Predicting the determinants of weed abundance: a model for the population dynamics of <i>Chenopodium album</i> in sugar beet

Freckleton, Robert P.; Watkinson, Andrew R.

doi:10.1111/j.1365-2664.1998.tb00008.x

Cited by 49 publications

(27 citation statements)

References 68 publications

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“…2006), weed ecology (e.g. Firbank 1985; Doyle, Cousens, & Moss 1986; Firbank & Watkinson 1986; Freckleton & Watkinson 1998; Maxwell & Luschei 2004) and simulating harvesting (Boot & Gullison 1995; Olmsted & Alvarez‐Buylla 1995; Freckleton & Watkinson 1998, Matos, Freckleton, & Watkinson 1999; Freckleton et al. 2003).…”

Section: Introductionmentioning

confidence: 99%

From meso‐ to macroscale population dynamics: a new density‐structured approach

Queenborough¹,

Burnet²,

Sutherland³

et al. 2010

Methods Ecol Evol

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Summary 1.To predict how plant populations may respond to changes in the environment or management, it is necessary to quantify the factors influencing their population dynamics and distributions over large spatial and ⁄ or temporal scales. 2. Most studies of plant population dynamics monitor demography at the sub-metre scale. Extrapolation or prediction from these studies is difficult because the data are sparse, parameter error cannot be ascertained and the data may not cover the range of expected environmental conditions. 3. Here, we describe a survey method based on density-structured models. These models use a discrete density state variable and model rates of transition between density states. Although analytically simple, these models are empirically useful as they may be parameterized using readily collected data. They also offer an empirical link between meso-scale and macro-scale population dynamics. 4. For a large-scale study on annual weeds, we describe the rapid estimation of densities using relatively coarse density estimates using visual estimates of density. Using information from detailed surveys, we describe how we use the method to measure populations of annual plants to a scale of 20 · 20 m in areas of up to 4 ha per population within 500 different arable fields over 3 years. 5. We show that the approach taken is repeatable within and among observers, and we quantify the degree of measurement error. We give examples of the resultant data, and compare these with the data obtained from nested small-scale plots. Finally, we show how the information from this type of survey can be incorporated into population models and used to measure within-population and inter-annual flux.

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Section: Introductionmentioning

confidence: 99%

From meso‐ to macroscale population dynamics: a new density‐structured approach

Queenborough¹,

Burnet²,

Sutherland³

et al. 2010

Methods Ecol Evol

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“…These plants are therefore particularly sensitive to changes in land use or management that reduce the proportion of the seed-bank germinating, seedling survival or the number of seeds per plant returning to the seed-bank [13]. A number of management changes, which impact on different stages of the plant life cycle, have been implicated in the decline of national arable plant populations.…”

Section: Introductionmentioning

confidence: 99%

The impact of agricultural intensification and land-use change on the European arable flora

Storkey

Meyer

Still³

et al. 2011

Proc. R. Soc. B.

330

252

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The impact of crop management and agricultural land use on the threat status of plants adapted to arable habitats was analysed using data from Red Lists of vascular plants assessed by national experts from 29 European countries. There was a positive relationship between national wheat yields and the numbers of rare, threatened or recently extinct arable plant species in each country. Variance in the relative proportions of species in different threat categories was significantly explained using a combination of fertilizer and herbicide use, with a greater percentage of the variance partitioned to fertilizers. Specialist species adapted to individual crops, such as flax, are among the most threatened. These species have declined across Europe in response to a reduction in the area grown for the crops on which they rely. The increased use of agro-chemicals, especially in central and northwestern Europe, has selected against a larger group of species adapted to habitats with intermediate fertility. There is an urgent need to implement successful conservation strategies to arrest the decline of this functionally distinct and increasingly threatened component of the European flora.

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“…Similar models have been constructed for rodents (Lima et al 2006(Lima et al , 2008, for mosquitoes (Yang et al 2008), and for large herbivores in a wet climate (Coulson et al 2001;Hone and Clutton-Brock 2007). Stochastic variation in K is also implicit in some plant population studies that have estimated related population parameters (e.g., Freckleton and Watkinson 1998;Freckleton et al 2000;Turnbull et al 2004Turnbull et al , 2007Adams et al 2005). There have also been a few theoretical explorations of the consequences of variation in K and in density-dependent population parameters in general (Turelli 1978;Gyllenberg et al 1994;Ferriere et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Temporal Variation in the Carrying Capacity of a Perennial Grass Population

Pease¹

2010

The American Naturalist

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. abstract: Density dependence and, therefore, K (carrying capacity, equilibrium population size) are central to understanding and predicting changes in population size (N). Although resource levels certainly fluctuate, K has almost always been treated as constant in both theoretical and empirical studies. We quantified temporal variation in K by fitting extensions of standard population dynamic models to 16 annual censuses of a population of the perennial bunchgrass Bouteloua rigidiseta. Variable-K models provided substantially better fits to the data than did models that varied the potential rate of population increase. The distribution of estimated values of K was skewed, with a long right tail (i.e., a few "jackpot" years). The population did not track K closely. Relatively slow responses to changes in K combined with large, rapid changes in K sometimes caused N to be far from K. In 13%-20% of annual intervals, K was so much larger than N that the population's dynamics were best described by geometric growth and the population was, in effect, unregulated. Explicitly incorporating temporal variation in K substantially improved the realism of models with little increase in model complexity and provided novel information about this population's dynamics. Similar methods would be applicable to many other data sets.

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Predicting the determinants of weed abundance: a model for the population dynamics of Chenopodium album in sugar beet

Cited by 49 publications

References 68 publications

From meso‐ to macroscale population dynamics: a new density‐structured approach

From meso‐ to macroscale population dynamics: a new density‐structured approach

The impact of agricultural intensification and land-use change on the European arable flora

Temporal Variation in the Carrying Capacity of a Perennial Grass Population

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