Animal Modeling
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ANIMAL MODELING Editor's note: The following submission by Raymond O'Connor resonated with many of us at the GAP program. It suggests there are ways to improve our modeling efforts, such as incorporating data on population fluctuations over time, and that consideration of such improvements may warrant redefining the GAP vision. After a few of our reviewers read the article, it began to inspire some spirited discussion about GAP's future products and purpose. To try to capture some of this discussion, the article by O'Connor is followed by an article by Svancara and others, who elaborate on some potential future considerations for the GAP program. Dr. O'Connor has graciously agreed to give Svancara and others the last word, even though it was not anticipated when he made his submission. He noted that he did not always agree with how some of the specifics of his article had been interpreted. However, he was satisfied with letting both articles stand as written, because they work well together to raise some important issues for the future of the GAP program. We are very appreciative of this constructive attitude and want to thank him and all the contributors involved in this volume. GAP Conservation and Science Goals: Rethinking the Underlying Biology RAYMOND J. O’CONNOR Department of Wildlife Ecology, University of Maine, Orono Any successful program develops a momentum of its own, a consensus among its community of participants about what should be done and what the next steps should be. The problem with success―and one that is evident within the GAP community―is that this agreement often concerns tactics, the short-term actions needed to implement long- term goals originally enunciated and tacitly assumed to have remained unchanged. Glance through the programs for recent GAP meetings or look through recent issues of the GAP Bulletin and what you find is emphasis on details of assessing the accuracy of GAP models, incremental improvements on classification procedures, discussion of expert systems for inference of species presence, and, of course, reports of landmarks of progress in the GAP projects in individual states or regions. Yet the more successful the GAP community has been, the more pressing is the need to ask whether all these very worthy activities are still directed at the most useful strategy? We can grant the merits of the original goal of GAP; we can grant the merits of the current efforts to improve GAP incrementally; but we can, and should, nonetheless ask whether the accumulating GAP results indicate any need to redefine the larger GAP vision. Some lessons from the Industrial Revolution may be apt here. Early steam engines pressed for more power output had a habit of blowing up. They could always be made more powerful and safer by overengineering in the light of the available practical experience. But the most rapid advances came when engine performance was analyzed in the light of thermodynamic principles. No longer were engineers restricted to “cut and try” approaches: instead engines could be designed successfully for use in novel environments within which they 1 had never before been deployed. So where, and how, might the GAP community be most innovative in deploying its collective skills and expertise? GAP’s basis is in mapping the distribution of potential habitat. If GAP scientists unequivocally demonstrate that Kirtland’s warbler (Dendroica kirtlandii) is a denizen of young jack pine (Pinus banksiana) stands in the eastern United States, what exactly does a map of jack pine distribution across Michigan, for example, imply in conservation terms? The origin of GAP was in the notion that it meant a lot. If no stands of jack pine were in some form of protected status, then GAP asserts that one can validly infer that the conservation of Kirtland’s warbler will be enhanced by acquiring protection for some blocks of this habitat. Whether the threshold for effective protection should be 10% or 50% or 90% is thereafter considered to be largely a research and conservation management question, to be solved by incremental research. Here I maintain that one can evaluate such threshold questions in unconventional ways that may be better than the incremental advance possible with conventional thinking. In particular I want to suggest that the GAP concept of species distributions is one of container habitats rather than one of habitat correlates. A container can hold the species but need not always do so, and the relevant strategic questions are therefore, first, how specific the specification of the container is to the species in mind, and second, under what conditions the species will actually be present in the container. In contrast, the habitat correlate concept of species distribution envisages an equilibrium world in which a species is always present in its habitat, and the problem is merely one of obtaining a yet better statistical model with which to describe that habitat. This can, in turn, be best done for a species in equilibrium and yields poor results otherwise. More important yet, though, is that this latter notion holds poorly in the growing appreciation of the role of limits and carrying capacities as constraints on species distribution (Huston 2002, O’Connor 2002). A GAP assumption is that one can determine a species niche accurately, that one can correctly identify the habitat or environment characteristic of a particular species. GAP sees jack pine forest as a container within which Kirtland’s warblers may occur and assumes that a tight one-to-one correspondence exists between the two: jack pine means Kirtland’s warblers (assumption I) and Kirtland’s warblers mean jack pine (assumption II). Reality quickly cuts in for the first assumption, and it is readily acknowledged that not all jack pine will necessarily hold Kirtland’s warbler. There are two possible reasons for this. The first is that it may not really be jack pine that is the habitat but rather (say) jack pine in which tree density is above some critical density, and if we but knew that fact we could redefine the habitat to be appropriately high-density jack pine stands. This merely moves the logic on one step. But even if we had perfect knowledge of all such issues, the perfectly defined habitat may yet remain only locally occupied. Some sites may be unoccupied because a severe winter (or dispersal or migration stresses) killed the birds that would have occupied them, in which case waiting for the population to build up again and fill these stochastic gaps will resolve a temporary violation of the GAP assumption. But other sites may be unoccupied simply because the population is limited in the long term by factors other than habitat―perhaps the local use of pesticides, lack of winter habitat, and so on. In that case, there are not enough birds around to occupy all the available jack pine habitat (or whatever variant of it needs to be specified to describe 2 optimal habitat), and the GAP assessment is in long-term error. For stock doves (Columba oenas) in Britain in the 1950s, for example, this was the case: thousands of square miles of arable farmland lay open to use, but organochlorine use there limited the population to less than replacement demographic rates (O’Connor and Mead 1984). Now consider assumption II above, that Kirtland’s warblers mean jack pine. For many species individuals make use of secondary habitat when densities are locally at high levels, and they contract back into the core habitat when the population shrinks (Fretwell and Lucas 1970, Lidicker 1962). For such species the niche is, so to speak, somewhat elastic. At low densities individuals are exclusively in a core, optimum habitat (habitat A; ignoring any influence of site fidelity from previous episodes of high density; O’Connor 1985). At high densities, on the other hand, some individuals are forced into a secondary habitat (habitat B) where breeding is also possible but with less success than in habitat A. (This sequence may extend to a third, fourth, etc., habitat in a hierarchy of breeding suitability.) When researchers determine what to treat as the habitat of this species, their conclusion will depend on the prevailing population level. It will be “habitat A or habitat B” if the information comes from a time of generally high densities, for both habitat types are in use in such conditions. But it will be “habitat A alone” if the information comes from a time of generally low densities. The latter is then merely an analogue of the stenotope example discussed above, but the former will lead us to consider, and possibly protect, areas of type B. But although such protection is designed to be most valuable should the species decline, habitat of type B is the very type of habitat that is not used when population levels are low! Thinking in terms of the principles of habitat occupancy in this way immediately transforms (or, at least, should transform) thinking among the GAP community about the nature of predictive modeling accuracy and about what the concepts of omission and commission error mean. In the warbler-jack pine example, omission error (failing to predict the occurrence of a species that actually occurs on the site), could result (1) if the jack pine habitat is too narrow a specification of the habitat tolerance of the warbler (e.g., if it routinely uses other stand types than just jack pine) or (2) if it is subject to Fretwell-Lucas dynamics, and the test of accuracy was done at a time of high population while the model of its habitat use was developed at times of low population.