ANIMAL POPULATION ECOLOGY AND CONTROL FUNDAMENTALS

Expensive, extensive and apparently lethal control measures have been applied against many species of pest vertebrates and invertebrates for decades. In spite of this, few pests have been annihilated, and in many cases the stated goals have become progressively more modest, so that now we speak of saving foliage or a crop, rather than extermination. It is of interest to examine the reasons why animals are so difficult to exterminate, because this matter, of course, has implications for the type of control policy we pursue in the future. Also, it has implications for the problem of evaluating comparatively various resource management strategies. There are many biological mechanisms which could, in principle, enhance the performance of an animal population after control measures have been applied against it. These are of four main types: genetic, physiological, populationa1, and environmental. We are all familiar with the fact that in applying a control measure, we are, from the pest's point of view, applying intense selection pressure in favor of those individuals that may be preadapted to withstand the type of control being used. The well-known book by Brown (1958) documents, for invertebrates, a tremendous number of such cases. Presumably, vertebrates can show the same responses. Not quite so familiar is the evidence that sub-lethal doses of a lethal chemical may have a physiologically stimulating effect on population performance of the few individuals that happen to survive (Kuenen, 1958). With further research, we may find that this phenomenon occurs throughout the animal kingdom. Still less widely recognized is the fact that pest control elicits a populational homeostatic mechanism, as well as genetic and physiological homeostatic mechanisms. Many ecologists, such as Odum and Allee (1950, Slobodkin (1955), Klomp (1962) and the present author (1961, 1963) have pointed out that the curve for generation survival, or the curve for trend index as a function of last generations density is of great importance in population dynamics.

Eastern temperate North Pacific offshore killer whales (Orcinus orca): Occurrence, movements, and insights into feeding ecology

Beginning in the late 1980s, large groups of previously unidentified killer whales (Orcinus orca) were sighted off the west coast of Vancouver Island and in the Queen Charlotte Islands, British Columbia. Scientists working in this region produced two killer whale photo-identification catalogues that included both transient (mammal-eating) whales and 65 individual whales that investigators believed represented a distinct killer whale community (Ford et al. 1992, Heise et al. 1993). It was thought that these killer whales maintained a generally offshore distribution and were provisionally termed “offshores”; a term that has since been used as a population identifier for the eastern temperate North Pacific offshore killer whale population. Then in September 1992, 75 unidentified whales entered the Strait of Juan de Fuca just south and east of Victoria, British Columbia (Walters et al. 1992). Although most of these whales had not been seen before, two were matched to killer whales in the Queen Charlotte photo-identification catalogue (Ford et al. 1992, Heise et al. 1993) and were thus listed as “offshore” killer whales. During a similar time period, other large groups of killer whales, previously unidentified, were also being sighted off Alaska and California (Dahlheim et al. 1997; Nancy Black and Alisa Schulman- Janiger, unpublished data, respectively).

Seeking a Second Opinion: Uncertainty in Disease Ecology

Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.