By Dinusha Jayathilake, University of Auckland

What is Species Distribution Modeling (SDM)?

SDM is a numerical tool to predict the potential distribution of a given species or a set of species by combining the occurrence records and environmental variables layers (Pearson 2007, Elith and Leathwick 2009).  For example, if we are interested in modelling the distribution of Posidonia oceanica seagrass species known to endemic in the Mediterranean sea, we need to have occurrence records (latitudes and longitudes) and the marine abiotic variables that known to suitable for the distribution of Posidonia oceanica.

SDMs are now broadly used in terrestrial, freshwater and marine domain (Elith and Leathwick 2009). The similar method is alternatively known as environmental niche modelling, ecological niche modelling, suitable habitat modelling, or climate envelope modelling (BCCLV 2016).

Why use MaxEnt?

It is easily accessible, free to use, easy to learn, and particularly useful for presence-only data. The last is s special importance for most biogeographic and ecological data where species absences are likely to reflect lack of sampling more than true absence.

It is also available on this useful platform for running SDM: Biodiversity and Climate Change Virtual Laboratory (BCCVL).

Download MaxEnt here 

Click here for sources of biological and environmental data.

Training in MaxEnt is available from

Pearson R. (2014). NERC Advanced training short course: Species distribution modelling. University College London. Available on https://www.youtube.com uploaded by UCL CBER.

Gizi (2014). MaxEnt. Slideserve ID: 2829355. Available on https://www.slideserve.com/gizi/maxent.

BCCLV (2016). Introduction to Species Distribution Models. Available on https://support.bccvl.org.au/support/solutions/articles/6000127048-introduction-to-species-distribution-models.

Good species distribution modelling practice (from Elith and Leathwick (2009)):

  1. Gathering data of high spatial accuracy and comprehensiveness.
  2. Use of relevant environmental variables.
  3. Have to have a proper plan to work with correlated predictor variables.
  4. Selecting the most suitable modelling algorithm.
  5. Fitting the model to the test and training data.
  6. Evaluating the model with response functions.
  7. Mapping predictions to geographic space.
  8. Selecting a threshold to convert continuous prediction to a binary map.

TIPS

Species occurrence records

  • If you are going to use open databases such as the GBIF or OBIS, make sure to clean as much as possible. It is essential to get occurrence records with more accurate latitude and longitude values.
  • MaxEnt has an option to remove duplicate records.

Environmental variable layers

  • You can use continuous data or categorical data. Categorical data should be converted to numerical data. For example seabed substratum; sandy, rocky, muddy.
  • Make sure that all the abiotic layers have the same cell size and same projection in ASCII format.

MaxEnt modelling

  • Read the manual (Phillips 2017).
  • Start with a simple model. Keep the Regularization multiplier in default value (1). Do one run with 20- 30% test points. Then change the parameters (basically regularization multiplier).
  • I recommend running models with all abiotic variables and then selectively remove them. Then run the model again. If the variables are correlated then you can identify them in jack-knife test. Then you can decide whether to keep them or re-run the model without those correlating variables.
  • Save each model in a new folder and state the parameters in the file name so you can easily find them later.
  • Do replicate runs for one model for better results. 10 replicates common in the literature.

References and further reading

Basher Z, Costello MJ. 2016. The past, present and future distribution of a deep-sea shrimp in the Southern Ocean. PeerJ 4, e1713. DOI 10.7717/peerj.1713

Basher, Z., Bowden, D.A., Costello, M.J. (2018). GMED: Global Marine Environment Datasets for environment visualization and species distribution modeling. Earth Syst. Sci. Data.  https://doi.org/10.5194/essd-2018-64.

Elith, J., Leathwick, J.R. (2009). Species distribution models: ecological explanation and prediction across space and time.Annual Review of Ecology, Evolution and Systematics, 40, 677–697.

Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology

Jayathilake D. R. M., Costello M. J. (2018). A modelled global distribution of the seagrass biome. Biological Conservation. 226. 120-126. https://doi.org/10.1016/j.biocon.2018.07.009

Pearson, R.G. (2007). Species’ Distribution Modeling for Conservation Educators and Practitioners. Synthesis.New York: Am. Mus. Natl. Hist. http://ncep.amnh.org .

Peterson, A.T., Papes, M., Soberon, J. (2008). Rethinking receiver operating characteristic analysis application in ecological niche modelling. Ecological Modelling. 213. 63-72.

Peterson, A.T., Soberón, J., Pearson, R.G., et al. (2011). Ecological Niches and Geographic Distributions. Princeton University Press, United States of America.

Phillips, S. J. (2017). A brief tutorial on Maxent. AT&T Labs-Research, Available from http://biodiversityinformatics.amnh.org/open_source/maxent.  Accessed on 10/1/2019

Phillips, S.J., Anderson, R.P., Schapire, R.E., (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S.J., Dudík, M., (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

Saeedi, H, Basher Z, Costello MJ. 2016. Modelling present and future global distributions of razor clams (Bivalvia: Solenidae). Helgoland Marine Research 70 (23). DOI 10.1186/s10152-016-0477-4

Saeedi, H., Dennis, T.E., Costello, M.J., (2016). Bimodal latitudinal species richness and high endemicity of razor clams (Mollusca). Journal of Biogeography. 44, 592–604. https://doi.org/10.1111/jbi.12903

Tittensor, D.P., Baco, A.R., Brewin, P.E., et al. (2009). Predicting global habitat suitability for stony corals on seamounts. Journal of Biogeography. 36, 1111–1128. https://doi.org/10.1111/j.1365-2699.2008.02062.x

Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281.

Yesson, C., Taylor, M.L., Tittensor, D.P., et al. (2012). Global habitat suitability of cold-water octocorals. Journal of Biogeography. 39, 1278–1292. doi:10.1111/j.1365-2699.2011.02681.x

 

Print Friendly, PDF & Email

Subscribe By Email

Get every new post delivered right to your inbox.

This form is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Skip to toolbar