Tag: Population dynamics

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.

Data Wrangling to Assess Data Availability: A Data Detective at Work

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

Data wrangling, in my own loose definition, is the necessary combination of both data selection and data collection. Wrangling your data requires accessing then assessing your data. Data collection is just what it sounds like: gathering all data points necessary for your project. Data selection is the process of cleaning and trimming data for final analyses; it is a whole new bag of worms that requires decision-making and critical thinking. During this process of data wrangling, I discovered there are two major avenues to obtain data: 1) you collect it, which frequently requires an exorbitant amount of time in the field, in the lab, and/or behind a computer, or 2) other people have already collected it, and through collaboration you put it to a good use (often a different use then its initial intent). The latter approach may result in the collection of so much data that you must decide which data should be included to answer your hypotheses. This process of data wrangling is the hurdle I am facing at this moment. I feel like I am a data detective.

Data wrangling illustrated by members of the R-programming community. (Image source: R-bloggers.com)

My project focuses on assessing the health conditions of the two ecotypes of bottlenose dolphins between the waters off of Ensenada, Baja California, Mexico to San Francisco, California, USA between 1981-2015. During the government shutdown, much of my data was inaccessible, seeing as it was in possession of my collaborators at federal agencies. However, now that the shutdown is over, my data is flowing in, and my questions are piling up. I can now begin to look at where these animals have been sighted over the past decades, which ecotypes have higher contaminant levels in their blubber, which animals have higher stress levels and if these are related to geospatial location, where animals are more susceptible to human disturbance, if sex plays a role in stress or contaminant load levels, which environmental variables influence stress levels and contaminant levels, and more!

Alexa, alongside collaborators, photographing transiting bottlenose dolphins along the coastline near Santa Barbara, CA in 2015 as part of the data collection process. (Image source: Nick Kellar).

Over the last two weeks, I was emailed three separate Excel spreadsheets representing three datasets, that contain partially overlapping data. If Microsoft Access is foreign to you, I would compare this dilemma to a very confusing exam question of “matching the word with the definition”, except with the words being in different languages from the definitions. If you have used Microsoft Access databases, you probably know the system of querying and matching data in different databases. Well, imagine trying to do this with Excel spreadsheets because the databases are not linked. Now you can see why I need to take a data management course and start using platforms other than Excel to manage my data.

A visual interpretation of trying to combine datasets being like matching the English definition to the Spanish translation. (Image source: Enchanted Learning)

In the first dataset, there are 6,136 sightings of Common bottlenose dolphins (Tursiops truncatus) documented in my study area. Some years have no sightings, some years have fewer than 100 sightings, and other years have over 500 sightings. In another dataset, there are 398 bottlenose dolphin biopsy samples collected between the years of 1992-2016 in a genetics database that can provide the sex of the animal. The final dataset contains records of 774 bottlenose dolphin biopsy samples collected between 1993-2018 that could be tested for hormone and/or contaminant levels. Some of these samples have identification numbers that can be matched to the other dataset. Within these cross-reference matches there are conflicting data in terms of amount of tissue remaining for analyses. Sorting these conflicts out will involve more digging from my end and additional communication with collaborators: data wrangling at its best. Circling back to what I mentioned in the beginning of this post, this data was collected by other people over decades and the collection methods were not standardized for my project. I benefit from years of data collection by other scientists and I am grateful for all of their hard work. However, now my hard work begins.

The cutest part of data wrangling: finding adorable images of bottlenose dolphins, photographed during a coastal survey. (Image source: Alexa Kownacki).

There is also a large amount of data that I downloaded from federally-maintained websites. For example, dolphin sighting data from research cruises are available for public access from the OBIS (Ocean Biogeographic Information System) Sea Map website. It boasts 5,927,551 records from 1,096 data sets containing information on 711 species with the help of 410 collaborators. This website is incredible as it allows you to search through different data criteria and then download the data in a variety of formats and contains an interactive map of the data. You can explore this at your leisure, but I want to point out the sheer amount of data. In my case, the OBIS Sea Map website is only one major platform that contains many sources of data that has already been collected, not specifically for me or my project, but will be utilized. As a follow-up to using data collected by other scientists, it is critical to give credit where credit is due. One of the benefits of using this website, is there is information about how to properly credit the collaborators when downloading data. See below for an example:

Example citation for a dataset (Dataset ID: 1201):

Lockhart, G.G., DiGiovanni Jr., R.A., DePerte, A.M. 2014. Virginia and Maryland Sea Turtle Research and Conservation Initiative Aerial Survey Sightings, May 2011 through July 2013. Downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/1201) on xxxx-xx-xx.

Citation for OBIS-SEAMAP:

Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good, L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography 22(2):104-115

Another federally-maintained data source that boasts more data than I can quantify is the well-known ERDDAP website. After a few Google searches, I finally discovered that the acronym stands for Environmental Research Division’s Data Access Program. Essentially, this the holy grail of environmental data for marine scientists. I have downloaded so much data from this website that Excel cannot open the csv files. Here is yet another reason why young scientists, like myself, need to transition out of using Excel and into data management systems that are developed to handle large-scale datasets. Everything from daily sea surface temperatures collected on every, one-degree of latitude and longitude line from 1981-2015 over my entire study site to Ekman transport levels taken every six hours on every longitudinal degree line over my study area. I will add some environmental variables in species distribution models to see which account for the largest amount of variability in my data. The next step in data selection begins with statistics. It is important to find if there are highly correlated environmental factors prior to modeling data. Learn more about fitting cetacean data to models here.

The ERDAPP website combined all of the average Sea Surface Temperatures collected daily from 1981-2018 over my study site into a graphical display of monthly composites. (Image Source: ERDDAP)

As you can imagine, this amount of data from many sources and collaborators is equal parts daunting and exhilarating. Before I even begin the process of determining the spatial and temporal spread of dolphin sightings data, I have to identify which data points have sex identified from either hormone levels or genetics, which data points have contaminants levels already quantified, which samples still have tissue available for additional testing, and so on. Once I have cleaned up the datasets, I will import the data into the R programming package. Then I can visualize my data in plots, charts, and graphs; this will help me identify outliers and potential challenges with my data, and, hopefully, start to see answers to my focal questions. Only then, can I dive into the deep and exciting waters of species distribution modeling and more advanced statistical analyses. This is data wrangling and I am the data detective.

What people may think a ‘data detective’ looks like, when, in reality, it is a person sitting at a computer. (Image source: Elder Research)

Like the well-known phrase, “With great power comes great responsibility”, I believe that with great data, comes great responsibility, because data is power. It is up to me as the scientist to decide which data is most powerful at answering my questions.

Data is information. Information is knowledge. Knowledge is power. (Image source: thedatachick.com)

 

What REALLY is a Wildlife Biologist?

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

The first lecture slide. Source: Lecture1_Population Dynamics_Lou Botsford

This was the very first lecture slide in my population dynamics course at UC Davis. Population dynamics was infamous in our department for being an ultimate rite of passage due to its notoriously challenging curriculum. So, when Professor Lou Botsford pointed to his slide, all 120 of us Wildlife, Fish, and Conservation Biology majors, didn’t know how to react. Finally, he announced, “This [pointing to the slide] is all of you”. The class laughed. Lou smirked. Lou knew.

Lou knew that there is more truth to this meme than words could express. I can’t tell you how many times friends and acquaintances have asked me if I was going to be a park ranger. Incredibly, not all—or even most—wildlife biologists are park rangers. I’m sure that at one point, my parents had hoped I’d be holding a tiger cub as part of a conservation project—that has never happened. Society may think that all wildlife biologists want to walk in the footsteps of the famous Steven Irwin and say thinks like “Crikey!”—but I can’t remember the last time I uttered that exclamation with the exception of doing a Steve Irwin impression. Hollywood may think we hug trees—and, don’t get me wrong, I love a good tie-dyed shirt—but most of us believe in the principles of conservation and wise-use A.K.A. we know that some trees must be cut down to support our needs. Helicoptering into a remote location to dart and take samples from wild bear populations…HA. Good one. I tell myself this is what I do sometimes, and then the chopper crashes and I wake up from my dream. But, actually, a scientist staring at a computer with stacks of papers spread across every surface, is me and almost every wildlife biologist that I know.

The “dry lab” on the R/V Nathaniel B. Palmer en route to Antarctica. This room full of technology is where the majority of the science takes place. Drake Passage, International Waters in August 2015. Source: Alexa Kownacki

There is an illusion that wildlife biologists are constantly in the field doing all the cool, science-y, outdoors-y things while being followed by a National Geographic photojournalist. Well, let me break it to you, we’re not. Yes, we do have some incredible opportunities. For example, I happen to know that one lab member (eh-hem, Todd), has gotten up close and personal with wild polar bear cubs in the Arctic, and that all of us have taken part in some work that is worthy of a cover image on NatGeo. We love that stuff. For many of us, it’s those few, memorable moments when we are out in the field, wearing pants that we haven’t washed in days, and we finally see our study species AND gather the necessary data, that the stars align. Those are the shining lights in a dark sea of papers, grant-writing, teaching, data management, data analysis, and coding. I’m not saying that we don’t find our desk work enjoyable; we jump for joy when our R script finally runs and we do a little dance when our paper is accepted and we definitely shed a tear of relief when funding comes through (or maybe that’s just me).

A picturesque moment of being a wildlife biologist: Alexa and her coworker, Jim, surveying migrating gray whales. Piedras Blancas Light Station, San Simeon, CA in May 2017. Source: Alexa Kownacki.

What I’m trying to get at is that we accepted our fates as the “scientists in front of computers surrounded by papers” long ago and we embrace it. It’s been almost five years since I was a senior in undergrad and saw this meme for the first time. Five years ago, I wanted to be that scientist surrounded by papers, because I knew that’s where the difference is made. Most people have heard the quote by Mahatma Gandhi, “Be the change that you wish to see in the world.” In my mind, it is that scientist combing through relevant, peer-reviewed scientific papers while writing a compelling and well-researched article, that has the potential to make positive changes. For me, that scientist at the desk is being the change that he/she wish to see in the world.

Scientists aboard the R/V Nathaniel B. Palmer using the time in between net tows to draft papers and analyze data…note the facial expressions. Antarctic Peninsula in August 2015. Source: Alexa Kownacki.

One of my favorite people to colloquially reference in the wildlife biology field is Milton Love, a research biologist at the University of California Santa Barbara, because he tells it how it is. In his oh-so-true-it-hurts website, he has a page titled, “So You Want To Be A Marine Biologist?” that highlights what he refers to as, “Three really, really bad reasons to want to be a marine biologist” and “Two really, really good reasons to want to be a marine biologist”. I HIGHLY suggest you read them verbatim on his site, whether you think you want to be a marine biologist or not because they’re downright hilarious. However, I will paraphrase if you just can’t be bothered to open up a new tab and go down a laugh-filled wormhole.

Really, Really Bad Reasons to Want to be a Marine Biologist:

  1. To talk to dolphins. Hint: They don’t want to talk to you…and you probably like your face.
  2. You like Jacques Cousteau. Hint: I like cheese…doesn’t mean I want to be cheese.
  3. Hint: Lack thereof.

Really, Really Good Reasons to Want to be a Marine Biologist:

  1. Work attire/attitude. Hint: Dress for the job you want finally translates to board shorts and tank tops.
  2. You like it. *BINGO*

Alexa with colleagues showing the “cool” part of the job is working the zooplankton net tows. This DOES have required attire: steel-toed boots, hard hat, and float coat. R/V Nathaniel B. Palmer, Antarctic Peninsula in August 2015. Source: Alexa Kownacki.

In summary, as wildlife or marine biologists we’ve taken a vow of poverty, and in doing so, we’ve committed ourselves to fulfilling lives with incredible experiences and being the change we wish to see in the world. To those of you who want to pursue a career in wildlife or marine biology—even after reading this—then do it. And to those who don’t, hopefully you have a better understanding of why wearing jeans is our version of “business formal”.

A fieldwork version of a lab meeting with Leigh Torres, Tom Calvanese (Field Station Manager), Florence Sullivan, and Leila Lemos. Port Orford, OR in August 2017. Source: Alexa Kownacki.