Month: May 2016

On niche partitioning and the Ohio State Buckeyes

By: Erin Pickett, MS student, Biotelemetry and Behavioral Ecology Laboratory & GEMM Lab, MMI

Buckeye anecdote

I recently found myself sitting at a Sunday brunch at the Westin in Washington, D.C., talking to my uncle about my research on the foraging ecology of penguins. Our entire extended family had gathered for a cousin’s wedding, and it was the first family gathering in a long time that I had been able to attend due to always being “out on some island”, as my cousin puts it. In fact, I got a shout-out during one of the dinner reception speeches for coming all the way from Antarctica for the wedding.

My uncle asked me about my research while our surrounding family members sipped their coffee and OJ and recounted the highlights of the previous night’s wedding reception. This conversation with my uncle was the first I’d had with a family member all weekend that had progressed past my ‘elevator speech’ of what I was studying in school. After I described my research questions about resource partitioning between Adelie and gentoo penguins, my uncle glanced around the room full of family members and said to me, “You know what….”? And then he went on to describe his thoughts about how our aunts, uncles, cousins and in-laws all occupied distinct niches within our family.

The definition of the word niche is broad, and for this reason it can be used to describe the roles of younger siblings, matriarchs, sisters, and Ohio State Buckeye fans within their families or communities. Take for example my entire family on the dance floor chanting O-H-I-O during the bands requisite rendition of “Hang on Sloopy” at the wedding reception. As Buckeyes, we were occupying a role distinct from that of the bride’s family, who are Notre Dame Fans. Within our immediate families, the roles of every sibling and parent are further differentiated. My uncle and I looked around the room and saw a family who despite a wide range of personalities and football allegiances, was managing to enjoy a pretty good time together!

Ecological niche theory and sympatric penguins

In ecology, the term niche is used to describe the ecological role that a species occupies within an ecosystem (Hutchinson 1957). The concept of an ecological niche is typically used in ecology to describe how similar species coexist within the same space. This coexistence is made possible through segregation mechanisms that facilitate resource partitioning, such as spatial or temporal differences in foraging location, or dietary segregation (Pianka 1974). With this in mind, the main objective of my master’s research is to quantify the ecological niches of Adelie and gentoo penguins in terms of space, time and diet, in order to investigate whether foraging competition is occurring between these two species. You’ll find more background on this project here.

The first step in my investigation of resource partitioning was to assess the extent and consistency of dietary overlap between these two species. The diets of Adelie and gentoo penguins vary regionally, but along the Antarctic Peninsula the prey of both species is typically dominated by Antarctic krill. This was the case when I studied the diets of these two species at Palmer Station in Antarctica. I also found that both species consume the same size classes of krill and that this was consistent across both low and high prey availability years (Figure 1).

Size class frequency distribution of Antarctic krill found in penguin diet samples (2010-2015). Krill size class bins shown on x-axis and proportions depicted on y-axis
Figure 1. Length-frequency distribution of Antarctic krill found in penguin diet samples (2010-2015). Krill size class bins shown on x-axis with the proportion of those size classes depicted on the y-axis. Palmer LTER unpublished data.

The next step of my project is to assess the foraging habits and space-use patterns of these two species. They share food, but do they forage in the same areas? I am in the process of analyzing spatial data obtained from satellite and TDR (time depth recording) tags temporarily attached to Adelie and gentoo penguins during the breeding season to determine the core foraging areas. I am using kernel density estimate (KDE) techniques to visually and quantitatively determine the size and extent of spatial overlap between both species foraging areas (Figure 2).

Figure 2.
Figure 2. An example plot of 3D kernel density estimates outlining 95% and 50% volume contours of foraging penguins during the 2010 breeding season. Orange and green depict the core foraging areas of gentoo and Adelies, respectively. Horizontal axes show northing and easting values and depth is shown in meters on the vertical axis.

The KDE method allows me to turn hundreds of satellite tag derived location points into a probability density surface which depicts where an animal is most likely to be found (Kie et al. 2010).  2D KDEs are sufficient to describe the ranges of many terrestrial animals, however, 3D KDEs are a more appropriate description of the space-use patterns of diving seabirds. By failing to incorporate the depth at which these two species are foraging, 2D KDEs might overestimate the extent of spatial overlap between two species who are foraging in the same location but at different depths. Similar to other studies (Cimino et al. 2016 & Wilson 2010), I am finding that Adelie and gentoo penguins may be partitioning resources by foraging at different depths, with gentoo penguins diving deeper than Adelies. By foraging at different depths, these two species are limiting foraging competition.

While I am working on these analyses, I am also thinking about my next step, which will be to determine whether foraging niche overlap between Adelie and gentoo penguins is a function of prey availability. Resource availability is a critical component of niche segregation. When resources are abundant, there is typically a higher tolerance for niche overlap (Pianka 1974, Torres 2009). Conversely, niches may become more distinct as resources decrease and successfully partitioning these resources will become more important to minimize competition. In order to address the effect of resource availability on niche partitioning between Adelie and gentoo penguins, I will be comparing their foraging niches during years of both low and high prey availability. This will allow me to truly evaluate the potential occurrence of foraging competition between these two species.

Conclusion

I’ll keep you updated on my progress with data analysis in future blogs, but before I go I’ll share one last piece of wisdom about niche theory that I’ve learned from my family. There is a niche for everyone unless you are a Michigan fan, then no amount of spatial or dietary partitioning in a room full of Ohio State Buckeyes will save you.

References 

Cimino, Megan A., et al. “Climate-driven sympatry may not lead to foraging competition between congeneric top-predators.” Scientific reports 6 (2016).

Hutchinson, G.E. “Concluding remarks. Population Studies: Animal Ecology and Demography.” Cold Spring Harbor Symposia on Quantitative Biology 22 (1957): 415-427.

Kie, John G., et al. “The home-range concept: are traditional estimators still relevant with modern telemetry technology?” Philosophical Transactions of the Royal Society of London B: Biological Sciences 365.1550 (2010): 2221-2231.

Pianka, Eric R. “Niche overlap and diffuse competition.” Proceedings of the National Academy of Sciences 71.5 (1974): 2141-2145.

Torres, Leigh G. “A kaleidoscope of mammal, bird and fish: habitat use patterns of top predators and their prey in Florida Bay.” Marine Ecology Progress Series 375 (2009): 289-304.

How can we reconstruct life-history pathways of whales?

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU

 

Have you ever heard of statistical modeling? What about Hierarchical Bayes Models?

Hard words, I know…

Modeling is when known data (previously collected) is analyzed using sophisticated computer algorithms to look for patterns in these data. Models can be very useful for filling in data gaps where and when no sampling occurred. Hierarchical Bayes model is a type of statistical model that hierarchically integrates the observed data to estimate parameters. This type of model can analyze long-term data from individual animals to predict into data gaps and inform us about population dynamics.

When studying wild animals we often only collect data from brief and random encounters. Therefore, many researchers struggle with the reconstruction of possible pathways that could connect different sightings of wild animals to determine where, when and how the animal was doing in between sightings.

For instance, consider an animal that was observed in healthy condition at one sighting but in a subsequent sighting it was in poor health. How can we estimate what happened to this animal between sightings? Can we estimate where, when and how health deteriorated?

This is where the modeling comes in! It is a powerful tool used by many researchers to fill in gaps in our scientific knowledge using data that we do have. We use these ‘known data’ to estimate patterns and determine probabilities. The hierarchical Bayes model is a type of modeling that can be used to estimate the probability of pathways between known events. Schick et al. (2013) used hierarchical Bayes models to estimate the many factors that impact whale health and survivorship including distribution and movement patterns, true health condition of the individual and survival rates.

Modeling is very advantageous when studying aquatic animals like dolphins and whales that are very hard to spot since they spend a higher proportion of their lives submerged than above water. Also, sea conditions can hamper visual detection.

Schick et al. (2013) analyzed decades of data from photo-identifications of North Atlantic right whale resightings along the east coast of North America. They assessed different information from these pictures including body condition, infestation of cyamids, presence of fishing gear entanglements, rake marks and skin condition. The authors also used information of age and calving of the individuals. A model using these data was constructed and a more complete scenario of health and movement patterns of individuals and the populations were estimated. Survival rates of each individual were also estimated using this model. This is an example of a well-informed model and is important to notice that a model is only as good as the data you put into the model.

Using this model, Schick et al. documented variations in annual spatial distribution patterns between sexes (Fig. 1). For example, females arrive earlier to the BOF region than males, and have greater estimated transitions to SEUS region at the end of the year. It is also possible to see that there is a lack of information for the region MIDA, characterizing another advantage of modeling since it can highlight areas where effort should be increased.

Figure 1: Movement transition estimates from North to South regions in the western Atlantic Ocean for male and female right whales over the course of a year. Size of the circles in each region at each month corresponds to the actual number of right whales observed. Lines connecting regions indicate probability of transition. Magnitude of probability is depicted by line thickness. (NRTH: North region; BOF: Bay of Fundy; JL: Jeffreys Ledge; GOM: Gulf of Maine; RB: Roseway Basin; NE: Northeast; GSC: Great South Channel; MIDA: Mid-Atlantic; and SEUS: Southeastern US). Source: Figures 5 and 6 from Schick et al. 2013.
Figure 1: Movement transition estimates from North to South regions in the western Atlantic Ocean for male and female right whales over the course of a year. Size of the circles in each region at each month corresponds to the actual number of right whales observed. Lines connecting regions indicate probability of transition. Magnitude of probability is depicted by line thickness.
(NRTH: North region; BOF: Bay of Fundy; JL: Jeffreys Ledge; GOM: Gulf of Maine; RB: Roseway Basin; NE: Northeast; GSC: Great South Channel; MIDA: Mid-Atlantic; and SEUS: Southeastern US).
Source: Figures 5 and 6 from Schick et al. 2013.

 

When the model is applied to individual whales, the authors were able to estimate survival and health rates across the whale’s life-span (Fig. 2). Whale #1077 was a rarely seen adult male, with a sparse sighting history over 25 years. The last sighting of this whale was in 2004 when its health status was poor due to a poor body condition. According with his condition in the last sighting, the model predicted a high decrease in his health over time and since the whale was not seen for more than six years, so was presumed dead, following the standards set by the North Atlantic Right Whale Consortium.

Figure 2: Health time series for whale #1077. Time series of health observations for body condition, cyamids, entanglements, rake marks and skin condition (circles), estimates with uncertainty of health (thick line and dashed lines) and estimates of survivals (height rectangle at bottom). Photographic observations are color and size coded by class (three categories for body condition: green is good, orange is fair and purple is poor; and two categories for skin condition: green is good and orange is poor). Source: Figure 11 from Schick et al. 2013.
Figure 2: Health time series for whale #1077. Time series of health observations for body condition, cyamids, entanglements, rake marks and skin condition (circles), estimates with uncertainty of health (thick line and dashed lines) and estimates of survivals (height rectangle at bottom). Photographic observations are color and size coded by class (three categories for body condition: green is good, orange is fair and purple is poor; and two categories for skin condition: green is good and orange is poor).
Source: Figure 11 from Schick et al. 2013.

 

As I begin data collection for my thesis project to examine gray whale health along the Oregon coast in relation to ocean noise and inter-annual variability, I am considering how to apply a similar modeling approach to enhance our understanding of what influences individual gray whale health and also connect pathways between our resightings.

The marine environment is constantly changing, across space and over time. Therefore, distinguishing what contributes most significantly to whale stress levels can be very challenging. However, through a model we may be able to decipher the contributions of several factors to individual stress among the many parameters we are monitoring: ocean noise, prey availability, environmental patterns, season, sex, age, geographic area, reproductive status and body condition.

Marine ecology is a complex world, and sometimes complex models are needed to help us to find patterns in our data! Once estimates of these ecological processes are created and different hypotheses are explored, information can then be provided to conservation and environmental management to aid decision making, such as defining thresholds of ambient ocean noise levels in the vicinity of baleen whales.

 

Bibliographic Reference:

Schick RS, Kraus SD, Rolland RM, Knowlton AR, Hamilton PK, Pettis HM, Kenney RD and Clark JS. 2013. Using Hierarchical Bayes to Understand Movement, Health, and Survival in the Endangered North Atlantic Right Whale. PLOS ONE 8(6):e64166.

Are Oregon gulls trash birds?

By Stephanie Loredo, MSc student

“Violent” and “greedy” are words often used to describe gulls in populous areas where food or trash are readily available.  Humans are used to seeing gulls in parking lots, parks, and plazas eating left over crumbs. Many people have even experienced menacing gulls ripping food away from their hands. Anecdotes like these have caused people to have negative perceptions of gulls. But could the repulsive attitude towards these birds be changed with evidence that not all gulls are the same? Well, Oregon may be home to an odd bunch.

Last year, the Seabird Oceanography Lab in conjunction with the GEMM Lab began putting GPS trackers on western gulls (Laurus occidentalis) off the Oregon Coast. One of the goals was to determine where gulls scavenge for food while raising chicks: at sea or on land in association with humans. We were particularly interested to see if western gulls in Oregon would behave similarly to western gulls in California, some of which make trips to the nearest landfill during the breeding season to bring not only food but also potentially harmful pathogens back to the colony.

During the 2015 breeding season, 10 commercially brand ‘i-gotU’ GPS data loggers were placed on gulls from ‘Cleft-in-the-Rock’ colony in Yachats, Oregon. The tags provided GPS locations at intervals of two minutes that determined the general habitat use areas (marine vs. terrestrial). After a two-week period, we were able to recapture six birds, remove tags, and download the data.   We found that these western gulls stayed close to the colony and foraged in nearby intertidal and marine zones (Figure 1). Birds showed high site faithfulness by visiting the same foraging spots away from colony. It was interesting to see that inland habitat use did not extend past 1.3 miles from shore and the only waste facility within such boundaries did not attract any birds (Figure 1). Tagged birds never crossed the 101 Highway, but rather occurred at beaches in state parks such as Neptune and Yachats Ocean Road.

Figure 1. Tracks from 6 western gulls, each color representing a unique bird, from the Cleft-in-the-Rock colony carrying micro-GPS units.
Figure 1. Tracks from 6 western gulls, each color representing a unique bird, from the Cleft-in-the-Rock colony carrying micro-GPS units.

While it is hard to determine whether gulls avoided anthropogenic sources of food at the beach, preliminary analysis shows a high percentage of time spent in marine and intertidal habitat zones by half of the individuals (Figure 2). At a first glance, this is not as much as it seemed on the tracking map (Figure 1), but it nonetheless confirms that these gulls seek food in natural areas. Moreover, time spent at the colony is represented as time spent on coastal habitat on the graph, and thus “coastal” foraging values are over represented. To get a more exact estimate of coastal habitat use, future analysis will have to exclude colony locations and distinguish foraging versus resting behaviors.

Figure 2. Bar plot of the percentage of time spent in three distinct habitats for each gull carrying a GPS unit. The three-letter code represents the unique Bird ID.
Figure 2. Bar plot of the percentage of time spent in three distinct habitats for each gull carrying a GPS unit. The three-letter code represents the unique Bird ID.

‘Cleft-in-the-Rock’ is unique and its surroundings may explain why there was high foraging in intertidal and marine zones rather than within city limits. (The Cleft colony can also be tricky to get to, with a close eye on the tide at all times – See video below).  The colony site is close to the Cape Perpetua Scenic Area and surrounded by recently established conservation zones: the Cape Perpetua Marine Reserve Area, Marine Protected Area, and Seabird Protected Area (Figure 1).  Each of these areas has different regulatory rules on what is allowed to take, which you can read about here. The implication of these protected areas in place means there is more food for wildlife!  Moreover, the city of Yachats has a small population of 703 inhabitants (based on 2013 U.S Census Bureau). The small population allows the city to be relatively clean, and the waste facility is not spewing rotten odors into the air like in many big cities such as Santa Cruz (population of 62,864) where our collaborative gull study takes place. Thus, in Yachats, there is more limited odor or visual incentive to attract birds to landfills.

Field crew descends headland slope to reach ‘Cleft-in-the-Rock’ gull island in Yachats, OR (colony can be seen in distance across the water). The team must wear wetsuits and carry equipment in dry bags for protection during water crossing.

In order to determine whether gull habitat use in Yachats is a trend for all western gulls in Oregon, we need to track birds at more sites and for a longer time. That is why during the breeding season of 2016, we will be placing 30 new tags on gulls and include a new colony into the study, ‘Hunters Island’. The new colony is situated near the Pistol River, between Gold Beach and Brookings in southern Oregon, and it is part of the Oregon Islands Wildlife Refuge.

We will have 10 ‘i-gotU’ tags (Figure 3) and 20 CATS tags (Figure 4), the latter are solar powered and can collect data for several weeks, months, and hopefully even years! These tags do not need to be retrieved for data download; rather data can be accessed remotely, providing minimal disturbance to the gulls and colony. With long-term data, we can explore further into the important feeding areas for western gulls, examine rates of foraging in different habitats, and determine how extensive intertidal and marine foraging is throughout the year.

Figure 3. Taping an i-gotU tag for temporary attachment on the tail feathers of a gull.
Figure 3. Taping an i-gotU tag for temporary attachment on the tail feathers of a gull.

 

Figure 4. Rehearsing the placement and harness attachment of a CATS tag which must be secured on the bird‘s back, looping around the wings and hips.

We are excited to kick start our field season in the next couple of weeks and see how well the new tags work. We know that some questions will be solved and many new questions will arise; and we cannot wait to start this gull-filled adventure!

References

Osterback, A.M., Frechette, D., Hayes, S., Shaffer, S., & Moore, J. (2015). Long-term shifts in anthropogenic subsidies to gulls and implications for an imperiled fish. Biological Conservation, 191: 606–613.

Grad School Headaches

By Florence Sullivan, MSc student GEMM lab

Over the past few months I have been slowly (and I do mean SLOWLY – I don’t believe I’ve struggled this much with learning a new skill in a long, long time) learning how to work in “R”.  For those unfamiliar with why a simple letter might cause me so much trouble, R is a programming language and free software environment suitable for statistical computing and graphing.

My goal lately has been to interpolate my whale tracklines (i.e. smooth out the gaps where we missed a whale’s surfacing by inserting artificial locations).  In order to do this I needed to know (1) How long does a gap between fixes need to be to identify a missed surfacing? (2) How many artificial points should be used to fill a given gap?

The best way to answer these queries was to look at a distribution of all of the time steps between fixes.  I started by importing my dataset – the latitude and longitude, date, time, and unique whale identifier for each point (over 5000 of them) we recorded last summer. I converted the locations into x & y coordinates, adjusted the date and time stamp into the proper format, and used the package adehabitatLT  to calculate the difference in times between each fix.  A package known as ggplot2 was useful for creating exploratory histograms – but my data was incredibly skewed (Fig 1)! It appeared that the majority of our fixes happened less than a minute apart from each other. When you recall that gray whales typically take 3-4 short breathes at the surface between dives, this starts to make a lot of sense, but we had anticipated a bimodal distribution with two peaks: one for the quick surfacings, and one for the surfacings between 4-5 minutes dives. Where was this second peak?

Histogram of the difference in time (in seconds) between whale fixes.
Fig. 1.  Histogram of the difference in time (in seconds on x-axis) between whale fixes.

Sometimes, calculating the logarithm of one of your axes can help tease out more patterns in your data  – particularly in a heavily skewed distribution like Fig. 1. When I logged the time interval data, our expected bimodal distribution pattern became evident (Fig. 2). And, when I back-calculate from the center of the two peaks we see that the first peak occurs at less than 20 seconds (e^2.5 = 18 secs) representing the short, shallow blow intervals, or interventilation dives, and that the second peak of dives spans ~2.5 minutes to  ~5 minutes (e^4.9 = 134 secs, e^5.7 = 298 secs). Reassuringly, these dive intervals are in agreement with the findings of Stelle et al. (2008) who described the mean interval between blows as 15.4 ± 4.73 seconds, and overall dives ranging from 8 seconds to 11 minutes.

Fig. 2. Histogram of the log of time difference between whale fixes.
Fig. 2. Histogram of the log of time difference between whale fixes.

So, now that we know what the typical dive patterns in this dataset are, the trick was to write a code that would look through each trackline, and identify gaps of greater than 5 minutes.  Then, the code calculates how many artificial points to create to fill the gap, and where to put them.

Fig. 3. A check in my code to make sure the artificial points are being plotted correctly. The blue points are the originals, and the red ones are new.
Fig. 3. A check in my code to make sure the artificial points are being plotted correctly. The blue points are the originals, and the red ones are new.

One of the most frustrating parts of this adventure for me has been understanding the syntax of the R language.  I know what calculations or comparisons I want to make with my dataset, but translating my thoughts into syntax for the computer to understand has not been easy.  With error messages such as:

Error in match.names(clabs, names(xi)) :

  names do not match previous names

Solution:  I had to go line by line and verify that every single variable name matched, but turned out it was a capital letter in the wrong place throwing the error!

Error in as.POSIXct.default(time1) :

  do not know how to convert ‘time1’ to class “POSIXct”

Solution: a weird case where the data was in the correct time format, but not being recognized, so I had to re-import the dataset as a different file format.

Error in data.frame(Whale.ID = Whale.ID, Site = Site, Latitude = Latitude,  :   arguments imply differing number of rows: 0, 2, 1

Solution: HELP! Yet to be solved….

Is it any wonder that when a friend asks how I am doing, my answer is “R is kicking my butt!”?

Science is a collaborative effort, where we build on the work of researchers who came before us. Rachael, a wonderful post-doc in the GEMM Lab, had already tackled this time-based interpolation problem earlier in the year working with albatross tracks. She graciously allowed me to build on her previous R code and tweak it for my own purposes. Two weeks ago, I was proud because I thought I had the code working – all that I needed to do was adjust the time interval we were looking for, and I could be off to the rest of my analysis!  However, this weekend, the code has decided it doesn’t work with any interval except 6 minutes, and I am lost.

Many of the difficulties encountered when coding can be fixed by judicious use of google, stackoverflow, and the CRAN repository.

But sometimes, when you’ve been staring at the problem for hours, what you really need is a little praise for trying your best. So, if you are an R user, go download this package: praise, load the library, and type praise() into your console. You won’t regret it (See Fig. 4).

Screenshot (74)
Fig. 4. A little compliment goes a long way to solving a headache.

Thank you to Rachael who created the code in the first place, thanks to Solene who helped me trouble shoot, thanks to Amanda for moral support. Go GEMM Lab!

Why do pirates have a hard time learning the alphabet?  It’s not because they love aaaR so much, it’s because they get stuck at “c”!

Stelle, L. L., W. M. Megill, and M. R. Kinzel. 2008. Activity budget and diving behavior of gray whales (Eschrichtius robustus) in feeding grounds off coastal British Columbia. Marine mammal science 24:462-478.