Research Article |
Corresponding author: Bharath Sundaram ( b.sundaram@apu.edu.in ) Academic editor: Mark van Kleunen
© 2015 Bharath Sundaram, Ankila J. Hiremath, Jagdish Krishnaswamy.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Sundaram B, Hiremath AJ, Krishnaswamy J (2015) Factors influencing the local scale colonisation and change in density of a widespread invasive plant species, Lantana camara, in South India. NeoBiota 25: 27-46. doi: 10.3897/neobiota.25.8354
|
Identifying factors that underlie invasive species colonisation and change in density could provide valuable insights into the mechanisms of biological invasions and for invasive species management. We examined a suite of factors potentially influencing the landscape-level invasion of Lantana camara L., one of the most ubiquitous invasive species in South Asia. These factors included disturbance factors like forest fires, historical habitat modification, and edge effects, in addition to factors like propagule pressure and habitat suitability. We examined the relative importance of these factors on the colonisation and change in density of L. camara in the Biligiri Rangaswamy Temple Tiger Reserve, Western Ghats, India. We used extensive (1997–2008) datasets tracking the presence and abundance of L. camara and combined these with corresponding data on disturbances, propagule pressure, and habitat suitability. We used an information-theoretic model selection approach to determine the relative importance of each factor on the colonisation and change in density of L. camara. Colonisation was mainly a function of proximity to already established populations (i.e. propagule pressure), whereas increase in L. camara density appeared to be constrained by high fire frequency. Research and management efforts need to recognize the multi-dimensional nature of mechanisms underlying L. camara’s success during different invasion phases when strategizing interventions to mitigate its effects.
Tropical dry forest, disturbance, propagule pressure, forest fire
The likelihood of an introduced species becoming invasive is determined by the interplay of the invader’s characteristics (e.g. its high propagule output;
Propagule pressure has also been found to act as an important driver in the invasion process (
Both colonisation (i.e. local arrival in a part of the landscape where it was earlier absent), and change in density (i.e. changes in the abundance over time) contributes to invasive plant spread. Change in density influences the probability of maintaining populations in colonised sites and the quantum of propagules released from colonised sites. However, factors that influence colonisation may not necessarily influence density. At the colonisation stage, factors such as proximity to propagule sources may come into play more than factors such as habitat heterogeneity (
Change in density, on the other hand, may be determined by the frequency of forest fires or other landscape-level disturbances (
Lantana camara is one of the most globally ubiquitous invasive species (
L. camara was introduced to India a little over two centuries ago, and is today one of the most widespread invasive plant species in the country (
We examined factors underpinning the colonisation and change in density of L. camara in the Biligiri Rangaswamy Temple Tiger Reserve, a seasonally dry tropical forest landscape in the Western Ghats biodiversity hotspot, India. Rapid L. camara invasion has occurred here over the past decade (
Lantana camara (Verbenaceae) is a straggling shrub native to South and Central America.
Lantana camara started to be mentioned in the literature as invasive about a hundred years after its introduction to India (Tireman 1918, Iyengar 1933). Today, L. camara is common in tropical dry forests, slash-and-burn fallows, and pasture-lands all over India (
The Biligiri Rangaswamy Temple Tiger Reserve (hereafter, BRT) in Karnataka, India, where this study was conducted, is part of the Western Ghats biodiversity hotspot (
Our study site is an ideal system to examine the mechanisms underlying L. camara invasion for two significant reasons. First, spatially explicit data on the presence and abundance of L. camara are available for 1997 (
Map of the Biligiri Rangaswamy Temple Tiger Reserve (BRT) showing sampling grids and roads (a), old and current podus (Soliga settlements), and areas of historical plantation activity (b). Inset map of India shows location of BRT.
There are several forest types in BRT, of which seasonally dry forests constitute approximately 90% of the study area (
Information on L. camara distribution in BRT from 1997 came from
Spatially explicit data on L. camara distribution from 1997 to 2008 enabled us to arrive at our response variables, colonisation and change in density. For examining colonisation between 1997 and 2008, we used the subset of plots that were uninvaded by L. camara in 1997 (n = 71). Lantana camara change in density between 1997 and 2008 was examined by computing the change in L. camara stem density from 1997 to 2008 (stem density in 2008/stem density in 1997). For this we used the subset of plots that were already invaded by L. camara in 1997 (n = 51). Although hypothetically plots may already have attained maximum L. camara density in 1997, results from
We developed a L. camara neighbourhood index (LNI) assuming that presence of L. camara in an adjoining grid cell would contribute to propagule pressure. To calculate LNI in 1997, we used data on L. camara presence/absence from
Published information (
Satellite images that were used for the derivation of degree of deciduousness by
Historical disturbance: Field observations from BRT indicated that a large proportion of historical plantations and agricultural sites are heavily invaded by L. camara (B. Sundaram personal observation). It is possible that these plantations and habitations were the original source locations from which L. camara spread. Historically, plantations of silver oak (Grevillea spp.) and teak (Tectona grandis) were established in multiple locations. Information about the locations of historical clear- and selection-felling sites was obtained from the field (using a hand-held GPS unit) and from Karnataka Forest Department records (
Contemporary disturbances: Several studies indicate that edge effects (estimated through proximity to habitation, and to roads and streams) may play an important role in L. camara invasion (
Fire frequency (FF): Burnt areas were mapped each year from 1997–2002 (R. Siddappa Setty, unpublished data), and 2004–2007 (this study), yielding fire maps for 10 of the 11 years over which change in L. camara distribution has been assessed. Each year during April-May, all motorable roads in BRT were traversed. Visibly burnt areas were marked on a topographical sheet (scale 1:50000, or 1 cm = 500 m). Additional burnt areas that were not visible from the roads were mapped from vantage points within BRT. At least 17 locations across the study area were consistently used as vantage points annually. The topographical sheets on which fires were mapped were scanned and burnt areas were digitized using MapInfo. Based on fire maps from 1997–2002 and 2004–2007 fire frequency was calculated for each grid cell as the total number of times the cell burned between 1997 and 2007. In case the area in a grid cell was incompletely burnt, the grid cell was scored as burnt only if the grid center (where plots were located) was burnt.
After deriving all predictors, we determined the level of correlation (Pearson’s r) among predictor variables. We found a significant positive correlation (r = 0.61) between the minimum distance to historical plantation sites and minimum distance to historical habitation sites. Since our final predictor variable for historical disturbance (HD) was derived from the lesser of the two distances to historical plantation sites or historical habitation sites, this correlation would not affect our model selection exercise. None of the other predictors (or their components) were correlated.
Analyses were performed separately on data subsets corresponding to colonisation and change in density. An information-theoretic, model-selection approach (
After response and predictor variables were derived, candidate sets of models (global model with all predictors, single predictor models, and models containing predictor pairs) were developed a priori to examine factors driving colonisation and change in density. For both candidate sets, the explanatory variables used were identical, and in each candidate set of models, all predictor variables were used an equal number of times. Having a balanced model set was vital for the purposes of calculating the importance of each predictor variable individually (
Both candidate sets (one each for colonisation and change in density) contained a global model that included all predictors. The global models of colonisation and change in density included only two-way interactions (e.g., FF:EDGE) between predictors. Three-, four- and five-way interactions could not be included on account of small sample sizes. After defining the global model in each candidate set, a subset of separate single-predictor models was then added (a total of six models, one for each predictor). Finally, a subset of separate two-way additive models containing all unique combinations of predictor variables was added (a total of 15 models). Thus each candidate set had a total of 22 models (1 global model + 6 single predictor models + 15 two-way additive models).
A generalized linear model (GLM) with binomial errors and a logit link was used to model colonisation. For modeling L. camara change in density, a GLM with Gaussian errors and a log link was used. To account for instances where the density of L. camara in 2008 was 0 (a total of seven cases), we added a miniscule number (0.001), so that the log value of 2008 density/1997 density could be calculated.
For both candidate sets of models, a set of background tests were conducted before progressing to the model-selection stage (
The probability of colonisation increased with an increase in the L. camara neighbourhood index around each plot in 1997 (Fig.
Variables that best explain L. camara colonisation (a), and change in L. camara density (b). P-values are taken from single-predictor GLM analyses.
Models to explain Lantana camara colonisation with their corresponding AICc (corrected AIC), Δi (value of AICc in the ith model – minimum value of AICc), Akaike weights and percent deviance explained. The single-predictor model explaining the maximum deviance is in bold typeface.
Model | AICc | Δi | Akaike weights | % deviance explained |
---|---|---|---|---|
L. camara colonisation (n= 71) | ||||
Global | 116.05 | 38.27 | 0.000 | 51.12 |
Fire frequency (FF) | 81.70 | 3.92 | 0.047 | < 1 |
Degree of deciduousness (DOD) | 82.19 | 4.41 | 0.036 | < 1 |
Proximity to historical disturbance (HD) | 82.04 | 4.26 | 0.039 | < 1 |
Proximity to contemporary disturbance (CD) | 82.26 | 4.48 | 0.035 | < 1 |
Proximity to edge (EDGE) | 82.42 | 4.64 | 0.032 | < 1 |
L. camara neighbourhood index (LNI) | 77.78 | 0.00 | 0.330 | 6.19 |
FF:DOD | 84.39 | 6.61 | 0.012 | 1.60 |
FF:HD | 84.56 | 6.78 | 0.011 | 1.34 |
FF:CD | 84.61 | 6.83 | 0.011 | 1.27 |
FF:EDGE | 84.82 | 7.04 | 0.010 | < 1 |
DOD:HD | 84.30 | 6.52 | 0.013 | 1.69 |
DOD:CD | 85.13 | 7.36 | 0.008 | < 1 |
DOD:EDGE | 85.27 | 7.49 | 0.008 | < 1 |
HD:CD | 85.04 | 7.26 | 0.009 | < 1 |
HD:EDGE | 85.16 | 7.39 | 0.008 | < 1 |
CD:EDGE | 85.36 | 7.59 | 0.007 | < 1 |
LNI:FF | 80.44 | 2.66 | 0.087 | 6.81 |
LNI:DOD | 80.68 | 2.90 | 0.077 | 6.50 |
LNI:HD | 79.96 | 2.18 | 0.111 | 7.45 |
LNI:CD | 80.02 | 2.24 | 0.108 | 7.38 |
LNI:EDGE | 123.41 | 45.63 | 0.000 | 7.24 |
Other disturbance factors that affect the study area, such as fire frequency and proximity to edge, did not emerge as being important for explaining L. camara colonisation when compared to the L. camara neighbourhood index alone. The global model (all predictors individually, and all two-way additive interactions of predictors) explained 51.1% of the deviance from the intercept-only model.
The spatial extent of L. camara in BRT increased dramatically from 1997 to 2008. Lantana camara was present in 41% of plots across the 540 km2 of BRT in 1997 (
The global model containing all predictors and their interactions explained 56.8% of the deviance from the intercept-only model for L. camara density data. The frequency of fire in each grid cell during 1997–2008 emerged as the best predictor of change in L. camara density and explained 23.6% of the deviance from the intercept-only model for the data (Table
Models to explain Lantana camara change in density with their corresponding AICc (corrected AIC), Δi (value of AICc in the ith model – minimum value of AICc), Akaike weights and percent deviance explained. The single-predictor model explaining the maximum deviance is in bold typeface.
Model | AICc | Δi | Akaike weights | % deviance explained |
---|---|---|---|---|
L. camara density (n=51) | ||||
Global | 219.71 | 50.76 | 0.000 | 56.75 |
Fire frequency (FF) | 168.94 | 0.00 | 0.400 | 23.59 |
Degree of deciduousness (DOD) | 182.67 | 13.72 | 0.000 | < 1 |
Proximity to historical disturbance (HD) | 182.51 | 13.57 | 0.000 | < 1 |
Proximity to contemporary disturbance (CD) | 182.64 | 13.70 | 0.000 | < 1 |
Proximity to edge (EDGE) | 181.98 | 13.03 | 0.001 | 1.35 |
L. camara neighbourhood index (LNI) | 182.36 | 13.42 | 0.000 | < 1 |
FF:DOD | 172.12 | 3.17 | 0.083 | 23.69 |
FF:HD | 171.27 | 2.32 | 0.127 | 24.95 |
FF:CD | 172.15 | 3.20 | 0.082 | 23.65 |
FF:EDGE | 170.31 | 1.37 | 0.204 | 26.35 |
DOD:HD | 185.69 | 16.74 | 0.000 | < 1 |
DOD:CD | 185.87 | 16.93 | 0.000 | < 1 |
DOD:EDGE | 185.18 | 16.24 | 0.000 | 1.41 |
HD:CD | 185.71 | 16.77 | 0.000 | < 1 |
HD:EDGE | 185.11 | 16.16 | 0.000 | 1.56 |
CD:EDGE | 185.21 | 16.27 | 0.000 | 1.36 |
LNI:FF | 171.74 | 2.79 | 0.100 | 24.26 |
LNI:DOD | 185.60 | 16.65 | 0.000 | < 1 |
LNI:HD | 185.27 | 16.32 | 0.000 | 1.25 |
LNI:CD | 185.57 | 16.63 | 0.000 | < 1 |
LNI:EDGE | 184.94 | 16.00 | 0.000 | 1.88 |
Weight of evidence in favour of each variable for explaining L. camara colonisation and change in density. The weights for each variable were calculated by summing the Akaike weights of all models where the predictor variable of interest appears (
Predictor variables | Colonisation | Change in density |
---|---|---|
Fire frequency | 0.12 | 0.62 |
Degree of deciduousness | 0.10 | 0.05 |
Proximity to historical disturbance | 0.13 | 0.08 |
Proximity to contemporary disturbance | 0.12 | 0.05 |
Proximity to edge | 0.04 | 0.13 |
L. camara neighbourhood index | 0.48 | 0.06 |
Results from this study indicate that the factors that are important for L. camara invasion differ across stages. Propagule pressure (as inferred from the L. camara neighbourhood index) plays an important role in increasing the probability of local colonization of L. camara. Following L. camara colonisation, fire appears to limit L. camara density.
While two factors have emerged as being important mechanisms of the L. camara invasion process, the three other disturbance factors taken into account in this study (distance to historical disturbance, distance to current human habitation, and distance to edge) and L. camara habitat suitability, also play a role. The absence of interactions among our predictor variables was surprising, given our initial expectation of synergistic interaction effects between predictors.
In addition to the factors identified by this study as potential underlying mechanisms of L. camara colonisation and change in density, contemporary reports of L. camara proliferation in the larger Western Ghats landscape, e.g., in nearby protected areas like Bandipur (
In BRT, L. camara produces large fruit crops, sometimes up to ten thousand fruits per plant over a single fruiting season (Monika Kaushik, unpublished data). It is likely that arrival of L. camara propagules is enhanced by the year-round fruiting of the species combined with the lack of dispersal limitation. Indeed, studies on the Island of Reunion have shown that larger populations of dispersers (e.g., the invasive red-whiskered bulbul) are supported in areas invaded by four bird-dispersed invasive plants that produce seeds year-round (including L. camara) compared to areas with a low invasive plant density. This suggests a positive feedback between presence of propagules and presence of dispersers (
In addition to its dispersal by frugivorous birds, we have observed L. camara seeds in feces of wild pigs and sloth bears in BRT, although there are no published records of L. camara seed dispersal by mammals. In addition to sexual modes of propagation, it should also be noted that L. camara propagates vegetatively through rootstock (
While the L. camara neighbourhood index emerged as the primary driver of L. camara colonisation, other predictors were not negligible, nor sizeable, and the global model explained 51.1% of the variance in the data. The effects of predictors associated with disturbance, such as distance to edge, fire frequency, and distance to both contemporary and historical disturbance, were almost similar. Both
On the other hand, the relatively lower weight of evidence in favour of degree of deciduousness when compared to the L. camara neighbourhood index (and also edge and fire frequency) may be related to the relative representation of different forest types within the BRT landscape. Although a large proportion of our study area is deciduous (~90%;
Results from our study have improved our understanding of the relationship between L. camara and fire. Some conceptual models of L. camara invasibility have hypothesized that an increase in fire frequency could potentially favour L. camara (
Just as the role of fire in limiting L. camara density was contrary to our expectations, so too was the minimal role of degree of deciduousness and the L. camara neighbourhood index in influencing L. camara density. Studies from Australia and Africa show that L. camara change in density is enhanced by the presence of open canopies or gaps (
Propagule pressure – as inferred from the L. camara neighbourhood index in this case – presumably does not play much of a role in influencing L. camara density because allochthonous dispersal (i.e., arrival of seeds from outside) may cease to be important following the colonisation of L. camara at a site. Given the young age to maturity of L. camara and the large numbers of fruits produced per individual, resulting autochthonous seed arrival (i.e., seeds produced on-site) may swamp the effects of seeds arriving from elsewhere.
In Australia and in India, L. camara invasion is probably driven by the capacity of propagule pressure to overwhelm ecological resistance to invasion.
Lantana camara invasion in BRT is the product of a complex interplay between propagule pressure and frequency of forest fires. Clearly, therefore, land managers and biologists need to take into account the inherent multivariate nature of L. camara invasion when coordinating eradication or control activities.
Once a species is established, the reduction of propagule pressure is a challenging task (
In addition to propagule pressure, our finding that fire limits L. camara density also has important management implications. In the Indian context, fires have been regarded by forest managers as uniformly detrimental (
We thank the staff of the Ashoka Trust for Research in Ecology and the Environment (ATREE) BRT Field Station, for their logistical support and help with field data collection, Devcharan Jathanna and Kavita Isvaran for their help with data analysis, and Mahesh Sankaran, Varun Verma, Noelie Maurel, and two anonymous reviewers for their constructive comments. We thank R. Siddappa Setty for sharing data on fire frequency and L. camara distribution, C. Made Gowda and Nitin Rai for sharing data on historical habitation sites, MC Kiran for collating fire frequency data, and Madhura Niphadkar for creating maps used in this study. The Department of Science and Technology, India, and the International Foundation for Science, Sweden, funded this study.