The impact of inter-observer variability on the accuracy, precision and utility of a commonly-used grassland condition index

Goodenough, Anne E.; Carpenter, William S.; McTavish, Lynne; Blades, Bryony; Clarke, E. A.; Griffiths, Shaun; Harding, Nadine; Scott, Rebekah; Walsh, Emily S.; Wilson, Louisa; Hart, Adam G.

doi:10.1016/j.ecolind.2020.106664

Cited by 6 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Observer data collection across scientific disciplines often possesses intrinsic interobserver variability for various reasons 51, 52 . Here, variation in H. zea injury estimates among the five observers is not surprising.…”

Section: Resultsmentioning

confidence: 86%

“…Observer data collection across scientific disciplines often possesses intrinsic interobserver variability for various reasons. 51,52 Here, variation in H. zea injury estimates among the five observers is not surprising. However, any study requires a predetermined level of acceptable variability to ensure confidence in the results and accurate interpretation of research findings.…”

Section: Variability In Standard Injury Assessmentsmentioning

confidence: 72%

“…In addition to injury, we used the computer algorithm to examine the spatial feeding pattern complexity or solidity of H. zea injury across Bt toxin expression. Prior research has documented feeding irregularities when H. zea larvae are exposed to Bt toxins (Cry1Ab) 54 and greater randomness in H. zea feeding patterns as injury increases 51 . Previous research has also shown that H. zea modify their feeding behavior (for example, avoidance, mobility) in response to Bt exposure 55, 56 .…”

Section: Resultsmentioning

confidence: 99%

“…Prior research has documented feeding irregularities when H. zea larvae are exposed to Bt toxins (Cry1Ab) 54 and greater randomness in H. zea feeding patterns as injury increases. 51 Previous research has also shown that H. zea modify their feeding behavior (for example, avoidance, mobility) in response to Bt exposure. 55,56 This phenomenon may be due to ear-level variation in toxin expression among maize kernels, although all Bt kernels likely express toxins at some level.…”

Section: Helicoverpa Zea Feeding Patternmentioning

confidence: 99%

See 3 more Smart Citations

Computer vision for detecting field‐evolved lepidopteran resistance to Bt maize

Dorman

Kudenov

Lytle

et al. 2021

Pest Management Science

View full text Add to dashboard Cite

BACKGROUND Resistance evolution of lepidopteran pests to Bacillus thuringiensis (Bt) toxins produced in maize and cotton is a significant issue worldwide. Effective toxin stewardship requires reliable detection of field‐evolved resistance to enable the implementation of mitigation strategies. Currently, visual estimates of maize injury are used to document changing susceptibility. In this study, we evaluated an existing maize injury monitoring protocol used to estimate Bt resistance levels in Helicoverpa zea (Lepidoptera: Noctuidae). RESULTS We detected high interobserver variability across multiple injury metrics, suggesting that the precision and accuracy of maize injury detection could be improved. To do this, we developed a computer vision‐based algorithm to measure H. zea injury. Algorithm estimates were more accurate and precise than a sample of human observers. Moreover, observer estimates tended to overpredict H. zea injury, which may increase the false‐positive rate, leading to prophylactic insecticide application and unnecessary regulatory action. CONCLUSIONS Automated detection and tracking of lepidopteran resistance evolution to Bt toxins are critical for genetically engineered crop stewardship to prevent the use of additional insecticides to combat resistant pests. Advantages of this computerized screening are: (i) standardized Bt injury metrics in space and time, (ii) preservation of digital data for cross‐referencing when thresholds are reached, and (iii) the ability to increase sample sizes significantly. This technological solution represents a significant step toward improving confidence in resistance monitoring efforts among researchers, regulators and the agricultural biotechnology industry.

show abstract

Section: Resultsmentioning

confidence: 86%

Section: Variability In Standard Injury Assessmentsmentioning

confidence: 72%

Section: Resultsmentioning

confidence: 99%

Section: Helicoverpa Zea Feeding Patternmentioning

confidence: 99%

See 2 more Smart Citations

Computer vision for detecting field‐evolved lepidopteran resistance to Bt maize

Dorman

Kudenov

Lytle

et al. 2021

Pest Management Science

View full text Add to dashboard Cite

show abstract

“…The dominant tree and shrub species are umbrella acacia ( Vachellia tortilis ) and sicklebush ( Dichrostachys cinerea ), with some marula ( Sclerocarya birrea ). In an extensive grassland survey in 2019 (Goodenough et al, 2020), 45 grass species were identified on site. These were dominated by broad curly leaf ( Eragrostis rigidior ), common russet ( Loudetia simplex ), finger grass ( Digitaria eriantha ), wool grass ( Anthephora pubescens ), herringbone ( Pogonarthria squarrosa ), tassle three‐awn ( Aristia congesta ) and wire grass ( Elionurus muticus ).…”

Section: Methodsmentioning

confidence: 99%

Response of southern African ungulate species to supplementary feeding during drought: Species‐specific differences in relative use, food choice and intraspecific behavioural interactions

Goodenough

Sparkes

Dawson³

et al. 2022

Afr J Ecol

Self Cite

View full text Add to dashboard Cite

In southern Africa, climate change is increasing drought severity and duration. One way to mitigate drought‐induced food shortages is supplementary feeding, but ungulate responses are largely unstudied. Here, we analyse ~250 h of video footage from camera traps at feeding stations in South Africa during a year when rainfall was ~50% of the decadal average. Feeding station usage was highly uneven: eland (Taurotragus oryx), greater kudu (Tragelaphus strepsiceros), sable (Hippotragus niger) and white rhino (Ceratotherium simum) were over‐represented relative to their local population; and impala (Aepyceros melampus), blesbok (Damaliscus dorxas phillipsi) and zebra (Equus quagga) were under‐represented. In total, 81% of individuals at feeding stations fed (0%–90% for different species). Guarding behaviour was observed, especially by eland and kudu, potentially excluding other (smaller) species. Overall, 17% visits involving ≥2 individuals involved intraspecific aggression (highest for sable (40%) and eland (27%); low/absent otherwise); there were very few (n = 6) inter‐specific encounters. Lucerne and teff hay were consumed more frequently than Boskos (pellets), which was favoured by non‐target primates and ungulates showing guarding behaviour. As benefits of feeding are unequal, and can encourage guarding and intraspecific aggression, we suggest feeding should be done with caution, especially if the aim is to benefit specific species.

show abstract

Identification of salmon redds using RPV‐based imagery produces comparable estimates to ground counts with high inter‐observer variability

Auerbach

Fremier

2022

River Research & Apps

View full text Add to dashboard Cite

An integral part of population monitoring within fisheries is ground‐based surveys of fish redds. Remotely piloted vehicles or drones (RPVs) could provide a complementary method but need verification due to a host of methodological differences. To compare methods, we counted summer Chinook redds (Oncorhynchus tshawytscha) (~6 m2 in size) using RPVs and compared them to ground‐based counts in the Wenatchee River (WA, USA). We found individual aerial counts were many times twice the corresponding ground counts. We also found large inter‐observer variability among aerial counters. The coefficient of variation among multiple aerial counts were 37%, 38%, and 50% across three sites, which are comparable to published variation in ground counts. We attribute inter‐observer variability to inherent uncertainties in redd identification similar to ground counting, and importantly, we did not see evidence that the clarity of substrate in the image influenced observer bias. Overall, our data suggest that redd counting using RPVs is an effective method, particularly in high‐density spawning locations. We conclude that RPV imagery accurately identifies redds in a clear, relatively wide (60 m) river, but suggest continuing research into increasing precision, limiting observer variability, and assessing the accuracy across methods and locations.

show abstract

The impact of inter-observer variability on the accuracy, precision and utility of a commonly-used grassland condition index

Cited by 6 publications

References 27 publications

Computer vision for detecting field‐evolved lepidopteran resistance to Bt maize

Computer vision for detecting field‐evolved lepidopteran resistance to Bt maize

Response of southern African ungulate species to supplementary feeding during drought: Species‐specific differences in relative use, food choice and intraspecific behavioural interactions

Identification of salmon redds using RPV‐based imagery produces comparable estimates to ground counts with high inter‐observer variability

Contact Info

Product

Resources

About