James Justus

As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. We counter by pointing out important inaccuracies about the science and rejecting the apparent theory-first focus. More broadly, the case study reveals significant limitations of the predominant epistemic-semantic conceptions of scientific progress and the considerable merits of pragmatic, practically-oriented accounts.

Pure mathematics can play an indispensable role explaining empirical phenomena if recent accounts of insect evolution are correct. In particular, the prime life cycles of cicadas and the geometric structure of honeycombs are taken to undergird an inference to the best explanation about mathematical entities. Neither example supports this inference or the mathematical realism it is intended to establish. Both incorrectly assume that facts about mathematical optimality drove selection for the respective traits and explain why they exist. We show how this problem can be avoided, identify limitations of explanatory indispensability arguments, and attempt to clarify the nature of mathematical explanation.

The niche is allegedly the conceptual bedrock underpinning the most prominent, and some would say most important, theorizing in ecology. We argue this point of view is more aspirational than veridical. Rather than critically dissect existing definitions of the concept, the supposedly significant work it is thought to have done in ecology is our evaluative target. There is no denying the impressive mathematical sophistication and theoretical ingenuity of the ecological modeling that invokes ‘niche’ terminology. But despite the pervasive labeling, we demonstrate that niche talk is nothing more than a gloss on theory developed without it, that doesn’t need it, and that doesn’t benefit from it.

As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. We counter by pointing out important inaccuracies about the science and rejecting the apparent theory-first focus. More broadly, the case study reveals significant limitations of the predominant epistemic-semantic conceptions of scientific progress and the considerable merits of pragmatic, practically-oriented accounts.

Ecology is indispensable to understanding the biological world and addressing the environmental problems humanity faces. Its philosophy has never been more important. In this book, James Justus introduces readers to the philosophically rich issues ecology poses. Besides its crucial role in biological science generally, climate change, biodiversity loss, and other looming environmental challenges make ecology's role in understanding such threats and identifying solutions to them all the more critical. When ecology is applied and its insights marshalled to address these problems and guide policy formation, interesting philosophical issues emerge. Justus sets them out in detail, and explores the often ethically charged dimensions of applied ecological science, using accessible language and a wealth of scientifically-informed examples.

Perhaps no concept has been thought more important to ecological theorizing than the niche. Without it, technically sophisticated and well-regarded accounts of character displacement, ecological equivalence, limiting similarity, and others would seemingly never have been developed. The niche is also widely considered the centerpiece of the best candidate for a distinctively ecological law, the competitive exclusion principle. But the incongruous array and imprecise character of proposed definitions of the concept square poorly with its apparent scientific centrality. I argue this definitional diversity and imprecision reflects a problematic conceptual indeterminacy that challenges its putative indispensability in ecology.

Rudolf Carnap is increasingly regarded as one of the most important philosophers of the twentieth century. He was one of the leading figures of the logical empiricist movement associated with the Vienna Circle and a central figure in the analytic tradition more generally. He made major contributions to philosophy of science and philosophy of logic, and, perhaps most importantly, to our understanding of the nature of philosophy as a discipline. In this volume a team of contributors explores the major themes of his philosophy and discusses his relationship with the Vienna Circle and with philosophers such as Frege, Husserl, Russell, and Quine. New readers will find this the most convenient and accessible guide to Carnap currently available. Advanced students and specialists will find a conspectus of recent developments in the interpretation of Carnap.

Explicit, quantitative procedures for identifying biodiversity priority areas are replacing the often ad hoc procedures used in the past to design networks of reserves to conserve biodiversity. This change facilitates more informed choices by policy makers, and thereby makes possible greater satisfaction of conservation goals with increased efficiency. A key feature of these procedures is the use of the principle of complementarity, which ensures that areas chosen for inclusion in a reserve network complement those already selected. This paper sketches the historical development of the principle of complementarity and its applications in practical policy decisions. In the first section a brief account is given of the circumstances out of which concerns for more explicit systematic methods for the assessment of the conservation value of different areas arose. The second section details the emergence of the principle of complementarity in four independent contexts. The third section consists of case studies of the use of the principle of complementarity to make practical policy decisions in Australasia, Africa, and America. In the last section, an assessment is made of the extent to which the principle of complementarity transformed the practice of conservation biology by introducing new standards of rigor and explicitness.

Richard Levins has advocated the scientific merits of qualitative modeling throughout his career. He believed an excessive and uncritical focus on emulating the models used by physicists and maximizing quantitative precision was hindering biological theorizing in particular. Greater emphasis on qualitative properties of modeled systems would help counteract this tendency, and Levins subsequently developed one method of qualitative modeling, loop analysis, to study a wide variety of biological phenomena. Qualitative modeling has been criticized for being conceptually and methodologically problematic. As a clear example of a qualitative modeling method, loop analysis shows this criticism is indefensible. The method has, however, some serious limitations. This paper describes loop analysis, its limitations, and attempts to clarify the differences between quantitative and qualitative modeling, in content and objective. Loop analysis is but one of numerous types of qualitative analysis, so its limitations do not detract from the currently underappreciated and underdeveloped role qualitative modeling could have within science.

Ecologists have proposed several incompatible definitions of ecological stability. Emulating physicists, mathematical ecologists commonly define it as Lyapunov stability. This formalizes the problematic concept by integrating it into a well‐developed mathematical theory. The formalization also seems to capture the intuition that ecological stability depends on how ecological systems respond to perturbation. Despite these advantages, this definition is flawed. Although Lyapunov stability adequately characterizes perturbation responses of many systems studied in physics, it does not for ecological systems. This failure reveals a limitation of its underlying mathematical theory, and an important difference between dynamic systems modeling in physics and ecology. *Received March 2006; revised June 2008. †To contact the author, please write to: Philosophy Department, 151 Dodd Hall, Florida State University, Tallahassee, FL 32306; e‐mail: [email protected].

The rise of experimental philosophy has placed metaphilosophical questions, particularly those concerning concepts, at the center of philosophical attention. X-phi offers empirically rigorous methods for identifying conceptual content, but what exactly it contributes towards evaluating conceptual content remains unclear. We show how x-phi complements Rudolf Carnap’s underappreciated methodology for concept determination, explication. This clarifies and extends x-phi’s positive philosophical import, and also exhibits explication’s broad appeal. But there is a potential problem: Carnap’s account of explication was limited to empirical and logical concepts, but many concepts of interest to philosophers are essentially normative. With formal epistemology as a case study, we show how x-phi assisted explication can apply to normative domains.

Methodological individualism has a long, successful, and controversial track record in the social sciences. Its record in ecology is much shorter but proving as successful and controversial with so-called individual-based models. Distinctions and debates about methodological individualism in social sciences clarify the commitments of this general, individualistic approach to modeling ecological phenomena and show that there is a lot recommending it. In particular, a representational priority on individual organisms yields a cogent albeit deflationary account of ecological emergence and helps reveal how quite disparate models and theories in ecology might be unified

The waning popularity of logical empiricism and the supposed discovery of insurmountable technical difficulties led most philosophers to abandon the project to formulate a formal criterion of empirical significance. Such a criterion would delineate claims that observation can confirm or disconfirm from those it cannot. Although early criteria were clearly inadequate, criticisms made of later, more sophisticated criteria were often indefensible or easily answered. Most importantly, Carnap’s last criterion was seriously misinterpreted and an amended version of it remains tenable

Robustness concepts are often invoked to manage two obstacles confronting models of ecological systems: complexity and uncertainty. The intuitive idea is that any result derived from many idealized but credible models is thereby made more reliable or is better confirmed. An appropriate basis for this inference has proven elusive. Here, several representations of robustness analysis are vetted, paying particular attention to complex models of ecosystems and the global climate. The claim that robustness is itself confirmatory because robustness analysis employs a Bayesian variety-of-evidence argument is criticized, but recent overwhelming pessimism about robustness may have a silver lining.

ebec and Queensland, we applied four methods to assess the extent to which environmental surrogates can represent biodiversity components: (1) surrogacy graphs; (2) marginal representation plots; (3) Hamming distance function; and (4) Syrjala statistical test for spatial congruence. For Qu´ebec we used 719 faunal and floral species as biodiversity components, and for Queensland we used 2348 plant species. We used four climatic parameter types (annual mean temperature, minimum temperature during the coldest quarter, maximum temperature during the hottest quarter, and annual precipitation), along with slope, elevation, aspect, and soil types, as environmental surrogates. To study the effect of scale, we analyzed the data at seven spatial scales ranging from 0.01◦ to 0.10◦ longitude and latitude. At targeted representations of 10% for environmental surrogates and biodiversity components, all four methods indicated that using a full set of environmental surrogates systematically provided better results than selecting areas at random, usually ensuring that ≥90% of the biodiversity components achieved the 10%.

It has been argued in the conservation literature that giving conservation absolute priority over competing interests would best protect the environment. Attributing infinite value to the environment or claiming it is ‘priceless’ are two ways of ensuring this priority (e.g. Hargrove 1989; Bulte and van Kooten 2000; Ackerman and Heinzerling 2002; McCauley 2006; Halsing and Moore 2008). But such proposals would paralyse conservation efforts. We describe the serious problems with these proposals and what they mean for practical applications, and we diagnose and resolve some conceptual confusions permeating the literature on this topic.

Loop analysis is a method of qualitative modeling anticipated by Sewall Wright and systematically developed by Richard Levins. In Levins’ (1966) distinctions between modeling strategies, loop analysis sacrifices precision for generality and realism. Besides criticizing the clarity of these distinctions, Orzack and Sober (1993) argued qualitative modeling is conceptually and methodologically problematic. Loop analysis of the stability of ecological communities shows this criticism is unjustified. It presupposes an overly narrow view of qualitative modeling and underestimates the broad role models play in scientific research, especially in helping scientists represent and understand complex systems.

Abstract   Recent criticisms of intuition from experimental philosophy and elsewhere have helped undermine the authority of traditional conceptual analysis. As the product of more empirically informed philosophical methodology, this result is compelling and philosophically salutary. But the negative critiques rarely suggest a positive alternative. In particular, a normative account of concept determination—how concepts should be characterized—is strikingly absent from such work. Carnap's underappreciated theory of explication provides such a theory. Analyses of complex concepts in empirical sciences illustrates and supports this claim, and counteracts the charge explication is only suitable for highly mathematical, axiomatic contexts. Explication is also defended against the influential criticism it is “philosophically unilluminating”. Content Type Journal Article Category Original paper in Philosophy of Science Pages 1-19 DOI 10.1007/s13194-011-0027-5 Authors James Justus, Philosophy Department, Florida State University and University of Sydney, Tallahassee, FL 32306, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912