Observer error,a type of nonsampling error,is pervasive in vegetation sampling and often of a consequential magnitude.Observer error rates should be reported along with published studies,although there currently exist...Observer error,a type of nonsampling error,is pervasive in vegetation sampling and often of a consequential magnitude.Observer error rates should be reported along with published studies,although there currently exists no standardized,easily comparable format.Here we describe five key metrics of observer error(i.e.imprecision between observers),how they are calculated,and how they can be reported and interpreted.Three metrics apply to species composition:pseudo-turnover,observer bias in species richness and underestimation of true species richness.Two metrics—cover agreement and observer bias in cover estimation—apply to categorical cover estimation.All metrics are simple to determine,could be calculated from virtually any multispecies sampling effort using two or more observers,and are easily compared with other studies.The metrics are all reported as percentages,allowing for relative comparisons among studies with greatly differing species diversities.We also describe how to decompose the amount of error in species composition and cover estimation into random and biased components.Such decomposition is useful in determining whether additional training may be necessary for some observers.Two of the five metrics—pseudo-turnover and cover agreement—have been quantified in previous studies,and we compile a list of published rates of pseudo-turnover within general habitat types,and published cover agreement categories,for comparison with future studies.Finally,we provide an example by calculating the observer error metrics for a real data set collected by three different observers.展开更多
Aims Observer error is an unavoidable aspect of vegetation surveys involving human observers.We quantified four components of interobserver error associated with long-term monitoring of prairie vegetation:overlooking ...Aims Observer error is an unavoidable aspect of vegetation surveys involving human observers.We quantified four components of interobserver error associated with long-term monitoring of prairie vegetation:overlooking error,misidentification error,cautious error and estimation error.We also evaluated the association of plot size with pseudoturnover due to observer error,and how documented pseudochanges in species composition and abundance compared with recorded changes in the vegetation over a 4-year interval.Methods This study was conducted at Tallgrass Prairie National Preserve,Kansas.Monitoring sites contained 10 plots;each plot consisted of a series of four nested frames(0.01,0.1,1 and 10 m^(2)).The herbaceous species present were recorded in each of the nested frames,and foliar cover was visually estimated within seven cover categories at the 10 m^(2)spatial scale only.Three hundred total plots(30 sites)were surveyed,and 28 plots selected at random were resurveyed to assess observer error.Four surveyors worked in teams of two.Important Findings At the 10 m^(2)spatial scale,pseudoturnover resulting from overlooking error averaged 18.6%,compared with 1.4%resulting from misidentification error and 0.6%resulting from cautious error.Pseudoturnover resulting from overlooking error increased as plot size decreased,although relocation error likely played a role.Recorded change in species composition over a 4-year interval(excluding potential misidentification error and cautious error)was 30.7%,which encompassed both pseudoturnover due to overlooking error and actual change.Given a documented overlooking error rate of 18.6%,this suggests the actual change for the 4-year period was only 12.1%.For estimation error,26.2%of the time a different cover class was recorded.Over the 4-year interval,46.9%of all records revealed different cover classes,suggesting that 56%of the records of change in cover between the two time periods were due to observer error.展开更多
Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error.Three types of errors are common:(i)overlooking error(i.e.not observing species actually present),(ii)misidentifica...Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error.Three types of errors are common:(i)overlooking error(i.e.not observing species actually present),(ii)misidentification error(i.e.not correctly identifying species)and(iii)estimation error(i.e.not accurately estimating abundance).I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies.Important FindingsAlmost all studies(92%)that tested for a statistically significant effect of observer error found at least one significant comparison.In surveys of species composition,mean pseudoturnover(the percentage of species overlooked by one observer but not another)was 10-30%.Species misidentification rates were on the order of 5-10%.The mean coefficient of variation(CV)among observers in surveys of vegetation cover was often several hundred%for species with low cover,although CVs of 25-50%were more representative of species with mean covers of>50%.A variety of metrics and indices(including commonly used diversity indices)and multivariate data analysis techniques(including ordinations and classifications)were found to be sensitive to observer error.Sources of error commonly include both characteristics of the vegetation(e.g.small size of populations,rarity,morphology,phenology)and attributes of the observers(e.g.mental fatigue,personal biases,differences in experience,physical stress).The use of multiple observers,additional training including active feedback approaches,and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys.展开更多
基金supported by the Inventory and Monitoring Program of the National Park Service。
文摘Observer error,a type of nonsampling error,is pervasive in vegetation sampling and often of a consequential magnitude.Observer error rates should be reported along with published studies,although there currently exists no standardized,easily comparable format.Here we describe five key metrics of observer error(i.e.imprecision between observers),how they are calculated,and how they can be reported and interpreted.Three metrics apply to species composition:pseudo-turnover,observer bias in species richness and underestimation of true species richness.Two metrics—cover agreement and observer bias in cover estimation—apply to categorical cover estimation.All metrics are simple to determine,could be calculated from virtually any multispecies sampling effort using two or more observers,and are easily compared with other studies.The metrics are all reported as percentages,allowing for relative comparisons among studies with greatly differing species diversities.We also describe how to decompose the amount of error in species composition and cover estimation into random and biased components.Such decomposition is useful in determining whether additional training may be necessary for some observers.Two of the five metrics—pseudo-turnover and cover agreement—have been quantified in previous studies,and we compile a list of published rates of pseudo-turnover within general habitat types,and published cover agreement categories,for comparison with future studies.Finally,we provide an example by calculating the observer error metrics for a real data set collected by three different observers.
基金funded by the National Park Service Inventory and Monitoring Program.
文摘Aims Observer error is an unavoidable aspect of vegetation surveys involving human observers.We quantified four components of interobserver error associated with long-term monitoring of prairie vegetation:overlooking error,misidentification error,cautious error and estimation error.We also evaluated the association of plot size with pseudoturnover due to observer error,and how documented pseudochanges in species composition and abundance compared with recorded changes in the vegetation over a 4-year interval.Methods This study was conducted at Tallgrass Prairie National Preserve,Kansas.Monitoring sites contained 10 plots;each plot consisted of a series of four nested frames(0.01,0.1,1 and 10 m^(2)).The herbaceous species present were recorded in each of the nested frames,and foliar cover was visually estimated within seven cover categories at the 10 m^(2)spatial scale only.Three hundred total plots(30 sites)were surveyed,and 28 plots selected at random were resurveyed to assess observer error.Four surveyors worked in teams of two.Important Findings At the 10 m^(2)spatial scale,pseudoturnover resulting from overlooking error averaged 18.6%,compared with 1.4%resulting from misidentification error and 0.6%resulting from cautious error.Pseudoturnover resulting from overlooking error increased as plot size decreased,although relocation error likely played a role.Recorded change in species composition over a 4-year interval(excluding potential misidentification error and cautious error)was 30.7%,which encompassed both pseudoturnover due to overlooking error and actual change.Given a documented overlooking error rate of 18.6%,this suggests the actual change for the 4-year period was only 12.1%.For estimation error,26.2%of the time a different cover class was recorded.Over the 4-year interval,46.9%of all records revealed different cover classes,suggesting that 56%of the records of change in cover between the two time periods were due to observer error.
文摘Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error.Three types of errors are common:(i)overlooking error(i.e.not observing species actually present),(ii)misidentification error(i.e.not correctly identifying species)and(iii)estimation error(i.e.not accurately estimating abundance).I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies.Important FindingsAlmost all studies(92%)that tested for a statistically significant effect of observer error found at least one significant comparison.In surveys of species composition,mean pseudoturnover(the percentage of species overlooked by one observer but not another)was 10-30%.Species misidentification rates were on the order of 5-10%.The mean coefficient of variation(CV)among observers in surveys of vegetation cover was often several hundred%for species with low cover,although CVs of 25-50%were more representative of species with mean covers of>50%.A variety of metrics and indices(including commonly used diversity indices)and multivariate data analysis techniques(including ordinations and classifications)were found to be sensitive to observer error.Sources of error commonly include both characteristics of the vegetation(e.g.small size of populations,rarity,morphology,phenology)and attributes of the observers(e.g.mental fatigue,personal biases,differences in experience,physical stress).The use of multiple observers,additional training including active feedback approaches,and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys.
