Appendix 1
Supplementary material for ‘Methods’ section
A. Study area
There was considerable inter-annual variability in rainfall from January 2008 to December 2010. For
the duration of the study from April 2008 to February 2010, the rainfall received during the monsoon
season also varied. In 2008, most of the rainfall was received between June and November while in
2009, most of the rainfall was received between July and August. December to February for the
duration of the study were characterised by < 100 mm rainfall.
B. Study Design
1.
Quantification of lantana categories in the MFDP:
Lantana is quantified at the Mudumalai Forest 1. Dynamics Plot in terms of the area covered by the
plant in an area of 10m x 10m. Lantana has been categorised as being ‘absent’, ‘present’, ‘common’,
’dense’ and ‘very dense’. In 2011, a field study was conducted in order to determine the biomass of
lantana in each of these categories (Suresh et al, unpublished data). Ten 1m x 1m plots were set outside
the MFDP in areas that were visually determined as belonging to one of the five lantana density
categories. Lantana was harvested, oven-dried and weighed again to obtain dry weight biomass. While
the lantana ‘absent’ category corresponded to no lantana, the biomass of the other categories were as
follows: ‘present’ – 48.3 ± 15.6 g/m2, ‘common’- 503 ± 30.5 g/m2, ‘dense’ – 858.5 ± 154.2 g/m2 and
‘very dense’- 2038.5 ± 226 6 g/m2.
2.
List of species of woody plants seedlings which were tagged and monitored from April 2008 to
March 2010. Sample size is the total number of individuals pooled for lantana absent (LA) and
lantana dense (LD) plots. Repeated measurements on seedling heights were made once every two
months.
Species
Family
Forest habitat Habit
preference
Sample
size (LA
plots)
Sample
size (LD
plots)
Albizia odoratissima (L.f.) Benth.
Fabaceae
Moist
Canopy tree
0
3
Anogeissus latifolia (DC.)Wall. ex Guill. &
Perr.
Combretaceae
Dry
Canopy tree
15
20
Antidesma diandrum (Roxb.) Roth
Euphorbiaceae
Moist
Understory tree
11
3
Ardesia solanacea Roxb.
Myrsinaceae
Moist
Shrub
1
0
Bauhinia malabarica Roxb.
Fabaceae
Moist
Canopy tree
1
0
Bridelia retusa (L.) Sprengel
Euphorbiaceae
Dry
Canopy tree
1
0
Canthium dicoccum (Gaertner) Teijsm. &
Binnend.
Rubiaceae
Dry
Understory tree
31
10
Casearia esculenta Roxb.
Flacourtiaceae
Moist
Canopy tree
7
1
Cassia fistula L.
Fabaceae
Ubiquitous
Understory tree
14
7
Dalbergia latifolia Roxb.
Fabaceae
Moist
Canopy tree
18
11
Dalbergia lanceolaria L.f.
Fabaceae
Dry
Understory tree
13
12
Diospyros montana Roxb.
Ebenaceae
Dry
Canopy tree
50
59
Flacourtia indica (Burman) Merr.
Flacourtiaceae
Ubiquitous
Understory tree
10
0
Garuga pinnata Roxb.
Burseraceae
Ubiquitous
Canopy tree
1
0
Grewia orbiculata Rottl.
Tiliaceae
Dry
Shrub
4
7
Grewia tiliifolia Vahl
Tiliaceae
Dry
Canopy tree
53
50
Helicteres isora L.
Sterculiaceae
Moist
Shrub
21
45
Hymenodictyon orixense (Roxb.) Mabb.
Rubiaceae
Moist
Canopy tree
1
1
Kydia calycina Roxb.
Malvaceae
Moist
Understory tree
21
1
Lagerstroemia microcarpa Wt.
Lythraceae
Moist
Canopy tree
2
2
Lannea coromandelica (Houtt.) Merr.
Anacardiaceae
Moist
Canopy tree
1
0
Mitragyna parviflora (Roxb.) Korth.
Rubiaceae
Dry
Canopy tree
0
1
Naringi crenulata (Roxb.) Nicolson
Rutaceae
Dry
Understory tree
0
1
Phyllanthus emblica L.
Phyllanthaceae
Dry
Understory tree
25
5
Pterocarpus marsupium Roxb.
Fabaceae
Ubiquitous
Canopy tree
4
0
Catunaregam spinosa (Thunb.) Tirv.
Rubiaceae
Dry
Understory tree
161
196
Radermachera xylocarpa (Roxb.) Schum.
Bignoniaceae
Dry
Canopy tree
0
1
Schleichera oleosa (Lour.) Oken
Sapindaceae
Moist
Canopy tree
29
30
Stereospermum personatum(Hassk.)
Chatterjee
Bignoniaceae
Moist
Canopy tree
0
1
Syzygium cumini (L.) Skeels
Myrtaceae
Moist
Canopy tree
63
73
Tectona grandis L.f.
Verbenaceae
Ubiquitous
Canopy tree
3
2
Terminalia crenulata
Combretaceae
Dry
Canopy tree
1
0
Wrightia tinctoria (Roxb.) R.Br.
Apocynaceae
Dry
Understory tree
1
0
3.
Rationale for using height as a measure of seedling growth:
Owing to the deciduous nature of many of the species examined, we used height instead of leaf area or
leaf number to quantify seedling growth response. Above-ground tissue was also frequently lost mostly
due to desiccation in the dry season and rarely due to herbivory by large mammals at other times, thus
stem diameter could not be used as reliable measures of growth.
C. Statistical Analyses
1.
Mixed effects models specified for community and species level analyses -
All species
(community model)
Growth Interval
2 & 4 months
All species
(community model)
22 months
Most Abundant
species
(Catunaregam
2 & 4 Months
Fixed Effects
Lantana Density
Inter-census Rainfall
Habitat preference
All 2 way
interactions
Lantana Density
Habitat preference
All 2 way
interactions
Lantana Density
Inter-census rainfall
All 2 way
Random Effects
Plot ID
Plot ID
Plot ID
spinosa, Syzygium
cumini, Grewia
tiliifolia, Diospyros
montana)
Most Abundant
species
(Catunaregam
spinosa, Syzygium
cumini, Grewia
tiliifolia, Diospyros
montana)
interactions
22 Months
Lantana Density
Plot ID
Rainfall was not considered as a fixed effect in analysing 22-month growth rate as the variation in this
predictor was very small for this duration. Habitat preference was not considered in species-specific
analyses because each species belonged to single habitat preference guild.
2.
Use of 95% CI for determining significance of model terms :
For determining differences between the levels of the categorical variables – lantana density and
species’ habitat preference, we used default orthogonal contrasts specified in R, with all levels being
compared to the alphabetically first level. The parameter estimates in an R mixed effects model output
correspond to differences between means of all levels of a categorical variable from the alphabetically
first reference level. For example, in the default ‘treatment contrast’ of linear mixed model packages,
the mean growth rates of moist- forest preferring and ubiquitous species would be compared with the
mean growth rate of dry forest species. If the 95% CI of an estimate overlapped zero (say, the lower CI
is a negative number, while the upper CI is a positive number), one cannot say with certainty if the
mean of one level is greater or lesser than the reference level. Estimates with confidence intervals that
did not overlap with zero could be conclusively inferred as being less or more than the reference group.
Thus, estimates with 95% CIs that did not overlap zero were considered as being statistically
significant.