Tag: marine mammal protection act

Why the precautionary principle matters for marine mammal conservation

Lindsay Wickman, Postdoctoral Scholar, Oregon State University Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

This summer, Rep. Nick Begich of (R-AK), submitted a draft bill that proposes to roll back key features of the 1972 U.S. Marine Mammal Protection Act (MMPA). The MMPA has been the centerpiece legislation protecting whales, dolphins, sea otters, manatees, polar bears and seals for over 50 years, bringing many species back from the brink of extinction and setting a benchmark that has been replicated worldwide. Among the changes proposed, the draft bill explicitly bars the use of the precautionary principle in marine mammal management. For example, the draft bill includes these changes:

  • changing wording from “has the potential to injure/disturb” to “injures or disturbs” when considering threats that need to be mitigated.
  • instead of managing marine mammal populations to “result in maximum productivity”, the draft bill would manage species at the size “necessary to support the continued survival”.

The draft bill also includes changes to how allowable levels of injury and mortality to marine mammal populations (called a “take”) in the MMPA are calculated. Until now, these take levels were calculated using safety factors that correct for scientific uncertainty and bias. The proposal removes these safety factors, which would essentially increase the number of allowable takes from each population before management intervention is required. The proposed changes also require a much higher burden of proof before populations can be considered “depleted” or “strategic”, which are identifiers that trigger conservation action.

 Proponents of the draft bill say the current MMPA has been too precautionary, unnecessarily increasing burdens on fishers and other resource users. Here, I argue that the precautionary principle is not a subjective judgement that favors marine mammals over people’s livelihoods. Instead, it is a rational decision-making tool, essential for making management decisions when information is uncertain.

A humpback whale (Megaptera novaeangliae) surfaces during a recent research survey. Humpback whales along the U.S. West Coast have increased in abundance since the end of commercial whaling and MMPA protections. Imagery collected under research permit #27426 issued to MMI.

What is the precautionary principle?

In practice, it means that a lack of data or uncertainty in statistical estimates or trends should not be used as an excuse for inaction in the face of a valid threat (Raffensperger and Tickner, 1999). Instead, decision-makers should incorporate “safety factors” that account for limited knowledge or imperfect science. As said by Holt and Talbot (1978), “the magnitude of the safety factor should be proportional to the magnitude of risk.” So, if the goal is to prevent extinction, severely depleted populations may require bigger safety factors than healthy populations.

How does the U.S. MMPA apply the precautionary principle? 

During the first few decades the MMPA, actions to protect marine mammals were primarily reactionary, in response to highly publicized issues like the dolphin-tuna problem (Taylor et al., 2000). Conservation actions were supposed to be triggered when scientists detected a declining trend in a population’s abundance, but obtaining precise estimates of population size is notoriously difficult for marine mammals. The amount of data required to prove a population was declining due to human activities was so high that protection was continually stalled due to uncertainty in statistical trends (e.g., Marine Mammal Commission 1982; Wade 1993; Taylor et al., 2000).

In 1994, the U.S. MMPA was amended, implementing a new way to determine which marine mammal populations were at risk. Instead of requiring a statistical trend in population abundance, the new method calculates the number of sustainable takes without putting the population at risk of decline. The 1994 amendments also explicitly applied the precautionary principle by incorporating safety factors into this calculation of this number of allowable takes, known as the Potential Biological Removal (PBR; Wade 1998), which increases the likelihood that the management goals stated by the MMPA are achieved (Taylor et al., 2000). 

Three reasons why the precautionary principle matters:

1. It accounts for uncertainty and potential bias

Consider air travel for a moment: Given the uncertainty in the amount of time it takes to arrive at the airport (e.g., traffic, parking) and the unknown possibilities for extra delays once there (e.g., security), most travelers shoot for airport arrival times significantly earlier than the flight boards.  However, what if instead of an exact flight time, you are told the plane leaves sometime between 9 and 11 am? Also, although you have some experience travelling, you have never used this particular airport, and you have no idea how long security and check-in might take. Given these hypothetical circumstances, how would you plan your travel?

When applying marine mammal science to management goals, decision-makers must contend with a similarly uncertain set of information. Marine mammals are wide-ranging and spend most of their lives underwater, making them particularly challenging to study. It is impossible to get exact estimates of population size for these animals, and even the best designed research produces abundance estimates with significant levels uncertainty (e.g., Taylor et al., 2000; Taylor et al. 2007). After decades of researching marine mammals, we also still have significant knowledge gaps about their population dynamics, space-use, and behaviors.

Currently, the MMPA accounts for scientific uncertainty by using minimum estimated population size (the lower 20th percentile) when calculating sustainable levels of human takes (Wade 1998; Taylor et al. 2000). This safety factor makes it more likely that calculations of allowable takes are at or below safe levels (Wade 1998; Taylor et al. 2000).

Relating back to the airport example, if you were told your flight could leave between 9 and 11 am, using minimum population size (instead of the maximum or center of the estimate) is analogous to planning for the flight to leave closer to 9 am. However, you still need to add in time for extra factors that may cause other possible delays in addition to the uncertain departure time.

So, in addition to minimum population size, the MMPA also uses another safety factor in its calculation of allowable takes, called the recovery factor (FR). FR scales the number of allowable takes relative to the level of risk to the population and the potential for biased or uncertain information (Wade 1998; Taylor et al. 2000).  A lower FR is given to depleted, high risk populations, while FR can be increased for well-studied populations at lower risk (Wade 1998; Taylor et al. 2000). In the travel analogy, FR is the amount of padding needed to ensure a passenger makes their flight, accounting for potentially unknown security lines and traffic.

2. It incentivizes the public and industry to collect more data to “fine-tune” management

The more experienced you are with a particular airport and the more certain you are of the departure time, the more confident you can be in your travel plans. If you know the plane leaves at 10 am, and security takes 15 minutes, you don’t need to add nearly as much extra travel time as if your travel details were more uncertain.

Importantly, as the scientific knowledge of a population increases, the magnitude of the safety factors in the calculation of allowable mortalities decreases. For example, as the number of surveys of a population increases and an abundance estimate gets more precise, the range of the abundance estimate gets smaller. So, getting a more precise abundance estimate is like changing your uncertain flight time from being between 9 – 11 am, to being between 9:30 – 10 am. While you still have some uncertainty, you can be confident that leaving a little later than originally planned would be ok.

Since better knowledge results in more targeted management, both the public and industry are motivated to invest in continued research. Fine-tuning management means that necessary precautions can be kept, but unnecessary burdens on industries are removed. Ultimately, the strategy of a precautionary approach is to “act now, fine-tune later,” instead of “delay action until we get detailed information.” In addition to potentially delaying urgent action, the latter approach also disincentivizes industry to invest in research or develop solutions. As explained below, delaying conservation due to uncertainty has led to past pitfalls in marine mammal conservation, necessitating the need for a more proactive approach.

3. It prevents unnecessary delays in conservation action

If you had an important flight to catch on Wednesday, but did not know the departure time, would you decide to not go to the airport at all? Would it be worth it to just get to the airport early, or would you wait at home for more information, but at the risk of missing your flight?

The choice to not act at all in the face of uncertain data is inherently risky. For the first couple of decades of the MMPA, managers attempted to prove a population was declining before conservation action could be taken. The problem is, determining population trends of marine mammals with any certainty can take decades (Taylor and Gerrodette, 1993; Wade 1993; Taylor et al., 2000). In the case of some species, by the time scientists have the statistical power to detect a trend, the population could already be in a catastrophic decline. For example, in the case of eastern tropical Pacific dolphins killed as bycatch by the tuna industry during the 1970s, proving their population decline led to a 14-year protection delay from the first abundance estimate of the population (Wade et al., 1994; Taylor et al., 2000).

The purpose of the 1994 MMPA amendments was to correct for these unnecessary delays that required extensive amounts of data (Taylor et al. 2000). Instead of requiring population trend data, the MMPA now uses values that are much easier to obtain — population size and maximum population growth rates (Wade 1998). From these, the number of individuals that can sustainably be removed from the population (PBR) can be calculated. This approach is a much faster and simpler method, allowing for quick action if estimated mortality (e.g., numbers of animals killed or injured) is higher than this calculated threshold (PBR).

Lastly, the precautionary principle assumes that if a threat is valid, it should be considered, even if the effects are not 100% proven yet. This approach is essential for marine mammals, where anthropogenic injuries and mortality are not always easily detected or recorded. In the case of ship strikes and fisheries entanglement, many individuals disappear before their deaths or injuries are recorded (e.g., Cassoff et al., 2011; Pace et al. 2021). Other threats, like the effects of sound and chemical pollution, may require long-term monitoring to fully understand their population-level impacts. By using language like “has the potential to injure,” management can be implemented more proactively, allowing for research to continue, but not at the detriment of population health during the lengthy time it can take to establish statistical certainty.

Final thoughts

The precautionary principle is a way of dealing with the fact that good science can cost precious time. Results rarely give “yes or no” answers and clear-cut solutions. Instead, decision-makers must weigh study design, statistical power, and the precision (i.e., uncertainty) of scientific findings. The precautionary principle provides a framework for how to effectively use science to make decisions, increasing the likelihood that management plans meet their goals.

If this blog makes you concerned about the future of the precautionary principle in the U.S. MMPA:

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References

Cassoff, R.M., Moore, K.M., McLellan, W.A., Barco, S.G., Rotstein, D.S., Moore, M.J. (2011). Lethal entanglement in baleen whales. Diseases of Aquatic Organisms, 96: 175– 185.

Holt, S. J., and L. M. Talbot. (1978). New principles for the conservation of wild living resources. Wildlife Monographs, 59.

Marine Mammal Commission. (1982). Marine Mammal Commission annual report to Congress. Bethesda, Maryland.

Pace, R.M., Williams, R., Kraus, S.D., Knowlton, A.R., Pettis, H.M. (2021). Cryptic mortality of North Atlantic right whales. Conservation Science and Practice, 3: e346.

Raffensperger C, Tickner J, eds. (1999). Protecting Public Health and the Environment: Implementing the Precautionary Principle. Washington, DC: Island Press.

Taylor, B. L., & Gerrodette, T. (1993). The Uses of Statistical Power in Conservation Biology: The Vaquita and Northern Spotted Owl. Conservation Biology, 7(3), 489–500.

Taylor, B. L., Wade, P. R., de Master, D. P., & Barlow, J. (2000). Incorporating uncertainty into management models for marine mammals. Conservation Biology, 14(5), 1243–1252.

Taylor, B. L., Martinez, M., Gerrodette, T., Barlow, J., & Hrovat, Y. N. (2007). Lessons From Monitoring Trends in Abundance of Marine Mammals. Marine Mammal Science, 23(1), 157–175.

Wade, P. R. (1993). Estimation of historical population size of the eastern spinner dolphin (Stenella longirostris orientalis). Fishery Bulletin, United States 91:775–787.

Wade, P. R. (1994). Abundance and population dynamics of two eastern Pacific dolphins, Stenella attenuata and Stenella longirostris orientalis. Ph.D. dissertation. Scripps Institution of Oceanography, University of California, San Diego.

Wade, P. R. (1998). Calculating limits to the allowable human-caused mortality of cetaceans and pinnipeds. Marine Mammal Science, 14(1), 1–37.

Makah Gray Whale Hunt Waiver – a long-time coming, but still premature?

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

Archaeological site of Ozette Village. Source: Makah Museum.

The Makah, an indigenous people of the Pacific Northwest Coast living in Washington State, have a long history with whaling. Deposits from a mudslide in the village of Ozette suggest that whaling may date back 2,000 years as archaeologists uncovered humpback and gray whale bones and barbs from harpoons (Kirk 1986). However, the history of Makah whaling is also quite recent. On January 29 of this year, the National Marine Fisheries Service (NMFS; informally known as NOAA Fisheries) announced a 45-day public comment period regarding a NMFS proposed waiver on the Marine Mammal Protection Act’s (MMPA) moratorium on the take of marine mammals to allow the Makah to take a limited number of eastern North Pacific gray whales (ENP). To understand how the process reached this point, we first must go back to 1855.

1855 marks the year in which the U.S. government and the Makah entered into the Treaty of Neah Bay (in Washington state). The Makah ceded thousands of acres of land to the U.S. government, and in return reserved their right to whale. Following the treaty, the Makah hunt of gray whales continued until the 1920s. At this point, commercial hunting had greatly reduced the ENP population, so much so that the Makah voluntarily ceased their whaling. The next seven decades brought about the formation of the International Whaling Commission (IWC), the enactment of the Whaling Convention Act, the listing of gray whales as endangered under the U.S. Endangered Species Act, and the enactment of the MMPA. For gray whales, these national and international measures were hugely successful, leading to the removal of the ENP from the Federal List of Endangered Wildlife in 1994 when it was determined that the population had recovered to near its estimated original population size.

One year later on May 5, 1995 (just one month after I was born!), the Makah asked the U.S. Department of Commerce to represent its interest to obtain a quota for gray whales from the IWC in order to resume their treaty right for ceremonial and subsistence harvest of the ENP. The U.S. government pursued this request at the next IWC meeting, and subsequently NMFS issued a final Environmental Assessment that found no significant impact to the ENP population if the hunt recommenced. The IWC set a catch limit and NMFS granted the Makah a quota in 1998. In 1999 the Makah hunted, struck and landed an ENP gray whale.

“Makahs cutting up whale, Neah Bay, ca. 1930. Photo by Asahel Curtis, Courtesy UW Special Collections (CUR767)”. Source and caption: History Link.

I will not go into detail about what happened between 1999 and now because frankly, a lot happened, particularly a lot of legal events including summary judgements, appeals, and a lot of other legal jargon that I do not quite understand. If you want to know the specifics of what happened in those two decades, I suggest you look at NMFS’ chronology of the Makah Tribal Whale Hunt. In short, cases brought against NMFS argued that they did not take a “hard [enough] look” at the National Environmental Policy Act when deciding that the Makah could resume the hunt. Consequently, the hunt was put on hold. Yet, in 2005 NMFS received a waiver request from the Makah on the MMPA’s take moratorium and NMFS published a notice of intent to review this request. A lot more happened between that event and now, including on January 29 of this year when NMFS announced the availability of transcripts from the Administrative Law Judge’s (ALJ) hearing (which happened from November 14-21, 2019) on the proposed regulations and waiver to allow the Makah to resume hunting the ENP. We are currently in the middle of the aforementioned 45-day public comment period on the formal rulemaking record. 

It has been 15 years since the Makah requested the waiver and while the decision has not yet been reached, we are likely nearing the end of this long process. This blog has turned into somewhat of a history lesson (not really my intention) but I feel it is important to understand the lengthy and complex history associated with the decision that is probably going to happen sometime this year. My actual intent for this blog is to ruminate on a few questions, some of which remain unanswered in my opinion, that are large and broad, and important to consider. Some of these questions point out gaps in our ecological knowledge regarding gray whales that I believe should be addressed for a truly informed decision to be made on NMFS’ proposed waiver now or anytime in the near future. 

1. Should the Pacific Coast Feeding Group (PCFG) of gray whales be recognized as its own stock?

Currently, the PCFG are considered a part of the ENP stock. This decision was published following a workshop held by a NMFS task force (Weller et al. 2013). The report concluded that based on photo-identification, genetics, tagging, and other data, there was a substantial level of uncertainty in the strength of the evidence to support the independence of the PCFG from the ENP. Nevertheless, mitochondrial genetic data have indicated a differentiation between the PCFG and the ENP, and the exchange rate between the two groups may be small enough for the two to be considered demographically independent (Frasier et al. 2011). Based on all currently available data, it seems that matrilineal fidelity plays a role in creating population structure within and between the PCFG and the ENP, however there has not been any evidence to suggest that whales from one feeding area (i.e. the PCFG range) are reproductively isolated from whales that utilize other feeding areas (i.e. the Arctic ENP feeding grounds) (Lang et al. 2011). Several PCFG researchers do argue that there needs to be recognition of the PCFG as an independent stock. It is clear that more research, especially efforts to link genetic and photo-identification data within and between groups, is required.

ENP gray whales foraging off the coast of Alaska on their main foraging grounds in the Bering Sea. Photo taken by ASAMM/AFSC. Funded by BOEM IAA No. M11PG00033. Source: NMFS.

2. Is emigration/immigration driving PCFG population growth, or is it births/deaths?

It is unclear whether the current PCFG population growth is a consequence of births and deaths that occur within the group (internal dynamics) or whether it is due to immigration and emigration (external dynamics). Likely, it is a combination of the two, however which of the two has more of an effect or is more prevalent? This question is important to answer because if population growth is driven more by external dynamics, then potential losses to the PCFG population due to the Makah hunt may not be as detrimental to the group as a whole. However, if internal dynamics play a bigger role, then the loss of just a few females could have long-term ramifications for the PCFG (Schubert 2019). NMFS has taken precautions to try and avoid such effects. In their proposed waiver, of the cumulative limit of 16 strikes of PCFG whales over the 10-year waiver period, no more than 8 of the strikes may be PCFG females (Yates 2019a). While a great step, it still begs the question how the loss of 8 females, admittedly over a rather long period of time, may affect population dynamics since we do not know what ultimately drives recruitment. Especially when taken together with potential non-lethal effects on whales (further discussed in question 5 below).

“Scarlet” is a PCFG female who has had multiple calves in the decades that researchers have seen her in the PCFG range. Image captured under NOAA/NMFS permit #21678. Source: L Hildebrand.

3. How important are individual patterns within the PCFG, and how might the loss of these individuals affect the population? 

The hunt will be restricted to the Makah Usual & Accustomed fishing area (U&A), which is off the Washington coast. It has been shown that site fidelity among PCFG individuals is strong. In fact, based on the 143 PCFG gray whales observed in nine or more years from 1996 to 2015, 94.4% were seen in at least one of nine different PCFG regions during six or more of the years they were seen (Calambokidis et al. 2017). While high site-fidelity seems to be common for some PCFG individuals in certain regions, interestingly, an analysis of sighting histories of all individuals that utilized the Makah U&A from 1985-2011 revealed that most PCFG whales do not have strong site fidelity to the Makah U&A (Scordino et al. 2017). Only about 20% of the whales were observed in six or more years of the total 26 years of data analyzed. Since high individual site fidelity does not appear to be strong in this area, perhaps a loss of genetic diversity, cultural knowledge, and behavioral individualism is not of great concern.

“Buttons” seems to have a preference for the southern Oregon coast as in the last 5 years the GEMM Lab has conducted research, he has only been sighted in 1 year in Newport but in all 5 years in Port Orford. However, perhaps such preferences are not common among all PCFG whales. Source: F. Sullivan.

4. How has the current UME affected the situation?

The ENP has experienced two Unusual Mortality Events (UMEs) in the past 20 years; one from 1999-2000 and the second began in May 2019. Many questions arise when thinking about the Makah hunt in light of the UME. 

  • What impacts will the current UME have on ENP and PCFG birth rates in subsequent years? 
  • Could the UME lead to shifts in feeding behavior of ENP whales and result in greater use of PCFG range by more individuals?
  • What caused the UME? Shifting prey availability and a changing climate? Or has the ENP reached carrying capacity? 
  • Will UMEs become more frequent in the future with continued warming of the Arctic? 
  • What is the added impact of such periodic UMEs on population trends?
“A gray whale found dead off Point Reyes National Seashore in northern California [during the 2019 UME]. Photo by M. Flannery, California Academy of Sciences.” Source and caption: NMFS.

A key assumption of the model developed by NMFS (Moore 2019) to forecast PCFG population size for the period 2016-2028, is that the population processes underlying the data from 2002-2015 (population size estimates developed by Calambokidis et al. 2017) will be the same during the forecasted period. In other words, it is assuming that PCFG gray whales will experience similar environmental conditions (with similar variation) during the next decade as the previous one, and that there will be no catastrophic events that could drastically affect population dynamics. The UME that is still ongoing could arguably affect population dynamics enough such that they are drastically different to effects on the population dynamics during the previous decade. The cause of  the 1999/2000 UME remains undetermined and the results of the investigation of the current UME will possibly not be available for several years (Yates 2019b). Even though the ENP did rebound following the 1999/2000 UME and the abundance of the PCFG increased during and subsequent to that UME, much has changed in the 20 years since then. Increased noise due to increased vessel traffic and other anthropogenic activities (seismic surveys, pile driving, construction to name a few) as well as increased coastal recreational and commercial fishing, have all contributed to a very different oceanscape than the ENP and PCFG encountered 20 years ago. Furthermore, the climate has changed considerably since then too, which likely has caused changes in the spatial distribution of habitat and quantity, quality, and predictability of prey. All of these factors make it difficult to predict what impact the UME will have now. If such events were to become more frequent in the future or the impacts of such events are greater than anticipated, then the PCFG population forecasts will not have accounted for this change. 

5. What impacts will the hunt and associated training exercises have on energy and stress levels of whales?

The proposed waiver would allow hunts to occur in the following manner: in even-years, the hunting period is from December 1 of an odd-numbered year through May 31 of the following even-numbered year. While in odd-years, the hunt is limited from July to October.

In the even-years, the hunt coincides with the northbound migration toward the foraging grounds for ENP whales and with the arrival of PCFG whales to their foraging grounds near the Makah U&A. During the northbound migration, gray whales are at their most nutritionally stressed state as they have been fasting for several months. They are therefore most vulnerable to energy losses due to disturbance at this point (Villegas-Amtmann 2019). Attempted strikes and training exercises would certainly cause some level of disturbance and stress to the whales. Furthermore, the timing of even-year hunts, means that hunters would likely encounter pregnant females, as they are the first to arrive at foraging grounds. A loss of just ~4% of a pregnant female’s energy budget could cause them to abort the fetus or not produce a calf that year (Villegas-Amtmann 2019).

In odd-years, the Makah hunt will most certainly target PCFG whales as the Makah U&A forms one of the nine PCFG regions where PCFG individuals will be feeding during those months. However, NMFS’ waiver limits the number of strikes during odd-years to 2 (Yates 2019a), which certainly protects the PCFG population.

Stress is a difficult response to quantify in baleen whales and research on stress through hormone analysis is still relatively novel. It is unlikely that a single boat training approach of a gray whale will have an adverse effect on the individual. However, a whale is never just experiencing one disturbance at a time. There are typically many confounding factors that influence a whale’s state. In an ideal world, we would know what all of these factors are and how to recognize these effects. Yet, this is virtually impossible. Therefore, while precautions will be taken to try to minimize harm and stress to the gray whales, there may very well still be unanticipated impacts that we cannot anticipate. 

Gray whale fluke. Image captured under NOAA/NMFS permit #21678. Photo: L Hildebrand.

Final thoughts

Many unknowns still remain about the ENP and PCFG gray whale populations. During the ALJ hearing, both sides tried to deal with these unknowns. After reading testimony from both sides, it is clear to me that some of the unknowns still have not been reconciled. Ultimately, a lot of the questions circle back to the first one I posed above: Are the PCFG an independent stock? If there is independent population structure, then the proposed waiver put forth by NMFS would likely change. While NMFS has certainly taken the PCFG into account during the declarations of several experts at the ALJ hearing and has aired on the side of caution, the fact that the PCFG is considered part of the ENP might underestimate the impact that a resumption of the Makah hunt may have on the PCFG. As you can see, there are still many questions that should be addressed to make fully informed decisions on such an important ruling. While this research may take several years to obtain results, the data are within reach through synthesis and collaboration that will fill these critical knowledge gaps. 

Literature cited

Calambokidis, J. C., J. Laake, and A. Pérez. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. International Whaling Commission SC/A17/GW/05.

Frasier, T. R., S. M. Koroscil, B. N. White, and J. D. Darling. 2011. Assessment of population substructure in relation to summer feeding ground use in eastern North Pacific gray whale. Endangered Species Research 14:39-48.

Kirk, Ruth. 1986. Tradition and change on the Northwest Coast: the Makah, Nuu-chah-nulth, southern Kwakiutl and Nuxalk. University of Washington Press, Seattle.

Lang, A. R., D. W. Weller, R. LeDuc, A. M. Burdin, V. L. Pease, D. Litovka, V. Burkanov, and R. L. Brownell, Jr. 2011. Genetic analysis of stock structure and movements of gray whales in the eastern and western North Pacific. SC/63/BRG10.

Moore, J. E. 2019. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Schubert, D. J. 2019. Rebuttal testimony in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Scordino, J. J., M. Gosho, P. J. Gearin, A. Akmajian, J. Calambokidis, and N. Wright. 2017. Individual gray whale use of coastal waters off northwest Washington during the feeding season 1984-2011: Implications for management. Journal of Cetacean Research and Management 16:57-69.

Villegas-Amtmann, S. 2019. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001.

Weller, D. W., S. Bettridge, R. L. Brownell, Jr., J. L. Laake, J. E. Moore, P. E. Rosel, B. L. Taylor, and P. R. Wade. 2013. Report of the National Marine Fisheries Service Gray Whale Stock Identification Workshop. NOAA-TM-NMFS-SWFSC-507. 

Yates, C. 2019a. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Yates, C. 2019b. Fifth declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

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.

Sea Otter Management in the U.S.

By Dominique Kone, Masters Student in Marine Resource Management

Since the first official legal protections in 1911, the U.S. has made great strides in recovering sea otter populations. While much of this progress is due to increased emphasis on understanding sea otter behavior, biology, and ecology, there are also several policies that have been just as instrumental in making sea otter conservation efforts successful. Here, I provide a brief overview of the current legal and regulatory policies used to manage sea otters in the U.S. and explain why having a base understanding of these tools can help our lab as we look into the potential reintroduction of sea otters to the Oregon coast.

Sea otter with pup, Prince William Sound, Alaska. Source: Patrick J. Endres

When we talk about sea otter management in the U.S., the two most obvious laws that come to mind are the Marine Mammal Protection Act (MMPA) and the Endangered Species Act (ESA). In short, the MMPA seeks to prevent the take – including kill, harass, capture, or disturb – or importation of marine mammals and marine mammal products[1]. While the ESA seeks to protect and recover imperiled species – not just marine mammals – and the ecosystems which they depend upon[2]. Both laws are similar in the sense that their primary objectives are to protect and recover at-risk species. However, marine mammals will always be protected under the MMPA, but will only be protected under the ESA if the species is considered threatened or endangered.

On the federal level, the U.S. Fish and Wildlife Service (the Service) is primarily responsible for managing sea otter populations. In the U.S., we manage sea otter populations as five distinct stocks, which differ in their population size and geographic distribution – located in California, Washington, and Alaska state waters (Fig. 1). Because sea otters are divided into these single stocks, management decisions – such as recovery targets or reintroductions – are made on a stock-by-stock basis and are dependent on the stock’s population status. Currently, two of these stocks are federally-listed as threatened under the ESA. Therefore, these two stocks are granted protection under both the ESA and MMPA, while the remaining three stocks are only protected by the MMPA (at the federal level; state management may also apply).

Figure 1. Distribution (approximations of population centers) of sea otter stocks in the U.S. (SW = Southwest Alaskan; SC = Southcentral Alaskan; SE = Southeast Alaskan; WA = Washington, SCA = Southern/Californian)

While the MMPA and ESA are important federal laws, I would be remiss if I didn’t mention the important role that state laws and state agencies have in managing sea otters. According to the MMPA and ESA, if a state develops and maintains a conservation or recovery program with protections consistent with the standards and policies of the MMPA and/or ESA, then the Service may transfer management authority over to the state1,2. However, typically, the Service has opted to manage any stocks listed under the ESA, while states manage all other stocks not listed under the ESA.

Sea otter management in the states of Washington and California is a clear example of this dichotomy. The Washington sea otter stock is not listed under the ESA, and is therefore, managed by the Washington Department of Fish and Wildlife (WDFW), which developed the stock’s recovery plan[3]. In contrast, sea otters along the California coast are listed as threatened under the ESA, and the Service primarily manages the stock’s recovery[4].

Interestingly, sea otter management in Alaska is an exception to this rule. The Southeast and Southcentral sea otter stocks are not listed under the ESA, yet are still managed by the Service. However, the state recognizes sea otters as a species of greatest conservation need in the state’s Wildlife Action Plan, which acts as a recommendation framework for the management and protection of important species and ecosystems[5]. Therefore, even though the state is not the primary management authority for sea otters by law, they still play a role in protecting Alaskan sea otter populations through this action plan.

Table 1. Federal and state listing status of all sea otter stocks within U.S. coastal waters.

States have also implemented their own laws for protecting at-risk species. For instance, while the Washington sea otter stock is not listed under the ESA, it is listed as endangered under Washington state law4. This example raises an important example demonstrating that even if a stock isn’t federally-listed, it may still be protected on the state level, and is always protected under the MMPA. Therefore, if the federal and state listing status do not match, which is the case for most sea otter stocks in the U.S. (Table 1.), the stock still receives management protection at some level.

So why does this matter?

Each of the previously mentioned laws are prohibitive in nature, where the objectives are to prevent and discourage activities which may harm the stock of interest. Yet, agencies may grant exceptions – in the form of permits – for activities, such as scientific research, translocations, commercial/recreational fisheries operations, etc. The permit approval process will oftentimes depend on: (1) the severity or likelihood of that action to harm the species, (2) the species’ federal and state listing status, and (3) the unique approval procedures enforced by the agency. Activities that are perceived to have a high likelihood of harming a species, or involve a species that’s listed under the ESA, will likely require a longer and more arduous approval process.

A sea otter release in Monterey Bay, California. Source: Monterey Bay Aquarium Newsroom.

Understanding these various approval processes is vitally important for our work on the potential reintroduction of sea otters to Oregon because such an effort will no doubt require many permits and a thoughtful permit approval process. Each agency may have their own set of permits, administrative procedures, and approval processes. Therefore, it behooves us to have a clear understanding of these various processes relative to the state, agency, or stock involved. If, hypothetically, a stock is determined as a suitable candidate for reintroduction into Oregon waters, having this understanding will allow us to determine where our research can best inform the effort, what types of information and data are needed to inform the process, and to which agency or stakeholders we must communicate our research.

 

References:

[1] Marine Mammal Protection Act of 1972

[2] Endangered Species Act of 1973

[3] State of Washington. 2004. Sea Otter Recovery Plan. Washington Department of Fish and Wildlife: Wildlife Program

[4] U.S. Fish & Wildlife Service. 2003. Final Revised Recovery Plan for the Southern Sea Otter (Enydra lutris nereis).

[5] Alaska Department of Fish and Game. 2015. Alaska wildlife action plan. Juneau.