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Supplementary Appendix 1. Studies included in the literature survey and main findings. ____________________________________________________________________________________________________________ Study Location 1 Habitat type Plot size Plot Number Type of error Attribute ____________________________________________________________________________________________________________ West (1938) South Africa Pasture 1 m2 1 Intra- and inter-observer Cover Main findings: Three observers each sampled the same plot. One observer sampled it twice. The overall coefficient of variation was 11.6. For the observer that sampled the plot twice, the second estimate was 16% lower than the first. Hope-Simpson (1940) UK Chalk grassland 0.25 ha 2 Intra-observer Species composition/ subjective frequency Main findings: Estimated abundance of species within 7 categories. One site was sampled twice a few days apart. Considering species composition: 83% of species were found on both occasions; pseudoturnover was 8.8%. Considering subjective frequency: 36% of species differed by 1 category, and 12% differed by 2 categories. Ellison (1942) Montana, USA Short-grass associations 0.25 - 0.5 m2 3 Intra- and inter-observer Cover Main findings: Five trained and experienced observers each sampled 3 plots using 3 different methods. Significant differences were found among and within observers. Interactions between observer and method were also documented. Smith (1944) Western USA Ungrazed rangeland 9.3 m2 (100 ft2) 1 9 Intra- and inter-observer Density Main findings: Eight experienced observers estimated density in 3 plots per day for 7 days. Significant inter-observer variation was found; individual observers also had significant intra- and inter-daily variation. The highest estimates were 2x that of lowest, even after a week of training. “Extremely poor agreement” was found between visual estimates and biomass as determined by clipping. Goodall (1952) Victoria, Australia Waste ground 300 points 1 Inter-observer Cover Main findings: Three experienced observers each sampled the same plot using the point quadrat method. Significant interobserver differences were found for three of seven species. Two observers agreed consistently, while the third observer’s estimates either exceeded or fell below those of the first two. Goebel et al. (1958) Utah, USA Desert range 2.3 m2 (25 ft2) 60 Inter-observer Density Main findings: Three observers with differing degrees of experience estimated density in two vegetation types (30 sites each) by use of a subdivided sampling frame (1/16 ft2 units). Estimates of the observers differed significantly in one vegetation type but not the other. Walker (1970) Zimbabwe Grasslands Varied 30 Inter-observer 8 different variables Main findings: Three teams employed eight different techniques, including three basal cover estimates, three density estimates, a frequency estimate, and a dry-weight-rank method. Basal cover estimates differed among observers by as much as 27%. Differences among observers in the dry-weight-rank method were “too great for the results to be acceptable”. The author considered differences among observers to be “a major feature of the results”. 2 Campbell & Arnold (1973) Western Australia Pastures 0.25 m2 25-50 Intra- and inter-observer Biomass Main findings: Eight or eleven observers estimated biomass in 8 different experiments. Without training, observers overestimated the effects of height and underestimated the effects of density in determining yield. Trained observers produced more accurate regressions of estimated yield vs. actual yield (as determined by harvesting). Hall & Okali (1978) Nigeria Second-growth tropical forest 625 m2 50 Inter-observer Species composition Main findings: Two sets of plots (n=15 and n=35) were sampled by two different groups of observers. For 12 of 16 life forms, the mean number of species observed was significantly higher for the more experienced group of observers. A species richness gradient was concealed by observer bias. Nilsson & Nilsson (1982) Sweden Islands in lake 0.03-2.19 ha 41 Intra-observer Species composition Main findings: Surveys were conducted in three years: 1976, 1978, and 1980. Species turnover calculated from observations in 1976 and 1980 was an order of magnitude higher than “certain + probable” turnover obtained from taking into account population abundances, estimated ages, and presence/absence sequences over all three years (14.8 vs. 1.2%). Nilsson & Nilsson (1983) Sweden Islands in lake 0.03-1.04 ha 6 Intra-observer Species composition Main findings: Surveys were conducted twice, one week apart. Found species turnover of 7.9%, which was entirely due to species missed in one of the surveys (i.e., pseudoturnover). 3 Sykes et al. (1983) UK Woodland 4, 50 and 200 m2 8 of each size Inter-observer Cover Main findings: Ten experienced observers estimated cover over 3 weeks. Significant inter-observer effects were found for all quadrat sizes, with greater variation for larger quadrats. Compared to point-quadrat methods, significant differences were found for half the species. Estimates of a random observer were estimated to be within 10-20% of true cover (90% confidence). Gotfryd & Hansell (1985) Toronto, Canada Oak-maple forest 405 m2 8 Inter-observer 20 different vegetation variables Main findings: Four trained observers sampled each of eight sites four times. A univariate ANOVA was used to test for an observer effect. Observers differed significantly in their measurements of 18 out of 20 vegetation variables, with an observer effect usually in the 20-30% range. Nilsson & Nilsson (1985) Sweden Islands in lake 0.03-2.19 ha 41 Inter-observer Species composition Main findings: Forty-one islands were surveyed by two independent teams. Average between-team pseudoturnover was 11.4% (range: 4.2-19.4 among islands). Kirby et al. (1986) UK Woodlands 200 m2 18 Inter-observer Species composition Main findings: Six observers, all with previous experience, made species lists for three woodlands by sampling 6 quadrats or walking 3-3.5 km in each woodland. Significant inter-observer error was found. Sampling error for single observers ranged from 7 – 63%. Scott & Hallam (2002) determined that their data correspond to a pseudoturnover rate of 23 – 36%. 4 Block et al. (1987) California, USA Forest 400 m2 75 Inter-observer 49 different variables Main findings: Three experienced observers each estimated 49 different vegetation variables; all variables were later measured after ocular estimates were obtained. Significant inter-observer differences were found for 31 of the 49 variables. Ocular estimates by at least one observer differed from measurements for 21 of the 49 variables. Everson & Clarke (1987) South Africa Grasslands 400 m2 36 Inter-observer Percentage frequency, biomass Main findings: Six surveyors each employed six different techniques, including three point methods, a quadrat method, a belt transect, and estimation of relative biomass. Although the authors concluded that there were “no marked differences” among surveyors, mean CVs across all methods ranged from 32 to 64% for the 5 most “important” species. Friedel & Shaw (1987a) Northern Territory, Australia Rangelands 0.1, 0.25 & 1.0 m2 up to 60 of each Inter-observer 4 different variables Main findings: Six observers used various techniques of herbage sampling at five sites, and comparisons were made for differing numbers of quadrats sampled (20, 30, 40, 50 and 60) at each site. Overall, “none of the tested methods provided reliable results when a number of observers were compared.” Friedel & Shaw (1987b) Northern Territory, Australia Rangelands Varied 5 Varied Inter-observer 3 different variables Main findings: Six observers used various techniques of tree and shrub sampling at five sites. Significant differences among observers were found for some sites but not for others. The authors concluded “for cover, CVs in the order of 20% can be achieved… so that only large changes can be detected.” Kennedy & Addison Alberta, (1987) Canada Forest 1 m2 20 Intra-observer Cover Main findings: Single observer surveyed same 20 plots nine times. Error was highly variable among species, spanning 3 orders of magnitude. Suggested changes in vegetation must be >20% before they can be attributed to factors other than measurement error and annual fluctuations. Tonteri (1990) Finland Forest 2 m2 25 Inter-observer Cover Main findings: Eleven observers with 4.5 months experience estimated % cover in the same 25 plots. Significant interobserver error was found for 2 of 6 species/cover layers. Mean of the highest observer estimate was 2x that of lowest. Error was 15-40% of the actual cover. West & Hatton (1990) Wyoming, USA Sagebrushsteppe 1 m2 20 Inter-observer Species richness, cover Main findings: Five experienced observers produced significantly different estimates of species diversity, richness, and evenness. Some observer’s estimates were subsets of others, indicating differences among observers in their ability to detect the full set of species present. Stampfli (1991) Switzerland Meadows 1.76 m2 6 10 Intra-observer Frequency Main findings: A team of 2 observers sampled 10 plots using a fixed point method with 176 points. CVs ranged from 7.9 to 316.2 among species, averaging 28.8. Lepš & Hadincová (1992) Czech Republic Meadows, clearcuts, peat bogs 25 m2 40 Inter-observer Species composition, cover Main findings: Two experienced observers sampled 40 relevés using the Braun-Blanquet scale. The average inter-observer difference in species composition was 13%. For cover estimates, 39.5% of species differed by 1 category, and 3% differed by 2 categories. No systematic observer bias was found. Rusch & van der Maarel (1992) Sweden Limestonegrassland 0.01 m2 50 Intra-observer Species composition Main findings: Surveys were conducted twice for 30 of the 50 plots, in each of three different years. “Spurious” turnover (number of plots in which a species was missed in one survey) ranged from 0-11 by species. Statistical analyses were not done, but the authors considered the observational error to be “quite high”. Bråkenhielm & Qinghong (1995) Sweden Forest and bog 0.25 m2 62 Intra- and inter-observer Cover Main findings: Two experienced observers estimated cover by various methods. One observer “almost consistently” made lower visual estimates of cover than the other. The magnitude of inter-observer error was “nearly the same” as that of intraobserver error for three methods of estimating cover. McCune et al. Oregon and Lichen 0.378 ha 7 3 Inter-observer Species composition (1997) Southeast USA communities Main findings: Observers (up to 11) with various levels of experience recorded the lichen species present in the same plots. Inexperienced observers found many fewer species; trainees found 38-66% of the expert’s total. Indices determined from ordination analyses were relatively consistent across observers (repeatable to within 2-10%), however, due to “redundancy of information provided by different species”. Stapanian et al. (1997) North Carolina, USA Forest 1 m2 36 Inter-observer Species composition and cover in strata Main findings: Three technicians and three trainers sampled the same plots. Technicians found significantly fewer species than the trainers in the ground stratum, and estimated significantly less cover in the uppermost stratum (n=4 strata total). The proportion of variation due to measurement error and temporal variation was <13%. Zhou et al. (1998) NSW, Australia Semi-arid rangeland 4 m2 140 Inter-observer Cover Main findings: Four observers sampled the same plots. Significant inter-observer differences were found. The average difference in estimate of percent cover among observers was 12%, with a maximum difference of 50%. Oredsson (2000) Sweden 56 different habitats 100 m2 214 Intra- and inter-observer Species composition Main findings: Intra-observer: Twenty-two plots surveyed twice had an average of 10% more species than 131 plots surveyed once (all by the same botanist). Inter-observer: One of six surveyors had “significantly different efficiency” based on median species counts. 8 Van Hees & Mead (2000) Alaska, USA Forest 100 m2 20 Inter-observer Horizontal/vertical profiling Main findings: Six experienced observers estimated space occupied by vegetation in layers on three occasions. A components of variance analysis found that the estimates of observers were not consistent relative to each other from one plot to the next or from one measurement period to the next. Measurement error was large enough to “question the validity” of this method. Klimeš et al. (2001) Czech Republic Grassland 9.8 cm24 m2 7 Inter-observer Species composition Main findings: Five trained observers recorded plant species present in each of 7 plots. A discrepancy of 10-20% was found among observers in larger plots, which increased to 33% in smaller plots. Murphy & Lodge (2002) NSW, Australia Grasslands 0.16 m2 60 Inter-observer Cover Main findings: Four observers with different experience levels but similar training estimated ground and canopy cover. Mean estimates were not significantly different among observers, for either cover type. Some significant differences were observed when comparing the slope and intercept of the regression of each observer estimate versus mean values. Scott & Hallam (2002) UK Various habitats 0.16 m2 110 Inter-observer Species composition Main findings: An average of ten 0.16 m2 cells were surveyed in each of eleven plots. An expert botanist resurveyed all cells. 5.9% of specimens were misidentified to species level. The average pseudoturnover rate was 24% (range: 0-69 across species types). The average percentage agreement between surveyors was 57%. The percentage agreement was higher for sites originally surveyed by botanists with greater expertise. 9 Kercher et al. (2003) Wisconsin, USA Wet meadows 1 m2 120 Inter-observer Species composition, cover Main findings: Two teams, each consisting of two trained field botanists, surveyed the same plots. Mean pseudoturnover was 19% (range among ten sites: 9 – 30%). The sum of cover classes per plot was significantly higher for one team than the other. Kéry & Gregg (2003) West Virginia, USA Meadow 41.2 m2 1 Inter-observer Presence/absence of 1 target species Main findings: Two observers independently searched the same plot for the orchid, Cleistes bifaria. Although detectability was different for each life stage, there were no differences in detectability between the two observers. Klimeš (2003) Czech Republic Grassland 9.8 cm24 m2 7 Inter-observer Cover Main findings: Five trained observers estimated cover for the whole stand and individual species. Estimates of total plant cover differed significantly among observers (ANOVA). The CV among observers was scale dependent, ranging from 3545% at small scales to 7-15% at large scales. The CV of the cover of individual species ranged from 0-225% and decreased with increasing plot size. Helm & Mead (2004) Alaska, USA Forest 167 m2 plots/ 7/various multiple size subplots Inter-observer Various Main findings: Six experienced observers surveyed all subplots using 5 different techniques. CVs among observers in estimates of cover averaged 172% (range: 61-533). Observer variability was the greatest component of variance. Observer 10 error was significant in most analyses of variance. All techniques had major components of observer variability, although more detailed techniques had lower observer variability. Plattner et al. (2004) Switzerland Various 10 and 12,500 m2 28/23 Inter-observer Species richness Main findings: Two botanists re-sampled the species richness of plots that had been sampled by other botanists as part of a long-term monitoring program at local (10 m2) and landscape (12,500 m2) scales. Absolute differences in estimates of mean species richness were only 0.1 at the local and 5.0 at the landscape scales. Species compositions and pseudoturnover were not considered, however. Carlsson et al. (2005) Sweden Grasslands 0.5 m2 15 Inter-observer Cover, frequency Main findings: Two observers with similar experience used two methods on the same set of plots. For both methods, interobserver differences were relatively small (<3% explained variance by partial Redundancy Analyses), but “highly significant”. One observer consistently estimated higher cover and frequency than the other. Gray & Azuma (2005) Oregon, USA Forests 170 m2/ 1 m2 48/288 Inter-observer Species composition, cover Main findings: Two experienced botanists sampled the same forest plots. Agreement for species identifications was 71% at the subplot level and 67% at the quadrat level (pseudoturnover rate of 29-33%). For cover estimates, 41% of quadrats differed by 1 category, and 6% differed by 2 categories. 11 Ringvall et al. (2005) Sweden Forest 0.33 and 0.01 m2 70 of each Inter-observer Species composition Main findings: 41 observers with different experience levels recorded presence/absence data within a limited list of only 6 species or species guilds, and their results compared with a reference survey. Standard deviations of surveyor differences relative to actual frequency ranged from 5% (more common species) to 20% (less common species). Observers with more experience were more accurate and consistent. Archaux et al. (2006) France Lowland forest 400 m2 17 Inter-observer Species composition Main findings: Four professional botanists recorded plant species for 1 hour in the same 17 plots. On average, single observers overlooked 20-30% of the species present. Identification errors represented 5.6-10.5% of all occurrences. AIC modeling revealed an observer effect to be included in the most parsimonious model. Archaux et al. (2007) France Lowland forest 2, 4, and 400 m2 18/72/72 Inter-observer Five different variables Main findings: Four professional botanists surveyed the same plots consisting of different size quadrats to estimate species richness, cover and 3 different indices. Significant observer effects were found, which varied among the variables evaluated. The authors concluded that plant censuses conducted on smaller quadrats were not more reliable than on larger ones. Vittoz & Guissan (2007) Swiss Alps Alpine meadows 0.4, 4, and 40 m2 12 12/3/3 Inter-observer Species composition, cover Main findings: Eight experienced botanists worked singly or in pairs. Only 45-63% of species were seen by all observers. Pseudoturnover ranged from 5.2-18.9%, and was smaller for pairs of observers. The coefficients of variation in cover estimates were mostly in the 50-100% range. Cheal (2008) Victoria, Australia Open shrubland unspecified 1 Inter-observer Cover Main findings: Sixteen experienced observers estimated the projective foliage cover of the dominant grass species, using both a 10% scale and the Braun-Blanquet (7 point) scale. Final cover estimates for this species varied three-fold using the 10% scale (range 20 – 60%), and over three categories by the Braun-Blanquet scale. Milberg et al. (2008) Sweden Forest 100 m2 342 Inter-observer Species composition, cover Main findings: Each plot was visited by one observer from a group of 36 and one of two quality assessment observers. Overall, 26% of all occurrences of species and 34% of all occurrences of species groups (range 3-74%) were missed by one of the two observers to visit a plot. An observer effect in cover estimates was significant in 5 of 17 taxa based on variance component analyses. Symstad et al. (2008) Northcentral USA Grasslands 1000 m2/ 0.5 m2 46/460 Inter-observer 8 different variables Main findings: Ten observers with a variety of skill levels but similar training doubled-sampled plots in teams of 2. Pseudoturnover was “substantial”, ranging from 6-57%. Differences between observer teams were significant for all 8 variables, although differences were a relatively small proportion of the values recorded. 13 Young et al. (2008) Missouri, USA Limestone glade 25 m2 Varied among years Intra-observer Number of individuals Main findings: Estimates of abundance (i.e., number of individuals) for a single species within 5 density classes were evaluated by comprehensive counts. The accuracy of visual estimates varied among density classes and ranged from 20– 100%. All misclassifications underestimated true abundance. Most errors were within 1 density class, but up to 50% (depending on the density class) underestimated true abundance by 2 classes. Archaux (2009) France Lowland forest 100 m2 8 Inter-observer Species composition Main findings: Eleven teams of botanists, each with one professional, recorded plant species in the same 8 plots. Eight different species richness estimators were evaluated. The species recorded varied among teams, and none of the evaluated estimators correctly accounted for differences in the completeness of species lists that existed among teams. All estimators were highly sensitive to misidentifications. Archaux et al. (2009) France Lowland forest 100 m2 8 Inter-observer Species composition Main findings: Eleven teams of professional botanists recorded plant species first independently, and then consensually. On average, 16% of the tree layer species composition and 19% of the ground vegetation layer composition was overlooked (missed during a census) by a team. Misidentification rates averaged 2.3% for the tree layer and 5.3% for the ground vegetation layer. Bergstedt et al. (2009) Sweden Coniferous forest 100 m2 14 16 Inter-observer Cover Main findings: Ten observers with greatly differing experience estimated cover in same plots. Observer bias in variance component analyses revealed a relative contribution of 8.2 - 47.8%, depending upon the species. Partial redundancy analyses revealed a significant effect of experience, with the variation explained by experience ranging from 0 - 11.8%, depending upon the species. Chen et al. (2009) China Evergreen broadleaf forest 400 m2 141 Inter-observer Presence/absence of 6 target species Main findings: Two observers, with different levels of experience but similar training, searched the same plots for 6 target species. There was no significant difference in detection probability between the two observers. Gorrod & Keith (2009) Australia Grassy woodlands 100 m2/ variable 20 Inter-observer Two indices with multiple attributes Main findings: Ten observers with varying experience surveyed the same 20 sites. Observer estimates varied substantially across multiple scoring categories for all attributes. CVs ranged from 40 – 300% for attributes with means ≤5; CVs were ≥20% for means up to 75 (all attribute scores scaled to 100). With a single exception, each observer made the most extreme estimate on at least one site. MacDonald (2010) Scottish Highlands Various upland habitat 25 m2 and 1 ha 52/10 Inter-observer Impact of herbivores Main findings: Up to 17 observers with a range of experience estimated impact of herbivores in 3 categories (high, medium, low). About a third of the estimates differed by one category, although differences of two categories were rare. The authors interpreted the reliability of their impact assessments as “substantially worse” than for most estimates of plant cover. 15 Moore et al. (2011) Victoria, Australia Grass and shrubland 400 m2 45 Inter-observer Presence/absence of 1 target species Main findings: Twelve observers, with a range of experience, searched for experimentally transplanted Orange hawkweed plants. Detection varied widely among observers, ranging from 9 – 100% of the plants actually encountered (within the search area of the observers). Vittoz et al. (2010) Swiss Alps and Scotland Alpine vegetation >100 m2/ 1 m2 8/8 Inter-observer Species composition, cover Main findings: Eight (Scotland) or nine (Switzerland) experienced botanists surveyed 8 large (>100 m2) sections and 8 small (1 m2) plots. For large sections, pseudoturnover ranged from 25-32% in Scotland and from 12-19% in Switzerland. For small plots, pseudoturnover ranged from 8-15% in Scotland and from 5-11% in Switzerland. The CV associated with cover averaged 108 for large sections and 66 for small plots (both sites combined). Clarke et al. (2012) South Australia Chenopod shrubland 1 ha 4 Inter-observer Presence/absence of 7 target species Main findings: Up to six different surveyors recorded the species present at 4 sites. Detectability was examined for 7 of the “most visible, persistent, and easily identifiable perennial vegetation species”. Detectability was imperfect for all but one species, and non-detection errors varied among surveyors. Burg et al. (2015) Swiss Alps Alpine summits 63716,720 m2 16 48 Inter-observer Species composition Main findings: Two different observers surveyed the plant species of the uppermost 10 m of 48 summits. Pseudoturnover ranged from 0-33.3%, with a mean of 13.5 ± 1.1 (SE). An average of 9 species per summit (range: 0-31) were found by only one of the two observers. Number of species missed increased with difference in botanizing time between observers and with a longer ascent to the summit. ____________________________________________________________________________________________________________ 1 Location reflects current geopolitical boundaries, which in a few cases differ from those in effect at the time the study was published. 17 REFERENCES Archaux F (2009). Could we obtain better estimates of plot species richness from multiple-observer plant censuses? Journal of Vegetation Science 20:603-611. Archaux F, Berges L, Chevalier R (2007). 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