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«SFI WORKING PAPER: 2014-06-018 SFI Working Papers contain accounts of scienti5ic work of the author(s) and do not necessarily ...»

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On Theory in Ecology

Pablo A. Marquet

Andrew P. Allen

James H. Brown, et al.

SFI WORKING PAPER: 2014-06-018

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SANTA FE INSTITUTE

On theory in ecology by Pablo A. Marquet1,2,3,4, Andrew P. Allen5, James H. Brown6, Jennifer A. Dunne3,7, Brian J.

Enquist3,8, James F. Gillooly9, Patricia A. Gowaty10,11, Jessica L. Green12,,John Harte13, Steve P. Hubbell10,11, James O´Dwyer14, Jordan G. Okie15, Annette Ostling16, Mark Ritchie17, David Storch18,19, and Geoffrey B. West3 1 Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, C.P. 6513677, Santiago, Chile. E-mail: pmarquet@bio.puc.cl 2 Institute of Ecology and Biodiversity (IEB), Casilla 653, Santiago, Chile.

3 The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA.

4 Instituto de Sistemas Complejos de Valparaíso (ISCV), Subida Artillería 470, Valparaíso, Chile.

5 Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia.

6 Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.

7 Pacific Ecoinformatics and Computational Ecology Lab, 1604 McGee Ave., Berkeley, CA 94703, USA 8 Department of Ecology and Evolutionary Biology, University of Arizona, BioSciences West, Tucson, AZ 85721, USA.

9 Department of Biology, University of Florida, Room 223 Bartram Hall, PO Box 118525, Gainesville, FL 32611-852 10 Department of Ecology & Evolutionary Biology and Institute of the Environment and Sustainability, University of California, Los Angeles, California 90095, USA.

11 Smithsonian Tropical Research Institute 12 Institute of Ecology and Evolutionary Biology, 335 Pacific Hall, 5289 University of Oregon, Eugene, Oregon 97403, USA.

13 University of California, Berkeley, CA 94720 14 Department of Plant Biology, University of Illinois, Urbana IL USA.

15 School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA.

16 Department of Ecology and Evolutionary Biology, University of Michigan 2005 Kraus Natural Science Building, 830 North University Ann Arbor, Michigan 48109USA.

17 Department of Biology, Syracuse University, 108 College Place, 122 Lyman Hall, Syracuse NY 13244, USA.

18 Center for Theoretical Study, Charles University of Academy Sciences of the CR, Praha, Czech Republic 19 Department of Ecology, Faculty of Science, Charles University, Praha, Czech Republic Corresponding author: Pablo A. Marquet E-mail: pmarquet@bio.puc.cl

–  –  –

We argue for expanding the role of theory in ecology to accelerate scientific progress, enhance ability to address environmental challenges, foster the development of synthesis and unification, and improve design of experiments and large-scale environmental monitoring programs. To achieve these goals it is essential to foster the development of what we call “efficient” theories, which have several key attributes. Efficient theories 1) are grounded in first principles, 2) are usually expressed in the language of mathematics, 3) make few assumptions and generate a large number of predictions per free parameter, 4) are approximate, and 5) their predictions provide well understood standards for comparison with empirical data. We contend that the development and successive refinement of efficient theories provide a solid foundation for advancing environmental science in the era of Big Data.

Keywords Theory unification; Metabolic Theory; Neutral Theory of Biodiversity; Maximum Entropy Theory of Ecology; Big Data.

–  –  –

Introduction Science aims to deepen our understanding of the natural world. Progress in science arises from the tension between induction and deduction, empiricism and theory. Data gathered via observation and experimentation provide clues about the structure and function of the natural world, and theory organizes existing data and new ideas into a cohesive conceptual framework to both explain existing observations and make novel predictions. Theory reduces the apparent complexity of the natural world, because it captures essential features of a system, provides abstracted characterizations, and makes predictions for as-yet unobserved phenomena that additional data can test.





There are many ideas about the nature of theory in science and in philosophy (Nagel 1961, NRC 2008). In ecology, there is currently no consensus regarding the definition, role, and generality of theories, as discussed in several essays (e.g. Pickett et al. 1994, Scheiner and Willig 2011). It is not surprising that ecologists use the label ‘theory’ to refer to many things.

Theory meanings range from highly specific to very general, from hypotheses (e.g., the hypothesis of clutch overlap, or the intermediate disturbance hypothesis) to conceptual frameworks for complete fields or research programs (e.g., macroecology, conservation biology). A survey of the ecological literature finds reference to 78 theories (see Supporting Material Table S1). Almost half of which have been mentioned in more than two publications (Figure 1, Supporting Material Table S1), suggesting that ecology is awash with theories. But, is ecology theory rich? Are there different types of theories in ecology, with different precepts and goals? What theories constitute a foundational conceptual framework for building a more predictive, quantitative, and useful science of ecology? A field with a large and diverse number of theories may be a healthy one, but this can also hamper progress, stifle innovation and lead to inadequate policy to manage, mitigate and adapt to undesirable environmental impacts. A discussion of the different types of theories in ecology is a timely and necessary exercise.

Here we define a theory as a hierarchical framework that contains clearly formulated postulates, based on a minimal set of assumptions, from which a set of predictions logically follows. Theory is inherently deductive. Advances in data stimulate theory, and new theory refines, expands and replaces old theory thereby correcting flaws, explaining and predicting phenomena in the domain in which they apply. As such, the evolution of a successful theory is for it to become more general, leading to detailed models that apply the theory to a more focused class of phenomena in a more restricted domain.

Is ecology theory rich?

The richness of theories in ecology is to some degree illusory. As mentioned above, several “theories” (see Supporting Material Table S1) are actually specific hypotheses or models. Ecologists and other scientists often use the terms “model” and “theory” indistinguishably (e.g., Leijonhufvud 1997; Ginzburg and Jensen 2004), but “model” and “theory” are fundamentally different. Models usually aim to 1) increase our understanding or 3 solve a particular problem in a particular domain (e.g., the role of nutrient additions to lake ecosystems) or 2) explore the consequences of relaxing one or more assumptions of a theory (e.g., inclusion of Allee effects in Metapopulation Theory). “Since models are simplified, partial statements of theories, several models may belong to the same theory, designed to elucidate different aspects of it” (Leijonhufvud (1997). Some models, if repeatedly tested and supported, can eventually give way to a theory, but they are not theories by themselves.

Discovery of new empirical statistical patterns and statistical relationships often leads to theoretical advances. However, the inductively revealed patterns do not themselves constitute a theory, and neither do statistical representations of data or model fitting exercises. Some ecologists consider a statistical regression model a theoretical construct (e.g.

Peters 1991), but regression fails to meet our definition of theory. Although regression can test a theory by evaluating predicted relationships among variables, it does not constitute theory in itself. Statistical analyses may lead to the creation and refinement of theory;

however, theory goes further to provide understanding of the processes that give rise to the relationships, independent of the statistical fit.

Notwithstanding the apparent richness of theories in ecology (Figure 1, Supplementary Material Table S1), there seems to be a general trend that the use and importance of theory is declining in biology as a whole (NRC 2008). Biologists appear to increasingly underappreciate the role that theory can play. The fact that so called “model organisms” are used in the majority of research in molecular biology and biomedicine implies that biologists are searching for general trends and unified principles, but there is seemingly little motivation to organize new findings into a rigorous hierarchical framework expressed in the language of mathematics. This may be, in part, because we are overwhelmed and overly impressed by the vast amount of data that can be obtained from the natural world. The era of Big Data or data deluge (e.g., Bollier and Firestone 2010) has fostered the proliferation of giant databases, data mining, machine learning, and other inductive approaches. Some have suggested that theories are irrelevant in the big data era – that correlations are sufficient to build a vigorous science (Anderson 2008).

We disagree. We argue that data are fundamentally necessary and important but not sufficient; healthy productive science needs both theory and data to feed the continuous interplay between induction and deduction. No matter how much data one can obtain from social, biological and ecological systems, the multiplicity of entities and interactions among them means that we will never be able to predict many salient features of their structures and dynamics. To discover the underlying principles, mechanisms, and organization of complex adaptive systems and to develop a quantitative, predictive, conceptual framework ultimately requires the close integration of both theory and data.

Are there different types of theories in ecology?

Many of the theories listed in Figure 1 comply with the definition of theory given above, but some theories are more efficient than others. To understand the importance of efficiency, it is instructive to revisit the remarks of the British statistician George E.P. Box (Box 1976, p.

792) who said, “Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William of Occam he should seek an 4 economical description of natural phenomena.” We claim that the same is true for evaluating alternative theories that purport to explain the same phenomena. As pointed out by the philosopher of science Larry Laudan, the evaluation of theories is a comparative matter (Laudan 1977) and one important criterion for comparison is efficiency. A theory is more efficient than its rivals if it can make more and better explanations and predictions with the same number of free parameters or constructs (Laudan 1977).

Here we describe our emerging strategy for developing efficient theories in ecology.

Our strategy is not normative. Specifically, we do not imply that this is the only way to advance ecological understanding, especially under a post-modern scientific framework (Funtowicz and Ravetz 1993, Allen et al. 2001). In the following discussion it is useful to bear in mind that theory, etymologically, comes from the ancient Greek theōria that means contemplation or looking at. In that sense, a theory is a way of looking at the world and not necessarily a way of knowing how the world is. Our main claim is that efficient theories provide a particular way of looking at the world that can be particularly insightful and useful.

Building efficient theories is of fundamental importance as we think it will allow for a faster advancement of our field.

We do not suggest that all ecologists should be theoreticians. We recognize the value of pluralistic approaches. We do believe that a healthy and advancing science of ecology needs some appropriate balance between empiricists and theoreticians. Such a balanced science will not only contribute to the development of a quantitative and predictive science of ecology, it also will contribute to the application of ecological science to address pressing climate, societal and health challenges. We hope that our reflections will contribute to better understanding of the role of theory in ecology and explain why we think the development of theory in ecology is such an important pursuit.

In what follows, we provide a detailed account of what we think the salient characteristics of efficient theories are in ecology.

What constitutes an efficient theory?

(i) First principles ground efficient theories. – Efficient theories should be built, as much as possible, on first principles. First principles are the bedrock of science: that is, quantitative law-like postulates about processes underlying a given class of phenomena in the natural world with well-established validity, both theoretically and empirically (i.e. core-knowledge).



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