Supplementary material for ‘Methods’ section




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Appendix 1

Supplementary material for ‘Methods’ section



  1. 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.



  1. 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a field study was conducted in order to determine the biomass of lantana in each of these categories (Suresh et al, unpublished data).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.

  1. 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 preference

Habit

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



  1. 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.

  1. Statistical Analyses

  1. Mixed effects models specified for community and species level analyses -






Growth Interval

Fixed Effects

Random Effects

All species (community model)

2 & 4 months

Lantana Density

Inter-census Rainfall

Habitat preference

All 2 way interactions



Plot ID

All species (community model)

22 months

Lantana Density

Habitat preference

All 2 way interactions


Plot ID

Most Abundant species (Catunaregam spinosa, Syzygium cumini, Grewia tiliifolia, Diospyros montana)

2 & 4 Months

Lantana Density

Inter-census rainfall



All 2 way interactions

Plot ID

Most Abundant species (Catunaregam spinosa, Syzygium cumini, Grewia tiliifolia, Diospyros montana)

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.

  1. 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.


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