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The effect of species response form on species distribution model prediction and inference

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  • Santika, Truly
  • Hutchinson, Michael F.

Abstract

Ecological theory and current evidence support the validity of various species response curves according to a variety of environmental gradients. Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response. It is also not well known how spatial correlation in species occurrence affects model performance. These effects were investigated by applying an environmental envelope method (BIOCLIM) and three regression-based methods: logistic regression (LR), generalized additive modelling (GAM), and classification and regression tree (CART) to simulated species occurrence data. Each simulated species was constructed as a sum of responses with varying weights. Three basic species response curves were assumed: Gaussian (bell-shaped), Beta (skew) and linear. The two non-linear responses conform to standard ecological niche theory. All three responses were applied in turn to three simulated environmental variables, each with varying degrees of spatial autocorrelation. GAM produced the most consistent model performance over all forms of simulated species response. BIOCLIM and CART were inclined to underrate the performance of variables with a linear response. BIOCLIM was less sensitive to data density. LR was susceptible to model misspecification. The use of a linear function in LR underestimated the performance of variables with non-linear species response and contributed to increased spatial autocorrelation in model residuals. Omission of important environmental variables with non-linear species response also contributed to increased spatial autocorrelation in model residuals. Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant. This is to be expected since the autologistic approach was designed primarily for prediction and not for inference. Given that various forms of species response to environmental determinants arise commonly in nature: (1) higher order functions should always be tested when applying LR in modelling species distribution; (2) spatial autocorrelation in species distribution model residuals can indicate that environmental determinants with non-linear response are missing from the model; and (3) deficiencies in LR model performance due to model misspecification can be addressed by adding a spatial autocovariate to the model, but care should be taken when interpreting the coefficients of the model parameters.

Suggested Citation

  • Santika, Truly & Hutchinson, Michael F., 2009. "The effect of species response form on species distribution model prediction and inference," Ecological Modelling, Elsevier, vol. 220(19), pages 2365-2379.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:19:p:2365-2379
    DOI: 10.1016/j.ecolmodel.2009.06.004
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    References listed on IDEAS

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    1. Daniel P. McMillen, 2003. "Spatial Autocorrelation Or Model Misspecification?," International Regional Science Review, , vol. 26(2), pages 208-217, April.
    2. Miller, Jennifer & Franklin, Janet & Aspinall, Richard, 2007. "Incorporating spatial dependence in predictive vegetation models," Ecological Modelling, Elsevier, vol. 202(3), pages 225-242.
    3. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    4. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
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    4. Bell, David M. & Schlaepfer, Daniel R., 2016. "On the dangers of model complexity without ecological justification in species distribution modeling," Ecological Modelling, Elsevier, vol. 330(C), pages 50-59.
    5. Huang, Minyi & Kong, Xiaoquan & Varela, Sara & Duan, Renyan, 2016. "The Niche Limitation Method (NicheLim), a new algorithm for generating virtual species to study biogeography," Ecological Modelling, Elsevier, vol. 320(C), pages 197-202.
    6. Halvorsen, Rune & Mazzoni, Sabrina & Dirksen, John Wirkola & Næsset, Erik & Gobakken, Terje & Ohlson, Mikael, 2016. "How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt?," Ecological Modelling, Elsevier, vol. 328(C), pages 108-118.

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