This paper reconstructs the history of the introduction and use of iterative algorithms in conservation biology in the 1980s and early 1990s in order to prioritize areas for protection as nature reserves. The importance of these algorithms was that they led to greater economy in spatial extent (“efficiency”) in the selection of areas to represent biological features adequately (that is, to a specified level) compared to older methods of scoring and ranking areas using criteria such as biotic “richness” (the number (...) of features of interest). The development of these algorithms was critical to producing a research program for conservation biology that was distinct from ecology and eventually led to what came to be called systematic conservation planning. Very similar algorithmic approaches were introduced independently in the 1980–1990 period in Australia, South Africa, and (arguably) the United Kingdom. The key rules in these algorithms were the use of rarity and what came to be called complementarity (the number of new or under-represented features in an area relative to those that had already been selected). Because these algorithms were heuristic, they were not guaranteed to produce optimal (most “efficient”) solutions. However, complementarity came to be seen as a principle rather than a rule in an algorithm and its use was also advocated for the former reason. Optimal solutions could be produced by reformulating the reserve selection problem in a mathematical programming formalism and using exact algorithms developed in that context. A dispute over the relevance of full optimality arose and was never resolved. Moreover, exact algorithms could not easily incorporate criteria determining the spatial configuration of networks of selected areas, in contrast to heuristic algorithms. Meanwhile metaheuristic algorithms emerged in the 1990s and came to be seen as a credible more effective alternative to the heuristic algorithms. Ultimately what was important about these developments was that the reserve selection problem came to be viewed a complex optimal decision problem under uncertainty, resource, and other constraints. It was a type of problem that had no antecedent in traditional ecology. (shrink)

Biodiversity has become one of the most important conservation values that drive our ecological management and directly inform our environmental policy. This paper highlights the dangers of strategically appropriating concepts from ecological sciences and also of uncritically inserting them into conservation debates as unqualified normative landmarks. Here, I marshal evidence from a cutting-edge research program in microbial ecology, which shows that if species richness is our major normative target, then we are faced with extraordinary ethical implications. This example challenges our (...) well-received beliefs about biodiversity and it invites us to critically rethink the nature of this concept so that a more robust understanding of biodiversity and of its role in conservation policy could emerge. (shrink)

As conservation biology has developed as a distinct discipline from ecology, conservation guidelines based on ecological theory have been largely cast aside in favor of theory-independent decision procedures for designing conservation reserves. I argue that this transition has failed to advance the field toward its aim of preserving biodiversity. The abandonment of island biogeography theory in favor of complementarity-based algorithms is a case in point. In what follows, I consider the four central objections raised against island biogeographic conservation guidelines, arguing (...) that they fail to undermine the credibility of this framework as a conservation tool. At best, these objections call for a more careful application of this framework to conservation problems, not its wholesale abandonment. At the same time, complementarily-based algorithms are biased in favor of networks of small reserves containing non-overlapping species. These conditions threaten to promote inbreeding depression, genetic drift and other factors that increase a population’s risk of extinction. Therefore, recent developments in the field of conservation biology have arguably not contributed to its ultimate aim of preserving the maximum amount of biodiversity in the long run. (shrink)

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. (shrink)

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. (shrink)

Arguably no concept is more fundamental to science than that of causality, for investigations into cases of existence, persistence, and change in the natural world are largely investigations into the causes of these phenomena. Yet the metaphysics and epistemology of causality remain unclear. For example, the ontological categories of the causal relata have been taken to be objects (Hume 1739), events (Davidson 1967), properties (Armstrong 1978), processes (Salmon 1984), variables (Hitchcock 1993), and facts (Mellor 1995). (For convenience, causes and effects (...) will usually be understood as events in what follows.) Complicating matters, causal relations may be singular (Socrates’s drinking hemlock caused Socrates’s death) or general (Drinking hemlock causes death); hence the relata might be tokens (e.g., instances of properties) or types (e.g., types of events) of the category in question. Other questions up for grabs are: Are singular causes metaphysically and/or epistemologically prior to general causes or vice versa (or neither)? What grounds the intuitive asymmetry of the causal relation? Are macro-causal relations reducible to micro-causal relations? And perhaps most importantly: Are causal facts (e.g., the holding of causal relations) reducible to non-causal facts (e.g., the holding of certain spatiotemporal relations)? (shrink)