The ecologist and the economist: Exploring parallels between disciplines

By Dawn Barlow1 and Johanna Rayl2

1PhD Candidate, Oregon State University Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

2PhD Student, Northwestern University Department of Economics

The Greek word “oikos” refers to the household and serves as the root of the words ecology and economics. Although perhaps surprising, the common origin reflects a shared set of basic questions and some shared theoretical foundations related to the study of how lifeforms on earth use scarce resources and find equilibrium in their respective “households”. Early ecological and economic theoretical texts drew inspiration from one another in many instances. Paul Samuelson, fondly referred to as “the father of modern economics,” observed in his defining work Foundations of Economic Analysis that the moving equilibrium in a market with supply and demand is “essentially identical with the moving equilibrium of a biological or chemical system undergoing slow change.” Likewise, early theoretical ecologists recognized the strength of drawing on theories previously established in economics (Real et al. 1991). Similar broad questions are central to researchers in both fields; in a large and dynamic system (termed “macro” in economics) scale, ecologists and economists alike work to understand where competitive forces find equilibrium, and an in individual (or micro) scale, they ask how individuals make behavior choices to maximize success given constraints like time, energy, wealth, or physical resources.

The central model economists have in mind when trying to understand human choices involves “constrained optimization”: what decision will maximize a person, family, firm, or other agent’s objectives given their limitations? For example, someone that enjoys relaxing but also seeks a livable income must choose how much time to devote to working versus relaxing, given the constraint of having just 24 hours in the day, and given the wage they receive from working. An economist studying this decision may want to learn about how changes in the wage will affect that person’s choice of working hours, or how much they dislike working relative to relaxing. Along similar lines, early ecologists theorized that organisms could be selected for one of two optimization strategies: minimizing the time spent acquiring a given amount of energy (i.e., calories from food), or maximizing total energy acquisition per unit of time (Real et al. 1991). Foundational work in the field of economics clarified numerous technical details about formulating and solving such optimization problems. Returning to the example of the leisure time decision, economic theory asks: does it matter if we model this decision as maximizing income given wages and limited time, or as minimizing hours spent working given a desired lifetime income?; can we formulate a “utility function” that  describes how well-off someone is with a given income and amount of leisure?; can we solve for the optimal amount of leisure with pen and paper? The toolkit arising from this work serves as a jumping off point for all contemporary economic research, and the kinds of choices understood under this framework is vast, from, where should a child attend school?; to, how should a government allocate its budget across public resources?

Early work in ecology drew from foundational concepts in economics, following the realization that the strategies by which organisms exploit resources most efficiently also involve optimization. This parallel was articulated by MacArthur and Pianka in their foundational 1966 paper Optimal Use of a Patchy Environment, in which they state: “In this paper we undertake to determine in which patches a species would feed and which items would form its diet if the species acted in the most economical fashion. Hopefully, natural selection will often have achieved such optimal allocation of time and energy expenditures.” Subsequently, this idea was refined into what is known in ecology as the marginal value theorem, which states that an animal should remain in a prey patch until the rate of energy gain drops below the expected energy gain in all remaining available patches (Charnov 1976). In other words, if it is more profitable to switch prey patches than to stay, an animal should move on. These optimization models therefore allow ecologists to pose specific evolutionary and behavioral hypotheses, such as examining energy acquisition over time to understand selective forces on foraging behavior.

As the largest animals on the planet, blue whales have massive prey requirements to meet energy demands. However, they must balance their need to feed with costs such as oxygen consumption during breath-holding, the travel time it takes to reach prey patches at depth, the physiological constraints of diving, and the necessary recuperation time at the surface. It has been demonstrated that blue whales forage selectively to optimize this energetic budget. Therefore, blue whales should only feed on krill aggregations when the energetic gain outweighs the cost (Fig. 1), and this pattern has been empirically demonstrated for blue whale populations in the Gulf of St. Lawrence, Canada (Doniol-Valcroze et al. 2011), in the California Current, (Hazen et al. 2015) and in New Zealand (Torres et al. 2020).

Figure 1. Figure reprinted from Hazen et al. 2015, illustrating how a blue whale should theoretically optimize foraging success in two scenarios. Energy gained from feeding is shown by the blue lines, whereas the cost of foraging in terms of declining oxygen stores during a dive is illustrated by the red lines. On the left (panel B), the whale maximizes its energy gain by increasing the number of feeding lunges (shown by black circles) at the expense of declining oxygen stores when prey density is high. On the right (panel C), the whale minimizes oxygen use by reducing the number of feeding lunges when prey density is low.

The notion of the marginal value theorem is likewise at work in countless economic settings. Economic theory predicts that a farmer cultivating two crops would allocate resources into each crop such that the returns to adding more resources into each crop are the same. If not, she should move resources from the less productive crop to the one where marginal gains are larger. A fisherman, according to this notion, continues to fish longer into the season until the marginal value of one additional day at sea equals the marginal cost of their time, effort, and expenses. These predictions are intuitive by the same logic as the blue whale choosing where to forage, and derive from the mathematics of constrained and unconstrained optimization. Reassuringly, empirical work finds evidence of such profit-maximizing behavior in many settings. In a recent working paper, Burlig, Preonas, and Woerman explore how farmers’ water use in California responds to changes in the price of electricity, which effectively makes groundwater irrigation more expensive due to electric pumping. They find that farmers are very responsive to these changes in marginal cost. Farmers achieve this reduction in water use predominantly by switching to less water-intensive crops and fallowing their land (Burlig, Preonas, and Woerman 2020).

Undoubtedly there are fundamental differences between an ecosystem with interacting biotic and abiotic components and the human-economic environment with its many social and political structures. But for certain types of questions, the parallels across the shared optimization problems are striking. The foundational theories discussed here have paved the way for subsequent advances in both disciplines. For example, the field of behavioral ecology explores how competition and cooperation between and within species affects fitness of populations. Reflecting on early seminal work lends some perspective on how an area of research has evolved. Likewise, exploring parallels between disciplines sheds light on common threads, in turn revealing insights into each discipline individually.


Burlig, Fiona, Louis Preonas, and Matt Woerman (2020). Groundwater, energy, and crop choice. Working Paper.

Charnov EL (1976) Optimal foraging: The marginal value theorem. Theoretical Population Biology 9:129–136.

Doniol-Valcroze T, Lesage V, Giard J, Michaud R (2011) Optimal foraging theory predicts diving and feeding strategies of the largest marine predator. Behavioral Ecology 22:880–888.

Hazen EL, Friedlaender AS, Goldbogen JA (2015) Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Science Advces 1:e1500469–e1500469.

MacArthur RH, Pianka ER (1966) On optimal use of a patchy environment. The American Naturalist 100:603–609.

Real LA, Levin SA, Brown JH (1991) Part 2: Theoretical advances: the role of theory in the rise of modern ecology. In: Foundations of ecology: classic papers with commentaries.

Samuelson, Paul (1947). Foundations of Economic Analysis. Harvard University Press.

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

Managing Oceans: the inner-workings of marine policy

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

When we hear “marine policy” we broadly lump it together with environmental policy. However, marine ecosystems differ greatly from their terrestrial counterparts. We wouldn’t manage a forest like an ocean, nor would we manage an ocean like a forest. Why not? The answer to this question is complex and involves everything from ecology to politics.

Oceans do not have borders; they are fluid and dynamic. Interestingly, by defining marine ecosystems we are applying some kind of borders. But water (and all its natural and unnatural content) flows between these ‘ecosystems’. Marine ecosystems are home to a variety of anthropogenic activities such as transportation and recreation, in addition to an abundance of species that represent the three major domains of biology: Archaea, Bacteria, and Eukarya. Humans are the only creatures who “recognize” the borders that policymakers and policy actors have instilled. A migrating gray whale does not have a passport stamped as it travels from its breeding grounds in Mexican waters to its feeding grounds in the Gulf of Alaska. In contrast, a large cargo ship—or even a small sailing vessel—that crosses those boundaries is subjected to a series of immigration checkpoints. Combining these human and the non-human facets makes marine policy complex and variable.

The eastern Pacific gray whale migration route includes waters off of Mexico, Canada, and the United States. Source:

Environmental policy of any kind can be challenging. Marine environmental policy adds many more convoluted layers in terms of unknowns; marine ecosystems are understudied relative to terrestrial ecosystems and therefore have less research conducted on how to best manage them. Additionally, there are more hands in the cookie jar, so to speak; more governments and more stakeholders with more opinions (Leslie and McLeod 2007). So, with fewer examples of successful ecosystem-based management in coastal and marine environments and more institutions with varied goals, marine ecosystems become challenging to manage and monitor.

A visual representation of what can happen when there are many groups with different goals: no one can easily get what they want. Image Source: The Brew Monks

With this in mind, it is understandable that there is no official manual on policy development.  There is, however, a broadly standardized process of how to develop, implement, and evaluate environmental policies: 1) recognize a problem 2) propose a solution 3) choose a solution 4) put the solution into effect and 4) monitor the results (Zacharias pp. 16-21). For a policy to be deemed successful, specific criteria must be met, which means that a common policy is necessary for implementation and enforcement. Within the United States, there are a multiple governing bodies that protect the ocean, including the National Oceanic and Atmospheric Administration (NOAA), Environmental Protection Agency (EPA), Fish and Wildlife Service (USFWS), and the Department of Defense (DoD)—all of which have different mission statements, budgets, and proposals. To create effective environmental policies, collaboration between various groups is imperative. Nevertheless, bringing these groups together, even those within the same nation, requires time, money, and flexibility.

This is not to say that environmental policy for terrestrial systems, but there are fewer moving parts to manage. For example, a forest in the United States would likely not be an international jurisdiction case because the borders are permanent lines and national management does not overlap. However, at a state level, jurisdiction may overlap with potentially conflicting agendas. A critical difference in management strategies is preservation versus conservation. Preservation focuses on protecting nature from use and discourages altering the environment. Conservation, centers on wise-use practices that allow for proper human use of environments such as resource use for economic groups. One environmental group may believe in preservation, while one government agency may believe in conservation, creating friction amongst how the land should be used: timber harvest, public use, private purchasing, etc.

Linear representation of preservation versus conservation versus exploitation. Image Source: Raoof Mostafazadeh

Furthermore, a terrestrial forest has distinct edges with measurable and observable qualities; it possesses intrinsic and extrinsic values that are broadly recognized because humans have been utilizing them for centuries. Intrinsic values are things that people can monetize, such as commercial fisheries or timber harvests whereas extrinsic values are things that are challenging to put an actual price on in terms of biological diversity, such as the enjoyment of nature or the role of species in pest management; extrinsic values generally have a high level of human subjectivity because the context of that “resource” in question varies upon circumstances (White 2013). Humans are more likely to align positively with conservation policies if there are extrinsic benefits to them; therefore, anthropocentric values associated with the resources are protected (Rode et al. 2015). Hence, when creating marine policy, monetary values are often placed on the resources, but marine environments are less well-studied due to lack of accessibility and funding, making any valuation very challenging.

The differences between direct (intrinsic) versus indirect (extrinsic) values to biodiversity that factor into environmental policy. Image Source:

Assigning a cost or benefit to environmental services is subjective (Dearborn and Kark 2010). What is the benefit to a child seeing an endangered killer whale for the first time? One could argue priceless. In order for conservation measures to be implemented, values—intrinsic and extrinsic—are assigned to the goods and services that the marine environment provides—such as seafood and how the ocean functions as a carbon sink. Based off of the four main criteria used to evaluate policy, the true issue becomes assessing the merit and worth. There is an often-overlooked flaw with policy models: it assumes rational behavior (Zacharias 126). Policy involves relationships and opinions, not only the scientific facts that inform them; this is true in terrestrial and marine environments. People have their own agendas that influence, not only the policies themselves, but the speed at which they are proposed and implemented.

Tourists aboard a whale-watching vessel off of the San Juan Islands, enjoying orca in the wild. Image Source: Seattle Orca Whale Watching

One example of how marine policy evolves is through groups, such as the International Whaling Commission, that gather to discuss such policies while representing many different stakeholders. Some cultures value the whale for food, others for its contributions to the surrounding ecosystems—such as supporting healthy seafood populations. Valuing one over the other goes beyond a monetary value and delves deeper into the cultures, politics, economics, and ethics. Subjectivity is the name of the game in environmental policy, and, in marine environmental policy, there are many factors unaccounted for, that decision-making is incredibly challenging.

Efficacy in terms of the public policy for marine systems presents a challenge because policy happens slowly, as does research. There is no equation that fits all problems because the variables are different and dynamic; they change based on the situation and can be unpredictable. When comparing institutional versus impact effectiveness, they both are hard to measure without concrete goals (Leslie and McLeod 2007). Marine ecosystems are open environments which add an additional hurdle: setting measurable and achievable goals. Terrestrial environments contain resources that more people utilize, more frequently, and therefore have more set goals. Without a problem and potential solution there is no policy. Terrestrial systems have problems that humans recognize. Marine systems have problems that are not as visible to people on a daily basis. Therefore, terrestrial systems have more solutions presented to mitigate problems and more policies enacted.

As marine scientists, we don’t always immediately consider how marine policy impacts our research. In the case of my project, marine policy is something I constantly have to consider. Common bottlenose dolphins are protected under the Marine Mammal Protection Act (MMPA) and inhabit coastal of both the United States and Mexico, including within some Marine Protected Areas (MPA). In addition, some funding for the project comes from NOAA and the DoD. Even on the surface-level it is clear that policy is something we must consider as marine scientists—whether we want to or not. We may do our best to inform policymakers with results and education based on our research, but marine policy requires value-based judgements based on politics, economics, and human objectivity—all of which are challenging to harmonize into a succinct problem with a clear solution.

Two common bottlenose dolphins (coastal ecotype) traveling along the Santa Barbara, CA shoreline. Image Source: Alexa Kownacki


Dearborn, D. C. and Kark, S. 2010. Motivations for Conserving Urban Biodiversity. Conservation Biology, 24: 432-440. doi:10.1111/j.1523-1739.2009.01328.x

Leslie, H. M. and McLeod, K. L. (2007), Confronting the challenges of implementing marine ecosystem‐based management. Frontiers in Ecology and the Environment, 5: 540-548. doi:10.1890/060093

Munguia, P., and A. F. Ojanguren. 2015. Bridging the gap in marine and terrestrial studies. Ecosphere 6(2):25.

Rode, J., Gomez-Baggethun, E., Krause, M., 2015. Motivation crowding by economic payments in conservation policy: a review of the empirical evidence. Ecol. Econ. 117, 270–282 (in this issue).

White, P. S. (2013), Derivation of the Extrinsic Values of Biological Diversity from Its Intrinsic Value and of Both from the First Principles of Evolution. Conservation Biology, 27: 1279-1285. doi:10.1111/cobi.12125

Zacharias, M. 2014. Marine Policy. London: Routledge.