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 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.
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.
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.
