You can consult SK Barik sir and Dibyendu da of NEHU

Dear Bishwajit,

There are a wide range of methods a available for ecological niche modelling. Recent development is an ensemble approach of modelling where more than one models (or modelling algorithms) are combined to obtain robust prediction. You can read following paper by Elith and Leathwick 2009 for a good understanding of SDM:

Elith, J., Leathwick, J.R., 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697.

and my published papers for ensemble SDM. One of the paper is related to medicinal plant.

Check in my page https://www.researchgate.net/profile/Sailesh_Ranjitkar

Best regards

Sailesh

Dear Biswajit,

The field of species distribution modelling is complex and it's better to conduct your own litterature review before getting started but here are some important points to think about:

1. Check the relevance of your occurrence data. You should have at least 20, preferably 30 presence points for each species to build a robust species distribution model. Also ask the following questions for each species:

-Is the species niche constant in space or can you identify several subspecies that respond differently to environmental conditions?

-Are your observations spatially correlated?

-Are there any sample bias in your sampling design (e.g. more extensive sampling along main roads, near from cities, etc.)?

Don't forget to clean your data (remove replicates, delete records that are too old or spatially inaccurate...)

2. Think about your absence data. Are true absences available or are you going to use background or pseudo-absence data? If so, how are you going to choose your pseudo-absence points? Are you going to account for sample bias while selecting pseudo-absences or will you simply select them randomly? See for example:

Barbet-Massin, M., Jiguet, F., Albert, C.H., Thuiller, W., 2012. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution 3(2), 327-338.

3. Based on previous studies and your ecological knowledge, identify the environmental factors that are important for your species. Identify the environmental data layers available in your study area and join them spatially to your presence and (pseudo)absence points.

4. Choose one or several modelling algorithms that you are going to use to build your species distribution models (see e.g. Guisan & Zimmermann's review: Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135, 147-186.)

. Because different methods can provide very different predictions, it's better to test at least 2 different algorithms and compare their performances. Also try different values of the parameters for each modelling technique (e.g. try different numbers of trees for Random Forest, different values of the regularization parameter for Maxent,...)

5. Evaluate the quality of your species distribution models. Use a validation index such as Area Under the ROC Curve (AUC), True Skill Statistic (TSS), and carefully examine the maps of predictions and the response curves to the different environmental variables; check if they are consistent with existing litterature and your ecological knowledge. See for example:

Anderson, R.P., Lew, D., Townsend Peterson, A., 2003. Evaluating predictive models of species’distributions: criteria for selecting optimal models. Ecological Modelling 162, 211-232.

Here is a good starting guide for beginners in species distribution modelling:

Philips, S., 2009. Species'Distribution Modeling for Conservation Educations and Practicioners. American Museum of Natural History, Lessons in Conservation. 136 p. Available at http://ncep.amnh.org/linc.”

I hope this will help you,

Jessica

Hello,

A few references are very good for beginners (at least were for me), to help with the many concepts involved. These books are a good example:

https://books.google.pt/books/about/Mapping_Species_Distributions.html?id=PyhQV3o6WngC&redir_esc=y

https://books.google.pt/books/about/Ecological_Niches_and_Geographic_Distrib.html?id=wyFnf9x9Vi0C&redir_esc=y

As mentioned above you should read the basic references in the field in order to fully understand what you're doing.

I've been using mostly biomod/biomod2: http://finzi.psych.upenn.edu/library/biomod2/doc/Simple_species_modelling.pdf

However you should choose the adequate methods considering data quality and type (presence only or presence/absence) and objective of the study.

Many people use maxent (I've never used it), since it might be easier (althought the concept in itself is not so easy): http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2010.00725.x/full

Hope this helps,

Cheers.

Hi,

If you have only presences, Maxent is a good tool, even with few records. 

Find attached a tutorial which is really good to start working with Maxent. After that, the references provided in the other replies should be read.

Good luck!

xavi.

I think you have some very good input above, particularly the references that were mentioned.  I would add a few more to that list:

J. Millspaugh and F. R. Thompson (editors), Models for Planning Wildlife Conservation in Large Landscapes, Academic Press, Burlington, MA. Pp. 251-285.

Boria, R. A., L. E. Olsen, S. M. Goodman, and R. P. Anderson. 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling 275:73-77.

Brotons, L., W. Thuiller, M. B. Araújo, and A. H. Hirzel. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437-448.

Drew, C. A., Y. F. Wiersma, and F. Huettmann. 2011a. Predictive Species and Habitat Modelling in Landscape Ecology. Springer, New York.

Elith, J., C. H. Graham, R. P. Anderson, M. Dudík, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. M. Overton, A. T. Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129-151.

Franklin, J. 2010. Mapping Species Distributions Spatial Inference and Prediction. Cambridge University Press, Cambridge, UK.

Franklin, J. 2013. Species distribution models in conservation biogeography: developments and challenges. Diversity and Distributions 19:1217-1223.

Graham, C. H., J. Elith, R. J. Hijmans, A. Guisan, A. T. Peterson, B. A. Loiselle and the NCEAS Predicting Species Distributions Working Group. 2008. The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology 45:239-247.

Hernandez, P., C. H. Graham, L. L. Master, and D. L. Albert. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773-785.

Scott, J. M., P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall, and F. B. Samson. 2002. Predicting Species Occurrences Issues of Accuracy and Scale. Island Press, Washington DC.

Wisz, M. S., R. J. Hijmans, J. Li, A. T. Peterson, C. H. Graham, A. Guisan, and the NCEAS Predicting Species Distributions Working Group. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions 14:763-773.

Jocelyn

hi ,

adding further to the above suggestions , I am attaching a few links which willl be help full for starters and try open sourse softwares.

 http://clp-foss4g-workshop.readthedocs.org/en/latest/

http://www.bioversityinternational.org/e-library/publications/detail/training-manual-on-spatial-analysis-of-plant-diversity-and-distribution/

best of luck

pavithra   

In addition to the colleagues, a paper attached for your inspiration. 

Would you consider to include bio-climatic predictors, you may find some clues in our sequel on bear. 

Article Fine resolution distribution modelling of endemics in Majell...

try hijmans and elith modellin guide 2017 on ---- https://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf

GARP and MaxEnt model is good for habitat distribution modeling . Before starting the modelling search some litrature also..

Dear Biswajit

I am also working on distribution modeling and and have been trying my best to collect habitat as well as environmental data. Hope you have gained momentum with MaxEnt and deliberately practicing the same. If possible could I have some discussion with you on the same.

you can start with downloading climate data for current and future scenarios. Then follow the steps mentioned in the tutorial

https://github.com/shandongfx/workshop_maxent_R/blob/master/code/Appendix1_case_study.md

I hope it helps you.