To study impact of climate change on the distribution pattern's of Chromolaena odorata (L.) R.M. King & H. Robinson using Maxent Entropy Approach.
Chromolaena odorata is a very widely distributed tropical shrub and considered as an highly invasive weed of field crops and natural environments in its introduced range. It continues to spread due to its effective short- and long-distance dispersal. It can form pure stands where established, often in disturbed areas, grasslands, fallow areas and forestry plantations, and is highly competitive. It has been reported to be the most problematic invasive species within protected rain-forests in Africa. In Western Africa it is well known for preventing the regeneration of tree species in areas of shifting cultivation. It affects species diversity in southern Africa. The plant's flammability greatly affects the forest edges. In Sri Lanka it is considered as a major weed in disturbed areas and coconut plantations.
Chromolaena odorata is still expanding its range, and is considered one of the world’s worst weeds. It is viewed as a major environmental weed, but is appreciated by some agriculturalists as it shortens fallow time in shifting cultivation.
Ecological Niche Modelling: The Maximum Entropy (MaxEnt) approach is used for species distribution modelling for present and future (MIROC5) conditions under RCP (Representative concentration pathways) 8.5 for 2050 (average for 2041-2060) and 2070 (average for 2061-2080). To model current and future scenario's we used following Bioclimatic variables Bio1 (Annual Mean Temperature), Bio2 (Mean Diurnal Range), Bio3 (Isothermality), Bio12 (Annual Precipitation), Bio14 (Precipitation of Driest Month), Bio15 (Precipitation Seasonality) and Bio18 (Precipitation of Warmest Quarter) for both present and future climatic conditions. These future climatic conditions are the most recent GCM climate projections that are used in the Fifth Assessment IPCC report.
The area under the receiver operating curve (ROC), known as the AUC is one of the most common statistics to assess model performance. The calculation of AUC was performed by setting aside 30% of random points during modelling process. The SDM showed good level of predictive performance as indicated by the AUC. AUC had had a mean value and a standard deviation respectively of 0.895 ± 0.0044.
In this study, we found that Bio 3, Bio12, Bio 18 are the three major contributors in model building. Bio3 contributed 50.1%, Bio 12 contributed 30.7%, Bio 18 contributed 6.1% and remaining variables contributed 13.1 % towards model building.
The results also showed that there is shrinkage in the highly Suitable habitat for both 2050 and 2070 under Rcp8.5 scenario.
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