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1. conceptual issues in the history of ecology (Justus a; Eliot a);. 2. characterizing problematically unclear ecological concepts, especially 'biodiver-.
Table of contents
- Conceptual Issues in Ecology
- Environmental Ecology: Introduction, Videos, Concepts and Questions
- A conceptual framework for ecological economics based on systemic principles of life
- Search form
Conceptual Issues in Ecology
For small populations, even if it is increasing in size on the average, a chance fluctuation can result in extinction. Stochastic models are necessary to predict parameters such as the probability of extinction within a specified time period or the expected time to extinction. In his dissertation, Shaffer attempted such an analysis for the grizzly bears Ursus arctos of Yellowstone which were believed to face the prospect of stochastic extinction.
Shaffer distinguished four sources of uncertainty that can contribute to random extinction:. Shaffer went on to argue that all these factors increase in importance as the population size decreases—a claim that will be questioned below—and, therefore, that their effects are hard to distinguish. Leaving aside the conventional elements of the definition given above, even for the same species, populations in marginally different habitat patches often show highly variable demographic trends resulting in highly variable MVP estimates for them, with each estimate depending critically on the local context.
This should not come as a surprise: what would have been more surprising is if legislative fiat had identified a scientifically valuable parameter. After the demise of the concept of the MVP, PVA began to be performed largely to estimate other parameters, especially the expected time to extinction of a population the estimation of which does not require any conventional choices—see the entry on conservation biology.
The first point to note is that genetic stochasticity is not even the same type of mechanism as the other three: its presence makes Shaffer's classification oddly heterogeneous. The reason for this is that genetic stochasticity is a consequence of demographic stochasticity: in small populations, a particular allele may reach fixation purely by chance reproductive events. It is even possible that stochasticity increases the rate at which a beneficial allele may go to fixation in a small population provided that the initial frequency of that allele is already high.
Leaving genetic stochasticity aside, do the other three categories provide a good classification of stochasticity, or is the classification more like that of animals in Borges' notorious Chinese encyclopedia? Relevance is determined contextually by the possibility of there being a coherent account of how those categories came to be defined. Including genetic stochasticity in the classification leads to a lack of such coherence—hence, its exclusion above; ii the categories should be jointly exhaustive, able to subsume all cases of the relevant phenomena; and iii the categories should be mutually exclusive, that is, no phenomenon should be subsumed under more than one category.
It is the third criterion that is often called into question by Shaffer's classification of stochasticity primarily because of formalization indeterminacy. Consider some small reptile that fails to breed because a flood creates a barrier across its habitat that it cannot cross and there are no available mates on its side of the barrier.
Is this environmental or demographic stochasticity? On the one hand, it is obviously environmental because floods are precisely the type of mechanism by which environmental stochasticity is expressed. On the other hand, it is equally obviously demographic because the failure to reproduce is due to the chance unavailability of a mate on the appropriate side of the barrier. Ultimately, as will be discussed below in some detail, whether this is a case of demographic or environmental stochasticity depends on how it is modeled.
There is an important philosophical lesson here: especially when a new discipline is being formed, the structure of the phenomena—how they are distinguished and classified—are in part determined by the models used to represent them. Consequently, classification is not theoretically innocent. This is as true in ecology as in any other scientific context. Thus, as a prelude to modeling, Lande et al. The impact of environmental stochasticity is roughly the same for small and large populations.
Random variation in individual fitness, coupled with sampling effects in a finite population, produces demographic stochasticity. Moreover, Lande and others regard random catastrophes as extreme cases of environmental stochasticity. Consequently, it requires explicit mathematical models in which these distinctions are made exact through formal definitions. If it does, the model in question is one of demographic stochasticity; if it does not, it is one of environmental stochasticity.
This choice captures the intuition mentioned earlier that the effect of the former depends on the population size whereas the effect of the latter does not. The mathematical analysis of these models is non-trivial. The most general and uncontroversial theoretical result to date is that progressively larger populations are required for safety in the face of demographic, environmental, and random catastrophic stochasticity.
Environmental Ecology: Introduction, Videos, Concepts and Questions
Moreover, because of the structural uncertainly of these models, apparently slight differences in assumptions and techniques routinely lead to widely divergent predictions. This can be illustrated using the well-studied example of the Yellowstone grizzlies. In Foley constructed a model for this population incorporating environmental stochasticity alone and depending on the intrinsic growth rate of the population and the carrying capacity of the environment. In Foley constructed another model incorporating both demographic and environmental stochasticity but with the option of setting either part equal to 0.
It does not. It predicts a much lower expected time to extinction. The interactive definition given above is attractive for two reasons: a mere association leaves little of theoretical or practical interest to study, while requiring some specified elevated levels of interaction introduces an unnecessary arbitrariness in a definition of community; and b the former would make any association of species a community [ 34 ] whereas the latter would typically introduce so much structure that virtually no association would constitute a community.
A conceptual framework for ecological economics based on systemic principles of life
Community models can be conveniently represented as loop, diagrams [ 35 ] generalized graphs that have each species as a vertex and edges connecting these vertices when the species interact. The edges indicate whether the relevant species benefit or are harmed by the interaction, that is, whether they tend to increase or decrease in abundance, by an interaction. As with population ecology, what is of most interest is are the changes in a community over time.
This brings us to one of the most interesting—and one of the most vexed—questions of ecology: the relationship between diversity and stability.
A deeply rooted intuition among ecologists has been that diversity begets stability. If this claim is true, it has significant consequences for biodiversity conservation—see conservation biology. Both communities have the same richness because they both have two species; however, there is a clear sense in which the first is more diverse—or less homogeneous—than the second. Moreover, the difference is likely to be relevant. If diversity does beget stability in these communities, then that stability must be a result some interaction between the two species.
If species B comprises only 0. There have been several attempts to define and quantify diversity beyond richness; one of them is described in Box 2. This is a measure of the diversity of a community in the same way that the Shannon measure of information content is a measure of the variety in a signal. Unfortunately, though, there has been little success in tying these concepts to theoretical rules or even empirical generalizations.
Stability turns out to be even more difficult to define. At the practical level, this definition faces the problem of vacuous scope: almost no natural community satisfies such a strict requirement of equilibrium. Moreover, almost every community experiences significant disturbances. With this in mind, stability has been variously explicated using a system's response to disturbances or its tendency not to change beyond specified limits even in the absence of disturbance.
Boxes 3 and 4 see below list some of the definitions of stability that have been in vogue and how they may be measured in the field. How do any of these measures of stability relate to diversity? The only honest answer is that no one is sure. If diversity is interpreted as richness, traditionally, it was commonly assumed that diversity is positively correlated with at least persistence.
However, there was never much hard evidence supporting this assumption.
If stability is interpreted as a return to equilibrium, mathematical models that should answer questions about stability are easy to construct but hard to analyze unless the system is already close to equilibrium. This is called local stability analysis. The most systematic analyses performed so far give no straightforward positive correlation.
The traditional assumption of a general positive correlation between diversity as richness and stability has been seriously challenged on both theoretical and empirical grounds since the s. Meanwhile, Pfisterer and Schmid have produced equally compelling empirical evidence that richness is inversely correlated with stability, interpreted as resilience and resistance.
All that is certain is that McCann's confident verdict in favor of a positive diversity-stability relationship was premature. At least at the theoretical level, this remains an open field for philosophers. Clear formal results would not go unnoticed by ecologists.
Within community ecology, philosophers have lately paid considerable attention to the theory of island biogeography and the controversies surrounding its relevance for the design of networks of biological reserves. But, what is the form of this relationship? Moreover, what is the mechanism responsible for it? In spite of sporadic work over almost an entire century, these remain open questions. Larger areas were presumed to have greater habitat heterogeneity and could, therefore, host a larger number of species each with its own specific needs.
In recent years the relation is more often attributed to the belief that larger areas can support larger populations of any species. Consequently, on the average, more species are likely to be present in larger areas than smaller ones even if both started with the same species richness.
Whether the species-area curve rather than the mere qualitative relation has any empirical support remains a matter of contention. There is a dynamic equilibrium in the sense that this number does not change over time though there is a turnover of species which changes the composition of the community. The immigration rate varies inversely with the degree of isolation while the extinction rate decreases with area.
Thus, this theory incorporates the second mechanism for the species-area relation mentioned in the last paragraph. While some initial experimental evidence seemed to support the theory, by the mids its status had become controversial.
- Consumption Takes Time: Implications for Economic Theory (The Graz Schumpeter Lectures).
- Acronym Master List;
- Conceptual Issues in the Ecology and Evolution of Circadian Rhythms.
Nevertheless, in the s, island biogeography began to be viewed as a model for biological reserves which, by being surrounded by anthropogenically transformed lands, were supposed to be similar to islands—see the entry on conservation biology. The initially prevalent view, based on island biogeography theory, was that reserves should be as large as possible. Meanwhile the species-area curve also began to generate serious skepticism.
Important early criticism of the use of island biogeography theory for reserve network design came from Margules and several collaborators in By , it became clear that there would be no winner in the SLOSS debate; since then there has been no unequivocal role for island biogeography theory to play in the design of biodiversity reserve networks. Should ecosystem ecology, then, be regarded as an instance of the unification of the physical and biological sciences?
There has been so little philosophical attention to ecology that this question does not appear ever to have been broached. They also demanded that other specialists, including geochemists and soil scientists, be brought in so that all the relevant physical parameters of ecosystems, besides the biological ones, could be tracked simultaneously.
This study constituted the biologists' attempt to engage in publicly-funded Big Science, initiated by the physicists during the Manhattan Project, and subsequently profitably exploited by social scientists since the s.