The Use of Cover and Frequency in the Detection of Pattern in Plant Communities

Kershaw, K. A.

doi:10.2307/1931688

Cited by 141 publications

(69 citation statements)

References 9 publications

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“…E-mail: craig.johnson@utas.edu.au ticularly to monitor and detect meaningful change in ecosystem state (Rand 1994, Bishop et al 2002. The search for a means to identify natural or ''characteristic'' length scales (hereafter CLSs) in ecological systems stems back at least to the 1950s (Grieg-Smith 1952, Kershaw 1957, but several more recent attempts have also addressed the problem (e.g., Carlile et al 1989, De Roos et al 1991, Schneider 1994. Most approaches have assumed either that ecological systems are stationary in space and time, that fluctuations are random around a stationary global average (e.g., Rand and Wilson 1995), or that any trends detected are linear (see Turner et al 1991).…”

Section: Introductionmentioning

confidence: 99%

Determining Natural Scales of Ecological Systems

Habeeb¹,

Trebilco²,

Wotherspoon³

et al. 2005

Ecological Monographs

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Abstract. A key issue in ecology is to identify the appropriate scale(s) at which to observe trends in ecosystem behavior. The characteristic length scale (CLS) is a natural scale of a system at which the underlying deterministic dynamics are most clearly observed. Any approach to estimating CLSs of a natural system must be able to accommodate complex nonlinear dynamics and must have realistic requirements for data. Here, we compare the robustness of two methods to estimate CLSs of dynamical systems, both of which use attractor reconstruction to account for the complex oscillatory dynamics of ecological systems. We apply these techniques to estimate CLSs of spatial multispecies systems of varying complexity, and show that both methods are robust for the simplest system, but as model complexity increases, the Pascual and Levin metric is more robust than that of Keeling et al. Both methods demonstrate some sensitivity to the choice of species used in the analysis, with closely connected species producing more similar CLSs than loosely connected species. In this context, connectivity is determined both by the topology of the interaction network and spatial organization in the system. Notably, systems showing complex spatial self-organization can yield multiple CLSs, with larger length scales indicating the emergent dynamics of interactions between patches. While the prediction r 2 metric of Pascual and Levin is suitable to estimate CLSs of complex systems, their method is not suitable to apply to most real ecosystems because of the requirement of long time series for attractor reconstruction. We offer two alternatives, both based on prediction r 2 , but where repetition in space is largely (the ''short time series'' method) or wholly (the ''sliding window'' method) substituted for repetition in time in attractor reconstruction. Both methods, and in particular the short time series based on only three or four sequential observations of a system, are robust in detecting the primary length scale of complex systems. We conclude that the modified techniques are suitable for application to natural systems. Thus they offer, for the first time, an opportunity to estimate natural scales of real ecosystems, providing objectivity in important decisions about scaling in ecology.

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Section: Introductionmentioning

confidence: 99%

Determining Natural Scales of Ecological Systems

Habeeb¹,

Trebilco²,

Wotherspoon³

et al. 2005

Ecological Monographs

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“…The exact relationship between variability and window size is difficult to predict (see empirical methods, for example, in Kershaw 1957, Cain and Castro 1959, Greig-Smith 1964, Mead 1974), but will be determined by the way spatial correlations fall off with distance (see, for example, Hubbell and Foster 1983, Robertson et al 1988, Carlile et al 1989. In general, the relationship will follow a power law within the correlation length of the system (which is determined by such influences as the disturbance size distribution, and the dispersal curve), and then will fall off asymptotically as the inverse of the number of cells in the window.…”

Section: Pattern and Scale In Terrestrial Systemsmentioning

confidence: 99%

Untitled

1992

Ecology

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Abstract. It is argued that the problem of pattern and scale is the central problem in ecology, unifying population biology and ecosystems science, and marrying basic and applied ecology. Applied challenges, such as the prediction of the ecological causes and consequences of global climate change, require the interfacing of phenomena that occur on very different scales of space, time, and ecological organization. Furthermore, there is no single natural scale at which ecological phenomena should be studied; systems generally show characteristic variability on a range of spatial, temporal, and organizational scales. The observer imposes a perceptual bias, a filter through which the system is viewed. This has fundamental evolutionary significance, since every organism is an "observer" of the environment, and life history adaptations such as dispersal and dormancy alter the perceptual scales of the species, and the observed variability. It likewise has fundamental significance for our own study of ecological systems, since the patterns that are unique to any range of scales will have unique causes and biological consequences.The key to prediction and understanding lies in the elucidation of mechanisms underlying observed patterns. Typically, these mechanisms operate at different scales than those on which the patterns are observed; in some cases, the patterns must be understood as emerging from the collective behaviors oflarge ensembles of smaller scale units. In other cases, the pattern is imposed by larger scale constraints. Examination of such phenomena requires the study of how pattern and variability change with the scale of description, and the development of laws for simplification, aggregation, and scaling. Examples are given from the marine and terrestrial literatures.

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“…The analysis of pattern in vegetation is well established in investigations of higher plants (e.g. Greig-Smith 1952;Kershaw 1957). Although Allen (1973) applied these techniques to the study of micro-to freshwater scopic pattern of an epiphyllous alga, they appear not to have been used previously in studies of phytoplankton.…”

Section: Many Limnologistsmentioning

confidence: 99%

The application of pattern analysis to freshwater phytoplankton communities1

Richards¹,

Happey‐Wood²

1979

Limnology & Oceanography

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The horizontal distribution of four species of planktonic algae (Asterionella formosa, Tabellaria flocculosa, Chlorella vulgaris, and Chlamydomonas moewussii) growing during summer in surface lake water is described. All showed considerable variation in standing crop. By application of a pattern analysis technique, these surface distributions of algae were shown to be contagious with patterns of horizontal distribution which differed between species in dimensions and intensity. More extensive analysis of the distribution of A. formosa at contrasting times of year showed the existence of pattern in horizontal distribution to be a recurrent phenomenon.

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The Use of Cover and Frequency in the Detection of Pattern in Plant Communities

Cited by 141 publications

References 9 publications

Determining Natural Scales of Ecological Systems

Determining Natural Scales of Ecological Systems

Untitled

The application of pattern analysis to freshwater phytoplankton communities1

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