Determinants of the abundance of invasive annual weeds: community structure and non–equilibrium dynamics

Freckleton, Robert P.; Watkinson, Andrew R.; Dowling, P. M.; Leys, A. R.

doi:10.1098/rspb.2000.1122

Cited by 31 publications

(36 citation statements)

References 31 publications

(36 reference statements)

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“…Bayliss 1989; Burgman et al . 1992; Freckleton et al 2000; Freckleton & Watkinson 2001). The technique of regressing log population changes on log population size has actually been recommended for detecting and modelling density dependence (Burgmann et al .…”

Section: Methodsmentioning

confidence: 99%

Census error and the detection of density dependence

Freckleton

Watkinson

Green³

et al. 2006

Journal of Animal Ecology

261

335

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Summary1. Studies aiming to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the densitydependent signal may in reality be weak or absent. 4. We distinguish two forms of census error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without sampling error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods.

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Section: Methodsmentioning

confidence: 99%

Census error and the detection of density dependence

Freckleton

Watkinson

Green³

et al. 2006

Journal of Animal Ecology

261

335

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“…It is also worth noting that all the yield–density relationships in the studies described above were derived from sown rather than naturally regenerating populations. Under such conditions the role of asymmetric competition will be minimized and it has been noted that competition–density functions may be rather different in sown compared with naturally regenerating stands (Freckleton et al . 2000; Buckley et al .…”

Section: Lack Of Overcompensating Yield–density Responsesmentioning

confidence: 99%

Are weed population dynamics chaotic?

Freckleton

Watkinson

2002

Journal of Applied Ecology

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Summary 1.There have been suggestions that the population dynamics of weeds may show chaotic dynamics, and that therefore it will not be possible to predict the impact of changing management regimes on weed abundance. The instability of weed populations is presumed to result either from overcompensating yield-density responses or from threshold management. 2. Using theoretical arguments and empirical evidence we argue that this contention is likely to be incorrect. 3. Overcompensating yield-density responses are unlikely in plant populations and this point has been extensively discussed. Such responses have only been observed in high-density artificially sown stands of weed populations. The form of chaos that results from threshold management is a consequence of high population growth resulting from the cessation of management when weed densities are lower than a threshold level. Consequently the dynamics of such populations may be argued to be extrinsically rather than intrinsically driven. 4. There are many studies that have shown weed populations to be dynamically stable, both spatially and temporally. Here we present an analysis of data from the Broadbalk experiment that demonstrates long-term stability of 12 species of common weeds over a 12-year period. Using parameter estimates derived from the literature we show that the stability of these populations is similar to other annual species, both weedy and non-weedy. 5. We argue that weed population dynamics are more generally better viewed as resulting from the impacts of broad-scale types of management, as well as temporal variability in population numbers. The significance of chaotic dynamics is likely to be minimal.

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“…First, the use of explicit models allows quantitative prediction of population dynamics, as opposed to qualitative measures of the presence or absence of competition (Peters 1991). Despite the profusion of empirical competition studies, relatively few have calculated the interspecific competition coefficients that are the focus of most theoretical studies of competition (but see Park 1948, Vandemeer 1969, Ayala et al 1973, Pfister 1995, Damgaard 1998, Inouye 1999b, Freckleton et al 2000. Results from experiments that use a substitution or additive design cannot be used to fit predictive DESIGNS FOR COMPETITION EXPERIMENTS FIG. 1.…”

Section: Introductionmentioning

confidence: 99%

Response Surface Experimental Designs for Investigating Interspecific Competition

Inouye

2001

Ecology

227

161

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Competition is an important interaction in ecology, and many experiments have been done to examine the effects of intraspecific and interspecific competition. Unfortunately, most of these experiments have been done using either substitution or additive experimental designs. Substitution designs, and to a lesser extent additive designs, severely limit the useful inferences that ecologists can draw from the resulting data. Response surface experimental designs, which vary the densities of two competing species independently, offer the advantage of being able to compare the fits of alternative competition models and to estimate model parameters. This kind of experimental design has been relatively little used in ecology to date. I consider the use of response surface designs from two perspectives. The first is the optimal allocation of experimental effort among density treatments, with the goal of distinguishing among alternative models. Second, I compare the ability of six response surface designs to estimate known competition coefficients correctly, using simulated data. Low densities and densities near equilibrium in one generation tend to return similar densities the following generation and thus provide little or no information for comparing alternative population models. Experimental designs that span a wide range of densities, including high densities above carrying capacities, provide more accurate parameter estimates in general, but low densities are helpful for estimating population growth rates. Designs that are not fully factorial are one compromise solution that can yield accurate parameter estimates for models and yet require less experimental effort than full factorial surfaces. In general, response surfaces are a powerful class of experimental design and offer potentially stronger connections between empirical and theoretical approaches than traditional experimental designs.

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Determinants of the abundance of invasive annual weeds: community structure and non–equilibrium dynamics

Cited by 31 publications

References 31 publications

Census error and the detection of density dependence

Census error and the detection of density dependence

Are weed population dynamics chaotic?

Response Surface Experimental Designs for Investigating Interspecific Competition

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