Journal of Vegetation Science 18: 307-312, 2007
© IAVS; Opulus Press Uppsala.
FORUM
- Is tree diversity different down-under? -
307
FORUM
Is tree diversity different in the Southern Hemisphere?
Burns, K.C.
School of Biological Sciences, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand;
Fax +64 4 463 5331; E-mail [email protected]; Web http://www.vuw.ac.nz/staff/kevin_burns/index.htm
Abstract
Questions: Is tree diversity higher in the southern hemisphere?
Are latitudinal asymmetries in diversity sensitive to sampling
effects?
Location: 198 forested locales worldwide.
Methods: I re-analysed the Gentry database, which I augmented
with an additional survey from New Zealand. Data were used to
test whether latitudinal declines in tree diversity differ between
the northern and southern hemispheres. Data were also used to
test whether hemispheric asymmetries in diversity are sensitive
to sampling effects, or geographic variation in tree densities.
Results: Area-based measurements of species diversity are
higher in the southern hemisphere. However, southern forests
house denser plant populations. After controlling for geographic
variation in tree densities, diversity patterns reverse, indicating
tree diversity is higher in the northern hemisphere.
Conclusions: Latitudinal changes in tree diversity differ
between hemispheres. However, the nature of hemispherical
asymmetries in species diversity hinges on how diversity
is defined, illustrating how different definitions of diversity
can yield strikingly different solutions to common ecological
problems.
Keywords: Latitudinal diversity gradient; Sampling effect;
Species diversity.
Nomenclature: Allan (1961) and Moore & Edgar (1970).
Introduction
Latitudinal variation in species diversity has intrigued
biologists for over two centuries (Hawkins 2001). However, it has only recently been appreciated that declines
in diversity towards the poles may differ substantially
between hemispheres. In a recent review of latitudinal
diversity asymmetries, Chown et al. (2004) argue that
“…simple exercises – such as plotting richness values
for different latitudes or latitudinal bands against each
other for the hemispheres and examining the resulting
relationship… – rarely appear in the literature. Thus, it is
not yet clear how common or strong hemisphere-related
asymmetry is.” (p. 460, Chown et al. 2004; cf. Hillebrand
2004).
In a pioneering study, Gentry (1988) suggested that
tree diversity might be higher ʻdown-underʼ in the southern hemisphere. He graphically illustrated that species
diversity of woody plants follows a bell-shaped distribution with latitude, rising from low diversity levels at the
poles to a peak near the equator. But the peak in species
diversity appeared to occur in the southern hemisphere.
Diversity also seemed to decline more rapidly with
latitude in the northern hemisphere. However, Gentry
(1988) based this observation on a limited number of
forest inventories and he did not statistically test for
differences in diversity between hemispheres.
Species diversity is deceptively difficult to characterize. The most common measure of diversity is on an areabasis (i.e. species density, α-diversity, or the number of
species present in a given area). However, area-based
estimates of species diversity can be confounded by
population density, if the number of individuals sampled
varies among sampling points (Bunge & Fitzpatrick 1993;
Gotelli 2001). Such ʻsampling effectsʼ can have important consequences to our understanding of how factors
such as insularity and productivity influence taxonomic
diversity (Hector et al. 2002; Forbes et al. 2001; Chiarucci
et al. 2004; Lawes et al. 2005). However, “standardizing
308
Burns, K.C.
data sets by area… may produce very different results
compared to standardizing by the number of individuals collected, and it is not always clear which measure
of diversity is appropriate” (p. 379, Gotelli & Colwell
2001).
Here, I test whether tree diversity is higher in the
southern hemisphere. Using the Gentry database, I
evaluate whether hemispherical clines in area-based
measurements of tree diversity (i.e. species richness
per unit area) differ between hemispheres. I then assess latitudinal trends in tree population density and
test whether latitudinal asymmetries in tree diversity
remain unchanged after controlling for variation in plant
density.
Methods
Analyses were conducted using the Alwyn H.
Gentry Forest Transect Data Set (Phillips & Miller
2002). Gentry and his colleagues sampled a total of
226 forested locales across the globe with a sampling
design comprised of 10 separate transects, each measuring 2 m × 50 m (Gentry 1982). The first transect
typically began from a randomly chosen starting point
in undisturbed forest and was oriented in a random
direction. Each subsequent transect was then oriented
in a random direction within a 180° arch from the end
of the previous transect. All plants > 2.5 cm diameter
at breast height (1.37 m) that were rooted within 1 m of
the transect line was identified to species. Plants were
classified as belonging to morpho-species when their
taxonomic identity was uncertain. Each plant was also
categorized according to growth habit, either as a tree,
liana or hemi-epiphyte. As a result, each subsequent 0.1
ha plot provides an estimate of plant population density
and species richness per unit area for each growth form.
The full dataset is freely available on line (http://www.
mobot.org/MOBOT/research/gentry/data.shtml) and is
discussed in detail by Phillips & Miller (2002).
Of the 226 plots included in the dataset, 29 differ
from the protocol described above. Lianas and hemiepiphytes were not recorded in 6 sites, and 23 sites were
not sampled over a full 0.1 ha (i.e. < 10 transects were
sampled). To promote unbiased comparisons, these plots
were omitted from analyses, leading to a sample size of
197 standardized inventories. Forest inventories were
also not distributed homogeneously across the globe.
First, temperate forests in the northern hemisphere were
sampled more intensely than south-temperate forests.
The highest latitude sampled in the southern hemisphere
was in south-central Chile (40°43' S, 72°18' W), while ten
sites in North America and Europe were located above
42° latitude. Sampling intensity also differed between
FORUM
continents (Africa = 18, Australasia = 38, Europe = 5,
North America = 57, South America = 126), with over
80% of sampling points coming from North and South
America.
In an attempt to increase the representation of poorly
sampled regions and latitudes, I used Gentryʼs (1982) protocol to sample an additional site in New Zealand. These
data were collected in Otari-Wiltonʼs Bush (41º14' S,
174º45' E), which contains a large, undisturbed stand of
conifer-broadleaf forest on the southern tip of the North
Island, New Zealand (see Burns & Dawson 2005 for a
detailed site description). Elevation ranges between 70
m and 280 m above sea-level, mean annual temperature
is 12.8 ºC and total annual rainfall averages 1249 mm
(Anon. 1996).
I tested for hemispherical differences in area-based
measures of species diversity (i.e. number of species
per 0.1 ha) using the general linear model procedure in
SPSS (Anon. 2002). The absolute value of latitude was
used as a covariate and hemisphere (north or south) was
considered a fixed factor. The full model, comprised of
the independent effects of the covariate and the fixedfactor and their interaction was assessed. Following
Engqvist (2005), the interaction term was used to test
for differences in the slope of relationships for each
hemisphere. Separate tests were conducted for trees,
lianas (lianas and hemi-epiphytes combined) and total
species diversity.
Next, I re-assessed hemispherical differences in
species diversity after controlling for latitudinal variation in plant density. Least-squares regression was used
to evaluate the relationship between plant density and
latitude, and the relationship between plant density and
species richness per unit area. Standardized residuals of
the relationship between plant density and area-based
measures of species diversity were then subject to the
same general linear model procedure described above,
using the same covariate (absolute value of latitude) and
fixed-factor (hemisphere). Diversity and density estimates
were log10 transformed to conform to normality assumptions. Separate tests were again conducted for trees, lianas
and total species diversity.
Several additional analyses were conducted to control
for geographic and latitudinal differences in sampling
intensity. In addition to the analysis of the full dataset (N
= 198), analyses were repeated after removing all sites
located above 42° latitude to account for latitudinal differences in sampling intensity (N = 188). Analyses were also
repeated on North and South American sites exclusively,
to account for differences in sampling intensity between
continents. In the separate analysis of American sites,
samples located on Caribbean islands were also omitted
to control for insularity effects (N = 148). In all three
sets of analyses, the dependent variable and covariate
FORUM
- Is tree diversity different down-under? -
309
Table 1. F-statistics from general linear models of the effect of latitude and hemisphere on species diversity of lianas (including
hemi-epiphytes), trees and both life forms combined. Results from area-based diversity estimates (species richness/0.1 ha) are shown
alongside individual-based diversity estimates (residuals of relationships between plant density and species richness per unit area).
Analyses were conducted on all fully-sampled Gentry plots (ʻfull global datasetʼ, N = 198), all plots located below 42° latitude to
control for latitudinal differences in sampling intensity (ʻsites < 42° latitudeʼ, N = 188) and all non-insular plots located in North
and South America below 42° latitude to control for continental differences in sampling intensity (N = 148). Subscripts refer to the
more diverse hemisphere (S = southern hemisphere, N = northern hemisphere).
per-area
Full global dataset
Sites < 42° latitude
North & South America (< 42°)
4.6S*
5.5S*
10.7S**
Lianas
per-individual
24.2N***
11.6N**
14.4N***
were variously transformed (i.e. squared, log10 or log10
+ 1) to conform to assumptions when necessary. Lastly,
I assessed climatic correlates of global variation in plant
density.
Temperature and precipitation data from were obtained from the International Panel for Climate Change,
which were arranged as an array of half-degree longitude
× latitude grid cells according to New et al. (1999). For
each site in the full dataset (N = 198), monthly averages
were obtained for the period 1961-1990 to calculate four
climatic variables: (1) mean monthly temperature, (2)
mean monthly precipitation, (3) the standard deviation of
mean monthly temperature, and (4) the standard deviation of mean monthly precipitation, following Hartley et
al. (2006). Standard deviations of monthly temperature
and precipitation data were obtained to estimate annual
fluctuations in climate. Relationships between total plant
density and the four climatic variables were then assessed
with multiple regression. Plant density, mean precipitation and standard deviation in temperature were log10
transformed, and mean temperature was squared, to
conform to normality assumptions.
per-area
10.5S**
4.5S*
4.3S*
Trees
per-individual
10.1N**
16.1N***
29.1N***
Trees & Lianas
per-area
per-individual
14.9S***
7.0S**
7.1S**
14.8N***
10.4N**
16.7N***
Results
A total of 346 woody plants were encountered in
New Zealand (see App. 1). These included 323 trees, 22
lianas and one hemi-epiphyte. A total of 35 species were
encountered, including 28 tree species, 6 liana species and
one hemi-epiphyte species. Results from New Zealand
were very similar to inventories of temperate forests in
Chile (Phillips & Miller 2002). The total dataset consisted
of 67 423 woody plants (trees = 55 913, lianas = 11 510)
and 20 974 species occurrences (trees = 16 206, lianas =
4768).
The total number of woody plant species occurring
in each plot declined with latitude at a different rate in
the northern and southern hemispheres (Table 1). The
slope of the diversity-latitude relationship for all woody
plants was steeper for the northern hemisphere (Fig.
1). Similar results were obtained in separate analyses
of trees and lianas. Similar results were also obtained
with the full data set, sites located below 42° latitude
and non-insular, North and South American sites.
Therefore, area-based diversity estimates indicate that
Fig. 1. A. Latitudinal variation in species richness per unit area (the total number of woody plant species per 0.1 ha); B. Latitudinal
variation in species richness after controlling for plant density (standardized residuals of the relationship between species richness
per unit area and plant density). Vertical dashed lines are drawn at the equator. Species richness per unit area declines more rapidly
with latitude in the northern hemisphere, indicating tree diversity is higher in the southern hemisphere. However, the pattern reverses
(i.e. diversity is higher in the northern hemisphere) after controlling for plant density.
310
Burns, K.C.
FORUM
Fig. 2. Relationships (A) between plant density (log10 transformed) and latitude, and (B) between species richness per unit area
and plant density (both log10 transformed). Southern hemisphere latitudes have negative values.
tree diversity is higher in the southern hemisphere.
Plant population densities differed between hemispheres (Fig. 2, see also Enquist & Niklas 2001; Currie
et al. 2004). Total plant density declined linearly with
latitude (r2 = 0.364, P < 0.001), and species richness per
unit area increased with plant density (r2 = 0.545, P <
0.001). General linear model analyses of the residuals
of the relationship between species richness per unit
area and plant density yielded different results from the
previous analyses. After controlling for differences in
plant density, diversity estimates again declined with
latitude, but in this case declines were more rapid in
the southern hemisphere (Fig. 1). Similar results were
obtained in separate analyses of different growth forms
and amalgamations of data (Table 1). Therefore, after
controlling for plant density, tree diversity is higher in
the northern hemisphere.
Plant densities were correlated with several climatic
variables. Total densities of woody plants were positively
related with mean annual temperature (T = 2.28, P =
0.007) and negatively related to the standard deviation
of average monthly temperatures (T = -5.12, P < 0.001).
Plant densities were unrelated to mean annual precipitation (T = 0.01, P = 0.991) and the standard deviation of
average monthly precipitation (T = -0.06, P = 0.953).
Multicollinearly assumptions were met in all four independent variables (variance inflation factor < 2.0 for
all).
Discussion
Overall results showed that tree diversity is indeed
different ʻdown-underʼ. However, the nature of hemispherical asymmetries in diversity is strongly dependent on how diversity is measured. When measured on
a per-area basis, diversity appears to be higher in the
southern hemisphere, which supports Gentryʼs (1988)
speculation. However, plant density also varies asymmetrically between hemispheres, increasing linearly from
north to south. After controlling for latitudinal differences
in plant density, the direction of hemispherical diversity
asymmetries reverses. On a per-individual basis, tree
diversity is higher in the northern hemisphere.
This result can be analogized to an imaginary landscape that is randomly populated by plants belonging
to a variety of species, but plant density is higher at one
end of the landscape than the other. If two equal sized
plots are placed on either side of the landscape, species
richness per unit area will be higher on the side of the
landscape containing more plants, because as more
plants are sampled, more species will be encountered
by chance (see Gotelli & Colwell 2001). Hemispherical
asymmetries in tree species diversity appear to be influenced by a similar type of sampling effect. Except that
the side of the landscape with more plants (the southern
hemisphere) actually contains fewer species. Or more
precisely, results suggest that one would encounter new
species less rapidly while randomly inspecting plants in
the more densely populated, southern hemisphere.
Sampling effects have been hypothesized to generate
the latitudinal diversity gradient. Higher productivity
at the equator might lead to denser populations, which
could then randomly ʻsampleʼ more species (see Evans et
al. 2005). Although I did not intend to test the sampling
effect hypothesis as an overarching explanation for the
FORUM
- Is tree diversity different down-under? -
latitudinal gradient, my analyses argue against it. Even
after removing the effect of plant density from area-based
estimates of species diversity, diversity still peaks at low
latitudes. Therefore, some factor associated with latitude
(see Hawkins & Diniz-Filho 2005) is an important driver
of geographic variation in individual-based estimates of
species diversity.
In a previous analysis of the Gentry dataset, Currie
et al. (2004) found that plant density is correlated with
evapotranspiration, albeit weakly (rs = 0.35). Analyses
conducted here showed that plant density is more strongly
associated with temperature variability (rs = – 0.42),
indicating that areas with less variable monthly temperatures (i.e. more stable temperature regimes) house denser
plant populations. This result suggests that temperature
variability can partially explain latitudinal asymmetries
in plant density. Annual fluctuations in temperature are
much reduced in the southern hemisphere (Chown et
al. 2004), due to smaller continental landmasses and
the ameliorating effect of the ocean, which stores heat
as latent energy. This effect appears to explain higher
plant population densities in the southern hemisphere.
However, the overall adjusted r2 value from this analysis is quite small, indicating that other factors, such as
historical effects or soil conditions, are also important.
Therefore, while the negative relationship between
latitude and plant density appears to be associated with
temperature variability, a comprehensive explanation for
this pattern remains to be elucidated.
Niklas et al. (2003) linked the relationship between
plant density and species richness per unit area to plant
size. Regardless of hemisphere, most species in Gentryʼs plots occur only as saplings. Therefore, sampling
regimes that neglect to census smaller plants might seriously bias estimates of species diversity. The reversal of
hemispherical asymmetries in diversity after controlling
for sampling effects highlights a similar concern; different ways of quantifying diversity can yield different
geographic patterns in biodiversity.
Overall results uncovered strong hemispherical
asymmetries in tree diversity. However, the direction
of hemispherical diversity asymmetries hinges on how
species diversity is defined. On an area-basis, forests in
the southern hemisphere house more species. However,
patterns in species diversity are strongly influenced by
geographic variation in plant density, and after correcting
for sampling effects, diversity is higher in the northern
hemisphere. Tree diversity therefore appears to be different down-under. However, identifying which hemisphere
is more diverse hinges on oneʼs definition of diversity.
311
Acknowledgements. Alessandro Chiarucci, Gordon Jenkins,
Michael Kessler, Michael Weiser and an anonymous reviewer
provided helpful comments on earlier drafts of the manuscript.
References
Anon. 1996. Otari-Wiltonʼs bush management plan, Wellington
City Council, Wellington, NZ.
Anon. 2002. Release 11.5.1 for windows. SPSS Inc., Chicago,
IL, US.
Allan, H.H. 1961. Flora of New Zealand. Vol. 1. Government
Printer, Wellington, NZ.
Bunge, J. & Fitzpatrick, M. 1993. Estimating the number of
species, a review. J. Am. Stat. Assoc. 88: 364-373.
Burns, K.C. & Dawson, J. 2005. Patterns in the diversity and
distribution of epiphytes and vines in a New Zealand forest.
Aust. Ecol. 30: 891-899.
Chiaruccci, A., Alongi, C. & Wilson, J.B. 2004. Competitive
exclusion and the no-interaction model operate simultaneously in microcosm plant communities. J. Veg. Sci. 15:
789-796.
Chown, S.L., Sinclair, B.J., Leinaas, H.P. & Gaston, K.J. 2004.
Hemispheric asymmetries in biodiversity – a serious matter
for ecology. PLoS Biol. 2: 1701-1707.
Currie, D.J., Mittelbach, G.G., Cornell, H.V., Field, R., Guégan,
J.-F., Hawkins, B.A., Kaufman, D.M., Kerr, J.T., Oberdorff,
T., OʼBrien, E. & Turner J.R.G. 2004. Predictions and tests
of climate-based hypotheses of broad-scale variation in
taxonomic richness. Ecol. Lett. 7: 1121-1134.
Engqvist, L. 2005. The mistreatment of covariate interaction
terms in linear model analyses of behavioural and evolutionary ecology studies. Anim. Behav. 70: 967-971.
Enquist, B.J. & Niklas, K.J. 2001. Invariant scaling relations
across tree-dominated communities. Nature 410: 665660.
Evans, K.L., Warren, P.H. & Gaston, K.J. 2005. Species-energy
relationships at the macroecological scale: a review of the
mechanisms. Biol. Rev. 80: 1-25.
Forbes, S., Schauwecker, T. & Weiher, E. 2001. Rarefaction
does not eliminate the species richness-biomass relationship
in calcareous blackland prairies. J. Veg. Sci. 12: 525-532.
Gentry, A.H. 1982. Patterns in neotropical plant species diversity. Evol. Biol. 15: 1-84.
Gentry, A.H. 1988. Changes in plant community diversity and
floristic composition on environmental and geographic
gradients. Ann. Miss. Bot. Gar. 75: 1-34.
Gotelli, N.J. 2001. Research frontiers in null model analyses.
Global Ecol. Biogeogr. 10: 337-343.
Gotelli, N.J. & Colwell, R.K. 2001. Quantifying biodiversity:
procedures and pitfalls in the measurement and comparison
of species richness. Ecol. Lett. 4: 379-391.
Hartley, S., Harris, R. & Lester, P.J. 2006. Quantifying uncertainty in the potential distribution of an invasive species:
climate and the Argentine ant. Ecol. Lett. 9: 1068-1079.
Hawkins, B.A. 2001. Ecologyʼs oldest pattern? Trends Ecol.
Evol. 16: 470.
Hawkins, B.A. & Diniz-Filho, J.A.F. 2005. ʻLatitudeʼ and
312
Burns, K.C.
geographic patterns in species diversity. Ecography 27:
268-272.
Hector, A., Bazeley-White, E., Loreau, M., Otway, S. & Schmid,
B. 2002. Overyielding in grassland communities: testing
the sampling effect hypothesis with replicated biodiversity
experiments. Ecol. Lett. 5: 502-511.
Hillebrand, H. 2004. On the generality of the latitudinal diversity gradient. Am. Nat. 163: 192-211.
Lawes, M.J., Lamb, B.C.C. & Boudreau, S. 2005. Area- but
no edge-effect on woody seedling abundance and species
richness in old Afromontane forest fragments. J. Veg. Sci.
16: 363-372.
FORUM
Moore, L.B. & Edgar, E. 1970. Flora of New Zealand, Vol. 2.
Government Printer, Wellington, NZ.
New, M., Hulme, M. & Jones, P.D. 1999. Representing twentieth century space-time climate variability. Part 1: development of a 1961-90 mean monthly terrestrial climatology.
J. Climatol. 12: 829-856.
Niklas, K.J., Midgley, J.J. & Rand, R.H. 2003. Size-dependent species richness: trends within plant communities and
across latitude. Ecol. Lett. 6: 631-636.
Phillips, O.L. & Miller, J.S. 2002. Global patterns of plant
biodiversity: Alwyn H. Gentryʼs forest transect data set.
Missouri Botanical Garden, St. Louis, MO, US.
Received 28 September 2006;
Accepted 23 December 2006;
Co-ordinating Editor: S. Díaz.
1
App. 1.
Family
Genus
Species
Individuals
Araliaceae
Araliaceae
Araliaceae
Araliaceae
Asteraceae
Asteraceae
Asteraceae
Asteraceae
Atherospermataceae
Apocynaceae
Apocynaceae
Apocynaceae
Corynocarpaceae
Corynocarpaceae
Corynocarpaceae
Cyatheaceae
Cyatheaceae
Cyatheaceae
Cyatheaceae
Cyatheaceae
Cyatheaceae
Cyatheaceae
Cyatheaceae
Dicksoniaceae
Elaeocarpaceae
Elaeocarpaceae
Elaeocarpaceae
Elaeocarpaceae
Griseliniaceae
Lauraceae
Lauraceae
Lauraceae
Lauraceae
Lauraceae
Lauraceae
Loganiaceae
Loganiaceae
Loganiaceae
Loganiaceae
Loganiaceae
Loganiaceae
Loganiaceae
Malvaceae
Malvaceae
Malvaceae
Meliaceae
Pseudopanax
Pseudopanax
Pseudopanax
Pseudopanax
Brachyglottis
Brachyglottis
Olearia
Olearia
Laurelia
Parsonsia
Parsonsia
Parsonsia
Corynocarpus
Corynocarpus
Corynocarpus
Cyathea
Cyathea
Cyathea
Cyathea
Cyathea
Cyathea
Cyathea
Cyathea
Dicksonia
Elaeocarpus
Elaeocarpus
Elaeocarpus
Elaeocarpus
Griselinia
Beilschmiedia
Beilschmiedia
Beilschmiedia
Beilschmiedia
Beilschmiedia
Beilschmiedia
Geniostoma
Geniostoma
Geniostoma
Geniostoma
Geniostoma
Geniostoma
Geniostoma
Hoheria
Hoheria
Hoheria
Dysoxylem
arboreus
arboreus
crassifolius
crassifolius
repanda
repanda
rani
rani
novae-zelandiae
heterophylla (L)
heterophylla (L)
heterophylla (L)
laevigatus
laevigatus
laevigatus
dealbata
dealbata
dealbata
dealbata
dealbata
dealbata
dealbata
dealbata
squarrosa
dentatus
dentatus
dentatus
dentatus
lucida (H)
tawa
tawa
tawa
tawa
tawa
tawa
rupestre
rupestre
rupestre
rupestre
rupestre
rupestre
rupestre
sexstylosa
sexstylosa
sexstylosa
spectabilie
1
2
1
1
1
1
2
1
1
2
1
1
1
1
1
5
5
2
7
2
2
2
2
1
3
3
1
1
1
3
4
4
4
1
7
2
1
1
1
1
1
1
7
1
1
1
Meliaceae
Dysoxylem
spectabilie
34
Meliaceae
Meliaceae
Dysoxylem
Dysoxylem
spectabilie
spectabilie
21
11
Meliaceae
Dysoxylem
spectabilie
17
Meliaceae
Dysoxylem
spectabilie
18
Meliaceae
Monimiaceae
Monimiaceae
Monimiaceae
Monimiaceae
Moraceae
Myrtaceae
Myrtaceae
Myrtaceae
Dysoxylem
Hedycarya
Hedycarya
Hedycarya
Hedycarya
Streblus
Metrosideros
Metrosideros
Metrosideros
spectabilie
arborea
arborea
arborea
arborea
heterophyllus
diffusa (L)
diffusa (L)
diffusa (L)
33
1
1
1
3
1
1
1
1
Diameter at breast height (multi-stemmed individuals in parentheses)
2.8
10.3, 28.0
12.0
12.2
4.0
3.0
19.0, 13.5
14.2
16.5
3.5, 5.7
3.1
3.0
14.0
3.8
29.5
15.5, 24.8, 21.5, 13.8, 24.0
21.1, 29.0, 24.5, 24.0, 21.5
20.8, 27.0
24.0, 28.5, 18.5, 16.5, 16.0, 21.0, 18.0
26, 22.0
24.0, 23.0
18.1, 18.2
19.2, 10.0
14.4
24.0, 44.5, 70.5
22.0, 4.2, 25.0
22.5
37.5
(6.0, 4.4, 5.2)
22, 47.0, 42.0
15.7, 17.9, 24.3, 14.6
55.0, 46.1, 42.4, 35.3
40.0, 6.5, 30.1, 20.0
39.5
19.8, 10.2, 15.0, 18.6, 30.4, 18.7, 18.0
2.5, (3.6, 4.4)
2.6
(4.0, 4.0, 3.7)
2.7
2.6
2.5
2.7
4.6, 3.3, 3.2, 3.0, 13.0, 3.5, 17.5
10.8
5.5
24.2
18.4, (3.5, 11.0), 4.3, 3.1, 4.8, 4.1, 5.5, 3.5, 4.0, 3.8, 3.7, 3.7, 4.1, 3.5, 14.7,
2.7, 6.3, 4.3, 5.0, 6.9, 5.0, 4.6, 4.5, 3.4, 4.6, 4.5, 3.0, 15.8, 6.2, 3.0, 3.4, 12.5,
5.5, 6.0
5.7, 8.9, 4.6, 3.6, 6.1, 7.8, 4.5, 3.0, 7.1, (8.1, 6.2), 8.7, (5.5, 6.3), (5.7, 4.0), 4.4,
3.5, 3.0, 7.2, 14.0, 7.4, 8.2, 5.9
13.9, 6.5, 23.0, 5.4, 12.0, 11.2, 3.0, (16.0, 21.1), 8.2, 13.8, 5.5
3.9, 9.4, 22.0, 17.6, 6.8, 8.8, 5.2, 5.8, 9.9, 19.3, 22.1, 4.2, 2.7, 18.7, 23.9, 3.2,
3.1
6.5, 2.9, 2.8, 2.8, 2.5, 2.5, 3.4, 10.0, 4.0, 41.1, 4.8, 3.0, 13.3, 9.4, 3.9, 2.8, 2.7,
2.5
36.4, 19.3, (14.2, 16.9), 8.9, 12.3, 15.7, 13.9, (15.5, 20.0), 33.2, (17.8, 18.2),
6.5, 2.9, 15.0, 6.5, 4.0, 3.5, 26.5, 2.5, 5.3, 22.0, (23.0, 7.1), 17.0, (26.0, 16.5),
17.0, 11.7, 9.0, 23.0, 36.6, 18.0, 23.0, 2.8, 3.2, 42.4
(4.5, 6.0)
(9.2, 13.9)
15.0
13.4, 9.3, 11.3
3.7
3.5
4.2
(2.7, 3.1)
App. 1. Internet supplement to: Burns, K.C. 2007.
Is tree diversity different in the Southern Hemisphere?. J. Veg. Sci. 18: 307-312.
2
Myrtaceae
Myrtaceae
Myrtaceae
Myrtaceae
Myrtaceae
Myrtaceae
Myrsinaceae
Oleaceae
Passifloraceae
Metrosideros
Metrosideros
Metrosideros
Metrosideros
Metrosideros
Metrosideros
Mysine
Nestegis
Passiflora
fulgens (L)
fulgens (L)
fulgens (L)
perforata (L)
perforata (L)
perforata (L)
australis
cunninghamii
tetandra (L)
1
2
3
1
1
3
2
2
1
Piperaceae
Piperaceae
Piperaceae
Piperaceae
Piperaceae
Piperaceae
Piperaceae
Pittosporaceae
Podocarpaceae
Podocarpaceae
Podocarpaceae
Podocarpaceae
Podocarpaceae
Podocarpaceae
Podocarpaceae
Proteaceae
Proteaceae
Proteaceae
Proteaceae
Proteaceae
Proteaceae
Rosaceae
Rosaceae
Rosaceae
Rubiaceae
Rubiaceae
Rubiaceae
Rutaceae
Rutaceae
Sapindaceae
Sapindaceae
Violaceae
Violaceae
Violaceae
Violaceae
Violaceae
Violaceae
Violaceae
Violaceae
Macropiper
Macropiper
Macropiper
Macropiper
Macropiper
Macropiper
Macropiper
Pittosporum
Dacrydium
Dacrydium
Dacrydium
Podocarpus
Prumnopitys
Prumnopitys
Prumnopitys
Knightia
Knightia
Knightia
Knightia
Knightia
Knightia
Rubus
Rubus
Rubus
Coprosma
Coprosma
Coprosma
Melicope
Melicope
Alectryon
Alectryon
Melicytus
Melicytus
Melicytus
Melicytus
Melicytus
Melicytus
Melicytus
Melicytus
excelsum
excelsum
excelsum
excelsum
excelsum
excelsum
excelsum
eugenioides
cupressinum
cupressinum
cupressinum
totora
ferruginea
taxifolia
taxifolia
excelsa
excelsa
excelsa
excelsa
excelsa
excelsa
cissoides (L)
cissoides (L)
cissoides (L)
grandifolia
grandifolia
grandifolia
simplex
simplex
excelsus
excelsus
ramiflorus
ramiflorus
ramiflorus
ramiflorus
ramiflorus
ramiflorus
ramiflorus
ramiflorus
10
1
3
1
3
2
5
1
1
1
1
2
1
1
1
1
2
2
1
1
2
1
1
1
2
2
1
1
2
2
1
11
4
5
1
4
3
2
1
2.5
(4.3, 5.0, 2.6), 3.3
2.6, 4.0, (4.5, 5.5)
(3.8, 3.2, 2.8)
2.9
8.2, 3.0, 3.1
2.5, 4.2
12.3, 13.5
(3.0, 4.0)
(7.0, 7.5, 8.2), (4.6, 5.8), (6.2, 2.8, 3.5), 6.4, (8.0, 7.5), (7.1, 6.3), (5.3, 3.8), 3.2,
7.4, (3.0, 3.0)
2.5
(5.0, 3.0, 3.0), 5.0, (6.0, 2.9, 3.2, 4.2)
5.3
(4.3, 5.7), 3.3, 2.6
5.7, 2.8
3.3, 5.2, 2.7, 4.0, 3.0
19.0
91.5
60.6
72.5
(25.5, 14.4), 48.5
60.5
31.0
13.0
46.9
3.1, 5.3
33.5, 11.0
37.5
16.0
51.0, 58.5
2.6
4.0
7.5
6.5, 3.4
3.1, 8.8
2.5
(13.5,7.2)
12.0, (6.5, 12.2, 8.2)
3.0, 8.2
3.1
(11.3, 9.5, 7.5), 10.5, (9.0, 6.5), 23.0, 5.6, 4.5, 8.0, (8.0, 3.7,4.0), 2.5, 13.0, 9.0
9.2, 6.5, (10.0, 11.2), (9.3, 9.1, 7.1)
3.0, 2.8, 3.1, 3.5, 3.2
3.1
(5.7, 15.9), (9.8, 9.8), 26.9, 10.7
11.0, 2.5, (2.8, 15.4)
17.5, 13.1
46.5
App. 1. Internet supplement to: Burns, K.C. 2007.
Is tree diversity different in the Southern Hemisphere?. J. Veg. Sci. 18: 307-312.