Recommendations towards greater transparency in the science, science communication, and values-driven processes of natural resource management

Facts, fact claims, and values ought to be disentangled and kept clear if scientists, governments, policy makers, and interest groups want to work effectively and uphold the public trust. Researchers investigate two case studies and make recommendations towards greater shared clarity.

Cognitive bias, institutional agendas, human interests, and values all present challenges for science and science communication. When science and science communication are then folded into governance, these complexities are multiplied. 

A new paper, published by a team of researchers including Raincoast scientists, dives into this tangle and pays special attention to the role of undisclosed value judgments. The authors examine two cases in depth, revealing some recommendations for improving transparency in governance, debate, and science itself.

The paper, “Transparency About Values and Assertions of Fact in Natural Resource Management”, was led by Adrian Treves from the University of Wisconsin-Madison, and is published in the open-access journal Frontiers in Conservation Science.

The authors argue that targets set in natural resource management policy (i.e., harvest or habitat prescriptions) are often hiding imported value judgements about resources and their preservation. In this way, personal, institutional, or societal preferences can become entangled in scientific research.

The case studies are the northern spotted owl policy debates in the U.S. and a population model used to plan the regulated killing of wolves in Wisconsin;they illustrate how natural resource management is often rooted in undisclosed value systems. For example, the allowable ‘harvest’ of wolves was set at an arbitrary level reflective of values, even though the level was presented by government managers as a scientifically-determined prescription.

To meet this disentangle values from the products of science, the authors propose several recommendations for scientists working in natural resource management, including:

  1. Work to recognize values in communication; more clearly delineate value judgments from scientific observations and predictions,
  2. Work to recognize and make explicit the far-reaching and subtle effects of values, and alternative values, in research frameworks,
  3. Uphold the public trust by implementing stricter codes of scientific integrity, especially regarding collecting and sharing data and institutional bias,
  4. Make values explicit, identify winners and losers, disclose assumptions rooted in values, like intergenerational equity, precautionary principle, anthropocentrism, public trust principles, and more.


Worldwide, unsustainable use of nature threatens many ecosystems and the services they provide for a broad diversity of life, including humans. Yet, governments commonly claim that the best available science supports their policies governing extraction of natural resources. We confront this apparent paradox by assessing the complexity of the intersections among value judgments, fact claims, and scientifically verified facts. Science can only describe how nature works and predict the likely outcomes of our actions, whereas values influence which actions or objectives society ought to pursue. In the context of natural resource management, particularly of fisheries and wildlife, governments typically set population targets or use quotas. Although these are fundamentally value judgments about how much of a resource a group of people can extract, quotas are often justified as numerical guidance derived from abstracted, mathematical, or theoretical models of extraction. We confront such justifications by examining failures in transparency about value judgments, which may accompany unsupported assertions articulated as factual claims. We illustrate this with two examples. Our first case concerns protection and human use of habitats harboring the northern spotted owl (Strix occidentalis caurina), revealing how biologists and policy scholars have argued for divergent roles of scientists within policy debates, and how debates between scientists engaged in policy-relevant research reveal undisclosed value judgments about communication of science beyond its role as a source of description (observation, measurement, analysis, and inference). Our second case concerns protection and use of endangered gray wolves (Canis lupus) and shows how undisclosed value judgments distorted the science behind a government policy. Finally, we draw from the literature of multiple disciplines and wildlife systems to recommend several improvements to the standards of transparency in applied research in natural resource management. These recommendations will help to prevent value-based distortions of science that can result in unsustainable uses and eventual extinctions of populations. We describe methods for communicating about values that avoid commingling factual claims and discuss approaches to communicating science that do not perpetuate the misconception that science alone can dictate policy without consideration of values. Our remedies can improve transparency in both expert and public debate about preserving and using natural resources, and thereby help prevent non-human population declines worldwide.


Treves A, Paquet PC, Artelle KA, Cornman AM, Krofel M and Darimont CT (2021) Transparency About Values and Assertions of Fact in Natural Resource Management. Front. Conserv. Sci. 2:631998. doi: 10.3389/fcosc.2021.631998

Select figures

Figure 2

Chart of wolf numbers over years - figure 2 from the paper.
Figure 2. The 1999 Plan Figure 7 “Wisconsin Wolf Population Growth If Carrying Capacity Is 500 Wolves.” The 1999 Plan forecast wolf population growth to 2020 from a superimposed, generic logistic growth curve (WDNR, 1999). The model treated the population estimates as a single time series, although according to Treves (2019a) this should have been presented as two time series because of a change in wolf census methods in the winter of 1994–1995. The “Delisting Level” was set at 250, when the legal removal of wolves from the state’s list of threatened and endangered species would begin. The “Management Goal” codified a population target (Ngoal = 350), which is still the state population target today (USFWS, 2020). Vertical lines represented 5-year intervals and horizontal lines represented hundreds of wolves. Arguably, the explicit value judgments (Delisting Level and Management Goal) appear as outputs of the model, though their origin was not explained in WDNR (1999).

Figure 3

Figure 3 from the paper outlining the four recommendations for natural resource management and their relationships.
Figure 3. Recommendations for where the science begins and ends in NRM. Pink boxes are aimed at the dialogue between science and public policy, the orange box is aimed at policy-makers, and the green box is aimed at scientists. Arrows indicate cause and effect relationships, e.g., being transparent and examining alternatives will help scientists to uphold the public trust although not sufficient by themselves.


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