A Multidisciplinary Treasure Hunt: Learning about Indigenous Whaling in Oregon

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

At this year’s virtual State of the Coast conference, I enjoyed tuning into a range of great talks, including one by Zach Penney from the Columbia River Inter-Tribal Fish Commission. In his presentation, “More Than a Tradition: Treaty rights and the Columbia River Inter-Tribal Fish Commission,” Penney described a tribal “covenant with resources,” and noted the success of this approach — “You don’t live in a place for 15,000 years by messing it up.”

Indigenous management of resources in the Pacific Northwest dates back thousands of years. From oak savannahs to fisheries to fires, local tribes managed diverse natural systems long before colonial settlement of the area that is now Oregon. We know comparatively little, however, about how Indigenous groups in Oregon interacted with whale populations before the changes brought by colonialism and commercial whaling.

Makah hunters in Washington bring a harvested whale into Neah Bay (Asahel Curtis/Washington State Historical Society).

I’m curious about how this missing knowledge could inform our understanding of the coastal Oregon ecosystems in which many GEMM Lab projects take place. My graduate research will be part of the effort to identify co-occurrence between whales and fishing in Oregon, with the goal of helping to reduce whale entanglement risk. Penney’s talk, ongoing conversations about decolonizing science, and my own concerns about becoming the scientist that I want to be, have all led me to ask a new set of questions: What did humans know in the past about whale distributions along the Oregon coast? What lost knowledge can be reclaimed from history?

As I started reading about historical Indigenous whale use in Oregon, I was struck by how little we know today, and how this learning process became a multidisciplinary treasure hunt. Clues as to how Indigenous groups interacted with whales along the Oregon coast lie in oral histories, myths, journals, and archaeological artifacts. 

Much of what I read hinged on the question: did Indigenous tribes in Oregon historically hunt whales? Many signs point to yes, but it’s a surprisingly tricky question to answer conclusively. Marine systems and animals, including seals and whales, remain an important part of cultures in the Pacific Northwest today – but historically, documentation of hunting whales in Oregon has been limited. Whale bones have been found in coastal middens, and written accounts describe opportunistic harvests of beached whales. However, people have long believed that only a few North American tribes outside of the Arctic regularly hunted whales. 

But in 2007, archaeologists Robert Losey and Dongya Yang found an artifact that started to shift this narrative. While studying a collection of tools housed at the Smithsonian Institution, they discovered the tip of a harpoon lodged in a whale flipper bone. This artifact came from the Partee site, which was inhabited around AD 300-1150 and is located near present-day Seaside, Oregon.

A gray whale ulna with cut marks found at the Partee site (Wellman, et al. 2017).

Through DNA testing, Losey and Yang determined that the harpoon was made of elk bone, and that the elk was not only harvested locally, but also used locally. This new piece of evidence suggested that whaling did in fact take place at the Partee site, likely by the Tillamook or Clatsop tribes that utilized this area.

Several years later, this discovery inspired Smithsonian Museum of Natural History archaeologist Torben Rick and University of Oregon PhD student Hannah Wellman to comb through the rest of the animal remains in the Smithsonian’s collection from northwest Oregon. Rick and Wellman scrutinized 187 whale bones for signs of hunting or processing, and found that about a quarter of the marks they inspected could have come from either hunting or the opportunistic harvest of stranded whales. They examined tools from the midden as well, and found that they were more suited to hunting animals, like seals and sea lions, or fishing. 

However, Wellman and Rick also used DNA testing to identify which whale species were represented in the midden – and the DNA analyses suggested a different story. Genetic results revealed that the majority of whale bones in the midden came from gray whales, a third from humpback whales, and a few from orca and minke. Modern gray whale stranding events are not uncommon, and so it follows logically that these bones could have simply come from people harvesting beached whales. However, humpback strandings are rare – suggesting that such a large proportion of humpback bones in the midden is likely evidence of people actively hunting humpback whales.

Percentage of whale species identified at the Partee site and percentage of species in the modern stranding record for the Oregon Coast (Wellman, et al. 2017).

These results shed new light on whale harvesting practices at the Partee Site, and, like so much research, they suggest a new set of questions. What does the fact that there were orca, minke, gray, and humpback whales off the Oregon coast 900 years ago tell us about the history of this ecosystem? Could artifacts that have not yet been found provide more conclusive evidence of hunting? What would it mean if these artifacts are found one day, or if they are never found?

As this fascinating research continues, I hope that new discoveries will continue to deepen our understanding of historic Indigenous whaling practices in Oregon – and that this information can find a place in contemporary conversations. Indigenous whaling rights are both a contemporary and contentious issue in the Pacific Northwest, and the way that humans learn about the past has much to do with how we shape the present. 

What we learn about the past can also change how we understand this ecosystem today, and provide new context as we try to understand the impacts of climate change on whale populations in Oregon. I’m interested in how learning more about historical Indigenous whaling practices could provide more information about whale population baselines, ideas for management strategies, and a new lens on the importance of whales in the Pacific Northwest. Even if we can’t fully reclaim lost knowledge from history, maybe we can still read enough clues to help us see both the past and present more fully.

Sources:

Braun, Ashley. “New Research Offers a Wider View on Indigenous North American Whaling.” Hakai Magazine, November 2016, www.hakaimagazine.com/news/new-research-offers-wider-view-indigenous-north-american-whaling/. 

Eligon, John. “A Native Tribe Wants to Resume Whaling. Whale Defenders Are Divided.” New York Times, November 2019. 

Hannah P. Wellman, Torben C. Rick, Antonia T. Rodrigues & Dongya Y. Yang (2017) Evaluating Ancient Whale Exploitation on the Northern Oregon Coast Through Ancient DNA and Zooarchaeological Analysis, The Journal of Island and Coastal Archaeology, 12:2, 255-275, DOI: 10.1080/15564894.2016.1172382

Losey, R., & Yang, D. (2007). Opportunistic Whale Hunting on the Southern Northwest Coast: Ancient DNA, Artifact, and Ethnographic Evidence. American Antiquity, 72(4), 657-676. doi:10.2307/25470439

Sanchez, Gabriel (2014). Conference paper: Cetacean Hunting at the Par-Tee site (35CLT20)?: Ethnographic, Artifact and Blood Residue Analysis Investigation.

Zooming in: A closer look at bottlenose dolphin distribution patterns off of San Diego, CA

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

Data analysis is often about parsing down data into manageable subsets. My project, which spans 34 years and six study sites along the California coast, requires significant data wrangling before full analysis. As part of a data analysis trial, I first refined my dataset to only the San Diego survey location. I chose this dataset for its standardization and large sample size; the bulk of my sightings, over 4,000 of the 6,136, are from the San Diego survey site where the transect methods were highly standardized. In the next step, I selected explanatory variable datasets that covered the sighting data at similar spatial and temporal resolutions. This small endeavor in analyzing my data was the first big leap into understanding what questions are feasible in terms of variable selection and analysis methods. I developed four major hypotheses for this San Diego site.

The study species: common bottlenose dolphin (Tursiops truncatus) seen along the California coastline in 2015. Image source: Alexa Kownacki.

Hypotheses:

H1: I predict that bottlenose dolphin sightings along the San Diego transect throughout the years 1981-2015 exhibit clustered distribution patterns as a result of the patchy distributions of both the species’ preferred habitats, as well as the social nature of bottlenose dolphins.

H2: I predict there would be higher densities of bottlenose dolphin at higher latitudes spanning 1981-2015 due to prey distributions shifting northward and less human activities in the northerly sections of the transect.

H3: I predict that during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego would be distributed more northerly, predominantly with prey aggregations historically shifting northward into cooler waters, due to (secondarily) increasing sea surface temperatures.

H4: I predict that along the San Diego coastline, bottlenose dolphin sightings are clustered within two kilometers of the six major lagoons, with no specific preference for any lagoon, because the murky, nutrient-rich waters in the estuarine environments are ideal for prey protection and known for their higher densities of schooling fishes.

Data Description:

The common bottlenose dolphin (Tursiops truncatus) sighting data spans 1981-2015 with a few gap years. Sightings cover all months, but not in all years sampled. The same transect in San Diego was surveyed in a small, rigid-hulled inflatable boat with approximately a two-kilometer observation area (one kilometer surveyed 90 degrees to starboard and port of the bow).

I wanted to see if there were changes in dolphin distribution by latitude and, if so, whether those changes had a relationship to ENSO cycles and/or distances to lagoons. For ENSO data, I used the NOAA database that provides positive, neutral, and negative indices (1, 0, and -1, respectively) by each month of each year. I matched these ENSO data to my month-date information of dolphin sighting data. Distance from each lagoon was calculated for each sighting.

Figure 1. Map representing the San Diego transect, represented with a light blue line inside of a one-kilometer buffered “sighting zone” in pale yellow. The dark pink shapes are dolphin sightings from 1981-2015, although some are stacked on each other and cannot be differentiated. The lagoons, ranging in size, are color-coded. The transect line runs from the breakwaters of Mission Bay, CA to Oceanside Harbor, CA.

Results: 

H1: True, dolphins are clustered and do not have a uniform distribution across this area. Spatial analysis indicated a less than a 1% likelihood that this clustered pattern could be the result of random chance (Fig. 1, z-score = -127.16, p-value < 0.0001). It is well-known that schooling fishes have a patchy distribution, which could influence the clustered distribution of their dolphin predators. In addition, bottlenose dolphins are highly social and although pods change in composition of individuals, the dolphins do usually transit, feed, and socialize in small groups.

Figure 2. Summary from the Average Nearest Neighbor calculation in ArcMap 10.6 displaying that bottlenose dolphin sightings in San Diego are highly clustered. When the z-score, which corresponds to different colors on the graphic above, is strongly negative (< -2.58), in this case dark blue, it indicates clustering. Because the p-value is very small, in this case, much less than 0.01, these results of clustering are strongly significant.

H2: False, dolphins do not occur at higher densities in the higher latitudes of the San Diego study site. The sightings are more clumped towards the lower latitudes overall (p < 2e-16), possibly due to habitat preference. The sightings are closer to beaches with higher human densities and human-related activities near Mission Bay, CA. It should be noted, that just north of the San Diego transect is the Camp Pendleton Marine Base, which conducts frequent military exercises and could deter animals.

Figure 3. Histogram comparing the latitudes with the frequency of dolphin sightings in San Diego, CA. The x-axis represents the latitudinal difference from the most northern part of the transect to each dolphin sighting. Therefore, a small difference would translate to a sighting being in the northern transect areas whereas large differences would translate to sightings being more southerly. This could be read from left to right as most northern to most southern. The y-axis represents the frequency of which those differences are seen, that is, the number of sightings with that amount of latitudinal difference, or essentially location on the transect line. Therefore, you can see there is a peak in the number of sightings towards the southern part of the transect line.

H3: False, during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego were more southerly. In colder (negative) ENSO months, the dolphins were more northerly. The differences between sighting latitude and ENSO index was significant (p<0.005). Post-hoc analysis indicates that the north-south distribution of dolphin sightings was different during each ENSO state.

Figure 4. Boxplot visualizing distributions of dolphin sightings latitudinal differences and ENSO index, with -1,0, and 1 representing cold, neutral, and warm years, respectively.

H4: True, dolphins are clustered around particular lagoons. Figure 5 illustrates how dolphin sightings nearest to Lagoon 6 (the San Dieguito Lagoon) are always within 0.03 decimal degrees. Because of how these data are formatted, decimal degrees is the easiest way to measure change in distance (in this case, the difference in latitude). In comparison, dolphins at Lagoon 5 (Los Penasquitos Lagoon) are distributed across distances, with the most sightings further from the lagoon.

Figure 5. Bar plot displaying the different distances from dolphin sighting location to the nearest lagoon in San Diego in decimal degrees. Note: Lagoon 4 is south of the study site and therefore was never the nearest lagoon.

I found a significant difference between distance to nearest lagoon in different ENSO index categories (p < 2.55e-9): there is a significant difference in distance to nearest lagoon between neutral and negative values and positive and neutral years. Therefore, I hypothesize that in neutral ENSO months compared to positive and negative ENSO months, prey distributions are changing. This is one possible hypothesis for the significant difference in lagoon preference based on the monthly ENSO index. Using a violin plot (Fig. 6), it appears that Lagoon 5, Los Penasquitos Lagoon, has the widest variation of sighting distances in all ENSO index conditions. In neutral years, Lagoon 0, the Buena Vista Lagoon has multiple sightings, when in positive and negative years it had either no sightings or a single sighting. The Buena Vista Lagoon is the most northerly lagoon, which may indicate that in neutral ENSO months, dolphin pods are more northerly in their distribution.

Figure 6. Violin plot illustrating the distance from lagoons of dolphin sightings under different ENSO conditions. There are three major groups based on ENSO index: “-1” representing cold years, “0” representing neutral years, and “1” representing warm years. On the x-axis are lagoon IDs and on the y-axis is the distance to the nearest lagoon in decimal degrees. The wider the shapes, the more sightings, therefore Lagoon 6 has many sightings within a very small distance compared to Lagoon 5 where sightings are widely dispersed at greater distances.

 

Bottlenose dolphins foraging in a small group along the California coast in 2015. Image source: Alexa Kownacki.

Takeaways to science and management: 

Bottlenose dolphins have a clustered distribution which seems to be related to ENSO monthly indices, and likely, their social structures. From these data, neutral ENSO months appear to have something different happening compared to positive and negative months, that is impacting the sighting distributions of bottlenose dolphins off the San Diego coastline. More research needs to be conducted to determine what is different about neutral months and how this may impact this dolphin population. On a finer scale, the six lagoons in San Diego appear to have a spatial relationship with dolphin sightings. These lagoons may provide critical habitat for bottlenose dolphins and/or for their preferred prey either by protecting the animals or by providing nutrients. Different lagoons may have different spans of impact, that is, some lagoons may have wider outflows that create larger nutrient plumes.

Other than the Marine Mammal Protection Act and small protected zones, there are no safeguards in place for these dolphins, whose population hovers around 500 individuals. Therefore, specific coastal areas surrounding lagoons that are more vulnerable to habitat loss, habitat degradation, and/or are more frequented by dolphins, may want greater protection added at a local, state, or federal level. For example, the Batiquitos and San Dieguito Lagoons already contain some Marine Conservation Areas with No-Take Zones within their reach. The city of San Diego and the state of California need better ways to assess the coastlines in their jurisdictions and how protecting the marine, estuarine, and terrestrial environments near and encompassing the coastlines impacts the greater ecosystem.

This dive into my data was an excellent lesson in spatial scaling with regards to parsing down my data to a single study site and in matching my existing data sets to other data that could help answer my hypotheses. Originally, I underestimated the robustness of my data. At first, I hesitated when considering reducing the dolphin sighting data to only include San Diego because I was concerned that I would not be able to do the statistical analyses. However, these concerns were unfounded. My results are strongly significant and provide great insight into my questions about my data. Now, I can further apply these preliminary results and explore both finer and broader scale resolutions, such as using the more precise ENSO index values and finding ways to compare offshore bottlenose dolphin sighting distributions.