Sign up to be a platform tester.

List of recent and recommended literature

Jan 19, 2021

Recent and selected literature 

We will continue to add to this list of useful scientific articles related to the ecological workflows available on EcoCommons.  If you would like us to add a specific article, please send a link to comms@ecocommons.org.au

Recent articles

A great overview of the variety of important considerations when developing SDMs

Zurell, D., Franklin, J., König, C., Bouchet, P. J., Dormann, C. F., Elith, J., … & Merow, C. (2020). A standard protocol for reporting species distribution models. Ecography, 43(9), 1261-1277.

An interesting Bayesian approach to SDM

Engel, M., Mette, T., & Falk, W. (2022). Spatial species distribution models: Using Bayes inference with INLA and SPDE to improve the tree species choice for important European tree species. Forest Ecology and Management, 507, 119983.

Comparison of multiple SDM methods highlighting good performance of ensemble and BRT with tuning

Hao, T., Elith, J., Lahoz Monfort, J. J., & Guillera‐Arroita, G. (2020). Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography, 43(4), 549-558.  https://doi.org/10.1111/ecog.04890

Shows how SDMs and the prioritisation software Zonation can be used to prioritise survey effort

Southwell, D., Wilkinson, D., Hao, T., Valavi, R., Smart, A., & Wintle, B. (2022). A gap analysis of reconnaissance surveys assessing the impact of the 2019–20 wildfires on vertebrates in Australia. Biological Conservation, 270, 109573.

A great summary on need to use SDMs in ecological assessmentsBaker, DJ, Maclean, IMD, Goodall, M, Gaston, KJ. Species distribution modelling is needed to support ecological impact assessments. J Appl Ecol. 2021; 58: 21– 26. https://doi.org/10.1111/1365-2664.13782

An excellent example the relationship between sampling and environmental space
Guerin, G. R., Williams, K. J., Sparrow, B., & Lowe, A. J. (2020). Stocktaking the environmental coverage of a continental ecosystem observation network. Ecosphere, 11(12), e03307.

A new SDM R package

Shipley, B. R., Bach, R., Do, Y., Strathearn, H., McGuire, J. L., & Dilkina, B. (2022). megaSDM: integrating dispersal and time step analyses into species distribution models. Ecography2022(1).

Interesting look at how modelling approach can impact results for conservation

Muscatello, A., Elith, J., & Kujala, H. (2021). How decisions about fitting species distribution models affect conservation outcomes. Conservation Biology, 35(4), 1309-1320.

An article which spells out some of the limitations of SDM

A. Lee-Yaw, J., L. McCune, J., Pironon, S. and N. Sheth, S. (2021), Species distribution models rarely predict the biology of real populations. Ecography. https://doi.org/10.1111/ecog.05877

The utility of indigenous knowledge in SDM

Skroblin, A., Carboon, T., Bidu, G., Chapman, N., Miller, M., Taylor, K., … & Wintle, B. A. (2021). Including indigenous knowledge in species distribution modeling for increased ecological insights. Conservation Biology, 35(2), 587-597.

Biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions improve SDM predictions for invasives

Srivastava, V., Roe, A.D., Keena, M.A. et al. Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world. Biol Invasions 23, 297–349 (2021). https://doi.org/10.1007/s10530-020-02372-9 

A more complex but promising approach to joint species distribution models

Pichler, M., & Hartig, F. (2021). A new joint species distribution model for faster and more accurate inference of species associations from big community data. Methods in Ecology and Evolution.  

Evaluation of Joint SDM results

Wilkinson, D. P., Golding, N., Guillera‐Arroita, G., Tingley, R., & McCarthy, M. A. (2021). Defining and evaluating predictions of joint species distribution models. Methods in Ecology and Evolution, 12(3), 394-404. 

Biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions improve SDM predictions for invasives

Srivastava, V., Roe, A.D., Keena, M.A. et al. Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world. Biol Invasions 23, 297–349 (2021). 

 

Generalised dissimilarity modelling (coming soon to EcoCommons)

Ferrier, S., Manion, G., Elith, J., & Richardson, K. (2007). Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and distributions, 13(3), 252-264. 

Ferrier, S., & Guisan, A. (2006). Spatial modelling of biodiversity at the community level. Journal of applied ecology, 43(3), 393-404. 

Species Distribution Model related

A good summary of things to consider when developing SDMs

Helen R Sofaer, Catherine S Jarnevich, Ian S Pearse, Regan L Smyth, Stephanie Auer, Gericke L Cook, Thomas C Edwards, Jr, Gerald F Guala, Timothy G Howard, Jeffrey T Morisette, Healy Hamilton, Development and Delivery of Species Distribution Models to Inform Decision-Making, BioScience, Volume 69, Issue 7, July 2019, Pages 544–557, 

A more complex approach to improve SDM predictions using an integrated approach

Koshkina, V., Wang, Y., Gordon, A., Dorazio, R. M., White, M., & Stone, L. (2017). Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection. Methods in Ecology and Evolution, 8(4), 420-430.  

Fit for purpose SDMs

Guillera‐Arroita, G., Lahoz‐Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., … & Wintle, B. A. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography, 24(3), 276-292

 Joint SDM example

Pollock, L. J., Tingley, R., Morris, W. K., Golding, N., O’Hara, R. B., Parris, K. M., … & McCarthy, M. A. (2014). Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5(5), 397-406. 

Use of target background to reduce model bias in SDM

Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., & Ferrier, S. (2009). Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecological applications, 19(1), 181-197.

 

Needs in Marine SDMs

Robinson, L. M., Elith, J., Hobday, A. J., Pearson, R. G., Kendall, B. E., Possingham, H. P., & Richardson, A. J. (2011). Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Global Ecology and Biogeography, 20(6), 789-802.

 Inclusion of static and dynamic variables in SDM climate projections

Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P., & Reşit Akçakaya, H. (2012). Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution, 3(2), 349-357.  

Learn more about EcoCommons.

Our partners

 

  • Australian Research Data Commons
  • National Collaborative Research Infrastructure Strategy
  • EcoCommons Australia received investment (https://doi.org/10.47486/PL108) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS).

Sign up to our newsletter