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Highlighted literature

Oct 17, 2021

Recent and selected literature 

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

Recent articles

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.

 

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.  

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  • 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).

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