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Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas

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  • Srivastava, Vivek
  • Griess, Verena C.
  • Padalia, Hitendra

Abstract

Biological invasion is considered the subsequent most important threat to biodiversity after habitat destruction and is documented as a main cause of global biodiversity loss and species extinction. Species distribution models can be used to identify areas that are at risk for invasions by harmful invasive alien species (IAS) if IAS have not yet spread to all suitable habitats. We tested the potential of combining two or more independent but complementary modelling methods to enhance the accuracy of spatial predictions. We used the modelling tools Maxent, GARP and BIOCLIM with presence-only data of the IAS Yushania maling (Maling bamboo), from Darjeeling, Himalaya to develop distribution maps. Modelling tools were chosen based on their performance with presence-only data (Area under curve (AUC) > 0.7) as well as their differences in underlying modelling concept and statistics. The models combine occurrence records with topographic, climatic, and vegetative predictors derived from satellite data. By combining the 3 selected models in an ensemble approach we were able to minimize the spatial uncertainty related to suitable habitat prediction for Yushania maling. While both GARP and BIOCLIM consistently performed at an AUC > 0.7, both our ensemble model and Maxent performed better with an AUC of 0.95 and 0.94 respectively. Moderately and highly suitable habitats for Yushania maling predicted by models correlated well with survey records except for GARP, where we found the model to over-predict suitability outside of the species’ known ecological niche. Our findings identify the best modelling approach enhancing overall explanatory power of habitat suitability models. Our findings show that an ensemble approach should be used to ensure appropriate mitigation measures are applied in the appropriate places, enhancing overall effectiveness both ecologically as well as economically. It was shown that Yushania maling is indeed a threat to the ecosystems in the region, and while the species’ potential habitat may decrease in some areas with climate change, other areas will become more suitable for it.

Suggested Citation

  • Srivastava, Vivek & Griess, Verena C. & Padalia, Hitendra, 2018. "Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas," Ecological Modelling, Elsevier, vol. 385(C), pages 35-44.
  • Handle: RePEc:eee:ecomod:v:385:y:2018:i:c:p:35-44
    DOI: 10.1016/j.ecolmodel.2018.07.001
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    1. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(3), pages 427-432, June.
    2. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(4), pages 629-637, August.
    3. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(5), pages 777-788, October.
    4. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(1), pages 151-160, February.
    5. Thomas J. Stohlgren & Peter Ma & Sunil Kumar & Monique Rocca & Jeffrey T. Morisette & Catherine S. Jarnevich & Nate Benson, 2010. "Ensemble Habitat Mapping of Invasive Plant Species," Risk Analysis, John Wiley & Sons, vol. 30(2), pages 224-235, February.
    6. Roy, Arijit & Bhattacharya, Sudeepto & Ramprakash, M. & Senthil Kumar, A., 2016. "Modelling critical patches of connectivity for invasive Maling bamboo (Yushania maling) in Darjeeling Himalayas using graph theoretic approach," Ecological Modelling, Elsevier, vol. 329(C), pages 77-85.
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    1. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.

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