Adaptive Inference for Distinguishing Credible from Incredible Patterns in Nature

University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Nebraska Cooperative Fish & Wildlife Research Nebraska Cooperative Fish & Wildlife Research Unit -- Staff Publications Unit 2002 Adaptive Inference for Distinguishing Credible from Incredible Patterns in Nature C. S. Holling University of Florida Craig R. Allen University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/ncfwrustaff Part of the Other Environmental Sciences Commons Holling, C. S. and Allen, Craig R., "Adaptive Inference for Distinguishing Credible from Incredible Patterns in Nature" (2002). Nebraska Cooperative Fish & Wildlife Research Unit -- Staff Publications. 1. https://digitalcommons.unl.edu/ncfwrustaff/1 This Article is brought to you for free and open access by the Nebraska Cooperative Fish & Wildlife Research Unit at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Nebraska Cooperative Fish & Wildlife Research Unit -- Staff Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Ecosystems (2002) 5: 319–328 DOI: 10.1007/s10021-001-0076-2 ECOSYSTEMS © 2002 Springer-Verlag Adaptive Inference for Distinguishing Credible from Incredible Patterns in Nature C. S. Holling1 and Craig R. Allen2* 1Department of Zoology, 110 Bartram, University of Florida, Gainesville, Florida 32611, USA; and 2US Geological Survey, Biological Resources Division, South Carolina Cooperative Fish and Wildlife Research Unit, Clemson University, Clemson, South Carolina 29634, USA ABSTRACT Strong inference is a powerful and rapid tool that member of a pair of hypotheses against each other, can be used to identify and explain patterns in adaptive inference relies on the exuberant inven- molecular biology, cell biology, and physiology. It is tion of multiple, competing hypotheses, after which effective where causes are single and separable and carefully structured comparative data are used to where discrimination between pairwise alternative explore the logical consequences of each. Herein we hypotheses can be determined experimentally by a present an example that demonstrates the at- simple yes or no answer. But causes in ecological tributes of adaptive inference that have developed systems are multiple and overlapping and are not out of a 30-year study of the resilience of ecosys- entirely separable. Frequently, competing hypoth- tems. eses cannot be distinguished by a single unambig- uous test, but only by a suite of tests of different Key words: adaptive inference; cross-scale dynam- kinds, that produce a body of evidence to support ics; ecosystem structure; hypothesis testing; lumps; one line of argument and not others. We call this resilience; strong inference; textural discontinuity process “adaptive inference”. Instead of pitting each hypothesis. INTRODUCTION several competing hypotheses, and multiple lines of evidence that converge on a particular argument. This article is a review of the approach and methods Understanding the complexity of structure and we have used in a continuing effort to develop and function in ecosystems requires a sequence of cycles test a novel theory for a particular class of ecological of investigation where each cycle starts with the systems—that is, ecosystems, or sets of ecosystems. general, moves to the specific, and returns to the In this class, interactions occur across scales from general again. At each phase in a given cycle, there centimeters and days to 10s of kilometers and cen- are different goals for acceptable evidence. Hence, turies, so that opportunities for manipulative exper- an investigation moves from general and suggestive iments are limited and partial and causes are mul- goals to specific and credible ones. For major studies tiple and nonlinear. This is the class emphasized by of complex systems, such as ecosystems, the se- Pickett and others (1994) in their critique of eco- quence of cycles takes decades and a consistent and logical theory, method, statistics, and practice. It is a persistent pattern of research that searches for nov- class that requires pluralistic scientific methods, elty, comparability, and credibility. We argue that the goal is not to arrive at certain conclusions but to develop credible lines of argument that include Received 14 November 2000; accepted 15 June 2001. some conclusions that might be certain, others that *Corresponding author; e-mail: [email protected] are likely, and still others that are uncertain. 319 320 C. S. Holling and C. R. Allen This article was initially motivated by two specific tems, including more recent work on lumpiness critiques (Manly 1996; Siemann and Brown 1999) and cross-scale dynamics. of one recent cycle of research on the “lumpy” We will deal principally with the challenge such character of the body masses of organisms in eco- studies present in terms of approach and method. systems (Holling 1992). But it has grown from that They pose the following problem: How can we inception and has been generalized to deal with the maintain a degree of skepticism about the existence fuller range of issues entailed by such investiga- of patterns and causes while at the same time en- tions. The specific examples we present here con- couraging the innovation needed to stimulate cre- cern ecosystem dynamics and structure. However, ative discovery about those patterns and their the profile is one that applies generally to studies of causes? Meeting this challenge is essential to studies global climate change, to analyses of the causes and of complexity. functions of biodiversity, to studies of ecosystem trophic cascade dynamics, and to designs for restor- APPROACH AND METHODS ing and maintaining resilience in regional systems where people and nature interact (Gunderson and It is not useful to frame the approach as a battle that Holling 2002). Our examples are drawn from our pits a philosophy that insists upon avoiding type I own work in the latter area. error and assumes the primacy of falsification The paper suggesting that ecosystem attributes against one that argues for avoiding type II error are distributed discontinuously (that is, they are and sees value in confirmatory observational evi- “lumpy”) (Holling 1992) arose from 30 years of dence. The first position runs the risk of over- research aimed at understanding the dynamics and restricting innovation for the sake of certainty; the structure of ecosystems. It triggered a strong reac- second, of an endless accumulation of anecdotes for tion from some of those active in statistical and the sake of speculation. At their extremes, the first erects premature stopping rules for inquiry; community ecology, because it seemed to fall whereas the second presents no clear stopping rule within those areas of inquiry. The critical responses at all. dealt only with the question of whether the distri- But at their intersection is a cycle of inquiry that butions considered were lumpy, arguing that either we call “adaptive inference”. The cycle of investiga- there were at most two lumps in the distribution of tion is initiated by speculation. Speculation is based animal body masses (Manly 1996) or no lumps at on untested but novel ideas that then can be ex- all (Siemann and Brown 1999) rather than the five plored using simple models, empirical observations, to 10 lumps originally proposed (Holling 1992). The and hard data. The ideas are then roughly tested in significant point we want to address here concerns as quick and effective a manner as possible, using our belief that these two critiques are based on an easily obtained data from any source. For example, approach and methods appropriate for a different well before its publication, the original research on tradition of science—one that does not place an lumps (Holling 1992) was submitted to just such a emphasis on the issues we dealt with and that, process. This was done as an extension of earlier taken alone, cannot readily identify properties of resilience research (Holling 1986) that suggested such complex systems. As an example of complex that there are a small number of structuring pro- systems, ecosystems have causes that are nonlinear cesses that control dynamics in ecosystems, each and multiscalar. Interactions and self-organization operating at its own scale. If so, one of the conse- occur across scales; in particular, they involve self- quences would be a lumpy distribution in the at- organization among both biotic and abiotic ele- tributes of ecosystems. One attribute that was easy ments. to obtain and measure was the body mass of ani- Our purpose here is therefore not to establish mals living in an extensive landscape. Analysis was how lumpy animal body mass distributions are, but first meant to be quick and to provide an approxi- rather to stand back and summarize the methods mate test of a large proposition. The first check of that we, along with others, have developed for the the full distribution of body masses of a group of analysis of systems with complex dynamics. It hap- animals did not reveal any obvious lumpiness. But pens that these approaches and methods were the a further analysis of departures from a unimodal ones that led us to the conclusion that the world is representation did seem to show that body weights lumpy and that continue to guide our ongoing re- were grouped into a small number of size classes — search on these and related subjects. We will illus- at least five to eight, and perhaps more. trate the features of the approach and methods by Thereafter, we made extensive efforts to devise reference to research on the resilience of ecosys- several competing hypotheses of causation, to ac- Adaptive Inference 321 cumulate evidence from several systems and situa- vatism or inertia in the scientific enterprise, often tions that would help to sift among hypotheses, to rationalized as the healthy skepticism characteristic develop and test specific predictive models, to form of the scientific temper.” But according to, Church- generalizations, and finally to return to speculation. man (1948), the best procedure is the one that Ultimately, through this well-articulated process, minimizes both types of error.

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