Tag: migration

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.

 

 

 

Managing Oceans: the inner-workings of marine policy

By Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

When we hear “marine policy” we broadly lump it together with environmental policy. However, marine ecosystems differ greatly from their terrestrial counterparts. We wouldn’t manage a forest like an ocean, nor would we manage an ocean like a forest. Why not? The answer to this question is complex and involves everything from ecology to politics.

Oceans do not have borders; they are fluid and dynamic. Interestingly, by defining marine ecosystems we are applying some kind of borders. But water (and all its natural and unnatural content) flows between these ‘ecosystems’. Marine ecosystems are home to a variety of anthropogenic activities such as transportation and recreation, in addition to an abundance of species that represent the three major domains of biology: Archaea, Bacteria, and Eukarya. Humans are the only creatures who “recognize” the borders that policymakers and policy actors have instilled. A migrating gray whale does not have a passport stamped as it travels from its breeding grounds in Mexican waters to its feeding grounds in the Gulf of Alaska. In contrast, a large cargo ship—or even a small sailing vessel—that crosses those boundaries is subjected to a series of immigration checkpoints. Combining these human and the non-human facets makes marine policy complex and variable.

The eastern Pacific gray whale migration route includes waters off of Mexico, Canada, and the United States. Source: https://www.learner.org/jnorth/tm/gwhale/annual/map.html

Environmental policy of any kind can be challenging. Marine environmental policy adds many more convoluted layers in terms of unknowns; marine ecosystems are understudied relative to terrestrial ecosystems and therefore have less research conducted on how to best manage them. Additionally, there are more hands in the cookie jar, so to speak; more governments and more stakeholders with more opinions (Leslie and McLeod 2007). So, with fewer examples of successful ecosystem-based management in coastal and marine environments and more institutions with varied goals, marine ecosystems become challenging to manage and monitor.

A visual representation of what can happen when there are many groups with different goals: no one can easily get what they want. Image Source: The Brew Monks

With this in mind, it is understandable that there is no official manual on policy development.  There is, however, a broadly standardized process of how to develop, implement, and evaluate environmental policies: 1) recognize a problem 2) propose a solution 3) choose a solution 4) put the solution into effect and 4) monitor the results (Zacharias pp. 16-21). For a policy to be deemed successful, specific criteria must be met, which means that a common policy is necessary for implementation and enforcement. Within the United States, there are a multiple governing bodies that protect the ocean, including the National Oceanic and Atmospheric Administration (NOAA), Environmental Protection Agency (EPA), Fish and Wildlife Service (USFWS), and the Department of Defense (DoD)—all of which have different mission statements, budgets, and proposals. To create effective environmental policies, collaboration between various groups is imperative. Nevertheless, bringing these groups together, even those within the same nation, requires time, money, and flexibility.

This is not to say that environmental policy for terrestrial systems, but there are fewer moving parts to manage. For example, a forest in the United States would likely not be an international jurisdiction case because the borders are permanent lines and national management does not overlap. However, at a state level, jurisdiction may overlap with potentially conflicting agendas. A critical difference in management strategies is preservation versus conservation. Preservation focuses on protecting nature from use and discourages altering the environment. Conservation, centers on wise-use practices that allow for proper human use of environments such as resource use for economic groups. One environmental group may believe in preservation, while one government agency may believe in conservation, creating friction amongst how the land should be used: timber harvest, public use, private purchasing, etc.

Linear representation of preservation versus conservation versus exploitation. Image Source: Raoof Mostafazadeh

Furthermore, a terrestrial forest has distinct edges with measurable and observable qualities; it possesses intrinsic and extrinsic values that are broadly recognized because humans have been utilizing them for centuries. Intrinsic values are things that people can monetize, such as commercial fisheries or timber harvests whereas extrinsic values are things that are challenging to put an actual price on in terms of biological diversity, such as the enjoyment of nature or the role of species in pest management; extrinsic values generally have a high level of human subjectivity because the context of that “resource” in question varies upon circumstances (White 2013). Humans are more likely to align positively with conservation policies if there are extrinsic benefits to them; therefore, anthropocentric values associated with the resources are protected (Rode et al. 2015). Hence, when creating marine policy, monetary values are often placed on the resources, but marine environments are less well-studied due to lack of accessibility and funding, making any valuation very challenging.

The differences between direct (intrinsic) versus indirect (extrinsic) values to biodiversity that factor into environmental policy. Image Source: Conservationscienceblog.wordpress.com

Assigning a cost or benefit to environmental services is subjective (Dearborn and Kark 2010). What is the benefit to a child seeing an endangered killer whale for the first time? One could argue priceless. In order for conservation measures to be implemented, values—intrinsic and extrinsic—are assigned to the goods and services that the marine environment provides—such as seafood and how the ocean functions as a carbon sink. Based off of the four main criteria used to evaluate policy, the true issue becomes assessing the merit and worth. There is an often-overlooked flaw with policy models: it assumes rational behavior (Zacharias 126). Policy involves relationships and opinions, not only the scientific facts that inform them; this is true in terrestrial and marine environments. People have their own agendas that influence, not only the policies themselves, but the speed at which they are proposed and implemented.

Tourists aboard a whale-watching vessel off of the San Juan Islands, enjoying orca in the wild. Image Source: Seattle Orca Whale Watching

One example of how marine policy evolves is through groups, such as the International Whaling Commission, that gather to discuss such policies while representing many different stakeholders. Some cultures value the whale for food, others for its contributions to the surrounding ecosystems—such as supporting healthy seafood populations. Valuing one over the other goes beyond a monetary value and delves deeper into the cultures, politics, economics, and ethics. Subjectivity is the name of the game in environmental policy, and, in marine environmental policy, there are many factors unaccounted for, that decision-making is incredibly challenging.

Efficacy in terms of the public policy for marine systems presents a challenge because policy happens slowly, as does research. There is no equation that fits all problems because the variables are different and dynamic; they change based on the situation and can be unpredictable. When comparing institutional versus impact effectiveness, they both are hard to measure without concrete goals (Leslie and McLeod 2007). Marine ecosystems are open environments which add an additional hurdle: setting measurable and achievable goals. Terrestrial environments contain resources that more people utilize, more frequently, and therefore have more set goals. Without a problem and potential solution there is no policy. Terrestrial systems have problems that humans recognize. Marine systems have problems that are not as visible to people on a daily basis. Therefore, terrestrial systems have more solutions presented to mitigate problems and more policies enacted.

As marine scientists, we don’t always immediately consider how marine policy impacts our research. In the case of my project, marine policy is something I constantly have to consider. Common bottlenose dolphins are protected under the Marine Mammal Protection Act (MMPA) and inhabit coastal of both the United States and Mexico, including within some Marine Protected Areas (MPA). In addition, some funding for the project comes from NOAA and the DoD. Even on the surface-level it is clear that policy is something we must consider as marine scientists—whether we want to or not. We may do our best to inform policymakers with results and education based on our research, but marine policy requires value-based judgements based on politics, economics, and human objectivity—all of which are challenging to harmonize into a succinct problem with a clear solution.

Two common bottlenose dolphins (coastal ecotype) traveling along the Santa Barbara, CA shoreline. Image Source: Alexa Kownacki

References:

Dearborn, D. C. and Kark, S. 2010. Motivations for Conserving Urban Biodiversity. Conservation Biology, 24: 432-440. doi:10.1111/j.1523-1739.2009.01328.x

Leslie, H. M. and McLeod, K. L. (2007), Confronting the challenges of implementing marine ecosystem‐based management. Frontiers in Ecology and the Environment, 5: 540-548. doi:10.1890/060093

Munguia, P., and A. F. Ojanguren. 2015. Bridging the gap in marine and terrestrial studies. Ecosphere 6(2):25. http://dx.doi.org/10.1890/ES14-00231.1

Rode, J., Gomez-Baggethun, E., Krause, M., 2015. Motivation crowding by economic payments in conservation policy: a review of the empirical evidence. Ecol. Econ. 117, 270–282 (in this issue).

White, P. S. (2013), Derivation of the Extrinsic Values of Biological Diversity from Its Intrinsic Value and of Both from the First Principles of Evolution. Conservation Biology, 27: 1279-1285. doi:10.1111/cobi.12125

Zacharias, M. 2014. Marine Policy. London: Routledge.