Surrogates underpin ecological understanding and practice

BioScience

Abstract

A surrogate is a proxy measure for an attribute of true interest that is too difficult or costly to measure directly. Surrogacy is widely used in the environmental sciences, as well as in other disciplines, such as clinical medicine and pharmacology (Barton et al. 2015, Lindenmayer et al. 2015a). In medicine, for example, easily quantified properties of blood, such as cholesterol level, are regularly used to infer a patient’s health, risk of disease, or response to a medical treatment (Barton et al. 2015). Similarly, ecologists often monitor attributes such as carbon stocks, species richness, or vegetation structure to infer the overall state of biodiversity, risk of undesired change, or response to a management intervention (Lindenmayer et al. 2015a).

Surrogates are often used in applied ecology to inform decisions about biodiversity management, atmospheric pollution, and conservation reserve selection (e.g., Rodrigues and Brooks 2007). However, proxy measures are also used widely in fundamental ecology. Ecosystem properties such as productivity, fire severity, and water quality are almost exclusively inferred from related but indirect measures (e.g., Keeley 2009). This implicit use of surrogacy is often not acknowledged outside of the applied disciplines. The conceptual and analytical frameworks developed to improve surrogacy in applied contexts therefore have much to offer research in fundamental ecology. Similarly, the causal frameworks and search for mechanism in fundamental ecology has much to offer applied surrogacy. In our view, integrating and communicating the lessons from each will lead to better outcomes for both.

Here, we consider how fundamental tenets from surrogate research, particularly those that deal with intrinsic uncertainty and risk, are underappreciated in broader ecological research. Our assertion is that explicit recognition of the use of surrogates will benefit all ecological research through improved evaluation of the accuracy, consistency, and certainty of the inferences drawn from measures, regardless of the context.