of the sinkhole, water does not constantly flow from the opening, so it is less likely to be from a potable water line break.  When there is an active rain/storm water event or soon after, water floods the playground area and travels into the school’s paved pick up and drop off area.

 

Sinkhole pond that develops when it rains

Alsea’s current water systems need additional mapping.  Sadi Stouder, a Civil Engineering graduate student contacted me.  She will be adding the necessary information, and is interested is using my on-site assistance.  The objective is to gather the additional GIS data.  Bob Miller, from Benton County Public Works, records the town’s water meter usage, and may be willing to provide meter base locations.  This will help to document stub out locations and meter bases locations in order to connect them on GIS to the potable water system.

The raster file is now at 10 foot contour intervals.  This will help determine where the water will travel through town.

10ft Contour Interval

10ft Contour at the sinkhole by the Alsea School

Facing North toward R

Close-up where gravel and a metal panel have been added for safety

Facing South toward Alsechool and pathway of the storm water drainage pipe

Permanent Stabilization Practices

A few examples of permanent stabilization of areas with open soil profiles are buildings, paved areas and seeding of all non paved areas. Surface roughening, planting and re-vegetation have decreased erosion around the Alsea School District (Pre-K through 12^th grade).  Individuals are functioning as Storm Water Pollution Prevention (SWPP) leaders in the community.  The horticulture group is also working on gardens and selling plants to raise money.  Erosion control works hand in hand with reducing the impacts of flood waters in small communities.

Land use will reduce the effects of flooding and the weather conditions that occur at the same time.  By planting trees as clusters or groups, the wind is less likely to topple over a few vs. just one.

Flood of 2rains that began overnight Tuesday and poured steadily on Wednesday triggered floods throughout western Oregon on Thursday, with high water

Quote:

http://www.gazettetimes.com/news/local/the-flood-of/article_96c74746-4339-11e1-be3a-0019bb2963f4.html

January 20, 2012 5:00 am   Corvallis Gazette-Times

“Waters rise: Floodwaters posed a threat throughout Benton County on Thursday as the Marys River reached a record flood stage of 21.41 feet.  Not as bad as 1996, but a strong reminder.  Heavy rains that began overnight Tuesday and poured steadily on Wednesday triggered floods throughout western Oregon on Thursday, with high water pouring over roads throughout Benton County and soggy ground helping to create landslides, downed trees and other weather mayhem….Area schools and universities were taking no chances, though: Oregon State University’s Corvallis campus will be closed today, as will schools in the Corvallis School District and Alsea… In the 24 hours ending at 8 a.m. Thursday, 4.02 inches of rain fell at Hyslop Farm outside of Corvallis – easily breaking the previous record for Jan. 19, 2.25 inches, a mark set in 1911.  In fact, Wednesday went into the record books as the third-rainiest day in 101 years, said Kathie Dello of the Oregon Climate Service at OSU. The only other days that saw more than 4 inches of rain in 24 hours were Nov. 19, 1996, when 4.45 inches fell, triggering massive flooding that cut off access to south Corvallis for several days, and Jan. 28, 1965, when 4.28 inches fell.  The effect of the rain was most evident at the Marys River. It reached 21.41 feet Thursday morning, breaking the old record of 20.9 feet, and flooded parts of Corvallis and Philomath…. The Alsea School District closed schools and its playing fields and some buildings were flooded. ”

The quote above explains the history of flooding in the Willamette Valley and Coastal Range.  The next flood event may be in 10 years or it might be the 500 year event but the true extent of such an event is unknown as the data has not yet been recorded for 500 years.

The Federal Emergency Management Agency (FEMA) website allows public access to download data associated with flooding.  This information is not in GIS format or even in a data table to convert.  The process then was to find the Alsea River locations that were used as reference points and determine the elevation of the flood plains.

The categories are:

• 2 % likelihood i.e. not very often (500yrs)
• 1% more often (100yrs)
• 2% chance of flood is within a lifetime (50 yrs)
• 10% probability of flood levels every (10yrs)r (Federal Emergency Management Agency)

Even though it seems odd that FEMA is working on a document that is called Flood Insurance Study, maybe it had the people’s best interests at heart.

The purpose is to examine the real vs. perceived risks of flood events and determine the actual differences between a 10 year flood and a 500 year flood.O

regon

 

Special Flood Hazard Area

There is only one map for the 10, 50, 100 and 500 year events.  This map looks like the 10yr flood event.

The elevations on the graph provide the values of flood plains during each of the 10, 50, 100 and 500 year events.

 

A number of controls have been put into place by the Emergency Preparedness Group in Alsea.  One control is lift stations, or pump stations. These help ensure that high surface water levels don’t cause wastewater or storm water to back flow into people’s homes.

Additionally, litter, debris, and construction chemicals that could be exposed to storm water should be prevented from becoming a pollutant source in storm water discharges. (10) Year Flood Event

accrossTen Year Flood Event Extent

The 10 year flood event appears in Alsea as:

 

Ten Year Flood Event in AlseaFifty Year Flood Event Extent

The 50 year flood event appears in Alsea as:

The 100 year flood event appears in Alsea as:

The 500 year flood event appears in Alsea as:

 

The overlays from the Benton County Public Works GIS Department show Alsea potable, waste and storm water systems, yet did not have the ArcGIS flood data.  This next level of research was found through the FEMA website and adapted into the existing map.  Once the flood data could be viewed with the existing layers, flood elevations were determined and placed into Table 2.

The base map was then edited through the toolbox within the customize tab.  When selected, the locations A-E were added as reference points.  Select the contour lines available. If 10 foot then convert to 2 ft so it will provide the results with better quality results and details. rocedural ArcMap GIS example in Alsea

When we think of floods, the worst comes to mind. Many in this are have known the effects first hand. This report is for those that haven’t experienced the high waters and to recognize the areas of concern. This will allow the development of other methods to reduce the negative impacts on our community, and our personal items in our basement.

The map below shows the 10 year flood in blue and then the additional areas affected by the 50 year flood in pink, the 100 year flood in purple, and then the 500 year flood in greenish-yellow.  If it’s in your home, the couple foot difference is not minimal, but in comparison the changes in area are slight.  These are also estimated and could be higher or lower depending on rain, snow loads in the mountains, water table, vegetation and many other factors.

Regional 10, 50,100 and 500 year Flood Extents

Alsea 10, 50,100 and 500 year Flood Extents

REFERENCES

Federal Emergency Management Agency:

https://msc.fema.gov/portal/search?AddressQuery=alsea%2C%20oregon

 

US Department of Fish and Wildlife – www.fws.gov

 

Doug Sachinger, GIS Coordinator, Benton County, Oregon, Public Works

 

(Staff Reporter) 2012,January 20, “The Flood” Corvallis Gazette-Times

 

http://heresmeme.com/AVV/Alsea%20Flood%202012/New/page_01.htm

 

My research objective is to investigate two spatial aspects of humpback whale (Megaptera novaeangliae) surface feeding in and around the Stellwagen Bank National Marine Sanctuary in the southern Gulf of Maine, USA. Specifically, I aim to:

• Quantify the spatial scale of this behavior.
• Investigate whether this behavior is more frequently observed in certain areas of the study region.

Most studies investigating the spatial scale of humpback whale movement have focused on large spatial and temporal scales, using 1-2 location points per day over the course of several days or weeks during which the animals traveled hundreds or thousands of kilometers (Dalla Rosa et al. 2008; Heide-Jorgensen and Laidre 2007; Kennedy et al. 2013). In contrast, the proposed study aims at investigating the detailed movement of these whales on the temporal and spatial scales of daily bouts of foraging events. Analyzing such fine-scale foraging movement patterns can contribute to our knowledge of how marine predators search for patchily distributed resources (Levin 1992; Pinaud and Weimerskirch 2005).

The existing data for this analysis stems from a long-term study investigating humpback whale behavior and ecology in the southern Gulf of Maine, USA, (for more details, see Friedlaender et al. 2009). Almost every summer since 2004, whales were equipped with non-invasive tags that recorded detailed information on the underwater movement of the whales or collected video-footage of the behavior of the tagged animal and associated whales. During daylight deployments that usually lasted for up to 8 hours, focal follows were conducted from a small boat following the tagged whale, during which detailed information on the behavior of the tagged whale at the water surface was collected. Because the tags did not contain a GPS, range and bearing information on the whale were also collected at least once when the whale was observed at the surface in between consecutive dives, usually resulting in the collection of one location point every 3-5 min. Continuous GPS locations of the boat were automatically collected. Based on the time stamps of the range/bearing data and the boat GPS data, the GPS location collected at or close to the time the range and bearing data was collected, were identified and together this data was used to calculate the location of the whale.

The analysis proposed here will use the whale behavioral observation data to identify focal follows during which surface feeding was observed. The location data of these focal follows will be used for the following analysis. For each focal follow of a surface feeding whale, I will use the R package adehabitat (Calenge 2011) to implement first-passage time analysis to calculate the spatial scale of surface feeding, and to identify areas of intense foraging effort. The following description of the method is based on Fauchald & Tveraa (2003). First-passage time is a metric used to quantify search effort along an animal movement path. Around each location point, a circle of a given radius is created, and the amount of time the animal spent within the area of the circle is measured. The measurement is then repeated for each location point of the animal’s path with successively increasing circle radii. Increasing the radii will increase the amount of time the animal spent within the circle, but the increase in time will be greater in areas of intense search effort compared to areas through which the animal was simply traveling. The radius at which the variance in first-passage time between the different location points is greatest represents the spatial scale of foraging effort. I intend to statistically test for differences in the spatial scales of surface feeding between individuals and as a function of group size. At the radius representing the spatial scale of foraging, those circles with the longest first-passage times identify areas where foraging effort is concentrated (Bailey & Thompson 2006). Comparing the locations of intense foraging effort between individuals, areas within the study region can be identified that represent suitable foraging habitat (Bailey & Thompson 2006). The expected outcome of this part of the analysis is a map of the study region displaying locations of suitable foraging habitat based on first-passage time calculation.

I expect to find that different individuals have a similar spatial scale of surface feeding, as I anticipate that the spatial scale of foraging is correlated with spatial metrics of prey schools (Benoit-Bird et al. 2013). Because the spatial scale of search effort is likely to be larger than the spatial scale of the prey patches themselves, I expect the spatial scale of foraging for all individuals to be larger than the average prey school length in the area, which is ca. 139 m (Hazen et al. 2009). I expect to find a positive correlation between the spatial scale of surface feeding and group size because I anticipate that larger groups will cover a wider area during their search. I expect to see a concentration of surface feeding locations in the western part of the sanctuary region as this has been found to be an important feeding area in a previous study using a subset of the data I am planning on analyzing here (Hazen et al. 2009). This is due to the substrate type, topography and oceanographic conditions in this area which serve to attract and aggregate prey (Hazen et al. 2009).

I currently have basic knowledge of ArcMap and R and no experience with Python or Modelbuilder.

 

Literature cited:

Bailey, H. & P. Thompson. 2006. “Quantitative Analysis of Bottlenose Dolphin Movement Patterns and Their Relationship with Foraging: Movement Patterns and Foraging.” Journal of Animal Ecology 75 (2): 456–65. doi:10.1111/j.1365-2656.2006.01066.x.

Benoit-Bird, K.J., B.C. Battaile, C.A. Nordstrom, and A.W. Trites. 2013. “Foraging Behavior of Northern Fur Seals Closely Matches the Hierarchical Patch Scales of Prey.” Marine Ecology Progress Series 479 (April): 283–302. doi:10.3354/meps10209.

Calenge, C. 2011. “Analysis of Animal Movements in R: The adehabitatLT Package.” Saint Benoist, Auffargis, France: Office Nationale de La Chasse. http://cran.gis-lab.info/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf.

Dalla Rosa, L., E. R. Secchi, Y. G. Maia, A. N. Zerbini, and M. P. Heide-Jørgensen. 2008. “Movements of Satellite-Monitored Humpback Whales on Their Feeding Ground along the Antarctic Peninsula.” Polar Biology 31 (7): 771–81. doi:10.1007/s00300-008-0415-2.

Fauchald, P. & T. Tveraa. 2003. “Using First-Passage Time in the Analysis of Area-Restricted Search and Habitat Selection.” Ecology 84 (2): 282–88.

Friedlaender, A.S., E.L. Hazen, D.P. Nowacek, P.N. Halpin, C. Ware, M.T. Weinrich, T. Hurst, and D. Wiley. 2009. “Diel Changes in Humpback Whale Megaptera Novaeangliae Feeding Behavior in Response to Sand Lance Ammodytes Spp. Behavior and Distribution.” Marine Ecology Progress Series 395 (December): 91–100. doi:10.3354/meps08003.

Hazen, E.L., A.S. Friedlaender, M.A. Thompson, C.R. Ware, M.T. Weinrich, P.N. Halpin, and D.N. Wiley. 2009. “Fine-Scale Prey Aggregations and Foraging Ecology of Humpback Whales Megaptera Novaeangliae.” Marine Ecology Progress Series 395 (December): 75–89. doi:10.3354/meps08108.

Heide-Jorgensen, M. P., and K. L. Laidre. 2007. “Autumn Space-Use Patterns of Humpback Whales (Megaptera Novaeangliae) in West Greenland.” Journal of Cetacean Research and Management 9 (2): 121.

Kennedy, A.S., A.N. Zerbini, O.V. Vásquez, N. Gandilhon, P.J. Clapham, and O. Adam. 2013. “Local and Migratory Movements of Humpback Whales (Megaptera Novaeangliae) Satellite-Tracked in the North Atlantic Ocean.” Canadian Journal of Zoology 92 (1): 9–18. doi:10.1139/cjz-2013-0161.

Levin, S.A. 1992. “The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture.” Ecology 73 (6): 1943. doi:10.2307/1941447.

Pinaud, D.D. & H. Weimerskirch. 2005. “Scale-Dependent Habitat Use in a Long-Ranging Central Place Predator.” Journal of Animal Ecology 74 (5): 852–63. doi:10.1111/j.1365-2656.2005.00984.x.

Adelie and Gentoo penguins are two closely related Pygoscelis penguins with overlapping breeding ranges on the Western Antarctic Peninsula. The foraging strategies of these two species vary widely across their breeding ranges and little is known about their foraging niches at Palmer Station, Anvers Island.

Is intraspecific competition driving population trends at Palmer Station?

It remains unclear whether intraspecific competition is occurring between Adelie and Gentoo penguins at Palmer station. This is a particularly important question to answer in this region, where climate-induced warming and sea ice loss has significantly altered habitat, marine food web dynamics, and the community structure of Pygoscelis penguins. In this area, populations of Adelie penguins have been declining over the last four decades while numbers of Gentoo penguins have been increasing. In this context, it is important to gain a better understanding of how these two species partition their shared prey resource. This study will contribute to knowledge to current theories explaining the contrasting population trends of Adelie and Gentoo penguins, and help to determine the extent that intraspecific competition is driving these trends.

As central place foragers, Adelie and Gentoo penguins are constrained by the spatial and temporal distribution of their prey, which in this region is principally Antarctic krill. In order to co-exist as sympatric predators, these species must partition their shared prey resource by foraging in different locations, at different depths and/or at different times of the day. The objective of my project is to investigate foraging ranges of Adelie and Gentoo penguins and quantify the degree of overlap between them, if any.

My hypothesis is that the foraging ranges of these two species will overlap. Due to the constraint imposed upon them as central place foragers, seabirds rely on foraging in locations with predictable prey patches. The islands that each of these species breed on are relatively close together (within the distance that each species is known to swim on a single foraging trip) and it is likely that both species are cued into aggregations of prey in similar areas.

I will be utilizing satellite telemetry to examine the foraging ranges of these species and to test this hypothesis. Fieldwork was conducted through a Long Term Ecological Research project based out of Palmer Station, Anvers Island. We tracked Adelie and Gentoo penguins between 5 Jan-2 Feb 2015 using platform terminal transmitters (PTTs). A total of 15 Adelie and 7 Gentoo penguins carried these tags for roughly 3 days each during this study period. Location data were downloaded from Argos. My first step in this project will be to pre-process these data by filtering out erroneous data points, and I am considering using the R package argosfilter to do this. Following this step, I intend to import these data points into ArcGIS and utilize the Spatial Analyst extension tool to create kernel density estimates of foraging ranges. I am interested in quantifying the total foraging area utilized by each species as well as core foraging areas. Previous studies have done this by using 50% and 95% kernel density estimates and calculating the area within each of those contour lines. This will allow me to visually and quantitatively describe the space that each of these species is utilizing to forage. Following these steps, I plan to calculate the proportion of overlap between each of these species ranges. In addition to learning how to conduct the analyses above, I am interested in exploring ArcView’s Animal Movement extension, and determining whether it might be useful for this project.

I anticipate that I will produce a series of maps that illustrate the foraging ranges of these two species over this study period. In addition to this I will calculate the total area (km²) of these ranges, and the percentage of overlap between these ranges. I expect that these results will provide us with a better understanding of whether intraspecific competition is a mechanism behind the contrasting population trends of Adelie and Gentoo penguins in the Palmer region. Up until this point, it has been difficult to discern the relative influence of sea ice loss on penguin habitat versus its effects on marine primary productivity and food web dynamics. The value of using these apex predators as indicator species will increase if we are able to determine the proximal causes behind their responses to environmental variability. This study will leave us better informed to infer population-level responses to increased competition in the Palmer region.

Regarding my previous experience with the tools we will be using in class- my knowledge of ArcGIS and R is basic, and I do not have any experience with Modelbuilder or Python.

Is there a pattern evident in the successional order of habitat types recolonized by certain diurnal raptor species within the last twenty years along urban/rural gradients in Washington, Oregon, and California? This is the question I hope explore throughout this course. I have yet to finalize which raptor species I will include in this analysis, but I will likely use observations of red-tailed hawks, merlins, American kestrels, and northern harriers. This analysis will be automated with a Python script so the number of species I choose to include is not critical at this stage of the research.

To answer this question, I will use citizen science bird observations and land cover data. The bird observations will come from the eBird database, an online citizen science monitoring and reporting system. For each eBird observation, an observer submits the species, the geographic location, and the date/time. These data exist at a range of spatial scales from several meters to several kilometers. Because I will analyze habitat colonization at the patch scale, I have already written Python code to select observations where lat/long is reported at or above a specified precision. The extent of these data stretches beyond the continental US. Temporally, eBird data are reported with a timestamp specified to the minute, however, my analysis will only require resolution to the month. I will use data from 1995 to 2014. Data exist for observations before 1995, but they are much more sparse.

The land cover data I will use is derived from LandTrendr, a series of algorithms for extracting land cover change information from Landsat time series imagery. For this project, I will use only land cover data from Washington, Oregon, and California as these data already exist. Land cover data for other parts of the country would need to be processed, a time-consuming endeavor that is unnecessary for the purposes of this class. Land cover data for Washington, Oregon, and California exist for all years from 1995 to 2014.

From this research, I expect to see two general patterns: 1) species prevalence for each selected species will increase inside highly developed patches from the beginning of the study period to the end, and 2) a successional order of colonized habitat types will be present for each species. A number of longitudinal studies have documented urban re-colonization by various diurnal raptor species. These re-colonization events may follow successional patterns. This process can be understood in the following way: when an urban area is first developed, existing raptor species likely retreat to remaining high quality habitat. As this high quality habitat reaches carrying capacity, pioneering individuals must find marginal habitat that still suits their ecological needs. This same process of demographic saturation is repeated, likely resulting in a successional pattern, as more marginal habitat is re-colonized.

From my limited knowledge of spatial statistics, it seems that a geographically weighted regression or ordinary least squares would be appropriate to tease out such patterns. eBird data are also notorious for spatial biases where observations are more numerous near easily accessible places (e.g. roads, trails, etc.) Perhaps a spatial autocorrelation analysis may work to assess the accuracy of successional pattern analysis results. To perform these analyses, I am proficient with ArcInfo, ModelBuilder, and Python. However, I don’t have any experience with R.

Within the last several decades, urbanization has rapidly altered available habitat for diurnal raptor species, leading to changes in geographic distribution. Greater understanding of urban raptor habitat selection may lead to more effective conservation. To date, however, data on urbanization patterns and species response are not available at the spatial and temporal scales needed to build this understanding. Answering the above research question will hopefully shed light on the effects of development on raptor distributions and aid city planners and conservationists in building more ecologically habitable cities.

I am interested in exploring the utility of MODIS Land Surface Temperature (LST) maps in creating heat stress maps for migrant farm workers in rural Oregon. Currently it is difficult to estimate heat exposure for field workers since point source data is very scattered. Monitoring stations are currently used to collect information at a large scale; however this information cannot accurately be interpolated for a large area. Often times there is only one monitoring station for a very large area, which would lead to serious issues when trying to create a continuous surface temperature map. The ability to use remotely sensed data for these heat stress models would allow researchers to more accurately assess individual exposure. This is crucial in identifying areas that need more attention or resources, and would greatly simplify the process of analyzing this data.

I would like to compare the values predicted by the MODIS LST for a given date with the temperature recorded by the National Weather Service (NWS) or another point source for temperature data. This will allow me to find the difference between the sources of information, as well as identify any patterns in the distribution of error for MODIS data. To do this, I will compare data for many dates across a variety of locations in order to identify any spatial or seasonal patterns. The main objectives of this project are as follows:

1. Identify the magnitude of the difference between these 2 sources of data
2. Create a regression model for comparing temperature data recorded by the NWS and MODIS LST maps for a set of given dates and locations
3. If the difference from the point source data to the MODIS LST image is too great, explore other ways to use MODIS LST images to predict heat stress for migrant populations
4. If necessary and/or possible, explore other remotely sensed data sources if MODIS does not work for this spatial problem

The first hurdle will be collecting all this data for the locations needed for the analysis. Also, the MODIS data and NWS data are not reported with the same timeframe (i.e. the high and low temperatures reported by the NWS may not match the average temperature recorded on the MODIS LST image). I will need to find some way to compare these values and normalize them to each other (currently my only idea is to create a function that will take the time of the high and low records from the NWS data, create an estimate of the temperature throughout the day based on these values, and compare the temperature at the specific time of the MODIS reading).  Ideally I would like to look at data for 5 sites of different terrain over 5 days from each season (20 days total, 100 data points).

Regarding my experience with the various tools for this class, I have moderate experience using R and ArcGIS, I have a strong introduction to using Python, and have a rudimentary knowledge of using ModelBuilder.