Use of Visual Cover Assessments as Quantitative Estimators of Some British Woodland Taxa

Sykes, Jonathan M.; Horrill, A.D.; Mountford, M. D.

doi:10.2307/2259726

Cited by 178 publications

(125 citation statements)

References 12 publications

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“…High observer-dependency in cover estimates has been found in other experiments (Sykes et al 1983, Floyd & Anderson 1987, Kennedy & Addison 1987, Mitchell et al 1988, van Hees & Mead 2000, Klimes 2003, Kercher et al 2003, Vittoz & Guisan 2007), but few have separated random variation and bias. In a survey context, random variation in data could be countered by increasing sample size, while bias has to be taken into account when presenting results.…”

Section: Random Error and Biasmentioning

confidence: 76%

“…In a survey context, random variation in data could be countered by increasing sample size, while bias has to be taken into account when presenting results. Sykes et al (1983) found bias consistent for an observer in relation to individual species, thus, making the use of a mean bias correction factor possible. This is probably not possible in a survey covering large areas with many different types of vegetation and, due to seasonal and weather variations, a variety of phenological stages.…”

Section: Random Error and Biasmentioning

confidence: 87%

“…Økland 1988Økland , Kercher et al 2003. The reliability of visual cover estimates has been the focus for a number of studies (Sykes et al 1983, Jukola-Sulonen and Salemaa 1985, Floyd and Anderson 1987, Kennedy and Addison 1987, Mitchell et al 1988, Dethier et al 1993, Bråkenhielm & Qinghong 1995, Carlsson et al 2005, Vittoz & Guisan 2007. Still, the contribution of bias due to individual observers versus other unexplained sources of error has rarely been reported (van Hees & Mead 2000, Milberg et al 2008.…”

Section: Introductionmentioning

confidence: 99%

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In the eye of the beholder: bias and stochastic variation in cover estimates

2009

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Cover estimates by eye is a prevailing method to assess abundance. We examined cover estimates with regard to bias and random variation. Ten observers working with a national forest vegetation survey estimated sixteen 100 m 2 -plots, placed in two different vegetation types. These had similar species composition but were clearly distinguishable in the field. In specieswise analyses, observer bias varied greatly, with Dicranum spp., Vaccinium vitis-idaea and V. myrtillus having the largest bias. Experience had a surprisingly small impact on variation. Power analysis revealed only small differences between observers in the ability to distinguish the two vegetation types, and little value in averaging the assessments from 2, 3 or 4 observers. Cover estimates did better than presence/absence data in separating the two vegetation types and multivariate analyses were more powerful than univariate ones.

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Section: Random Error and Biasmentioning

confidence: 76%

Section: Random Error and Biasmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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In the eye of the beholder: bias and stochastic variation in cover estimates

2009

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“…But in comparison to biomass, density tends to be an inferior predictor of competitive effects (Goldberg 1987;Keddy 2001) because density data do not describe plant sizes. Finally, ocular estimates summarize plant sizes and can be gathered quickly without much equipment, but visual estimates can embody large observation error variances (Kennedy and Addison 1987), and, more critically, visual estimates are easily biased (Sykes et al 1983).…”

Section: Introductionmentioning

confidence: 99%

Using Light Attenuation to Estimate Leafy Spurge Impacts on Forage Production

Rinella¹,

Sheley²

2006

REM

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Rangeland managers often must decide whether to suppress dicotyledonous weed populations with expensive and time-consuming management strategies. Often, the underlying goal of weed suppression efforts is to increase production of native forage plants. Many managers suppress weeds only when they feel the unwanted plants are substantially impacting their forage base. Currently, intuition and guesswork are used to determine whether weed impacts are severe enough to warrant action. We believe scientific impact assessments could be more effective than these casual approaches to decision making. Scientific approaches will necessitate data on weed abundances because the severity of a weed's impact is highly correlated with its abundance. The need for weed abundance data poses major obstacles because gathering these data with readily available techniques is time consuming. Most managers cannot or will not spend a lot of time gathering vegetation data. In this paper, we explore a rapidly measured index (,2 minutes per sample location) that is highly correlated with weed (i.e., leafy spurge Euphorbia esula L.) abundance per unit area. This index is based on the light attenuation leafy spurge causes. After measuring light attenuation in plots planted to leafy spurge and grasses, we developed a probabilistic model that predicts leafy spurge impacts on forage production. Data from experiments where herbicides suppressed leafy spurge provided an opportunity to evaluate prediction accuracy of the model. In each case herbicide experiment data fell within the range of values (i.e., credibility intervals) the model predicted, even though the model development experiments were separated from the herbicide experiments by several hundred kilometers in space and 4 years in time. Therefore, we conclude that the model successfully accounts for spatial and temporal variation. We believe light attenuation could help natural resource managers quickly quantify some kinds of weed impacts. ResumenLos manejadores de pastizales a menudo deben decidir si suprimen o no las poblaciones de malezas dicotiledó neas con estrategias de manejo caras y que consumen mucho tiempo. Frecuentemente, la meta de suprimir las malezas es incrementar la producció n de las plantas forrajeras nativas. Muchos manejadores solo suprimen las malezas cuando sienten que las plantas indeseables está n impactando substancialmente su producció n de forraje. Actualmente, la intuició n y suposiciones son usadas para determinar si los impactos de la maleza son los suficientemente severos o no para justificar la acció n de supresió n. Nosotros creemos que las evaluaciones científicas del impacto de la maleza pueden ser má s efectivas que las estos métodos casuales de toma de decisiones. Los métodos científicos necesitará n datos de la abundancia de la maleza, porque la severidad del impacto de la maleza esta altamente correlacionado con su abundancia. La necesidad de datos de abundancia de la maleza presenta grandes obstá culos porque la recopilació n de estos datos con...

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“…However, the fine-texture of grasses and the frequently large areas to be assessed lead to errors in estimation that are either large, unknown or observerspecific (Walker 1970, Sykes et al 1983). These problems are confounded in arid environments where the patchy nature of the vegetation produces infrequent recordings and, as a result, wide confidence intervals (Friedel and Shaw 1987).…”

Section: Introductionmentioning

confidence: 99%

Journal of Range Management, Volume 53, Number 6 (November 2000)

2000

REM

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Use of Visual Cover Assessments as Quantitative Estimators of Some British Woodland Taxa

Cited by 178 publications

References 12 publications

In the eye of the beholder: bias and stochastic variation in cover estimates

In the eye of the beholder: bias and stochastic variation in cover estimates

Using Light Attenuation to Estimate Leafy Spurge Impacts on Forage Production

Journal of Range Management, Volume 53, Number 6 (November 2000)

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