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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
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