Transcript a ppt presentation

Jan Leps, Dept of Botany,
University of South Bohemia,
České Budejovice,
Czech Republic
Biodiversity of seminatural
meadows under various
management regimes: a 16
years experimental study
OHRAZENI – a seminatural
meadow
Regular mowing ceased in late eighties
Molinia caerulea
Nardus stricta
Species diversity and “interesting plants” (e.g. red list species)
concentrated in “traditional”, i.e. mown, unfertilized
Dactylorhiza majalis
Senecio rivularis
14 Carex species
Carex pulicaris
C. hartmanii
Scorzonera humilis
Myosotis nemorosa
Factorial experiment, 3 replications
• Mowing (once a year, in June)
• Ferilization (65 [50] g of commercial NPK/m2 - 12% N
(nitrate and ammonium), 19% P (as P2O5) and 19% K
(as K2O))
• Dominant (i.e. Molinia caerulea) removal (in spring
1995, but re-weeding necessary time from time)
• Yielding 24 plots, 2m × 2m each
• Central 1m x 1m sampled, followed by detailed analysis
of 50cm × 50cm grid of 10cm × 10 cm – including
seedling counts
• Experiment started 1994, baseline data available
Ohrazení (http://mapy.atlas.cz)
Detailed recording of vegetation in all the 16 years
Sprouts of woody plants removed
Response of plant community to the
treatments in terms of
• Species richness and composition
• Species traits
– Methodological notes: how to analyse the species traits?
Mown - unfertilized (=traditional)
Mown - unfertilized & Molinia
removed
Mown - fertilized (=intensive)
Mown - fertilized & Molinia removed
Unmown - unfertilized (=abandoned)
Unmown - unfertilized & Molinia removed
Unmown - fertilized (=abandoned eutrofized meadow)
Unmown - fertilized & Molinia removed
Cover of M olinia is negatively affected by both, mowing Error bars
and fertilization
=95%
70
confidence
intervals
60
40
30
20
UNFERTILIZED
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
YEAR
0
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
10
YEAR
cover of Molinia
50
FERTILIZED
Problem for
interpretation,
in late years,
Molinia
removal would
have little
effect in
fertilized mown
(as it have very
low cover also
in the control
plots)
UNMOWN
MOWN
Species richness
Unfertilized plots
In mown plots,
increase of the
number of
species during
first six years,
regardless of
removal of
Molinia, in
unmown plots,
removal has
positive effect
on species
richness
50
40
35
30
25
20
15
10
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
YEAR
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
5
YEAR
Number of species (m
-2
)
45
UNMOWN
NSP of vascular plants per m2
MOWN
CONTROL
REMOVAL
Fertilized plots
In unmown plot,
continuous
decrease. Initial
positive effect
of removal
ceased after 10
yrs. In mown
plots, initial
increase (5
years) followed
by decrease, no
effect of
removal.
50
40
35
30
25
20
15
10
UNMOWN
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
YEAR
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
5
YEAR
Number of species (m
-2
)
45
MOWN
CONTROL
REMOVAL
Take home message
• Increase in soil nutrients can lead to
competitive exclusion - nevertheless, in
community of established perennial plants,
the exclusion can take rather long (six years
in our case).
Species richness dynamics
depends on spatial scale
• Repeated measures ANOVA
• 3 main plot factors (Mowing, Fertilization,
Removal)
• 2 Rep Mes faktors – year and plot size
(from 10x10cm2 to 50x50cm2)
• Number of species log transformed – i.e.
relative change of the richness
• Very long ANOVA table
Effect
Intercept
kosen
hnoj
mollik
kosen*hnoj
kosen*mollik
hnoj*mollik
kosen*hnoj*mollik
Error
YR
YR*kosen
YR*hnoj
YR*mollik
YR*kosen*hnoj
YR*kosen*mollik
YR*hnoj*mollik
YR*kosen*hnoj*mollik
Error
SIZE
SIZE*kosen
SIZE*hnoj
SIZE*mollik
SIZE*kosen*hnoj
SIZE*kosen*mollik
SIZE*hnoj*mollik
SIZE*kosen*hnoj*mollik
Error
YR*SIZE
YR*SIZE*kosen
YR*SIZE*hnoj
YR*SIZE*mollik
YR*SIZE*kosen*hnoj
YR*SIZE*kosen*mollik
YR*SIZE*hnoj*mollik
YR*SIZE*kosen*hnoj*mollik
Error
Repeated Measures Analysis of Variance (Spreadsheet3)
Sigma-restricted parameterization
Effective hypothesis decomposition
SS
Degr. of
MS
F
p
Freedom
2271.738
1 2271.738 11331.30 0.000000
5.242
1
5.242
26.15 0.000104
15.512
1
15.512
77.37 0.000000
2.688
1
2.688
13.41 0.002105
1.246
1
1.246
6.21 0.024022
1.199
1
1.199
5.98 0.026405
0.002
1
0.002
0.01 0.928131
0.129
1
0.129
0.64 0.435081
3.208
16
0.200
6.208
14
0.443
28.11 0.000000
0.739
14
0.053
3.35 0.000067
2.764
14
0.197
12.51 0.000000
0.311
14
0.022
1.41 0.150534
1.149
14
0.082
5.20 0.000000
0.215
14
0.015
0.98 0.478951
0.886
14
0.063
4.01 0.000004
0.365
14
0.026
1.65 0.066893
3.533
224
0.016
97.016
4
24.254 4365.49 0.000000
0.278
4
0.069
12.49 0.000000
0.015
4
0.004
0.70 0.597317
0.023
4
0.006
1.02 0.402189
0.063
4
0.016
2.82 0.032050
0.017
4
0.004
0.76 0.552968
0.148
4
0.037
6.66 0.000152
0.006
4
0.001
0.27 0.897611
0.356
64
0.006
0.350
56
0.006
9.66 0.000000
0.052
56
0.001
1.44 0.021420
0.102
56
0.002
2.83 0.000000
0.044
56
0.001
1.21 0.139860
0.097
56
0.002
2.68 0.000000
0.024
56
0.000
0.68 0.967328
0.032
56
0.001
0.87 0.741054
0.036
56
0.001
1.00 0.485832
0.580
896
0.001
Many interactions
significant, effects are
not additive
Only selected terms will
be interpretted
unmown
unfertilized
2.0
Log(number of species)
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
During the first eight years,
number of species increases
on small plots, but is constant
on larger plots
10x10
20x20
30x30
40x40
50x50
0.2
0.0
YR: 2
4
6
8
control
10
12
14
YR: 2
4
6
8
10
removal
12
14
Positive effect of mowing is most
pronounced on the small spatial scale
1.5
1.4
50x50
Log(number of species)
1.3
1.2
1.1
1.0
0.9
0.8
10x10
0.7
0.6
0.5
0.4
0.3
unmown
mown
Data from
2008,
averaged over
fertilization
and removal
treatments
SIZE*FERT*REMOVAL; LS Means
Current effect: F(4, 64)=6.6605, p=.00015
2.0
1.8
log(number of species)
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
control
removal
SIZE: 1
control
removal
control
removal
control
removal
control
removal
SIZE: 2
SIZE: 3
SIZE: 4
SIZE: 5
non-fertilized
fertrilized
In small plots, the effect of removal is more pronounced in non-fertilized
plots, in large plots, in fertilized plots
Seedling number - Average over 1996-2007
2.4
2.2
log(number of seedlings+1)
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
control
removal
0.4
unmown
unfertilized
mown
unmown
mown
fertilized
Species composition
DCA - Molinia is passive species - log transformed cover.
Starting points in 1994 show the random variability - the
divergence of trajectories show the differentiation according to
treatments between 1994 and 2007.
1.8
MR
M
R
0
FM
FMR
1994
FR
F
0.6
0.0
2.5
Centroids of Year x Treatment
4.0
Principal response curves
aulapalu
3.0
0.8
MR
2.0
M
1.0
R
PRC1
FMR
rhitsqua
nardstri
anthodor
prunvulg
carepilu
brizmed
luzumult
carepale
hylosple festovin poteerec
selicarv carepani climdend
ranuacer
succprat
planlanc
pseupuru
scorhumi lychflos careumbr
brachyte
carepuli
ranunemo
siegdecu
triangles - mown
circles unmown
FM 0.0
-1.0
FR
F
full symbol - fertil.
open symbol - unfert
festrubr
poa triv solid line - control
descespi
-2.0
-0.6
1994
1996
1998
2000
2002
2004
2006
YEAR
broken l. - removal
Principal response curves
• Multivariate counterpart of Repeated
measurement ANOVA - the first axis, which
is plotted against the time, captures the
main differentiation among categories of
YEAR * TREATMENT interaction
• The common temporal trend is subtracted
from the data - YEAR as covariable(s)
• The horizontal axis corresponds to the
control (in our case, unmown, unfertilized,
no removal)
4.0
Principal response curves
aulapalu
3.0
0.8
MR
2.0
M
1.0
R
PRC1
FMR
rhitsqua
nardstri
anthodor
prunvulg
carepilu
brizmed
luzumult
carepale
hylosple festovin poteerec
selicarv carepani climdend
ranuacer
succprat
planlanc
pseupuru
scorhumi lychflos careumbr
brachyte
carepuli
ranunemo
siegdecu
triangles - mown
circles unmown
FM 0.0
-1.0
FR
F
full symbol - fertil.
open symbol - unfert
festrubr
poa triv solid line - control
descespi
-2.0
-0.6
1994
1996
1998
2000
2002
2004
2006
YEAR
broken l. - removal
The species lost from the
community under various
management types
• Are not a random subsample, but species
with specific ecological characteristics
(contrary to Hubbell’s theory)
• IMPORTANCE OF SPECIES TRAITS
• (mostly based on 2004 biomass data)
Two approaches to analyse species
trait response
• Species based: can we predict the species response
on the basis of its traits?
– Traits are predictors of species response (trait value is a
fixed characteristics of individual species)
• Community (plot) based: how do the community
(weighted) average [or variability] respond to
environmental characteristics?
– Traits (averages, variability) are response. Trait
plasticity can be evaluated
Species based approach
• 1. Calculate the environmental response for
each individual species (we have used
constrained ordination framework / RDA)
• 2. Predict the species response on the basis
of species traits (various regressions,
regression trees)
Species response to fertilization (RDA score, positive values mean
that the0.6species gains from fertilization)
0.4
RDA (fert)
0.2
0.0
-0.2
-0.4
-0.6
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Maximum height (from local Flora)
2.0
2.2
Species response to mowing (RDA score, positive values mean that
the species is supported by mowing)
0.8
0.6
RDA (mow)
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
0
20
40
60
80
Plant height - flora
100
120
140
Plantheightflora>=67.5
|
Tall plants
Unassisted seed
disperals
Regression
tree
prediction of
response to
mowing
unasist>=0.5
-0.3255
Erosulate>=0.5
0.2487
rosettes
-0.2417
0.1467
Potential height is good predictor of
response to fertilization and mowing
• With increasing nutrients, the plants are released
from competition for nutrients, but
simultaneously, the importance of competition for
light increases - the taller plants are in advantage.
• The taller plants are harmed more by mowing
• Higher asymmetry of competition for light (in
comparison with competition for nutrients)
explains decline in species richness.
Methodological note 1
• Regression tree – highly non-parametric
regression approach
– Well designed to account for the non-additivity
• The tall plants are always harmed by mowing
• In a group of “not so tall” plants, also other factors
play a role: e.g. those with a rosette respond more
positively
Methodological note 2
• Species based approach – species are
considered independent observations –
problem of phylogenetic relatedness
– Do we need phylogenetic correction?
– To which extend could be the similarity of
species responses explained by the similarity of
their traits, and to which extend by the
phylogenetic relatedness?
The role of the dominant
(Molinia caerulea)
consequences of its removal
Litter - Molinia produces large amount of slowly decaying
litter. Its removal causes decrease of litter amount (with
exception of mown & fertilized conditions)
60
As a
consequence,
seedling
recruitment is
supported by
Molinia removal.
litter (estimated cover, average over years)
55
50
45
40
35
30
25
20
Molinia competes
also after its
death.
15
10
5
0
UNMOWN
MOWN
UNFERTILIZED
UNMOWN
MOWN
FERTILIZED
CONTROL
REMOVAL
In the presence of Molinia, the peak of biomass is shifted from June to
August (and is slightly higher) - only weak statistical support
Unfertilized plots only shown
2500
The presence of a
single species
can considerably
shift the seasonal
biomass
dynamics
1500
1000
500
UNMOWN
MOWN
October
August
June
April
MONTH:
March
October
August
June
April
0
MONTH:
March
Biomass [g.m -2 ]
2000
CONTROL
REMOVAL
In mown plots, the removed Molinia is replaced by other grasses,
however, there is no other grass which would be able to replace
Molinia in unmown plots.
Take home
message: The
dominant species
(when removed) is
not always
replaced with the
species from the
same functional
group.
Proportion of grasses in biomass
1.0
0.8
0.6
0.4
0.2
0.0
UNMOWN
MOWN
UNFERTILIZED
UNMOWN
MOWN
FERTILIZED
CONTROL
REMOVAL
Molinia is rather extreme in some
traits, e.g.
•
•
•
•
Very late phenology
Very slow rate of litter decomposition
Very deep and strong roots
Extremely constant biomass over the years
• It is very likely that the presence/absence of
Molinia has crucial role in various ecosystem
processes (e.g. nutrient cycling)
Thanks for the help
Thanks for money
Czech Science Foundation
(GACR)
Framework V – VISTA project
Iva Spackova, Alena Vitova, Petr Macek, Francesco
de Bello, Jiri Dolezal, Vojtech Lanta, Jonathan Titus,
Eva Chaloupecka, Katerina Palkova, David Zeleny
Thank you for your attention