Geospatial Ecology of Marine Megafauna Laboratory

Sonar savvy: using echo sounders to characterize zooplankton swarms

By Natalie Chazal, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

I’m Natalie Chazal, the GEMM Lab’s newest PhD student! This past spring I received my MS in Biological and Agricultural Engineering with Dr. Natalie Nelson’s Biosystems Analytics Lab at North Carolina State University. My thesis focused on using shellfish sanitation datasets to look at water quality trends in North Carolina and to forecast water quality for shellfish farmers in Florida. Now, I’m excited to be studying gray whales in the GEMM Lab!

Since the beginning of the Fall term, I’ve jumped into a project that will use our past 8 years of sonar data collected using a Garmin echo sounder during the GRANITE project work with gray whales off the Newport, OR coast. Echo sounder data is commonly used recreationally to detect bottom depth and for finding fish and my goal is to use these data to assess relative prey abundance at gray whale sightings over time and space.

There are also scientific grade echo sounders that are built to be incredibly precise and very exact in the projection and reception of the sonar pulses. Both types of echosounders can be used to determine the depth of the ocean floor, structures within the water column, and organisms that are swimming within the sonar’s “cone” of acoustic sensing. The precision and stability of the scientific grade equipment allows us to answer questions related to the specific species of organisms, the substrate type at the sea floor, and even animal behavior. However, scientific grade echo sounders can be expensive, overly large for our small research vessel, and require expertise to operate. When it comes to generalists, like gray whales, we can answer questions about relative prey abundances without the use of such exact equipment (Benoit-Bird 2016; Brough 2019).

While there are many variations of echo sounders that are specific to their purpose, commercially available, single beam echo sounders generally function in the same way (Fig. 1). First, a “ping” or short burst of sound at a specific frequency is produced from a transducer. The ping then travels downward and once it hits an object, some of the sound energy bounces off of the object and some moves into the object. The sound that bounces off of the object is either reflected or scattered. Sound energy that is either reflected or scattered back in the direction of the source is then received by the transducer. We can figure out the depth of the signal using the amount of travel time the ping took (SeaBeam Instruments 2000).

Figure 1. Diagram of how sound is scattered, reflected, and transmitted in marine environments (SeaBeam Instruments, 2000).

The data produced by this process is then displayed in real-time, on the screen on board the boat. Figure 2 is an example of the display that we see while on board RUBY (the GEMM Lab’s rigid-hull inflatable research boat):

Figure 2. Photo of the echo sounder display on board RUBY. On the left is a map that is used for navigation. On the right is the real time feed where we can see the ocean bottom shown as the bright yellow area with the distinct boundary towards the lower portion of the screen. The more orange layer above that, with the more “cloudy” structure is a mysid swarm.

Once off the boat, we can download this echo sounder data and process it in the lab to recreate echograms similar to those seen on the boat. The echograms are shown with the time on the x-axis, depth on the y-axis, and are colored by the intensity of sound that was returned (Fig. 3). Echograms give us a sort of picture of what we see in the water column. When we look at these images as humans, we can infer what these objects are, given that we know what habitat we were in. Below (Fig. 3) are some example classifications of different fish and zooplankton swarms and what they look like in an echogram (Kaltenberg 2010).

Figure 3. Panel of echogram examples, from Kaltenberg 2010, for different fish and zooplankton aggregations that have been classified both visually (like we do in real time on the boat) as well as statistically (which we hope to do with the mysid aggregations).

For our specific application, we are going to focus on characterizing mysid swarms, which are considered to be the main prey target of PCFG whales in our study area. With the echograms generated by the GRANITE fieldwork, we can gather relative mysid swarm densities, giving us an idea of how much prey is available to foraging gray whales. Because we have 8 years of GRANITE echosounder data, with 2,662 km of tracklines at gray whale sightings, we are going to need an automated process. This demand is where image segmentation can come in! If we treat our echograms like photographs, we can train models to identify mysid swarms within echograms, reducing our echogram processing load. Automating and standardizing the process can also help to reduce error.

We are planning to utilize U-Nets, which are a method of image segmentation where the image goes through a series of compressions (encoders) and expansions (decoders), which is common when using convolutional neural nets (CNNs) for image segmentation. The encoder is generally a pre-trained classification network (CNNs work very well for this) that is used to classify pixels into a lower resolution category. The decoder then takes the low resolution categorized pixels and reprojects them back into an image to get a segmented mask. What makes U-Nets unique is that they re-introduce the higher resolution encoder information back into the decoder process through skip connections. This process allows for generalizations to be made for the image segmentation without sacrificing fine-scale details (Brautaset 2020; Ordoñez 2022; Slonimer 2023; Vohra 2023).

Figure 4. Diagram of the encoder, decoder architecture for U-Nets used in biomedical image segmentation. Note the skip connections illustrated by the gray lines connecting the higher resolution image information on the left, with the decoder process on the right (Ronneberger 2015)

What we hope to get from this analysis is an output image that provides us only the parts of the echogram that contain mysid swarms. Once the mysid swarms are found within the echograms, we can use both the intensity and the size of the swarm in the echogram as a proxy for the relative abundance of gray whale prey. We plan to quantify these estimates across multiple spatial and temporal scales, to link prey availability to changing environmental conditions and gray whale health and distribution metrics. This application is what will make our study particularly unique! By leveraging the GRANITE project’s extensive datasets, this study will be one of the first studies that quantifies prey variability in the Oregon coastal system and uses those results to directly assess prey availability on the body condition of gray whales.

However, I have a little while to go before the data will be ready for any analysis. So far, I’ve been reading as much as I can about how sonar works in the marine environment, how sonar data structures work, and how others are using recreational sonar for robust analyses. There have been a few bumps in the road while starting this project (especially with disentangling the data structures produced from our particular GARMIN echosounder), but my new teammates in the GEMM Lab have been incredibly generous with their time and knowledge to help me set up a strong foundation for this project, and beyond.

References

Kaltenberg A. (2010) Bio-physical interactions of small pelagic fish schools and zooplankton prey in the California Current System over multiple scales. Oregon State University, Dissertation. https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/z890rz74t
SeaBeam Instruments. (2000) Multibeam Sonar Theory of Operation. L-3 Communications, East Walpole MA. https://www3.mbari.org/data/mbsystem/sonarfunction/SeaBeamMultibeamTheoryOperation.pdf
Benoit-Bird K., Lawson G. (2016) Ecological insights from pelagic habitats acquired using active acoustic techniques. Annual Review of Marine Science. https://doi.org/10.1146/annurev-marine-122414-034001
Brough T., Rayment W., Dawson S. (2019) Using a recreational grade echosounder to quantify the potential prey field of coastal predators. PLoS One. https://doi.org/10.1371/journal.pone.0217013
Brautaset O., Waldeland A., Johnsen E., Malde K., Eikvil L., Salberg A, Handegard N. (2020) Acoustic classification in multifrequency echosounder data using deep convolutional neural networks. ICES Journal of Marine Science 77, 1391–1400. https://doi.org/10.1093/icesjms/fsz235
Ordoñez A., Utseth I., Brautaset O., Korneliussen R., Handegard N. (2022) Evaluation of echosounder data preparation strategies for modern machine learning models. Fisheries Research 254, 106411. https://doi.org/10.1016/j.fishres.2022.106411
Slonimer A., Dosso S., Albu A., Cote M., Marques T., Rezvanifar A., Ersahin K., Mudge T., Gauthier S., (2023) Classification of Herring, Salmon, and Bubbles in Multifrequency Echograms Using U-Net Neural Networks. IEEE Journal of Oceanic Engineering 48, 1236–1254. https://doi.org/10.1109/JOE.2023.3272393
Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. https://doi.org/10.48550/arXiv.1505.04597

Migrating back east

By: Kate Colson, MSc Oceans and Fisheries, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

With the changing of the season, gray whales are starting their southbound migration that will end in the lagoons off the Baja California Mexico. The migration of the gray whale is the longest migration of any mammal—the round trip totals ~10,000 miles (Pike, 1962)!

*Map of the migration route taken by gray whales along the west coast of North America. (Image credit: Angle, Asplund, and Ostrander, 2017* *https://www.slocoe.org/resources/parent-and-public-resources/what-is-a-california-gray-whale/california-gray-whale-migration/*)

Like these gray whales, I am also undertaking my own “migration” as I leave Newport to start my post-Master’s journey. However, my migration will be a little shorter than the gray whale’s journey—only ~3,000 miles—as I head back to the east coast. As I talked about in my previous blog, I have finished my thesis studying the energetics of gray whale foraging behaviors and I attended my commencement ceremony at the University of British Columbia last Wednesday. As my time with the GEMM Lab comes to a close, I want to take some time to reflect on my time in Newport.

*Me in my graduation regalia (right) and my co-supervisor Andrew Trites holding the university mace (left) after my commencement ceremony at the University of British Columbia rose garden.*

Many depictions of scientists show them working in isolation but in my time with the GEMM Lab I got to fully experience the collaborative nature of science. My thesis was a part of the GEMM Lab’s Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project and I worked closely with the GRANITE team to help achieve the project’s research goals. The GRANITE team has annual meetings where team members give updates on their contributions to the project and flush out ideas in a series of very busy days. I found these collaborative meetings very helpful to ensure that I was keeping the big picture of the gray whale study system in mind while working with the energetics data I explored for my thesis. The collaborative nature of the GRANITE project provided the opportunity to learn from people that have a different skill set from my own and expose me to many different types of analysis.

*GRANITE team members hard at work thinking about gray whales and their physiological response to noise.*

This summer I also was able to participate in outreach with the partnership of the Oregon State University Marine Mammal Institute and the Eugene Exploding Whales (the alternate identity of the Eugene Emeralds) minor league baseball team to promote the Oregon Gray Whale License plates. It was exciting to talk to baseball fans about marine mammals and be able to demonstrate that the Gray Whale License plate sales are truly making a difference for the gray whales off the Oregon coast. In fact, the minimally invasive suction cup tags used in to collect the data I analyzed in my thesis were funded by the OSU Gray Whale License plate fund!

Photo of the GEMM Lab promoting Oregon Gray Whale License plates at the Eugene Exploding Whales baseball game. If you haven’t already, be sure to “Put a whale on your tail!” to help support marine mammal research off the Oregon Coast.

Outside of the amazing science opportunities, I have thoroughly enjoyed the privilege of exploring Newport and the Oregon coast. I was lucky enough to find lots of agates and enjoyed consistently spotting gray whale blows on my many beach walks. I experienced so many breathtaking views from hikes (God’s thumb was my personal favorite). I got to attend an Oregon State Beavers football game where we crushed Stanford! And most of all, I am so thankful for all the friends I’ve made in my time here. These warm memories, and the knowledge that I can always come back, will help make it a little easier to start my migration away from Newport.

*Me and my friends outside of Reser Stadium for the Oregon State Beavers football game vs Stanford this season. Go Beavs!!!*

*Me and my friends celebrating after my defense.*

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References

Pike, G. C. (1962). Migration and feeding of the gray whale (Eschrichtius gibbosus). Journal of the Fisheries Research Board of Canada, 19(5), 815–838. https://doi.org/10.1139/f62-051

Blue whales, krill, and climate change: introducing the SAPPHIRE project

By Dr. Dawn Barlow, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

The world is warming. Ocean ecosystems are experiencing significant and rapid impacts of climate change. However, the cascading effects on marine life are largely unknown. Thus, it is critical to understand how – not just if – environmental change impacts the availability and quality of key prey species in ocean food webs, and how these changes will impact marine predator health and population resilience. With these pressing knowledge gaps in mind, we are thrilled to launch a new project “Marine predator and prey response to climate change: Synthesis of Acoustics, Physiology, Prey, and Habitat in a Rapidly changing Environment (SAPPHIRE).” We will examine how changing ocean conditions affect the availability and quality of krill, and thus impact blue whale behavior, health, and reproduction. This large-scale research effort is made possible with funding from the National Science Foundation.

The SAPPHIRE project takes place in the South Taranaki Bight (STB) region of Aotearoa New Zealand, and before diving into our new research plans, let’s reflect briefly on what we know so far about this study system based on our previous research. Our collaborative research team has studied blue whales in the STB since 2013 to document the population, understand their ecology and habitat use, and inform conservation management. We conducted boat-based surveys and used hydrophones to record the underwater soundscape, and found the following:

Blue whales in Aotearoa New Zealand are a unique population, genetically distinct from all other known populations in the Southern Hemisphere, with an estimated population size of 718 (95% CI = 279 – 1926).¹
Blue whales reside in the STB region year-round, with feeding and breeding vocalizations detected nearly every day of the year.^2,3
Wind-driven upwelling over Kahurangi shoals moves a plume of cold, nutrient-rich waters into the STB, supporting aggregations of krill, and thereby critical feeding opportunities for blue whales in spring and summer.^4–6
We developed predictive models to forecast blue whale distribution up to three weeks in advance, providing managers with a real-time tool in the form of a desktop application to produce daily forecast maps for dynamic management.⁷
During marine heatwaves, blue whale feeding activity was substantially reduced in the STB. Interestingly, their breeding activity was also reduced in the following season when compared to the breeding season following a more productive, typical foraging season. This finding indicates that shifting environmental conditions, such as marine heatwaves and climate change, may have consequences to not just foraging success, but the population’s reproductive patterns.³

A blue whale comes up for air in the South Taranaki Bight. Photo by Leigh Torres.

Project goals

Building on this existing knowledge, we aim to gain understanding of the health impacts of environmental change on krill and blue whales, which can in turn inform management decisions. Over the next three years (2024-2026) we will use multidisciplinary methods to collect data in the field that will enable us to tackle these important but challenging goals. Our broad objectives are to:

Assess variation in krill quality and availability relative to rising temperatures and different ocean conditions,
Document how blue whale body condition and hormone profiles change relative to variable environmental and prey conditions,
Understand how environmental conditions impact blue whale foraging and reproductive behavior, and
Integrate these components to develop novel Species Health Models to predict predator and prey whale population response to rapid environmental change.

Kicking off fieldwork

This coming January, we will set sail aboard the R/V Star Keys and head out in search of blue whales and krill in the STB! Five of our team members will spend three weeks at sea, during which time we will conduct surveys for blue whale occurrence paired with active acoustic assessment of krill availability, fly Unoccupied Aircraft Systems (UAS; “drones”) over whales to determine body condition and potential pregnancy, collect tissue biopsy samples to quantify stress and reproductive hormone levels, deploy hydrophones to record rates of foraging and reproductive calls by blue whales, and conduct on-board controlled experiments on krill to assess their response to elevated temperature.

The team in action aboard the R/V Star Keys in February 2017. Photo by L. Torres.

The moving pieces are many as we work to obtain research permits, engage in important consultation with iwi (indigenous Māori groups), procure specialized scientific equipment, and make travel and shipping arrangements. The to-do lists seem to grow just as fast as we can check items off; such is the nature of coordinating an international, multidisciplinary field effort. But it will pay off when we are underway, and I can barely contain my excitement to back on the water with this research team.

Our team has not collected data in the STB since 2017. We know so much more now than we did when studies of this blue whale population were just beginning. For example, we are eager to put our blue whale forecast tool to use, which will hopefully enable us to direct survey effort toward areas of higher blue whale density to maximize data collection. We are keen to see what new insights we gain, and what new questions and challenges arise.

Research team

The SAPPHIRE project will only be possible with the expertise and coordination of the many members of our collaborative group. We are all thrilled to begin this research journey together, and eager to share what we learn.

Principal Investigators:

Leigh Torres (Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, OSU)

Research partners and key collaborators:

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References:

1. Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-Hymes CT, Klinck H. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res. 2018;36:27–40.

2. Barlow DR, Klinck H, Ponirakis D, Holt Colberg M, Torres LG. Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring. J Mammal. 2022;

3. Barlow DR, Klinck H, Ponirakis D, Branch TA, Torres LG. Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort. Ecol Evol. 2023;13:e9770.

4. Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep. 2021;11(6915):1–10.

5. Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG. Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser. 2020;642:207–25.

6. Torres LG, Barlow DR, Chandler TE, Burnett JD. Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ. 2020;8:e8906.

7. Barlow DR, Torres LG. Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management. J Appl Ecol. 2021;58(11):2493–504.

A non-invasive approach to pregnancy diagnosis in Gray whales is possible!

Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab.

In a previous post (link to blog), I discussed the crucial importance of acquiring knowledge on the reproductive parameters of individual animals in wild populations for designing effective strategies in conservation biology. Specifically, the ability to quantify the number of pregnancies within a population offers valuable insights into the health of individual females and the population as a whole [1,2]. This knowledge provides tools to describe important life-history parameters, including the age of sexual maturity, frequency of pregnancy, duration of gestation, timing of reproduction, and population fecundity; all of which are essential components for monitoring trends in reproduction and the overall health of a species [3]. Additionally, I explained some of the challenges inherent in obtaining such information when working with massive wild animals that spend most of their time underwater in vast expanses of the oceans. Yes, I am talking about whales.

As a result of the logistical and methodological challenges that involve the study of large whales, detailed knowledge of the life-history and general reproductive biology of whales is sparse for most species and populations. In fact, much of the available information is derived from whaling records [4], which may be outdated for application in population models [5].

If you are an avid reader of the GEMM Lab blog posts, you might be familiar with the gray whale (Eschrichtius robustus), and with the distinct subgroup of gray whales, known as the Pacific Coast Feeding Group (PCFG). PCFG gray whales are characterized by their shorter migration to spend their feeding season in the coastal waters of Northern California, Oregon, and southeastern Alaska [6], relative to the larger Eastern North Pacific gray whale that forage in the Arctic region.

The GEMM Lab has monitored individual gray whales within the PCFG off the Oregon coast since 2016 (check the GRANITE project). Each individual whale presents a unique pigmentation pattern, or unique marks that we can use to identify who is who among the whales who visit the Oregon coast. In this way, we keep a detailed record of re-sightings of known individuals (visit IndividuWhale to learn more), and we have high individual re-sighting rates, resulting in a long-term data series for individual whales which enables us to monitor their health, body condition, and thus further develop and advance our non-invasive study methods.

Drone-based image of a Gray whale defecating. Source: GEMM Lab, NOAA/NSF permit #16111

In our recently manuscript published in the Royal Society Open Science journal, armed with our robust dataset comprising fecal hormone metabolites, drone-based photogrammetry, and individual sightings, we delved into the strengths and weaknesses of various diagnostic tools for non-invasive pregnancy diagnosis. Ultimately, we propose a methodological approach that can help with the challenging and important task of identifying pregnancies in gray whales. In particular, we explored the variability in fecal progesterone metabolites and body morphology relative to observed reproductive status and estimated the pregnancy probability for mature females using statistical models.

In mammals, the progesterone hormone is secreted in the ovaries during the estrous cycle and gestation, making it the predominant hormone responsible for sustaining pregnancy [7]. As the hormones are cleared from the blood into the gut, they are metabolized and eventually excreted in feces; fecal samples represent a cumulative and integrated concentration of hormone metabolites [8;9], which are useful indicators for endocrine assessments of free-swimming whales. Additionally, our previous studies in this population [10] detected differences in body condition (see KC blog for more details about how we measure whales) that suggest that changes in the whale’s body widths could be useful in detecting pregnancies.

Our exploratory analyses show that in individual whales, the levels of fecal progesterone were elevated when pregnant as compared to when the same whale was not pregnant. But when looking at progesterone levels at the population level, these differences were masked with the intrinsic variability of this measurement. In turn, the body morphometrics, in particular the body width at the 50% of the total body length, helped discriminate pregnancies better, and the statistical models that included this width variable, effectively classified pregnant from non-pregnant females with a commendable accuracy. Thus, our morphometric approach showcased its potential as a reliable alternative for pregnancy diagnosis.

Below, a comparison of body widths at 5% increments along total body length (from 20 % to 70 %) in female gray whales of known reproductive status from UAS-based photogrammetry (example photograph shown at top). Pregnant females (PF; in blue), presumed nonpregnant juvenile females (JF; yellow), and lactating females (LF; orange). Fernandez Ajó et al. 2023.

Notably, when we ran the pregnancy prediction models on data from our 2022 season and compared results with observations of whales in 2023, we identified a known whale from our study area “Clouds” accompanied by a calf, indicating that she was pregnant in 2022. Our model predicted Clouds to be pregnant with a 70% probability. This validation lends strong confidence to our approach to diagnosing pregnancy. Conversely, some whales predicted to be pregnant in 2022 were not observed with a calf during the 2023 season. However, the absence of calves accompanying these females is likely due to the relatively high mortality of newborn calves in gray whales due to predation or other causes [11].

Overall, our findings underscore some limitations of fecal progesterone metabolite in accurately identifying pregnant PCFG gray whales. However, while acknowledging the challenges associated with fecal sample collection and hormone analysis, we advocate for ongoing exploration of alternative hormone quantification methods and antibodies. Our study highlights the importance of continued research in refining these techniques. The unique attributes of our study system, including high individual re-sighting rates and non-invasive fecal hormone analysis, position it as a cornerstone for future advancements in understanding gray whale reproductive health. By improving our ability to monitor reproductive metrics in baleen whale populations, we pave the way for more effective conservation strategies, ensuring the resilience of these magnificent creatures in the face of a changing marine ecosystems.

References

[1] Burgess EA, Lanyon JM, Brown JL, Blyde D, Keeley T. 2012 Diagnosing pregnancy in free-ranging dugongs using fecal progesterone metabolite concentrations and body morphometrics: A population application. Gen Comp Endocrinol 177, 82–92. (doi:10.1016/J.YGCEN.2012.02.008)

[2] Slade NA, Tuljapurkar S, Caswell H. 1998 Structured-Population Models in Marine, Terrestrial, and Freshwater Systems. J Wildl Manage 62. (doi:10.2307/3802363)

[3] Madliger CL, Love OP, Hultine KR, Cooke SJ. 2018 The conservation physiology toolbox: status and opportunities. Conserv Physiol 6, 1–16. (doi:10.1093/conphys/coy029)

[4] Rice DW, Wolman AA. 1971 Life history and ecology of the gray whale (Eschrichtius robustus). Stillwater, Oklahoma: American Society of Mammalogists.

[5] Melicai V, Atkinson S, Calambokidis J, Lang A, Scordino J, Mueter F. 2021 Application of endocrine biomarkers to update information on reproductive physiology in gray whale (Eschrichtius robustus). PLoS One 16. (doi:10.1371/journal.pone.0255368)

[6] Calambokidis J, Darling JD, Deecke V, Gearin P, Gosho M, Megill W, et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to south-eastern Alaska in 1998. J Cetacean Res Manag 2002;4:267–76.

[7] Bronson, F. H. (1989). Mammalian reproductive biology. University of Chicago Press.

[8] Wasser SK, Hunt KE, Brown JL, Cooper K, Crockett CM, Bechert U, Millspaugh JJ, Larson S, Monfort SL (2000) A generalized fecal glucocorticoid assay for use in a diverse array of nondomestic mammalian and avian species. Gen Comp Endocrinol120:260–275.

[9] Hunt, K.E., Rolland, R.M., Kraus, S.D., Wasser, S.K., 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (Eubalaena glacialis). Gen. Comp. Endocrinol. 148, 260–272. https://doi.org/10.1016/j.ygcen.2006.03.01215.

[10] Soledade Lemos L, Burnett JD, Chandler TE, Sumich JL, Torres LG. 2020 Intra‐ and inter‐annual variation in gray whale body condition on a foraging ground. Ecosphere 11. (doi:10.1002/ecs2.3094)

[11] James L. Sumich, James T. Harvey, Juvenile Mortality in Gray Whales (Eschrichtius robustus), Journal of Mammalogy, Volume 67, Issue 1, 25 February 1986, Pages 179–182, https://doi.org/10.2307/1381019

A smaller sized gray whale: recent publication finds PCFG whales are smaller than ENP whales

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab

A recent blog post by GEMM Lab’s PhD Candidate Clara Bird gave a recap of our 8^th consecutive GRANITEfield season this year. In her blog, Clara highlighted that we saw 71 individual gray whales this season, 61 of which we have seen in previous years and identified as belonging to the Pacific Coast Feeding Group (PCFG). With an estimated population size of around 212 individuals, this means that we saw almost 1/3 of the PCFG population this season alone. Since the GEMM Lab first started collecting data on PCFG gray whales in 2016, we have collected drone imagery on over 120 individuals, which is over half the PCFG population. This dataset provides incredible opportunity to get to know these individuals and observe them from year to year as they grow and mature through different life history stages, such as producing a calf. A question our research team has been interested in is what makes a PCFG whale different from an Eastern North Pacific (ENP) gray whale, which has a population size around 16,000 individuals and feed predominantly in the Arctic during the summer months? For this blog, I will highlight findings from our recent publication in Biology Letters (Bierlich et al., 2023) comparing the morphology (body length, skull, and fluke size) between PCFG and ENP populations.

Body size and shape reflect how an animal functions in their environment and can provide details on an individual’s current health, reproductive status, and energetic requirements. Understanding how animals grow is a key component for monitoring the health of populations and their vulnerability to climate change and other stressors in their environment. As such, collecting accurate morphological measurements of individuals is essential to model growth and infer their health. Collecting such morphological measurements of whales is challenging, as you cannot ask a whale to hold still while you prepare the tape measure, but as discussed in a previous blog, drones provide a non-invasive method to collect body size measurements of whales. Photogrammetry is a non-invasive technique used to obtain morphological measurements of animals from photographs. The GEMM Lab uses drone-based photogrammetry to obtain morphological measurements of PCFG gray whales, such as their body length, skull length (as snout-to-blowhole), and fluke span (see Figure 1).

Figure 1. Morphological measurements obtained via photogrammetry of a Pacific Coast Feeding Group (PCFG) gray whale. These measurements were used to compare to individuals from the Eastern North Pacific (ENP) population.

As mentioned in this previous blog, we use photo-identification to identify unique individual gray whales based on markings on their body. This method is helpful for linking all the data we are collecting (morphology, hormones, behavior, new scarring and skin conditions, etc.) to each individual whale. An individual’s sightings history can also be used to estimate their age, either as a ‘minimum age’ based on the date of first sighting or a ‘known age’ if the individual was seen as a calf. By combining the length measurements from drone-based photogrammetry and age estimates from photo-identification history, we can construct length-at-age growth models to examine how PCFG gray whales grow. While no study has previously examined length-at-age growth models specifically for PCFG gray whales, another study constructed growth curves for ENP gray whales using body length and age estimates obtained from whaling, strandings, and aerial photogrammetry (Agbayani et al., 2020). For our study, we utilized these datasets and compared length-at-age growth, snout-to-blowhole length, and fluke span between PCFG and ENP whales. We used Bayesian statistics to account and incorporate the various levels of uncertainty associated with data collected (i.e., measurements from whaling vs. drone, ‘minimum age’ vs. ‘known age’).

We found that while both populations grow at similar rates, PCFG gray whales reach smaller adult lengths than ENP. This difference was more extreme for females, where PCFG females were ~1 m (~3 ft) shorter than ENP females and PCFG males were ~0.5 m (1.5 ft) shorter than ENP males (Figure 2, Figure 3). We also found that ENP males and females have slightly larger skulls and flukes than PCFG male and females, respectively. Our results suggest PCFG whales are shaped differently than ENP whales (Figure 3)! These results are also interesting in light of our previous published study that found PCFG whales are skinnier than ENP whales (see this previous blog post).

Figure 2. Growth curves (von Bertalanffy–Putter) for length-at-age comparing male and female ENP and PCFG gray whales (shading represents 95% highest posterior density intervals). Points represent mean length and median age. Vertical bars represent photogrammetric uncertainty. Dashed horizontal lines represent uncertainty in age estimates.

Figure 3. Schematic highlighting the differences in body size between Pacific Coast Feeding Group (PCFG) and Eastern North Pacific (ENP) gray whales.

Our results raise some interesting questions regarding why PCFG are smaller: Is this difference in size and shape normal for this population and are they healthy? Or is this difference a sign that they are stressed, unhealthy and/or not getting enough to eat? Larger individuals are typically found at higher latitudes (this pattern is called Bergmann’s Rule), which could explain why ENP whales are larger since they feed in the Arctic. Yet many species, including fish, birds, reptiles, and mammals, have experienced reductions in body size due to changes in habitat and anthropogenic stressors (Gardner et al., 2011). The PCFG range is within closer proximity to major population centers compared to the ENP foraging grounds in the Arctic, which could plausibly cause increased stress levels, leading to decreased growth.

The smaller morphology of PCFG may also be related to the different foraging tactics they employ on different prey and habitat types than ENP whales. Animal morphology is linked to behavior and habitat (see this blogpost). ENP whales feeding in the Arctic generally forage on benthic amphipods, while PCFG whales switch between benthic, epibenthic and planktonic prey, but mostly target epibenthic mysids. Within the PCFG range, gray whales often forage in rocky kelp beds close to shore in shallow water depths (approx. 10 m) that are on average four times shallower than whales feeding in the Arctic. The prey in the PCFG range is also found to be of equal or higher caloric value than prey in the Arctic range (see this blog), which is interesting since PCFG were found to be skinnier.

It is also unclear when the PCFG formed? ENP and PCFG whales are genetically similar, but photo-identification history reveals that calves born into the PCFG usually return to forage in this PCFG range, suggesting matrilineal site fidelity that contributes to the population structure. PCFG whales were first documented off our Oregon Coast in the 1970s (Figure 4). Though, from examining old whaling records, there may have been PCFG gray whales foraging off the coasts of Northern California to British Columbia since the 1920s.

Figure 4. First reports of summer-resident gray whales along the Oregon coast, likely part of the Pacific Coast Feeding Group. Capital Journal, August 9, 1976, pg. 2.

Altogether, our finding led us to two hypotheses: 1) the PCFG range provides an ecological opportunity for smaller whales to feed on a different prey type in a shallow environment, or 2) the PCFG range is an ecological trap, where individuals gain less energy due to energetically costly feeding behaviors in complex habitat while potentially targeting lower density prey, causing them to be skinnier and have decreased growth. Key questions remain for our research team regarding potential consequences of the smaller sized PCFG whales, such as does the smaller body size equate to reduced resilience to environmental and anthropogenic stressors? Does smaller size effect fecundity and population fitness? Stay tuned as we learn more about this unique and fascinating smaller sized gray whale.

References

Agbayani, S., Fortune, S. M. E., & Trites, A. W. (2020). Growth and development of North Pacific gray whales (Eschrichtius robustus). Journal of Mammalogy, 101(3), 742–754. https://doi.org/10.1093/jmammal/gyaa028

Bierlich, K. C., Kane, A., Hildebrand, L., Bird, C. N., Fernandez Ajo, A., Stewart, J. D., Hewitt, J., Hildebrand, I., Sumich, J., & Torres, L. G. (2023). Downsized: gray whales using an alternative foraging ground have smaller morphology. Biology Letters, 19(8). https://doi.org/10.1098/rsbl.2023.0043

Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L., & Heinsohn, R. (2011). Declining body size: A third universal response to warming? Trends in Ecology and Evolution, 26(6), 285–291. https://doi.org/10.1016/j.tree.2011.03.005

Intermittent upwelling impacts zooplankton and their gray whale predators

Allison Dawn, MSc, GEMM Lab graduate, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

The second year of my master’s flew by. Gone were the days of feeling new to graduate school. While I was feeling more comfortable navigating courses, balancing time at both Corvallis and HMSC campuses, and leading recruitment and logistics for the TOPAZ/JASPER field seasons, I certainly felt intimidated by my long, yet exciting, list of research goals I planned to accomplish in order to graduate in Summer 2023. Now, I am proud to say we have come a long way from my (overly ambitious) research proposal and simple Pearson correlations.

At this time last year, I had narrowed down a few key environmental factors to assess relationships between zooplankton in reef systems where PCFG gray whales feed and environmental variability. Even still, I was feeling frustrated at my preliminary analysis results that suggested upwelling had little to no impact on zooplankton abundance or whale foraging effort. This result was dissatisfying given what we know about the upwelling and the California Current System (CCS), so I wondered if my analysis approach, upwelling metrics, or both, were limited. However, thanks to the dedicated mentorship of Leigh, and an informal chat on the water with Aaron Galloway while dive tending for CamDO deployments, I was encouraged to dig even deeper into the literature (and subsequent debates) on the role of upwelling in the nearshore. After several inspiring meetings with the lab about our latest literature deep-dive, I reconfigured my initial hypotheses and charged ahead into the next phases of analysis with a different metric of upwelling than I had calculated before, which I will describe further below. While my final chapter includes a total of seven environmental factors that capture both broad- and fine-temporal temporal scales, for the purposes of this blog I will just share the result of the broad-scale impact of intermittent upwelling on both zooplankton abundance and gray whale foraging effort.

First, a brief recap on upwelling — during the spring and summer in the CCS, strong northerly winds push surface waters offshore, bringing cold, nutrient rich waters from the deep; which creates coastal upwelling. However, upwelling is not persistent. There are periods of time when these northerly winds relax, reducing surface water advection, and upwelling stalls. This relaxation period allows for nearshore retention of primary productivity, which permeates trophic levels with important nutrients. The alternation between upwelling and relaxation is called “intermittent upwelling”, and researchers are finding that the occurrence of relaxation periods are just as important as upwelling itself. Both support biophysical mechanisms that deliver and retain nutrients in the system.

For an example of intermittent upwelling in the CCS, Figure 1 shows a northerly wind stress plot taken from a coastal buoy near our Port Orford study area during 2016. On the y-axis we have northerly wind stress, where positive values show less strong northerly winds, indicating downwelling favorable conditions, and negative values represent strong northerly winds, indicating upwelling favorable conditions. The x-axis is months over time. Here, you can see how in the winter downwelling prevails, but in the summer time we mainly have upwelling favorable winds. However, these summer periods are punctuated by positive values of wind stress, demonstrating that alternations between upwelling and relaxation occur several times throughout the spring and summer period.

Figure 1: Example plot of northerly wind stress plot taken from NOAA Buoy 4601 in 2016 (near our Port Orford, Oregon study area).

The role of upwelling intermittency has been explored in previous work and was posited as the Intermittent Upwelling Hypothesis (IUH) by Menge and Menge 2013. Figure 2, left, demonstrates this hypothesis in theoretical plots. In panel A we see that the rates of ecological processes such as primary productivity and prey response are maximal in at middle values of persistent upwelling and downwelling. In panel B. we see ecological processes positively increase with an index of upwelling intermittency. In Figure 2, right, the authors tested this hypothesis on chlorophyll-a and barnacle and mussel larval recruitment across several study sites and found the results did closely match theory.

Figure 2: Left, Intermittent Upwelling Hypothesis (IUH) theoretical plots showing predicted unimodal relationship between nutrient availability and prey response along a gradient between persistent upwelling and persistent downwelling (panel A) and the expected linear relationship between nutrient availability and upwelling intermittency (panel B); Right, Chlorophyll-a, barnacle, and mussel recruitment responses to an upwelling and intermittency index. Menge and Menge 2013.

Nearshore systems in the CCS, like the ones described in this Menge and Menge 2013 paper, are vastly understudied. And while there is a growing body of literature investigating the role of intermittent upwelling on various prey metrics (Mace & Morgan, 2006; Roegner et al 2007; Benoit-Bird et al., 2019) as well as cetacean movement (Ryan et al., 2022), to our knowledge no study has yet assessed the role of intermittent upwelling on nearshore prey availability and marine mammal occurrence.

To investigate the role of intermittent upwelling, we used the coastal upwelling transport index (CUTI) as our proxy for upwelling. Using daily CUTI values we generated a cumulative upwelling index and number of relaxation events for each year of the study (Figure 3.). This cumulative upwelling information was used to define the day of spring transition (ST) and end of the upwelling season for each year (2016-2021), following the upwelling phenological definitions from Bograd et al. 2009.

Figure 3: Summed running mean of Cumulative Upwelling Transport Index (CUTI) at latitude 42°N across years 2016-2021, initial data source https://oceanview.pfeg.noaa.gov/products/upwelling/cutibeuti

Using of five-year dataset we investigated functional relationships between each environmental variable and either zooplankton abundance or whale foraging effort using Boosted Regression Tree analysis (Elith et al., 2008). Model results demonstrate that for both zooplankton and whales, species occurrence is high at the intersection between moderate values of accumulated upwelling and with an increasing number of relaxation events. Overall, this work identifies intermittent upwelling as a primary driver of zooplankton abundance and gray whale foraging effort in a nearshore region of Oregon.

Winds in the California Current System are projected to get stronger with climate change, and if upwelling-favorable winds increase in duration and intensity, this could potentially threaten this balance between relaxation and upwelling. While these changes may mean greater primary productivity on some scales, how exactly this increase might affect the very nearshore regions and intermittent upwelling is unknown. Thus, research should continue long-term monitoring of nearshore areas to assist with adaptive management solutions in the face of environmental change.

Preparing this manuscript for my first-first author publication has been another new and exciting process. I feel so grateful for my time as a Master’s student in the GEMM Lab, and for the support of my lab mates, the HMSC community, family, and friends who cheered me on each step of the way to the finish line.

Figure 4: Toasting to a successful master’s defense seminar with GEMM Lab mates, friends and family.

References

Benoit‐Bird, K. J., Waluk, C. M., & Ryan, J. P. (2019). Forage species swarm in response to coastal upwelling. Geophysical Research Letters, 46(3), 1537-1546.

Bograd, S. J., Schroeder, I., Sarkar, N., Qiu, X., Sydeman, W. J., & Schwing, F. B. (2009). Phenology of coastal upwelling in the California Current. Geophysical Research Letters, 36(1).

Curtis Roegner, G., Armstrong, D. A., Hickey, B. M., & Shanks, A. L. (2003). Ocean distribution of Dungeness crab megalopae and recruitment patterns to estuaries in southern Washington State. Estuaries, 26, 1058-1070.

Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of animal ecology, 77(4), 802-813.

Mace, A. J., & Morgan, S. G. (2006). Biological and physical coupling in the lee of a small headland: contrasting transport mechanisms for crab larvae in an upwelling region. Marine Ecology Progress Series, 324, 185-196.

Menge, B. A., & Menge, D. N. (2013). Dynamics of coastal meta‐ecosystems: the intermittent upwelling hypothesis and a test in rocky intertidal regions. Ecological Monographs, 83(3), 283-310.

Oestreich, W. K., Abrahms, B., McKenna, M. F., Goldbogen, J. A., Crowder, L. B., & Ryan, J. P. (2022). Acoustic signature reveals blue whales tune life‐history transitions to oceanographic conditions. Functional Ecology, 36(4), 882-895.

Roegner, G. C., Armstrong, D. A., & Shanks, A. L. (2007). Wind and tidal influences on larval crab recruitment to an Oregon estuary. Marine Ecology Progress Series, 351, 177-188.

Ryan, J. P., Benoit‐Bird, K. J., Oestreich, W. K., Leary, P., Smith, K. B., Waluk, C. M., … & Goldbogen, J. A. (2022). Oceanic giants dance to atmospheric rhythms: Ephemeral wind‐driven resource tracking by blue whales. Ecology Letters, 25(11), 2435-2447.

The whales keep coming and we keep learning: a wrap up of the eighth GRANITE field season.

Clara Bird, PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

As you may remember, last year’s field season was a remarkable summer for our team. We were pleasantly surprised to find an increased number of whales in our study area compared to previous years and were even more excited that many of them were old friends. As we started this field season, we were all curious to know if this year would be a repeat. And it’s my pleasure to report that this season was even better!

We started the season with an exciting day (6 known whales! see Lisa’s blog) and the excitement (and whales) just kept coming. This season we saw 71 individual whales across 215 sightings! Of those 71, 44 were whales we saw last year, and 10 were new to our catalog, meaning that we saw 17 whales this season that we had not seen in at least two years! There is something extra special about seeing a whale we have not seen in a while because it means that they are still alive, and the sighting gives us valuable data to continue studying health and survival. Another cool note is that 7 of our 12 new whales from last year came back this year, indicating recruitment to our study region.

Included in that group of 7 whales are the two calves from last year! Again, indicating good recruitment of new whales to our study area. We saw both Lunita and Manta (previously nick-named ‘Roly-poly’) throughout this season and we were always happy to see them back in our area and feeding on their own.

We had an especially remarkable encounter with Lunita at the end of this season when we found this whale surface feeding on porcelain crab larvae (video 1)! This is a behavior that we rarely observe, and we’ve never seen a juvenile whale use this behavior before, inspiring questions around how Lunita knew how to perform this behavior.

Not only did we resight our one-year-old friends, but we found two new calves born to well-known mature females (Clouds and Spotlight). We had previously documented Clouds with a calf (Cheetah) in 2016 so it was exciting to see her with a new calf and to meet Cheetah’s sibling! Cheetah has become one of our regulars so we’re curious to see if this new calf joins the regular crew as well. We’re also hoping that Spotlight’s calf will stick around; and we’re optimistic since we observed it feeding alone later in the season.

Collage of new calves from 2023! Left: Clouds and her calf, Center: Spotlight and her calf, Right: Spotlight’s calf independently foraging

Of course, 71 whales means heaps of data! We spent 226 hours on the water, conducted 132 drone flights (a record!), and collected 61 fecal samples! Those 132 flights were over 64 individual whales, with Casper and Pacman tying for “best whale to fly over” with 10 flights each. We collected 61 fecal samples from 26 individual whales with a three-way tie for “best pooper” between Hummingbird, Scarlett, and Zorro with 6 fecal samples each. And we continued to collect valuable prey and habitat data through 80 GoPro drops and 79 zooplankton net tows.

Above: (left) drone image of the team on our boat next to Pacman, (right) KC piloting the drone. Below: the team celebrating good fecal samples!

And if you were about to ask, “but what about tagging?!”, fear not! We continued our suction cup tagging effort with a successful window in July where we were joined by collaborators John Calambokidis from Cascadia Research Collective and Dave Cade from Hopkins Marine Station and deployed four suction-cup tags.

The team during our tagging effort this season.

It’s hard to believe all the work we’ve accomplished in the past five months, and I continue to be honored and proud to be on this incredible team. But as this season has come to a close, I have found myself reflecting on something else. Learning. Over the past several years we have learned so much about not only these whales in our study system but about how to conduct field work. And while learning is continuous, this season in particular has felt like an exciting time for both. In the past year our group has published work showing that we can detect pregnancy in gray whales using fecal samples and drone imagery (Fernandez Ajó et al., 2023), that PCFG gray whales are shorter and smaller than ENP whales (Bierlich et al., 2023), and that gray whales are consuming high levels of microplastics (Torres et al., 2023). We also have several manuscripts in review focused on our behavior work from drones and tags. While this information does not directly affect our field work, it does mean that while we’re observing these whales live, we better understand what we’re observing and we can come up with more specific, in-depth questions based on this foundation of knowledge that we’re building. I have enjoyed seeing our questions evolve each year based on our increasing knowledge and I know that our collaborative, inquisitive chats on the boat will only continue inspiring more exciting research.

On top of our gray whale knowledge, we have also learned so much about field work. When I think back to the early days compared to now, there is a stark difference in our knowledge and our confidence. We do a lot on our little boat! And so many steps that we once relied on written lists to remember to do are now just engrained in our minds and bodies. From loading the boat, to setting up at the dock, to the go pro drops, fecal collections, drone operations, photo taking, and photo ID, our team has become quite the well-oiled machine. We were also given the opportunity to reflect on everything we’ve learned over the past years when it was our turn to train our new team member, Nat! Nat is a new PhD student in the GEMM lab who is joining team GRANITE. Teaching her all the ins and outs of our fieldwork really emphasized how much we ourselves have learned.

On a personal note, this was my third season as a drone pilot, and honestly, I was pleasantly surprised by my experience this season. Since I started piloting, I have experienced pretty intense nerves every time I’ve flown the drone. From stress dreams, to mild nausea, and an elevated heart rate, flying the drone was something that I didn’t necessarily look forward to. Don’t get me wrong – it’s incredibly valuable data and a privilege to watch the whales from a bird’s eye view in real time. But the responsibility of collecting good data, while keeping the drone and my team members safe was something that I felt viscerally. And while I gained confidence with every flight, the nerves were still as present as ever and I was starting to accept that I would never be totally comfortable as a pilot. Until this season, when the nerves finally cleared, and piloting became as innate as all the other field work components. While there are still some stressful moments, the nerves don’t come roaring back. I have finally gone through enough stressful situations to not be fazed by new ones. And while I am fully aware that this is just how learning works, I write this reflection as a reminder to myself and anyone going through the process of learning any new skill to push through that fear. Remember there can be a disconnect between the time when you know how to do something well, or well-enough, and the time when you feel comfortable doing it. I am just as proud of myself for persevering as I am of the team for collecting so much incredible data. And as I look ahead to my next scary challenge (finishing my PhD!), this is a feeling that I am trying to hold on to.

Stay tuned for updates from team GRANITE!

References

Bierlich, K. C., Kane, A., Hildebrand, L., Bird, C. N., Fernandez Ajo, A., Stewart, J. D., Hewitt, J., Hildebrand, I., Sumich, J., & Torres, L. G. (2023). Downsized: Gray whales using an alternative foraging ground have smaller morphology. Biology Letters, 19(8), 20230043. https://doi.org/10.1098/rsbl.2023.0043

Fernandez Ajó, A., Pirotta, E., Bierlich, K. C., Hildebrand, L., Bird, C. N., Hunt, K. E., Buck, C. L., New, L., Dillon, D., & Torres, L. G. (2023). Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science, 10(7), 230452. https://doi.org/10.1098/rsos.230452

Torres, L. G., Brander, S. M., Parker, J. I., Bloom, E. M., Norman, R., Van Brocklin, J. E., Lasdin, K. S., & Hildebrand, L. (2023). Zoop to poop: Assessment of microparticle loads in gray whale zooplankton prey and fecal matter reveal high daily consumption rates. Frontiers in Marine Science, 10. https://www.frontiersin.org/articles/10.3389/fmars.2023.1201078

Cruising through space and time – a GEMM Lab’s journey in the Northern California Current

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Last month I had the privilege to participate in the 2023 September Northern California Current (NCC) cruise onboard the NOAA RV Bell M. Shimada. These cruises are part of a long-term NOAA/NWFSC effort to study the NCC ecosystem and they have been taking place every February, May, and September since 2002. Thanks to a collaboration with NOAA (and more specifically the NCC cruise chief scientist Jennifer Fisher), the GEMM Lab has been able to put marine mammal observers on these cruises since 2018.

As a postdoc working on the OPAL project, I have been the main person in charge of processing and analyzing the cetacean data collected across the 10 (now 11!) cruises that the GEMM lab participated in. These data have played a paramount role in improving our understanding of rorqual whale (e.g., blue, humpback, fin) distribution and habitat use off the coast of Oregon (Derville et al., 2022) and assessing the resulting risk of entanglement in fishing gear that they face while migrating and feeding in our waters (Derville et al., 2023). But while I have been very involved in the data analysis side of things, up to now I had never been able to contribute to data collection for this project. First, I was working remotely at the height of the COVID pandemic and second, because the NCC cruises are onboard a NOAA vessel, they have strict limitations on non-US citizens participation. So, you can imagine how excited I was (as a French citizen) to finally set foot on the famous Bell M. Shimada that I had heard so many stories about!

The NCC cruises illustrate how valuable long-term ecosystem monitoring is. Station after station, miles surveyed after miles surveyed, little by little, we learn about the complex ecological relationships and changing patterns that shape life in the ocean. The data, the experience, and the memories accumulated over the years are a true legacy that I have felt very proud to be part of. Finally, being on this ship, I felt like I was walking in the path of so many of my friends who had held those same binoculars before. Florence Sullivan, who pioneered the GEMM Lab’s NCC cruise observer effort in the harsh winter weather of February 2018. Alexa Kownacki (May 2018, May 2019), whose detailed field notes I read years later with emotion and appreciation as they helped me figure out how the Seebird software used to collect data back then (and abandoned since!). Dawn Barlow (Sep 2018, Sep 2019, Sep 2020, May 2021, May 2022), our master observer who is said to be able to detect a whale’s blow 10 miles away in a 10-foot swell and Beaufort sea state 6, all while sipping an Affogato coffee. Clara Bird (Sep 2020, May 2022), who abandoned her beloved nearshore gray whales (twice!) to sail all the way to the NH-200 station (200 nautical miles from land!). Rachel Kaplan (May 2021, May 2022, Sep 2022), our jack of all trades who concurrently studies krill and whales, and by doing so probably broke the record of numbers of times running up and down between the flying bridge and the echosounder screen room down below. Renee Albertson (Sep 2022), a Marine Mammal Institute research associate who shared observations with Rachel until the cruise was cut short by an engine issue that led them to the docks of Seattle. And finally, Craig Hayslip (May 2023), who swapped his usual observer work on the United States Coast Guard’s helicopters as part of the OPAL project for two weeks onboard the Shimada.

What a team!

From left to right and top to bottom: Florence; Alexa; Dawn; Clara; Renee, Rachel and the rest of the science team including Jennifer Fisher and Anna Bolm; Craig; Rachel; and I!

The 11^th NCC cruise with GEMM Lab observers onboard was equal to its predecessors as it provided a perfect combination of camaraderie, natural beauty, Pacific Northwest weather, and unexpected change of plans. After being delayed by one day, we discovered that a big storm system was coming upon us and would have us retreat to Yaquina Bay in Newport for 4 days! Overall, I spent 5 days surveying for marine mammals from the flying bridge, in conditions that went from a beautiful sunny Friday on September 22^nd to an impressive Beaufort sea state 7 on the 29^th. This experience was the king of weather in which it became particularly cool to be on a ship as big as the Shimada (63 m, 208 feet long!) that can withstand swell and wind better than any ship I had worked on before.

Overall, I observed 36 groups of cetaceans, including seven different species of dolphins and whales: one sperm whale, a possible Sei whale, several fin whales, blue whales, humpback whales, and pods of Pacific white-sided dolphins, Dall’s porpoises and common dolphins. Among the highlights of this cruise was the observation of several blue whales and humpback whales that seemed to be feeding on the western slope of the Heceta bank. My personal favorite memory was also to observe common dolphins -a species that despite its name is not that common (at least not in the nearshore environment) and that I had never seen before in my life! How magnificent and graceful they were… and how lucky was I to be part of this voyage.

From left to right, top to bottom: a CTD deployment from the Bell M. Shimada; a whale’s dinner? Krill collected with a bongo net during a previous cruise; a very distant yet unmistakable sperm whale dorsal knob; a group of common dolphins; a marine mammal observer’s work tools; a blue whale surfacing at dusk.

More NCC cruise stories…

Surprises at Sea

Yonder Whales and Nearby Prey: A New Look at a Familiar System

Roger that, we are currently enamored

Marine mammals of the Northern California Current, 2020 edition

Surveying for marine mammals in the Northern California Current

Marine Mammal Observing: Standardization is key

Albatrosses at sunrise, dolphins at sunset: Northern California Current cruise

The Recipe for a “Perfect” Marine Mammal and Seabird Cruise

Living the Dream – life as a marine mammal observer

References

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, 868566. https://doi.org/10.3389/fmars.2022.868566

Derville, S., Buell, T. V, Corbett, K. C., Hayslip, C., & Torres, L. G. (2023). Exposure of whales to entanglement risk in Dungeness crab fishing gear in Oregon, USA, reveals distinctive spatio-temporal and climatic patterns. Biological Conservation, 109989. https://doi.org/10.1016/j.biocon.2023.109989

Zoop to poop: Recent GEMM Lab publication reveals high microparticle ingestion by zooplankton and gray whales

By Lisa Hildebrand, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Baleen whales face a multitude of threats on a daily basis. The exposure to some of these threats can be assessed visually. For example, the presence of propeller scars on a whale are indicative that the individual was struck by a boat. However, there are some threats that are not easily detected from visual assessments. One of these threats is the ingestion of microparticles (MPs), which include microplastics and other anthropogenic debris. While MP research has entered its second decade and documentation of MPs in the marine environment is common, we still lack empirical information on the rates of MP ingestion by baleen whales and their prey. Hence, one of the objectives of the Coastal Oregon Zooplankton Investigation (COZI; read more about it in a previous blog), which GEMM Lab PI Leigh Torres led, was to determine to what extent Pacific Coast Feeding Group (PCFG) gray whales and their nearshore zooplankton prey are impacted by MPs. The results of this work were recently published in the journal Frontiers in Marine Science and I am going to summarize them for you here today.

A number of studies have documented MP ingestion in baleen whales, including in humpback (Besseling et al., 2015), fin (Fossi et al., 2012, 2014, 2016, 2017), Bryde’s, and sei whales (Zantis et al., 2022). The effects of ingesting MPs on baleen whales are theorized to include blockage of internal organs, mechanical damage of the digestive tract, false feeling of satiation (full from eating), and potentially leaching of toxicants depending on the length of the digestive period (Donohue et al., 2019; Hudak & Sette 2019; Zhu et al., 2019; Novillo et al., 2020). Despite the fact that MPs have been documented in a number of baleen whale species, there is still a lack of knowledge regarding MP ingestion rates by baleen whales from empirical data, although modeled estimates have been derived for a few species (Zantis et al., 2022; Kahane-Rapport et al., 2022). Basically, we know whales eat MPs because it has been detected in their stomachs, but we do not know how much MPs they consume. The COZI team therefore aimed to quantify baleen whale MP consumption rates from empirically counted MP loads in zooplankton prey and to look at MP exposure of baleen whales from “zoop to poop” (Figure 1).

**Figure 1** Schematic depicting our “zoop to poop” approach. Taken from Torres et al., 2023.

In order to accomplish this aim, we used “zoop” and “poop” samples collected between 2017 to 2019 during the GEMM Lab’s long-term GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) project. We analyzed MP loads in three prey zooplankton species found in nearshore Oregon waters (the amphipod Atylus tridens and the mysid shrimp Holmesimysis sculpta and Neomysis rayii), all of which are known PCFG gray whale prey (Hildebrand et al., 2021), as well as five fecal samples collected from four unique individual gray whales. While the field collection of these samples was led by the GEMM Lab, the processing and MP analysis was led by Dr. Susanne Brander and conducted by a number of undergraduate student workers. MP analysis is no easy feat as it involves many, many meticulous and time-intensive steps in order to get from a sample of gray whale prey or poop to a known number of MPs that the sample contained. The process involves (1) sorting and identifying the prey into the different species; (2) rinsing the individuals to ensure no external MPs are counted; (3) digesting the sample in potassium hydroxide (KOH) for 24-72 hours; (4) sieving and filtering the digested samples; (5) picking out suspected MPs from the filters and measuring them; (6) analyzing the suspected MPs to confirm chemical composition. On top of all of these steps, anyone working with the samples has to try and minimize potential MP contamination, which is not easy since MPs are practically everywhere, such as synthetic fibers from our clothes or microplastics that are floating around in the air.

**Figure 2** Microparticle (MP) loads and morphotypes by zooplankton species. **(A)** the number of MPs per 1 gram per species, with the dotted line representing the average MP level in controls. **(B)** the proportion of MP morphotypes found in each zooplankton species. **(C)** the proportion of Fourier transform infrared (FTIR) spectroscopy categories of MPs found in each zooplankton species. The sample size for each sample is denoted above all columns. *Taken from* *Torres et al., 2023.*

After many long years of lab work (COVID lab restrictions included), we are excited (and a little daunted) to share the results of this collaborative project with you. We detected MPs in all 26 zooplankton prey samples that we analyzed and found that the number of MPs in the three species were pretty similar, with an average of 4 MPs per gram of zooplankton (Figure 2). Over 50% of the 418 suspected MPs that we identified in the zooplankton samples were fibers. We also detected MPs in all five gray whale fecal samples that we analyzed. While we also detected fibers among the 37 suspected MPs pulled from the fecal samples, we found a higher proportion of larger MPs such as fragments and pellets in the “poop” samples, than we did in the “zoop” samples (Figure 3). We also tested some seawater samples as controls to see how the composition of MPs in seawater compared to that of zooplankton and gray whale feces. We found that seawater was dominated by fibers, similar to the zooplankton prey. This finding suggests that the larger MPs (e.g., fragments, pellets) that we found in gray whale feces must be coming from somewhere other than their prey and the ambient seawater. This led us to hypothesize that gray whales are likely exposed to MPs through two pathways, via (1) trophic transfer from their zooplankton prey and (2) indiscriminate consumption of ambient MPs in the benthos while foraging benthically (Figure 1).

**Figure 3** Microparticle (MP) loads and morphotypes found in each of the five gray whale fecal samples analyzed. **(A)**the number of MPs per gram of fecal sample, with the dotted line representing the average MP level in controls. **(B)** the proportion of MP morphotypes found in each fecal sample. **(C)** the proportion of Fourier transform infrared (FTIR) spectroscopy categories of MPs found in each fecal sample. The sample size for each sample is denoted above all columns. Taken from Torres et al., 2023.

Next we wanted to estimate the daily ingestion rates of MPs by gray whales. For this estimation, we used our known values of zooplankton MP ingestion (from our analyzed samples) and extrapolated them using daily energetic needs of gray whales (i.e., how many calories does the whale need each day). The only published values of daily gray whale caloric needs are for pregnant and lactating females (Villegas-Amtmann et al., 2015, 2017), which is why we were only able to estimate daily MP ingestion rates for these two demographic groups. The numbers we calculated were rather staggering (and led us to double-, triple-, and quadruple-check our math) as we estimate that if a pregnant gray whale only ate the mysid N. rayii in a day, she would consume 9.55 million MP per day. We made these estimates for all three prey species that we analyzed as well as a “composite preyscape” (an average of the three prey species) and you can see all of those results in Table 1.

**Table 1** Estimates of the number of microparticles (MPs) that a pregnant and lactating female gray whale consumes per day generated through extrapolation of results from this study (Microparticles per individual zooplankton; first row) to their daily energetic needs by zooplankton prey species from Hildebrand et al., 2021. Taken from Torres et al., 2023.

These results are frightening. They still are to me even though I have spent months with this knowledge after having done a lot of the data analysis for this project. I think it is particularly frightening to think about the fact that MPs are not the only anthropogenic threat that gray whales (and really any organism in the ocean) are exposed to. The good news is that you can do something to help reduce this threat in the oceans. Below are just a few suggestions of what you can do to reduce MP pollution to the environment:

A major source of pollution in the ocean comes from microfibers through our laundry (as you saw in our results). You can help stop this pathway by simply using a Cora Ball or installing a filter (such as this one) in your washing machine that captures microfleece & polyester fibers.
Minimize your use of single-use plastics. There are so many ways to do so including reuseable water bottles, travel mugs for coffee or tea, fabric totes as shopping bags, carry a set of utensils for takeout food, beeswax wraps instead of plastic wrap or sandwich bags.
Use public transport when possible as another huge source of microplastics comes from tire treads! This solution also helps reduce your carbon footprint.

References

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Donohue M. J., Masura J., Gelatt T., Ream R., Baker J. D., Faulhaber K., et al. (2019). Evaluating exposure of northern fur seals, callorhinus ursinus, to microplastic pollution through fecal analysis. Mar. pollut. Bull. 138, 213–221. doi: 10.1016/j.marpolbul.2018.11.036

Fossi M. C., Panti C., Guerranti C., Coppola D., Giannetti M., Marsili L., et al. (2012). Are baleen whales exposed to the threat of microplastics? a case study of the Mediterranean fin whale (Balaenoptera physalus). Mar. pollut. Bull. 64, 2374–2379. doi: 10.1016/j.marpolbul.2012.08.013

Fossi M. C., Coppola D., Baini M., Giannetti M., Guerranti C., Marsili L., et al. (2014). Large Filter feeding marine organisms as indicators of microplastic in the pelagic environment: the case studies of the Mediterranean basking shark (Cetorhinus maximus) and fin whale (Balaenoptera physalus). Mar. Environ. Res. 100, 17–24. doi: 10.1016/j.marenvres.2014.02.002

Fossi M. C., Marsili L., Baini M., Giannetti M., Coppola D., Guerranti C., et al. (2016). Fin whales and microplastics: the Mediterranean Sea and the Sea of cortez scenarios. Environ. pollut. 209, 68–78. doi: 10.1016/j.envpol.2015.11.022

Fossi M. C., Romeo T., Baini M., Panti C., Marsili L., Campani T., et al. (2017). Plastic debris occurrence, convergence areas and fin whales feeding ground in the Mediterranean marine protected area pelagos sanctuary: a modeling approach. Front. Mar. Sci. 4. doi: 10.3389/fmars.2017.00167

Hildebrand L., Bernard K. S., Torres L. G. (2021). Do gray whales count calories? comparing energetic values of gray whale prey across two different feeding grounds in the eastern north pacific. Front. Mar. Sci. 8. doi: 10.3389/fmars.2021.683634

Hudak C. A., Sette L. (2019). Opportunistic detection of anthropogenic micro debris in harbor seal (Phoca vitulina vitulina) and gray seal (Halichoerus grypus atlantica) fecal samples from haul-outs in southeastern Massachusetts, USA. Mar. pollut. Bull. 145, 390–395. doi: 10.1016/j.marpolbul.2019.06.020

Kahane-Rapport S. R., Czapanskiy M. F., Fahlbusch J. A., Friednlaender A. S., Calambokidis J., Hazen E. L., et al. (2022). Field measurements reveal exposure risk to microplastic ingestion by filter-feeding megafauna. Nat. Commun. 13, 6327. doi: 10.1038/s41467-022-33334-5

Novillo O., Raga J. A., Tomás J. (2020). Evaluating the presence of microplastics in striped dolphins (Stenella coeruleoalba) stranded in the Western Mediterranean Sea. Mar. pollut. Bull. 160, 111557. doi: 10.1016/j.marpolbul.2020.111557

Torres, L. G., Brander, S. M., Parker, J. I., Bloom, E. M., Norman, R., Van Brocklin, J. E., Lasdin, K.S., Hildebrand, L. (2023) Zoop to poop: assessment of microparticle loads in gray whale zooplankton prey and fecal matter reveal high daily consumption rates. Front. Mar. Sci. https://doi.org/10.3389/fmars.2023.1201078

Villegas-Amtmann S., Schwarz L. K., Sumich J. L., Costa D. P. (2015). A bioenergetics model to evaluate demographic consequences of disturbance in marine mammals applied to gray whales. Ecosphere 6, 1–19. doi: 10.1890/ES15-00146.1

Villegas-Amtmann S., Schwarz L. K., Gailey G., Sychenko O., Costa D. P. (2017). East Or west: the energetic cost of being a gray whale and the consequence of losing energy to disturbance. Endangered Species Res.34, 167–183. doi: 10.3354/esr00843

Zantis L. J., Bosker T., Lawler F., Nelms S. E., O’Rorke R., Constantine R., et al. (2022). Assessing microplastic exposure of large marine filter-feeders. Sci. Total Environ. 818, 151815. doi: 10.1016/j.scitotenv.2021.151815

Zhu J., Yu X., Zhang Q., Li Y., Tan S., Li D., et al. (2019). Cetaceans and microplastics: first report of microplastic ingestion by a coastal delphinid, Sousa chinensis. Sci. Total Environ. 659, 649–654. doi: 10.1016/j.scitotenv.2018.12.389

Krill Intentions: Bringing Lessons Home from a Winter of Fieldwork

By Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Hello from Palmer Station, Antarctica! I’ve spent the last five months here in a kind of parallel universe to that of my normal life in Oregon. It’s spring here at the Western Antarctic Peninsula (WAP), and since May I’ve been part of a team studying Antarctic krill (Euphausia superba) – a big change from the Oregon species I typically study, and one that has already taught me so much.

I am here as part of a project titled “The Omnivore’s Dilemma: The effect of autumn diet on winter physiology and condition of juvenile Antarctic krill”. Through at-sea fieldwork and experiments in the lab, we have spent this field season investigating how climate-driven changes in diet impact juvenile and adult krill health during the long polar night. Winter is a crucial time for krill survival and recruitment, and an understudied season in this remote corner of the world.

*Figure 1.* Recently collected Antarctic krill *(Euphausia superba)* await identification and measuring.

Within this field season, we have been part of two great research cruises along the WAP, and spent the rest of the time at Palmer Station, running long-term experiments to learn how diet influences krill winter growth and development. The time has passed incredibly fast, and it’s hard to believe that we’ll be heading home in just a couple weeks.

There have been so many wonderful parts to our time here. While at sea, I was constantly aware that each new bay and fjord we sampled was one of the most beautiful places I would ever have the privilege to visit. I was also surprised and thrilled by the number of whales we saw – I recorded over one hundred sightings, including humpbacks, minke, and killer whales. As consumed as I was by looking for whales during the few hours of daylight, it was also rewarding to broaden my marine mammal focus and learn about another krill predator, the crabeater seal, from a great team researching their ecology and physiology.

In between our other work, I have been processing active acoustic (echosounder) data collected during a winter 2022 cruise that visited many of the same regions of the WAP. Antarctic krill have been much more thoroughly studied than the main krill species that occur off the coast of Oregon, Euphausia pacifica and Thysanoessa spinifera, and it has been amazing to draw upon this large body of literature.

*Figure 2.* The active acoustic data I’m working with from the Western Antarctic Peninsula, pictured here, was collected along a wiggly cruise track in 2022, giving me the opportunity to learn how to process this type of survey data and appreciate the ways in which a ship’s movements translate to data analysis.

Working with a new flavor of echosounder data has presented me with puzzles that are teaching me to navigate different modes of data collection and their analytical implications, such as for the cruise track data above. I’ll never take data collected along a standardized grid for granted again!

I’ve also learned new techniques that I am excited to apply to my research in the Northern California Current (NCC) region. For example, there are two primary different ways of detecting krill swarms in echosounder data: by comparing the results of two different acoustic frequencies, and by training a computer algorithm to recognize swarms based on their dimensions and other characteristics. After trying a few different approaches with the Antarctic data this season, I developed a way to combine these techniques. In the resulting dataset, two different methods have confirmed that a given area represents krill, which gives me a lot of confidence in it. I’m looking forward to applying this technique to my NCC data, and using it to assess some of my next research questions.

*Figure 3.* A combination of krill detection techniques selected these long krill aggregations off the coast of the Western Antarctic Peninsula (WAP).

Throughout it all, the highlight of this season has been being part of an amazing field team. I’m here with Kim Bernard (as a co-advised student, I refer to Kim as my “krill advisor” and Leigh as my “whale advisor”), and undergraduate Abby Tomita, who just started her senior year at OSU remotely from Palmer. From nights full of net tows to busy days in the lab, we’ve become a well-oiled machine, and laughed a lot along the way. Working with the two of them makes me sure that we’ll be able to best any difficulties that come up.

Now, our next challenge is wrapping up our last labwork, packing up equipment and samples, and getting ready to say goodbye. Leaving this wild, remote place is always heartbreaking – you never really know if you’ll be back. But there’s a lot to look forward to as we journey north, too: I can’t wait to hug my family and friends, eat a salad, and drive out to Newport to see the GEMM Lab. I’m excited to head back to the world with everything I’ve learned here, and to keep working.

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