Ecological research focuses on understanding how species and ecosystems interact and function, as well as understanding what drives changes in these interactions and functions over time. Thus, ecology is a critical component of conservation biology. Although uncertainty is present in any research, it is a pervasive characteristic of ecology and conservation biology, often due to our inability to control the complexity of natural systems. Uncertainty poses challenges to decision-making, policy development, and effective conservation strategies, and therefore needs to be understood and addressed when conducting ecological studies and conservation efforts.
There are several sources of uncertainty in ecological research and conservation biology. One of the primary sources arises from incomplete or limited data (epistemic uncertainty). Ecological systems are complex, and obtaining comprehensive data on all relevant variables and scales is often challenging or impossible. Data may be lacking or unavailable for certain species, habitats, or regions, which can hinder the ability to fully understand ecological dynamics and make accurate predictions. Additionally, ecological data may be uncertain or variable due to measurement errors (see blog post), sampling biases, or changes in data collection methods over time (Regan et al. 2002). Furthermore, another source of uncertainty arises from language (linguistic uncertainty). Linguistic uncertainty can result from lack of agreement in the terms and definitions used in the scientific vocabulary (see blog post), which can often result in ambiguous, vague, or context dependent interpretations (Regan et al 2002). These two source-types of uncertainty can create a complex set of challenges.
Uncertainty in ecological research and conservation biology has important implications for decision-making and policy development. When faced with uncertain information, decision-makers may adopt a cautious approach, leading to delayed or ineffective conservation actions. Alternatively, they may make decisions based on incomplete or biased data, which can lead to unintended consequences or wasted resources. Uncertainty can also affect the public’s perception of ecological issues, leading to skepticism, misinformation, or lack of support for conservation initiatives. In addition, uncertainty can also pose challenges in setting conservation priorities. With limited resources, conservation organizations and policymakers must prioritize efforts to protect species or habitats that are at the greatest risk. However, uncertainties in data or predictions can affect the accuracy of risk assessments, leading to potential misallocation of resources. Finally, uncertainty may also arise when assessing the success of conservation interventions, making it difficult to determine the effectiveness of the conservation actions.
Despite the challenges posed by uncertainty, there are ways to address and mitigate its impacts in ecological research and conservation biology. Here are some strategies that the GEMM Lab implements to navigate these nuances in ecological research:
Improving data quality and quantity: Robust data can provide a more accurate understanding of ecological dynamics and facilitate evidence-based decision-making. In this direction, the GEMM Lab develops comprehensive data collection and monitoring efforts that can help reduce uncertainty. The TOPAZ and GRANITE projects, which study gray whale ecology off the Oregon coast, are good examples in this direction due to continuous research efforts since 2015. With these projects we have developed and standardized data collection and analytical methods, improved data accuracy and precision, and are filling knowledge gaps through targeted research.
Emphasizing adaptive management: Adaptive management is an approach that involves learning from ongoing conservation actions and adjusting strategies based on new information (Allen et al. 2015). This approach recognizes that uncertainties are inherent in ecological systems and promotes flexibility in conservation planning. Monitoring and evaluating conservation interventions, and adjusting management strategies, accordingly, can help mitigate the impacts of uncertainty. With OBSIDIAN, OPAL, and HALO projects the GEMM Lab works towards a better understanding of cetaceans’ distribution and its interactions with the oceanographic conditions (e.g., ocean temperature). These research projects can help to forecast the occurrence of whale aggregations and inform management to reduce conflicts when overlapping with human activities. For instance, results from the OPAL project have been incorporated into Dungeness Crab fishing regulations to reduce entanglement risk to whales, and the GEMM Lab is now investigating the effectiveness of these regulations in the SLATE project.
With these projects, along with the many other research efforts conducted by the GEMM lab and the MMI, we are advancing research in marine ecology, through the development and application the best possible science to generate the needed ecological data for effective conservation and management of the marine environment.
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Sources:
Regan, H. M., Colyvan, M., & Burgman, M. A. (2002). A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological applications, 12(2), 618-628.
Allen, C. R., & Garmestani, A. S. (2015). Adaptive management (pp. 1-10). Springer Netherlands.
By Morgan O’Rourke-Liggett, Master’s Student, Oregon State University, Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
Avid readers of the GEMM Lab blog and other scientists are familiar with the incredible amounts of data collected in the field and the informative figures displayed in our publications and posters. Some of the more time-consuming and tedious work hardly gets talked about because it’s the in-between stage of science and other fields. For this blog, I am highlighting some of the behind-the-scenes work that is the subject of my capstone project within the GRANITE project.
For those unfamiliar with the GRANITE project, this multifaceted and non-invasive research project evaluates how gray whales respond to chronic ambient and acute noise to inform regulatory decisions on noise thresholds (Figure 1). This project generates considerable data, often stored in separate Excel files. While this doesn’t immediately cause an issue, ongoing research projects like GRANITE and other long-term monitoring programs often need to refer to this data. Still, when scattered into separate long Excel files, it can make certain forms of analysis difficult and time-consuming. It requires considerable attention to detail, persistence, and acceptance of monotony. Today’s blog will dive into the not-so-glamorous side of science…data management and standardization!
Figure 1. Infographic for the GRANITE project. Credit: Carrie Ekeroth
Of the plethora of data collected from the GRANITE project, I work with the GPS trackline data from the R/V Ruby, environmental data recorded on the boat, gray whale sightings data, and survey summaries for each field day. These come to me as individual yearly spreadsheets, ranging from thirty entries to several thousand. The first goal with this data is to create a standardized survey effort conditions table. The second goal is to determine the survey distance from the trackline, using the visibility for each segment, and calculate the actual area surveyed for the segment and day. This blog doesn’t go into how the area is calculated. Still, all these steps are the foundation for finding that information so the survey area can be calculated.
The first step requires a quick run-through of the sighting data to ensure all dates are within the designated survey area by examining the sighting code. After the date is a three-letter code representing a different starting location for the survey, such as npo for Newport and dep for Depoe Bay. If any code doesn’t match the designated codes for the survey extent, those are hidden, so they are not used in the new table. From there, filling in the table begins (Figure 2).
Figure 2. A blank survey effort conditions table with each category listed at the top in bold.
Segments for each survey day were determined based on when the trackline data changed from transit to the sighting code (i.e., 190829_1 for August 29th, 2019, sighting 1). Transit indicated the research vessel was traveling along the coast, and crew members were surveying the area for whales. Each survey day’s GPS trackline and segment information were copied and saved into separate Excel workbook files. A specific R code would convert those files into NAD 1983 UTM Zone 10N northing and easting coordinates.
Those segments are uploaded into an ArcGIS database and mapped using the same UTM projection. The northing and easting points are imported into ArcGIS Pro as XY tables. Using various geoprocessing and editing tools, each segmented trackline for the day is created, and each line is split wherever there was trackline overlap or U shape in the trackline that causes the observation area to overlap. This splitting ensures the visibility buffer accounts for the overlap (Figure 3).
Figure 3. Segment 3 from 7/22/2019 with the visibility of 3 km portrayed as buffers. There are more than one because the trackline was split to account for the overlapping of the survey area. This approach accounts for the fact that this area where all three buffers overlap was surveyed 3 times.
Once the segment lines are created in ArcGIS, the survey area map (Figure 4) is used alongside the ArcGIS display to determine the start and end locations. An essential part of the standardization process is using the annotated locations in Figure 4 instead of the names on the basemap for the location start and endpoints. This consistency with the survey area map is both for tracking the locations through time and for the crew on the research vessel to recognize the locations. The step assists with interpreting the survey notes for conditions at the different segments. The time starts and ends, and the latitude and longitude start and end are taken from the trackline data.
Figure 4. Map of the survey area with annotated locations (Created by L. Torres, GEMM Lab)
The sighting data includes the number of whales sighted, Beaufort Sea State, and swell height for the locations where whales were spotted. The environmental data from the sighting data is used as a guide when filling in the rest of the values along the trackline. When data, such as wind speed, swell height, or survey condition, is not explicitly given, matrices have been developed in collaboration with Dr. Leigh Torres to fill in the gaps in the data. These matrices and protocols for filling in the final conditions log are important tools for standardizing the environmental and condition data.
The final product for the survey conditions table is the output of all the code and matrices (Figure 5). The creation of this table will allow for accurate calculation of survey effort on each day, month, and year of the GRANITE project. This effort data is critical to evaluate trends in whale distribution, habitat use, and exposure to disturbances or threats.
Figure 5. A snippet of the completed 2019 season effort condition log.
The process of completing the table can be a very monotonous task, and there are several chances for the data to get misplaced or missed entirely. Attention to detail is a critical aspect of this project. Standardizing the GRANITE data is essential because it allows for consistency over the years and across platforms. In describing this aspect of my project, I mentioned three different computer programs using the same data. This behind-the-scenes work of creating and maintaining data standardization is critical for all projects, especially long-term research such as the GRANITE project.
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One of the most frequent questions graduate students get asked (besides when you are going to graduate) is what their plans are after university. For me, the answer has always adamantly been continuing to do research, most likely as a government researcher because I don’t want teaching commitments to take away from my ability to conduct research.
However, one of the most fulfilling parts of my degree at University of British Columbia has actually been teaching four terms of a 100-level undergraduate science course focused on developing first-year students’ critical thinking, data interpretation, and science communication skills. My role in the course has been facilitating active learning activities that exercise these skills and reviewing material the students go over in their pre-class work. Through this course, I have experienced the teaching styles of six different professors and practiced my own teaching. As with any skill, there is always room for improvement, so when I had a chance to read a book titled How Learning Works: Seven Research-Based Principles for Smart Teaching (Ambrose et al. 2010), I took it as an opportunity to further refine my teaching and explore why some practices are more effective than others.
In the book, Ambrose et al. present principles of learning, the research surrounding these principles and examples for incorporating them into a university level course. Some of the principles gave me ideas for strategies to incorporate into my teaching to benefit my students. These described how prior knowledge impacts student learning and how to use goal-oriented practice and give feedback relative to target criteria that the students can apply to the next practice task. For example, I learned to be more conscious about how I explain and clarify course material to make connections with what the students have learned previously, so they can draw on that prior knowledge. Other principles presented by Ambrose et al. were more complex and offered a chance for greater reflection.
Beyond presenting strategies for improving teaching, the book also presented research that supported what I had learned firsthand through teaching. These principles related to the factors that motivate students to learn and why the course climate matters for learning. I have seen how student motivation is impacted by the classroom climate and culture put forth by the teaching team. Perhaps the most frustrating experiences I have had teaching were when one member of the teaching team does not see the importance of fostering a supportive course environment.
For this reason, my favorite assignments have been the Thrive Contract and the Group Contract. Each term, the Thrive Contract is the first major class activity, and the Group Contract is the first group assignment. These assignments serve as a means for everyone to co-create guidelines and expectations and establish a positive classroom culture for the rest of the term. After an exceptionally poor classroom experience my first time teaching, I have highlighted the importance of the Thrive Contract in all subsequent terms. Now, I realize the significance I lent this assignment is supported by the research on the importance for a supportive environment to maximize student motivation and encourage classroom engagement (Figure 1).
Another powerful lesson I have learned through teaching is the importance of clarifying the purpose of an activity to the students. Highlighting a task’s objective is also supported by research to ensure that students ascribe value to the assigned work, increasing their motivation (Figure 1). In my teaching, I have noticed a trend of lower student participation and poorer performance on assignments when a professor does not emphasize the importance of the task. Reviewing the research that shows the value of a supportive course climate has further strengthened my belief in the importance of ensuring that students understand why their teaching team assigns each activity.
Figure 1. How environment, student efficacy, and value interact to impact motivation. The above figure shows that motivation is optimized when students see the value in a goal, believe they have the skills to achieve the goal, and are undertaking the goal in a supportive class environment (the bright blue box in the bottom right corner). If this situation were to occur in an unsupportive class environment, defiant behaviour (e.g. “I’ll prove you wrong” attitude) is likely to occur in response to the lack of support, as the student sees the value in the goal and believes in their ability to achieve the goal. Rejecting behaviour (e.g., disengagement) occurs when the student does not associate value to a task and does not believe in their ability to complete the goals regardless of the environment. Evading behaviour (e.g., lack of attention or minimal effort) results when students are confident in their ability to complete a task, but do not see the goal as meaningful in both supportive and unsupportive environment. When a student sees the importance of the goal but are not confident in their ability to complete it, they become hopeless (e.g., have no expectation of success and act helpless) when in an unsupportive environment and fragile (e.g., feign understanding, deny difficulty, or make excuses for poor performance) in a supportive environment. Diagram adapted from Ambrose et al. (2010) Figure 3.2 incorporating the works of Hansen (1989) & Ford (1992).
Potentially my favorite part about the structure of Ambrose’s book was that it offered me a chance to reflect not only on teaching, but also on my own learning and cognitive growth since I started my master’s degree. Graduate students are often in a unique position in which we are both students and teachers depending on the context of our surroundings. The ability to zoom out and realize how far I have come in not only teaching others, but also in teaching myself, has been humbling. My reflection on my own learning and growth has been driven by learning about how organizing knowledge affects learning, how mastery is developed and how students become self-directed learners.
One of the main differences between novices and experts in how they organize their knowledge is the depth of that knowledge and the connections made between different pieces of information. Research has shown that experts hold more connections between concepts, which allows for faster and easier retrieval of information that translates into ease in applying skills to different tasks (Bradshaw & Anderson, 1982; Reder & Anderson, 1980; Smith, Adams, & Schorr, 1978). Currently in my degree, I am experiencing this ease when it comes to coding my analysis and connecting my research to the broader implications for the field. By making these deeper connections across various contexts, I am building a more complex knowledge structure, thus progressing towards holding a more expert organization of knowledge.
In the stages of mastery concept proposed by Sprague and Stewart (2000), learners progress from unconscious incompetence where the student doesn’t know what they don’t know, to conscious incompetence where they have become aware of what they need to know (Figure 2). This was where I was when I started my master’s — I knew what objectives I wanted to achieve with my research, but I needed to learn the skills necessary for me to be able to collect the data and analyze it to answer my research questions. The next stage of mastery is conscious competence, in which the ability of the learner to function in their domain has greatly increased, but practicing the necessary skills still requires deliberate thinking and conscious actions (Figure 2). This is the level I feel I have progressed to — I am much more comfortable performing the necessary tasks related to my research and talking about how my work fills existing knowledge gaps in the field. However, it still helps to talk out my proposed plans with true masters in the field. The final stage of mastery, unconscious competence, is where the learner has reached a point where they can practice the skills of their field automatically and instinctively such that they are no longer aware of how they enact their knowledge (Figure 2).
Figure 2. Stages of mastery showing how the learner consciousness waxes and then wanes as competence is developed. Unconscious states refer to those where the learner is not aware of what they are doing or what they know, whereas conscious states have awareness of thoughts and actions. Competence refers to the ability of the learner to perform tasks specific to the field they are trying to master. Diagram adapted from Ambrose et al. (2010) Figure 4.2 incorporating the works of Sprague & Stewart (2000).
In line with my progression to higher levels of mastery has come the development of metacognitive skills that have helped me become a better self-directed learner. Metacognition is the process of learning how to learn, requiring the learner to monitor and control their learning through various processes (Figure 3). The most exciting part of my metacognitive growth I have noticed is the greater independence I have in my learning. I am much better at assessing what is needed to complete specific tasks and planning my particular approach to successfully achieve that goal (e.g., the construction of a Hidden Markov model from my last blog). By becoming more aware of my own strengths and weaknesses as a learner, I am better able to prepare and give myself the support needed for completing certain tasks (e.g., reaching out to experts to help with my model construction as I knew this was an area of weakness for me). By becoming more aware of how I am monitoring and controlling my learning, I know I am setting myself up for success as a lifelong learner.
Figure 3. Metacognition requires learner to monitor and control their learning through various processes. These processes involve the learner assessing the necessary skills needed for a task, evaluating their strengths and weaknesses with regards to the assigned task, and planning a way to approach the task. Once a plan has been made, the learner then must apply the strategies involved from the plan and monitor how those strategies are working to accomplish the assigned task. The learner must then be able to decide if the planned approach and applied strategies are effectively accomplishing the assigned task and adjust as needed with a re-assessment of the task that begins the processing cycle over again. Underlying each of these metacognitive processes are the learner’s belief in their own abilities and their perceptions of their intelligence. For example, students who believe their intelligence cannot be improved and do not have a strong sense of efficacy will be less likely to expend effort in metacognitive processes as they believe the extra effort will not influence the results. This contrasts with students who believe their intelligence will increase with skills development and have a strong belief in their abilities, as these learners will see the value in putting in the effort of trying multiple plans and adjusting strategies. Diagram adapted from Ambrose et al. (2010) Figure 7.1 incorporating the cycle of adaptive learning proposed by Zimmerman (2001).
References:
Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching (1st ed.). San Francisco, CA: Jossey-Bass.
Bradshaw, G. L., & Anderson, J. R. (1982). Elaborative encoding as an explanation of levels of processing. Journal of Verbal Learning and Verbal behaviours, 21,165-174.
Ford, M. E. (1992). Motivating humans: Goals, emotions and personal agency beliefs. Newbury Park, CA: Sage Publications, Inc.
Hansen, D. (1989). Lesson evading and dissembling: Ego strategies in the classroom. American Journal of Education, 97, 184-208.
Reder, L. M., & Anderson, J. R. (1980). A partial resolution of the paradox of interference: The role of integrating knowledge. Cognitive Psychology, 12, 447-472.
Smith, E. E., Adams, N., & Schorr, D. (1978). Fact retrieval and the paradox of interference. Cognitive Psychology, 10, 438-464.
Sprague, J., & Stewart, D. (2000). The speaker’s handbook. Fort Worth, TX: Harcourt College Publishers.
Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement (2nd ed., pp. 1-38). Hillsdale, NJ: Erlbaum.
Since its start, the GEMM Lab has been interested in the effect of vessel disturbance on whales. From former student Florence’s masters project to Leila’s PhD work, this research has shown that gray whales on their foraging grounds have a behavioral response to vessel presence (Sullivan & Torres, 2018) and a physiological response to vessel noise (Lemos et al., 2022). Presently, our GRANITE project is continuing to investigate the effect of ambient noise on gray whales, with an emphasis on understanding how these effects might scale up to impact the population as a whole (Image 1).
To date, all this work has been focused on gray whales feeding off the coast of Oregon, but I’m excited to share that this is about to change! In just a few weeks, Leigh and I will be heading south for a pilot study looking at the effects of whale watching vessels on gray whale mom/calf pairs in the nursing lagoons of Baja California, Mexico.
Image 1. Infographic for the GRANITE project. Credit: Carrie Ekeroth
We are collaborating with a Fernanda Urrutia Osorio, a PhD candidate at Scripps Institute of Oceanography, to spend a week conducting fieldwork in one of the nursing lagoons. For this project we will be collecting drone footage of mom/calf pairs in both the presence and absence of whale watching vessels. Our goal is to see if we detect any differences in behavior when there are vessels around versus when there are not. Tourism regulations only allow the whale watching vessels to be on the water during specific hours, so we are hoping to use this regulated pattern of vessel presence and absence as a sort of experiment.
Image 2. A mom and calf pair. NOAA/NMFS permit #21678.
The lagoons are a crucial place for mom/calf pairs, this is where calves nurse and grow before migration, and nursing is energetically costly for moms. So, it is important to study disturbance responses in this habitat since any change in behavior caused by vessels could affect both the calf’s energy intake and the mom’s energy expenditure. While this hasn’t yet been investigated for gray whales in the lagoons, similar studies have been carried out on other species in their nursing grounds.
Video 1. Footage of “likely nursing” behavior. NOAA/NMFS permit #21678.
We can use these past studies as blueprints for both data collection and processing. Disturbance studies such as these look for a wide variety of behavioral responses. These include (1) changes in activity budgets, meaning a change in the proportion of time spent in a behavior state, (2) changes in respiration rate, which would reflect a change in energy expenditure, (3) changes in path, which would indicate avoidance, (4) changes in inter-individual distance, and (5) changes in vocalizations. While it’s not necessarily possible to record all of these responses, a meta-analysis of research on the impact of whale watching vessels found that the most common responses were increases in the proportion of time spent travelling (a change in activity budget) and increased deviation in path, indicating an avoidance response (Senigaglia et al., 2016).
One of the key phrases in all these possible behavioral responses is “change in ___”. Without control data collected in the absence of whale watching vessels, it impossible to detect a difference. Some studies have conducted controlled exposures, using approaches with the research vessel as proxies for the whale watchers (Arranz et al., 2021; Sprogis et al., 2020), while others use the whale watching operators’ daily schedule and plan their data collection schedule around that (Sprogis et al., 2023). Just as ours will, all these studies collected data using drones to record whale behavior and made sure to collect footage before, during, and after exposure to the vessel(s).
One study focused on humpback mom/calf pairs found a decrease in the proportion of time spent resting and an increase in both respiration rate and swim speed during the exposure (Sprogis et al., 2020). Similarly, a study focused on short-finned pilot whale mom/calf pairs found a decrease in the mom’s resting time and the calf’s nursing time (Arranz et al., 2021). And, Sprogis et al.’s study of Southern right whales found a decrease in resting behavior after the exposure, suggesting that the vessels’ affect lasted past their departure (Sprogis et al., 2023, Image 3). It is interesting that while these studies found changes in different response metrics, a common trend is that all these changes suggest an increase in energy expenditure caused by the disturbance.
However, it is important to note that these studies focused on short term responses. Long term impacts have not been thoroughly estimated yet. These studies provide many valuable insights, not only into the response of whales to whale watching, but also a look at the various methods used. As we prepare for our fieldwork, it’s useful to learn how other researchers have approached similar projects.
Image 3. Visual ethogram from Sprogis et al. 2023. This shows all the behaviors they identified from the footage.
I want to note that I don’t write this blog intending to condemn whale watching. I fully appreciate that offering the opportunity to view and interact with these incredible creatures is valuable. After all, it is one of the best parts of my job. But hopefully these disturbance studies can inform better regulations, such as minimum approach distances or maximum engine noise levels.
As these studies have done, our first step will be to establish an ethogram of behaviors (our list of defined behaviors that we will identify in the footage) using our pilot data. We can also record respiration and track line data. An additional response that I’m excited to add is the distance between the mom and her calf. Former GEMM Lab NSF REU intern Celest will be rejoining us to process the footage using the AI method she developed last summer (Image 4). As described in her blog, this method tracks a mom and calf pair across the video frames, and allows us to extract the distance between them. We look forward to adding this metric to the list and seeing what we can glean from the results.
Image 4. Example of a labelled frame from SLEAP, highlighting labels: rostrum, blowhole, dorsal, dorsal-knuckle, and tail. This labels are drawn to train the software to recognize the whales in unlabelled frames.
While we are just getting started, I am excited to see what we can learn about these whales and how best to study them. Stay tuned for updates from Baja!
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References
Arranz, P., Glarou, M., & Sprogis, K. R. (2021). Decreased resting and nursing in short-finned pilot whales when exposed to louder petrol engine noise of a hybrid whale-watch vessel. Scientific Reports, 11(1), 21195. https://doi.org/10.1038/s41598-021-00487-0
Lemos, L. S., Haxel, J. H., Olsen, A., Burnett, J. D., Smith, A., Chandler, T. E., Nieukirk, S. L., Larson, S. E., Hunt, K. E., & Torres, L. G. (2022). Effects of vessel traffic and ocean noise on gray whale stress hormones. Scientific Reports, 12(1), Article 1. https://doi.org/10.1038/s41598-022-14510-5
Senigaglia, V., Christiansen, F., Bejder, L., Gendron, D., Lundquist, D., Noren, D., Schaffar, A., Smith, J., Williams, R., Martinez, E., Stockin, K., & Lusseau, D. (2016). Meta-analyses of whale-watching impact studies: Comparisons of cetacean responses to disturbance. Marine Ecology Progress Series, 542, 251–263. https://doi.org/10.3354/meps11497
Sprogis, K. R., Holman, D., Arranz, P., & Christiansen, F. (2023). Effects of whale-watching activities on southern right whales in Encounter Bay, South Australia. Marine Policy, 150, 105525. https://doi.org/10.1016/j.marpol.2023.105525
Sprogis, K. R., Videsen, S., & Madsen, P. T. (2020). Vessel noise levels drive behavioural responses of humpback whales with implications for whale-watching. ELife, 9, e56760. https://doi.org/10.7554/eLife.56760
Sullivan, F. A., & Torres, L. G. (2018). Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. Journal of Wildlife Management, 82(5), 896–905. https://doi.org/10.1002/jwmg.21462
Reports of whale entanglements have been on the rise over the last decade on the US West Coast, with Dungeness crab fishing gear implicated in many cases (Feist et al., 2021; Samhouri et al., 2021; Santora et al., 2020). State agencies are responsible for managing this environmental issue that has implications both for the endangered whale sub-populations that are subject to entanglements, and for the fishing activities, which play an important social, cultural, and economic role for coastal communities. In Oregon, the Oregon Whale Entanglement Working Group (today the Oregon Entanglement Advisory Committee, facilitated by ODFW – Oregon Department of Fish and Wildlife) formed in 2017, tasked with developing options to reduce entanglement risk. The group members composed of managers, researchers and fishermen identified that a lack of information and understanding of whale distribution in Oregon waters was a significant knowledge gap of high priority.
In response, the GEMM Lab and its collaborators at ODFW developed the OPAL project (Overlap Predictions About Large whales, phase 1: 2018-2022). The first phase of the project (phase 1) was developed to 1) model and predict large whale distribution off the coast of Oregon in relation to dynamic environmental conditions, and 2) assess overlap with commercial crab fishing gear to inform conservation efforts. Although this first phase was extended up to June as a result of COVID, it is now coming to an end. As a postdoc in the GEMM Lab, I have been the main analyst working on this project. The habitat use models that I generated from several years of aerial and boat-based surveys provide improved knowledge about where and when rorqual whales (combining blue, humpback and fin) are most abundant (Derville et al., 2022). Moreover, we are about to publish an analysis of overlap between whale predicted densities and commercial Dungeness crab fishing effort. This analysis of co-occurrence over 10 years shows distinct spatio-temporal patterns in relation to climatic fluctuations affecting the northern California Current System (Derville et al., In review).
Although we are quite satisfied with the outputs of these four years of research, this is not the end of it! Project OPAL continues into a second phase (2022-2025; supported by NOAA Section 6 funding), during which models will be improved and refined via incorporation of new survey data (helicopter and boat-based) as well as prey data (krill and fish distribution). PhD student Rachel Kaplan is a key contributor to this research, and I will do my best to keep assisting her in this journey in the years to come.
Announcing SLATE!
As this newly acquired knowledge leads to potentially new management measures in Oregon, it becomes essential for managers to evaluate their impacts on the entanglement issue. But how do we know exactly how many entanglements occur during any year within Oregon waters? Is recording reports of entanglements or signs of entanglements in stranded whales enough? The simple answer is no. Entanglements are notoriously under-detected and under-reported (Tackaberry et al., 2022). Over the US West Coast, entanglements are also relatively rare events that can easily go unnoticed in the immensity of the ocean. Moreover, entangled large whales are often able to carry the fishing gear for some time away from the initial gearset location, which makes it hard to locate the origin of the gear causing problems (van der Hoop et al., 2017).
Figure 1: Graphical representation of the SLATE project representing the different tasks described below. Work in progress…
Our approach to the challenge of assessing humpback whale entanglement rates in Oregon waters is to use scar analysis. Our new “SLATE” (Scar-based Long-term Assessment of Trends in whale Entanglements, Figure 1) project will be using scar-based methods as a proxy to detect unobserved entanglement events (e.g., Basran et al., 2019; Bradford et al., 2009; George et al., 2017; Knowlton et al., 2012; Robbins, 2012). Indeed, this approach has been effective to detect potential interactions with fishing gear at a much higher frequency than entanglement reports in the Atlantic Ocean (e.g., only 10% of entanglements of humpback whales in the Gulf of Maine were estimated to be reported; Robbins, 2012). We will be examining hundreds of photographs of humpback whales observed in Oregon waters to try to detect wrapping scars and notches that result from entanglement events. Based on this scar pattern, we will assign each whale a qualitative probability of prior entanglement (i.e., uncertain, low, high). We will specifically be looking at the caudal peduncle (the attachment point of the whale’s fluke, see Figure 2) following a methodology developed in the Gulf of Maine by Robbins & Mattila, (2001).
Figure 2: Examples of unhealed injuries interpreted as entanglement related in 2010 in the Gulf of Maine. Figure reproduced from (Robbins, 2012).
Data please?
While this approach is to-date the most applicable way to assess otherwise undetected entanglements, it is sometimes limited by sample size. Although we plan to collect more photos in the field in summer 2023 and 2024, this long-term analysis of scarring patterns would not be possible without the contribution of the Cascadia Research Collective (CRC) led by John Calambokidis. The CRC humpback whale catalogue will be crucial to assessing entanglement rates at the individual level over the last decade.
Moreover, as we have been contemplating the task ahead of us, we realized that the data collected through traditional scientific surveys might not be sufficient to achieve our goal. We need the help of the people who live off the ocean and encounter whales on a day-to-day basis: fishermen. That is why we decided to solicit interested fishermen to take photographs of whales while at sea. Starting this year, we will work with at least three self-selected fishermen who are interested in supporting this program and collecting data to support the research efforts. Participants will be provided a stipend, equipped with a high-quality camera, and trained to photograph whales while following National Oceanic and Atmospheric Administration (NOAA) Marine Mammal Protection Act (MMPA) guidelines.
And here come the statistics…
If we have some of my previous blogs (e.g., May 2022, June 2018), you know that I usually participate in projects that have a significant statistical modeling component. As part of the SLATE project, I will be trying out some new approaches that I never had the opportunity to work with before, which makes me feels both super excited and slightly apprehensive!
First, I will analyze humpback whale scarring at the population level. That means I will be using all available photos of whales in Oregon waters without considering individual identification, and I will model the probability of entanglement scars in relation to space and time. This model will help us answer questions such as: did whales have a higher chance of becoming entangled in certain years over others? Did whales observed in a certain zone in Oregon waters have a higher risk of getting entangled?
Second, I will analyze humpback whale scarring at the individual level. This time, we will only use encounters of a selected number of individuals that have a long recapture history, meaning that they were photo-identified and resighted several times throughout the last decade. Using a genetic database produced by the Cetacean Conservation and Genomic Laboratory (CCGL, Marine Mammal Institute), we will also be able to tell to which “Distinct Population Segment” (DPS) some of these individual whales belong. Down the line, this is an important piece of information because humpback whale DPS do not breed in the same areas, and these groups have different levels of population health. Then, we will use what is known as a “multi-event mark-recapture model” to estimate the probability of entanglement as a function of time and spatial residency or DPS assignment, while accounting for detection probability and survival.
Through these analyses, our goal is to produce a single indicator to help managers assess the effects of mandatory or voluntary changes in Oregon fishing practices. In the end, we hope that these models will provide a measurable and robust way of monitoring whale entanglements in fishing gear off the coast of Oregon.
References
Basran, C. J., Bertulli, C. G., Cecchetti, A., Rasmussen, M. H., Whittaker, M., & Robbins, J. (2019). First estimates of entanglement rate of humpback whales Megaptera novaeangliae observed in coastal Icelandic waters. Endangered Species Research, 38(February), 67–77. https://doi.org/10.3354/ESR00936
Bradford, A. L., Weller, D. W., Ivashchenko, Y. v., Burdin, A. M., & Brownell, R. L. (2009). Anthropogenic scarring of western gray whales (Eschrichtius robustus). Marine Mammal Science, 25(1), 161–175. https://doi.org/10.1111/j.1748-7692.2008.00253.x
Derville, S., Barlow, D. R., Hayslip, C. E., & Torres, L. G. (2022). Seasonal, Annual, and Decadal Distribution of Three Rorqual Whale Species Relative to Dynamic Ocean Conditions Off Oregon, USA. Frontiers in Marine Science, 9, 1–19. https://doi.org/10.3389/fmars.2022.868566
Derville, S., Buell, T., Corbett, K., Hayslip, C., & Torres, L. G. (n.d.). Exposure of whales to entanglement risk in Dungeness crab fish-ing gear in Oregon, USA, reveals distinctive spatio-temporal and climatic patterns. Biological Conservation.
Feist, B. E., Samhouri, J. F., Forney, K. A., & Saez, L. E. (2021). Footprints of fixed-gear fisheries in relation to rising whale entanglements on the U.S. West Coast. Fisheries Management and Ecology, 28(3), 283–294. https://doi.org/10.1111/fme.12478
George, J. C., Sheffield, G., Reed, D. J., Tudor, B., Stimmelmayr, R., Person, B. T., Sformo, T., & Suydam, R. (2017). Frequency of injuries from line entanglements, killer whales, and ship strikes on bering-chukchi-beaufort seas bowhead whales. Arctic, 70(1), 37–46. https://doi.org/10.14430/arctic4631
Knowlton, A. R., Hamilton, P. K., Marx, M. K., Pettis, H. M., & Kraus, S. D. (2012). Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: A 30 yr retrospective. Marine Ecology Progress Series, 466(Kraus 1990), 293–302. https://doi.org/10.3354/meps09923
Robbins, J. (2012). Scar-Based Inference Into Gulf of Maine Humpback Whale Entanglement : 2010 (Issue January). Report to the Northeast Fisheries Science Center National Marine Fisheries Service, EA133F09CN0253 Item 0003AB, Task 3.
Robbins, J., & Mattila, D. K. (2001). Monitoring entanglements of humpback whales ( Megaptera novaeangliae ) in the Gulf of Maine on the basis of caudal peduncle scarring. SC/53/NAH25. Report to the Scientific Committee of the International Whaling Commission, 14, 1–12. http://www.ccbaymonitor.org/pdf/scarring.pdf
Samhouri, J. F., Feist, B. E., Fisher, M. C., Liu, O., Woodman, S. M., Abrahms, B., Forney, K. A., Hazen, E. L., Lawson, D., Redfern, J., & Saez, L. E. (2021). Marine heatwave challenges solutions to human-wildlife conflict. Proceedings of the Royal Society B: Biological Sciences, 288, 20211607. https://doi.org/10.1098/rspb.2021.1607
Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., Sydeman, W. J., Wells, B. K., Calambokidis, J., Saez, L., Lawson, D., & Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nature Communications, 11, 536. https://doi.org/10.1038/s41467-019-14215-w
Tackaberry, J., Dobson, E., Flynn, K., Cheeseman, T., Calambokidis, J., & Wade, P. R. (2022). Low Resighting Rate of Entangled Humpback Whales Within the California , Oregon , and Washington Region Based on Photo-Identification and Long-Term Life History Data. Frontiers in Marine Science, 8(January), 1–13. https://doi.org/10.3389/fmars.2021.779448
van der Hoop, J., Corkeron, P., & Moore, M. (2017). Entanglement is a costly life-history stage in large whales. Ecology and Evolution, 7(1), 92–106. https://doi.org/10.1002/ece3.2615
ByRachel Kaplan, PhD student, OSU College of Earth, Ocean and Atmospheric Sciences and Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
Ocean ecosystems are complex and dynamic, shaped by the interconnected physical and biogeochemical processes that operate across a variety of timescales. A trip on the “ocean conveyer belt”, which transports water from the North Atlantic across the global ocean and back in a process called thermohaline circulation, takes about a thousand years to complete. Phytoplankton blooms, which cycle nutrients through the surface ocean and feed marine animals, often occur at the crucial, food-poor moment of spring, and last for weeks or months. The entanglement of a whale in fishing gear, a major anthropogenic threat to ocean life that drives the GEMM Lab’s Project OPAL, can happen in seconds.
Compounding this complexity, even the timescales that research has clarified are changing. Many processes in the ocean are shifting – and often accelerating – due to global climate change. Images of melting sea ice, calving glaciers, and coastal erosion all exemplify our natural world’s rapid reorganization, and even discrete events can have dramatic repercussions and leave their mark for years. For example, a marine heatwave that occurred in 2014-2015 raised temperatures up to 2.5° C warmer than usual, redistributed species northward along the United States’ West Coast, spurred harmful algal blooms, and shut down fisheries. The toxic blooms also caused marine mammal strandings, domoic acid poisoning in California sea lions, and seabird mass death events (McCabe et al., 2016).
Figure 1. Figures like this Stommel diagram reveal the broad temporal and spatial scales over which ocean phenomena occur. Source: Sloyan et al., 2019
As humans seek to manage ocean ecosystems and mitigate the effects of climate change, our political processes have their own time scales, interconnected cycles, and stochasticity, just like the ocean. At the federal level in the United States, the legislative process takes place over months to decades, sometimes punctuated by relatively quicker actions enacted through Executive Orders. In addition, just as plankton have their turnover times, so do governmental branches. Both the legislative branch and the executive branch change frequently, with new members of Congress coming in every two years, and the president and administration changing every four or eight years. Turnover in both of these branches may constitute a total regime shift, with new members seeking to redirect science policy efforts.
The friction between oceanic and political timescales has historically made crafting effective ocean conservation policy difficult. In recent years, the policy approach of “adaptive management” has sought to respond to the challenges at the tricky intersection of politics, climate change, and ocean ecosystems. The U.S. Department of the Interior’s Technical Guide to Adaptive Management highlights its capacity to deal with the uncertainty inherent to changing ecosystems, and its ability to accommodate progress made through research: “Adaptive management [is a decision process that] promotes flexible decision making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process” (Williams et al, 2009).
Over the last several years, adaptive management policy approaches have been key as resource managers along the West Coast have responded to the problem of whale entanglement in fishing gear. When the 2014-2015 marine heatwave event caused anomalously low krill abundance in the central California Current region, humpback whales used a tactic called “prey-switching”, and fed on inshore anchovy schools rather than offshore krill patches. The resulting habitat compression fueled an increase in humpback whale entanglement events in Dungeness crab fishing gear (Santora et al, 2020).
This sudden uptick in whale entanglements necessitated strategic management responses along the West Coast. In 2017, the California Dungeness Crab Fishing Gear Working Group developed the Risk Assessment and Mitigation Program (RAMP) to analyze real-time whale distribution and ocean condition data during the fishing season, and provide contemporaneous assessments of entanglement risk to the state’s Department of Fish and Wildlife. The Oregon Whale Entanglement Working Group (OWEWG) formed in 2017, tasked with developing options to reduce risk. Oregon Department of Fish and Wildlife (ODFW) has guided whale entanglement reduction efforts by identifying four areas of ongoing work: accountability, risk reduction, best management practices, and research – with regular, scheduled reviews of the regulations and opportunities to update and adjust them.
Figure 2. Entanglement in fishing gear can occur in seconds and may negatively impact whales for years. Source Scott Benson/NOAA
The need for research to support the best possible policy is where the GEMM Lab comes in. ODFW has established partnerships with Oregon State University and Oregon Sea Grant in order to improve understanding of whale distributions along the coast that can inform management efforts. Being involved in this cooperative “iterative learning process” is exactly why I’m so glad to be part of Project OPAL. Initial results from this work have already shaped ODFW’s regulations, and the framework of adaptive management and assessment means that regulations can continue being updated as we learn more through our research.
Ecosystem management will always be complex, just like ecosystems themselves. Today, the pace at which the climate is changing causes many people concern and even despair (Bryndum-Buchholz, 2022). Building adaptive approaches into marine policymaking, like the ones in use off the West Coast, introduces a new timescale into the U.S. policy cycle – one more in line with the rapid changes that are occurring within our dynamic ocean.
References
Williams, B. L., Szaro, R. C., and Shapiro, C. D. 2009. Adaptive management: the U.S. Department of the Interior Technical Guide. Adaptive Management Working Group, v pp.
Bryndum-Buchholz, A. (2022). Keeping up hope as an early career climate-impact scientist. ICES Journal of Marine Science, 79(9), 2345–2350. https://doi.org/10.1093/icesjms/fsac180
McCabe, R. M., Hickey, B. M., Kudela, R. M., Lefebvre, K. A., Adams, N. G., Bill, B. D., Gulland, F. M., Thomson, R. E., Cochlan, W. P., & Trainer, V. L. (2016). An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys Res Lett, 43(19), 10366–10376. https://doi.org/10.1002/2016GL070023
Santora, J. A., Sydeman, W. J., Schroeder, I. D., Wells, B. K., & Field, J. C. (2011). Mesoscale structure and oceanographic determinants of krill hotspots in the California Current: Implications for trophic transfer and conservation. Progress in Oceanography, 91(4), 397–409. https://doi.org/10.1016/j.pocean.2011.04.002
Sloyan, B. M., Wilkin, J., Hill, K. L., Chidichimo, M. P., Cronin, M. F., Johannessen, J. A., Karstensen, J., Krug, M., Lee, T., Oka, E., Palmer, M. D., Rabe, B., Speich, S., von Schuckmann, K., Weller, R. A., & Yu, W. (2019). Evolving the Physical Global Ocean Observing System for Research and Application Services Through International Coordination. Frontiers in Marine Science, 6, 449. https://doi.org/10.3389/fmars.2019.00449
In October 1972, the tides turned for U.S. environmental politics: the Marine Mammal Protection Act (MMPA) was passed. Its creation ushered in a new flavor of conservation and management. With phrases like “optimum sustainable population” baked into its statutory language, it marked among the first times that ecosystem-based management — an approach which directly calls upon knowledge of ecology to inform action — was required by law (Ray and Potter 2022). Transitioning from reductionist, species-siloed policies, the MMPA instead placed the interdependency of species at the core of ecosystem function and management.
Beyond deepening the role of science on Capitol Hill, the MMPA’s greatest influence may have been spurred by the language that prohibited “the taking and importation of marine mammals” (16 U.S.C. 1361). Because the word “taking” is multivalent, it carries on its back many interpretations. “Taking” a marine mammal is not limited to intentionally hunting or killing them, or even accidental bycatch. “Taking” also includes carelessly operating a boat when a marine mammal is present, feeding a marine mammal in the wild, or tagging a marine mammal without the appropriate scientific permit. “Taking” a marine mammal can also extend to the fatal consequences caused by noise pollution — not intent, but incident (16 U.S.C. 1362).
The latter circumstances remain reverberant for the U.S. Navy. To comply with the MMPA, they are granted “incidental, but not intentional, taking of small numbers of marine mammals….[when] engag[ing] in a specified activity (other than commercial fishing)” (87 FR 33113). So, if the sonar activities required for national security exercises adversely impact marine mammals, the Navy has a bit of leeway but is still expected to minimize this impact. To further mitigate this potential harm, the Navy thus invests heavily in marine mammal research. (If you are interested in learning more about how the Navy has influenced the trajectory of oceanographic research more broadly, you may find this book interesting.)
Beaked whales are an example of a marine mammal we know much about due to the MMPA’s call for research when incidental take occurs. Three decades ago, many beaked whales stranded ashore following a series of U.S. Navy sonar exercises. Since then, the Navy has flooded research dollars toward better understanding beaked whale hearing, vocal behavior, and movements (e.g., Klinck et al. 2012). Through these efforts, a deluge of research charged with developing effective tools to acoustically monitor and conserve beaked whales has emerged.
These studies have laid the foundation for my Ph.D. research, which is dedicated to the Holistic Assessment of Living marine resources off Oregon (HALO) project. Through both visual and acoustic surveys, the HALO project’s mission is to understand how changes in ocean conditions — driven by global climate change — influence living marine resources in Oregon waters.
In my research specifically, I aim to learn more about beaked whales off the Oregon coast. Beaked whales represent nearly a fourth of cetacean species alive today, with at least 21 species recorded to date (Roman et al. 2013). Even so, 90% of beaked whales are considered data deficient: we lack enough information about them to confidently describe the state of their populations or decide upon effective conservation action.
Much remains to be learned about beaked whales, and I aim to do so by eavesdropping on them. By referring to the “acoustic repertoire” of beaked whales — that is, their vocalizations and corresponding behaviors — I aim to tease out their vocalizations from the broader ocean soundscape and understand how their presence in Oregon waters varies over time.
Beaked whales are notoriously cryptic, elusive to many visual survey efforts like those aboard HALO cruises. In fact, some species have only been identified via carcasses that have washed ashore (Moore and Barlow 2013). Acoustic studies have elucidated ecological information (beaked whales forage at night at seamounts summits; Johnston et al. 2008) and have also introduced promising population-level monitoring efforts (beaked whales have been acoustically detected in areas with a historical scarcity of sightings; Kowarski et al. 2018). Their deep-diving nature often renders them inconspicuous, and they forage at depths between 1,000 and 2,000 m, on dives as long as 90 minutes (Moore and Barlow 2013; Klinck et al. 2012). Their echolocation clicks are produced at frequencies within the hearing range of killer whales, and previous studies have suggested that Blainville’s beaked whales are only vocally active during deep foraging dives and not at the surface, possibly to prevent being acoustically detected by predatory killer whales. Researchers refer to this phenomenon as “acoustic crypsis,” or when vocally-active marine mammals are strategically silent to avoid being found by potential predators (Aguilar de Soto et al. 2012).
We expect to see evidence of Blainville’s beaked whales in Oregon waters, as well as Baird’s, Cuvier’s, Stejneger’s, Hubb’s, and other beaked whale species. Species-specific echolocation clicks were comprehensively described a decade ago in Baumann-Pickering et al. 2013 (Figure 1). While this study laid the groundwork for species-level beaked whale acoustic detection, much more work is still needed to describe their acoustic repertoire with higher resolution detail. For example, though Hubb’s beaked whales live in Oregon waters, their vocal behavior remains scantly defined.
Figure 1: Baird’s, Blainville’s, Cuvier’s, and Stejneger’s beaked whales are among the most comprehensively acoustically described beaked whales inhabiting central Oregon waters, though more work would improve accuracy in species-specific acoustic detection. Credit: Marissa Garcia. Infographic draws upon beaked whale imagery from NOAA Fisheries and spectrograms and acoustical statistics published in Baumann-Pickering et al. 2013.
The HALO project seeks to add a biological dimension to the historical oceanographic studies conducted along the Newport Hydrographic (NH) line ever since the 1960s (Figure 2). Rockhopper acoustic recording units are deployed at sites NH 25, NH 45, and NH 65. The Rockhopper located at site NH 65 is actively recording on the seafloor about 2,800 m below the surface. Because beaked whales tend to be most vocally active at these deep depths, we will first dive into the acoustic data on NH 65, our deepest unit, in hopes of finding beaked whale recordings there.
Figure 2: The HALO project team conducts quarterly visual surveys along the NH line, spanning between NH 25 and NH 65. Rockhopper acoustic recording units continuously record at the NH 25, NH 45, and NH 65 sites. Credit: Leigh Torres.
Beaked whales’ acoustic repertoire can be broadly split into four primary categories: burst pulses (aka “search clicks”), whistles, buzz clicks, and rasps. Beaked whale search clicks, which are regarded as burst pulses when produced in succession, have distinct qualities: their upswept frequency modulation (meaning the frequency gets higher within the click), their long duration especially when compared to other delphinid clicks, and a consistent interpulse interval which is the time of silence between signals (Baumann-Pickering et al. 2013). Acoustic analysts can identify different species based on how the frequency changes in different burst pulse sequences (Baumann-Pickering et al. 2013; Figure 1). For this reason, when I conduct my HALO analyses, I intend to automatically detect beaked whale species using burst pulses, as they are the best documented beaked whale signal, with unique signatures for each species.
In the landscape of beaked whale acoustics, the acoustic repertoire of Blainville’s beaked whales (Mesoplodon densirostris) — a species of focus in my HALO analyses — is especially well defined. Blainville’s beaked whale whistles have been recorded up to 900 m deep, representing the deepest whistle recorded for any marine mammal to date in the literature (Aguilar de Soto et al. 2012). While Blainville’s beaked whales only spend 40% of their time at depths below 170 m, two key vocalizations occur at these depths: whistles and rasps. While they remain surprisingly silent near the surface, beaked whales produce whistles and rasps at depths up to 900 m. The beaked whales dive together in synchrony, and right before they separate from each other, they produce the most whistles and rasps, further indicating that these vocalizations are used to enhance foraging success (Aguilar de Soto et al. 2006). As beaked whales transition to foraging on their own, they predominantly produce frequently modulated clicks and buzzes. Beaked whales produce buzzes in the final stages of prey capture to receive up-to-date information about their prey’s location. The buzzes’ high repetition enables the whale to achieve 300+ updates on their intended prey’s location in the last 3 m before seizing their feast (Johnson et al. 2006; Figure 3).
Figure 3: Blainville’s beaked whales generally have four categories within their acoustic repertoire, including burst pulses, whistles, buzz clicks, and rasps. Credit: Marissa Garcia.
All of this knowledge about beaked whale acoustics can be linked back to the MMPA, which has also achieved broader success. Since the MMPA’s implementation, marine mammal population numbers have risen across the board. For marine mammal populations with sufficient data, approximately 65% of these stocks are increasing and 17% are stable (Roman et al. 2013).
Nevertheless, perhaps much of the MMPA’s true success lies in the research it has indirectly fueled, by virtue of the required compliance of governmental bodies such as the U.S. Navy. And the response has proven to be a boon to knowledge: if the U.S. Navy has been the benefactor of marine mammal research, beaked whale acoustics has certainly been the beneficiary. We hope the beaked whale acoustic analyses stemming from the HALO Project can further this expanse of what we know.
References
Aguilar de Soto, N., Madsen, P. T., Tyack, P., Arranz, P., Marrero, J., Fais, A., Revelli, E., & Johnson, M. (2012). No shallow talk: Cryptic strategy in the vocal communication of Blainville’s beaked whales. Marine Mammal Science, 28(2), E75–E92. https://doi.org/10.1111/j.1748-7692.2011.00495.x
Baumann-Pickering, S., McDonald, M. A., Simonis, A. E., Solsona Berga, A., Merkens, K. P. B., Oleson, E. M., Roch, M. A., Wiggins, S. M., Rankin, S., Yack, T. M., & Hildebrand, J. A. (2013). Species-specific beaked whale echolocation signals. The Journal of the Acoustical Society of America, 134(3), 2293–2301. https://doi.org/10.1121/1.4817832
Johnston, D. W., McDonald, M., Polovina, J., Domokos, R., Wiggins, S., & Hildebrand, J. (2008). Temporal patterns in the acoustic signals of beaked whales at Cross Seamount. Biology Letters (2005), 4(2), 208–211. https://doi.org/10.1098/rsbl.2007.0614
Johnson, M., Madsen, P. T., Zimmer, W. M. X., de Soto, N. A., & Tyack, P. L. (2004). Beaked whales echolocate on prey. Proceedings of the Royal Society. B, Biological Sciences, 271(Suppl 6), S383–S386. https://doi.org/10.1098/rsbl.2004.0208
Johnson, M., Madsen, P. T., Zimmer, W. M. X., de Soto, N. A., & Tyack, P. L. (2006). Foraging Blainville’s beaked whales (Mesoplodon densirostris) produce distinct click types matched to different phases of echolocation. Journal of Experimental Biology, 209(Pt 24), 5038–5050. https://doi.org/10.1242/jeb.02596
Klinck, H., Mellinger, D. K., Klinck, K., Bogue, N. M., Luby, J. C., Jump, W. A., Shilling, G. B., Litchendorf, T., Wood, A. S., Schorr, G. S., & Baird, R. W. (2012). Near-real-time acoustic monitoring of beaked whales and other cetaceans using a Seaglider. PloS One, 7(5), e36128. https://doi.org/10.1371/annotation/57ad0b82-87c4-472d-b90b-b9c6f84947f8
Kowarski, K., Delarue, J., Martin, B., O’Brien, J., Meade, R., Ó Cadhla, O., & Berrow, S. (2018). Signals from the deep: Spatial and temporal acoustic occurrence of beaked whales off western Ireland. PloS One, 13(6), e0199431–e0199431. https://doi.org/10.1371/journal.pone.0199431
Madsen, P. T., Johnson, M., de Soto, N. A., Zimmer, W. M. X., & Tyack, P. (2005). Biosonar performance of foraging beaked whales (Mesoplodon densirostris). Journal of Experimental Biology, 208(Pt 2), 181–194. https://doi.org/10.1242/jeb.01327
McCullough, J. L. K., Wren, J. L. K., Oleson, E. M., Allen, A. N., Siders, Z. A., & Norris, E. S. (2021). An Acoustic Survey of Beaked Whales and Kogia spp. in the Mariana Archipelago Using Drifting Recorders. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.664292
Moore, J. E. & Barlow, J. P. (2013). Declining abundance of beaked whales (family Ziphiidae) in the California Current large marine ecosystem. PloS One, 8(1), e52770–e52770. https://doi.org/10.1371/journal.pone.0052770
Ray, G. C. & Potter, F. M. (2011). The Making of the Marine Mammal Protection Act of 1972. Aquatic Mammals, 37(4), 522.
Roman, J., Altman, I., Dunphy-Daly, M. M., Campbell, C., Jasny, M., & Read, A. J. (2013). The Marine Mammal Protection Act at 40: status, recovery, and future of U.S. marine mammals. Annals of the New York Academy of Sciences, 1286(1), 29–49. https://doi.org/10.1111/nyas.12040
Models can be extremely useful tools to describe biological systems and answer ecological questions, but they are often tricky to construct. If I have learned anything in my statistics classes, it is the importance of resisting the urge to throw everything but the kitchen sink into a model. However, this is usually much easier said than done, and model construction takes a lot of practice. The principle of simplicity is currently at the forefront of my thesis work, as I try to embody the famous quote by Albert Einstein:
“Everything should be made as simple as possible, but no simpler.”
As you might remember from my earlier blog, the goal of my thesis is to use biologging data to define different foraging behaviors of Pacific Coast Feeding Group (PCFG) gray whales, and then calculate the energetic cost of those behaviors. I am defining PCFG foraging behaviors at two scales: (1) dives that represent different behavior states (e.g., travelling vs foraging), and (2) roll events, which are periods during dives where the whale is rolled onto their side, that represent different foraging tactics (e.g., headstanding vs side-swimming).
Initially, I was planning to use a clustering analysis to define these different foraging behaviors at both the dive and roll event scale, as this method has been used to successfully classify different foraging strategies for Galapagos sea lions (Schwarz et al., 2021). In short, this clustering analysis uses summary variables from events of interest to group events based on their similarity. These can be any metric that describes the event such as duration and depth, or body positioning variables like median pitch or roll. The output of the clustering analysis method results in groups of events that can each be used to define a different behavior.
However, while this method works for defining the foraging tactics of PCFG gray whales, my discussions with other scientists have suggested that there is a better method available for defining foraging behavior at the dive scale: Hidden Markov Models (HMMs). HMMs are similar to the clustering method described above in that they use summary variables at discrete time scales to define behavior states, but HMMs take into account the bias inherent to time series data – events that occur closer together in time are more likely to be more similar. This bias of time can confound clustering analyses, making HMMs a better tool for classifying a series of dives into different behavior states.
Like many analytical methods, the HMM framework was first proposed in a terrestrial system where it was used to classify the movement of translocated elk (Morales et al., 2004). The initial framework proposed using the step length, or the spatial distance between the animal’s locations at the start of subsequent time intervals, and the corresponding turning angle, to isolate “encamped” from “exploratory” behaviors in each elk’s movement path (Figure 1, from Morales et al., 2004). “Encamped” behaviors are those with short step lengths and high turning angles that show the individual is moving within a small area, and they can be associated with foraging behavior. On the other hand, “exploratory” behaviors are those with long step lengths and low turning angles that show the individual is moving in a relatively straight path and covering a lot of ground, which is likely associated with travelling behavior.
Figure 1. The difference between “encamped” and “exploratory” behavior states from a simple Hidden Markov Model (HMM) in a translocated elk equipped with a GPS collar (Fig. 1 in Morales et al., 2004). The top rose plots show the turning angles while the bottom histograms show the step lengths as a daily movement rate. The “encamped” state has short step lengths (low daily movement rate) and high turning angles while the “exploratory” state has long step lengths (high daily movement rate) and low turning angle. These behavior states from the HMM can then be interpollated to elk behavior, as the low daily movement and tight turns of the “encamped” behavior state likely indicates foraging while the high daily movement and direct path of the “exploratory” behavior state likely indicates traveling. Thus, it is important to keep the biological relevance of the study system in mind while constructing and interpreting the model.
In the two decades following this initial framework proposed by Morales et al. (2004), the use of HMMs in anlaysis has been greatly expanded. One example of this expansion has been the development of mutlivariate HMMs that include additional data streams to supplement the step length and turning angle classification of “encamped” vs “exploratory” states in order to define more behaviors in movement data. For instance, a multivariate HMM was used to determine the impact of acoustic disturbance on blue whales (DeRuiter et al., 2017). In addition to step length and turning angle, dive duration and maximum depth, the duration of time spent at the surface following the dive, the number of feeding lunges in the dive, and the variability of the compass direction the whale was facing during the dive were all used to classify behavior states of the whales. This not only allowed for more behavior states to be identified (three instead of two as determined in the elk model), but also the differences in behavior states between individual animals included in the study, and the differences in the occurrence of behavior states due to changes in environmental noise.
The mutlivariate HMM used by DeRuiter et al. (2017) is a model I would ideally like to emulate with the biologging data from the PCFG gray whales. However, incorporating more variables invites more questions during the model construction process. For example, how many variables should be incorporated in the HMM? How should these variables be modeled? How many behavior states can be identified when including additional variables? These questions illustrate how easy it is to unnecessarily overcomplicate models and violate the principle of simiplicity toted by Albert Einstein, or to be overwhelmed by the complexity of these analytical tools.
Figure 2. Example of expected output of Hidden Markov Model (HMM) for the PCFG gray whale biologging data (GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678). The figure shows the movement track the whale swam during the deployment of the biologger, with each point representing the start of a dive. The axes show “Easting” and “Northing” rather than map coordinates because this is the relative path the whale took rather than GPS coordinates of the whale’s location. Each color represents a different behavior state—blue has short step lengths and high turning angles (likely foraging), red has intermediate step lengths and turning angles (likely searching), and black has long step lengths and low turning angles (likely transiting). These results will be refined as I construct the multivariate HMM that will be used in my thesis.
Luckily, I can draw on the support of Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project collaborators Dr. Leslie New and Dr. Enrico Pirotta to guide my HMM model construction and assist in interpreting the outputs (Figure 2). With their help, I have been learning the importance of always asking if the change I am making to my model is biologically relevent to the PCFG gray whales, and if it will help give me more insight into the whales’ behavior. Even though using complex tools, such as Hidden Markov Models, has a steep learning curve, I know that this approach is not only placing this data analysis at the cutting edge of the field, but helping me practice fundamental skills, like model construction, that will pay off down the line in my career.
Sources
DeRuiter, S. L., Langrock, R., Skirbutas, T., Goldbogen, J. A., Calambokidis, J., Friedlaender, A. S., & Southall, B. L. (2017). A multivariate mixed Hidden Markov Model for blue whale behaviour and responses to sound exposure. Annals of Applied Statistics, 11(1), 362–392. https://doi.org/10.1214/16-AOAS1008
Morales, J. M., Haydon, D. T., Frair, J., Holsinger, K. E., & Fryxell, J. M. (2004). Extracting more out of relocation data: Building movement models as mixtures of random walks. Ecology, 85(9), 2436–2445. https://doi.org/10.1890/03-0269
Schwarz, J. F. L., Mews, S., DeRango, E. J., Langrock, R., Piedrahita, P., Páez-Rosas, D., & Krüger, O. (2021). Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator. Oecologia, 195(2), 313–325. https://doi.org/10.1007/s00442-021-04850-w
By Abby Tomita, undergraduate student, OSU College of Earth, Ocean, and Atmospheric Sciences
From long days in Newport performing the patience-testing task of bomb calorimetry, to spending hours transfixed by the microscopic world that exists in our oceans, I recently got an amazing glimpse into the world of marine biological research working with PhD student Rachel Kaplan. She has been an amazing teacher to my fellow intern Hadley and I, showing us the basics of the research process and introducing us to so many wonderful people at NOAA and the GEMM Lab. I am in my third year studying oceanography here at OSU and had no real lab experience before this, so I was eager to explore this area of research, and not only learn new information about our oceans, but also to see the research process up close and personal.
After being trained by Jennifer Fisher, a NOAA Research Fisheries Biologist, I sorted through zooplankton samples collected on the R/V Bell M. Shimada from the Northern California Current region. This data will be used to get an idea of where krill are found throughout the year, and in what abundances. Though my focus was mainly on two species of krill, I also found an assortment of other organisms, such as larval fish, squid, copepods, crabs, and tons of jellies, which were super interesting to see.
A small group of larval squid and other unknown species (photo by Abby Tomita).
I also studied krill through a technique called bomb calorimetry, which is not for the faint of heart! It takes a tough soul to be able to put these complex little creatures into a mortar and pestle and grind them into a dust that hits your nose like pepper. They then take their final resting place into the bomb calorimetry machine (which can and will find something to fuss over) until it finally manages to ignite and dispose of the krill’s remains. The light that guided me through this dark tunnel was the knowledge that these sacrificial krill were taken in the name of science, with the aim of eventually decreasing whale entanglements.
Abby placing the pellet within the coil for the bomb.
That, and Rachel’s contagious positivity. In the early stages, we would spend the majority of our time troubleshooting after constant “misfires”, in which the machine fails to combust the sample properly. Bomb calorimetry involves many tedious steps, and working with such small quantities of tissue – a single krill could weigh 0.01 grams or even less – poses a plethora of its own challenges. One of my biggest takeaways from this experience was to have patience with this kind of work and know when to take a much-needed dance break. Things often do not work out according to plan, and getting to see first-hand how to adapt to confounding variables and hitches in the procedure was an invaluable lesson.
I also got to see how collaborative the research process is. We received helpful advice from other members of the GEMM Lab at lunch, as well as constant help from our esteemed Resident Bomb Cal Expert, Elizabeth Daly. It was comforting for me to see that even when you are doing independent research, you are not expected to only work alone, and there can be so much community in higher level research.
A larval snailfish.A larval fish (sp. unknown) still in its egg.A larval octopus. Female Thysanoessa spinifera krill (photos by Abby Tomita).
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After my graduation, since I have tropical blood running in my veins, I literally crossed the entire country in search of blue and sunny skies, warm weather and ocean, and of course different opportunities to continue doing research involving stressors and physiological responses in marine mammals and other marine organisms. It didn’t take me long to start a position as a postdoctoral associate with the Institute of Environment at Florida International University. I have learned so much in these past two years while mainly working with toxicology and stress biomarkers in a wide range of marine individuals including corals, oysters, fish, dolphins, and now manatees. I have started a new chapter in my life, and I am very eager to see where it takes me.
Talking about chapters… my Ph.D. thesis comprised four different chapters and I had published only the first one when I left Oregon: “Intra- and inter-annual variation in gray whale body condition on a foraging ground”. In this study we used drone-based photogrammetry to measure and compare gray whale body condition along the Oregon coast over three consecutive foraging seasons (June to October, 2016-2018). We described variations across the different demographic units, improved body condition with the progression of feeding seasons, and variations across years, with a better condition in 2016 compared to the following two years. Then in 2020, I was able to publish my second chapter entitled “Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales”. In this study, we used gray whale fecal samples to validate and quantify four different hormone metabolite concentrations (progestins, androgens, glucocorticoids, and thyroid hormone). We reported variation in progestins and androgens by demographic unit and by year. Almost a year later, my third chapter “Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability” was published. In this chapter, we documented a negative correlation between body condition and glucocorticoids, meaning that slim whales were more stressed than the chubby ones.
These three chapters were “relatively easy” to publish compared to my fourth chapter, which had a long and somewhat stressful process (which is funny as I am trying to report stress responses in gray whales). Changes between journals, titles, analyses, content, and focus had to be made over the past year and a half for it to be accepted for publication. However, I believe that it was worth the extra work and invested time as our research definitely became more robust after all of the feedback provided by the reviewers. This chapter, now entitled “Effects of vessel traffic and ocean noise on gray whale stress hormones” was finally published earlier this month at the Nature Scientific Reports journal, and I’ll describe it further below.
Increased human activities in the last decades have altered the marine ecosystem, leaving us with a noisier, warmer, and more contaminated ocean. The noise caused by the dramatic increase in commercial and recreational shipping and vessel traffic1-3 has been associated with negative impacts on marine wildlife populations4,5. This is especially true for baleen whales, whose frequencies primarily used for communication, navigation, and foraging6,7 are “masked” by the noise generated by this watercraft. Several studies have reported alterations in marine mammal behavioral states8-11, increased group cohesion12-14, and displacement8,15 due to this disturbance, however, just a few studies have considered their physiological responses. Examples of physiological responses reported in marine mammals include altered metabolic rate15,16 and variations in stress-related hormone (i.e., glucocorticoids) concentrations relative to vessel abundance and ambient noise17,18. Based on this context and on the scarcity of such assessments, we attempted to determine the effects of vessel traffic and associated ambient noise, as well as potential confounding variables (i.e., body condition, age, sex, time), on gray whale fecal glucocorticoid concentrations.
In addition to the data used in my previous three chapters collected from gray whales foraging off the Oregon coast, we also collected ambient noise levels using hydrophones, vessel count data from the Oregon Department of Fish and Wildlife (ODFW), and wind data from NOAA National Data Buoy Center (NDBC). Our first finding was a positive correlation between vessel counts and underwater noise levels (Fig. 1A), likely indicating that vessel traffic is the dominant source of noise in the area. To confirm this, we also compared underwater noise levels with wind speed (Fig. 1B), but no correlations were found.
Figure 1: Linear correlations between noise levels (daily median root mean square [rms] sound pressure level [SPL] in dB [re 1 μPa]; 50–1000 Hz) recorded on a hydrophone deployed outside the Newport harbor entrance during June to October of 2017 and 2018 and (A) vessel counts in Newport and Depoe Bay, Oregon, USA, and (B) daily median wind speed (m/s) from an anemometer station located on South Beach, Newport, Oregon, USA (station NWPO3). Asterisk indicates significant correlations between SPL and vessel counts in both years.
We also investigated noise levels by the hour of the day (Fig. 2), and we found that noise levels peaked between 6 and 8 am most days, coinciding with the peak of vessels leaving the harbor to get to fishing grounds. Another smaller peak is seen at 12 pm, which may represent “half-day fishing charter” vessels returning to the harbor. In contrast, wind speeds (in the lower graph) peaked between 3 and 4 pm, thus confirming the absence of correlation between noise and wind and providing more evidence that noise levels are dominated by the vessel activity in the area.
Figure 2: Median noise levels (root mean square sound pressure levels—SPLrms) for each hour of each day recorded on a hydrophone (50–10,000 Hz) deployed outside the Newport harbor entrance during June to October of 2017 (middle plot) and 2018 (upper plot), and hourly median noise level (SPL) against hourly median wind speed (lower plot) from an anemometer station located on South Beach, Newport, Oregon, USA (station NWPO3) over the same time period.
Finally, we assessed the effects of vessel counts, month, year, sex, whale body condition, and other hormone metabolites on glucocorticoid metabolite (GCm; “stress”) concentrations. Since we are working with fecal samples, we needed to consider the whale gut transit time and go back in time to link time of exposure (vessel counts) to response (glucocorticoid concentrations). However, due to uncertainty regarding gut transit time in baleen whales, we compared different time lags between vessel counts and fecal collection. The gut transit time in large mammals is ~12 hours to 4 days3,19,20, so we investigated the influence of vessel counts on whale “stress hormone levels” from the previous 1 to 7 days. The model with the most influential temporal scale included vessel counts from previous day, which showed a significant positive relationship with GCm (the “stress hormone level”) (Fig. 3).
Figure 3: The effect of vessel counts in Newport and Depoe Bay (Oregon, USA) on the day before fecal sample collection on gray whale fecal glucocorticoid metabolite (GCm) concentrations.
Thus, the “take home messages” of our study are:
The soundscape in our study area is dominated by vessel noise.
Vessel counts are strongly correlated with ambient noise levels in our study area.
Gray whale glucocorticoid levels are positively correlated with vessel counts from previous day meaning that gray whale gut transit time may occur within ~ 24 hours of the disturbance event.
These four chapters were all very important studies not only to advance the knowledge of gray whale and overall baleen whale physiology (as this group is one of the most poorly understood of all mammals given the difficulties in sample collection21), but also to investigate potential sources for the unusual mortality event that is currently happening (2019-present) to the Eastern North Pacific population of gray whales. Such studies can be used to guide future research and to inform population management and conservation efforts regarding minimizing the impact of anthropogenic stressors on whales.
I am very glad to be part of this project, to see such great fruits from our gray whale research, and to know that this project is still at full steam. The GEMM Lab continues to collect and analyze data for determining gray whale body condition and physiological responses in association with ambient noise (Granite, Amber and Diamond projects). The gray whales thank you for this!
Cited Literature
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