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Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data

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  • Moreno-Amat, Elena
  • Mateo, Rubén G.
  • Nieto-Lugilde, Diego
  • Morueta-Holme, Naia
  • Svenning, Jens-Christian
  • García-Amorena, Ignacio

Abstract

Maximum entropy modeling (Maxent) is a widely used algorithm for predicting species distributions across space and time. Properly assessing the uncertainty in such predictions is non-trivial and requires validation with independent datasets. Notably, model complexity (number of model parameters) remains a major concern in relation to overfitting and, hence, transferability of Maxent models. An emerging approach is to validate the cross-temporal transferability of model predictions using paleoecological data. In this study, we assess the effect of model complexity on the performance of Maxent projections across time using two European plant species (Alnus glutinosa (L.) Gaertn. and Corylus avellana L.) with an extensive late Quaternary fossil record in Spain as a study case. We fit 110 models with different levels of complexity under present time and tested model performance using AUC (area under the receiver operating characteristic curve) and AICc (corrected Akaike Information Criterion) through the standard procedure of randomly partitioning current occurrence data. We then compared these results to an independent validation by projecting the models to mid-Holocene (6000 years before present) climatic conditions in Spain to assess their ability to predict fossil pollen presence–absence and abundance. We find that calibrating Maxent models with default settings result in the generation of overly complex models. While model performance increased with model complexity when predicting current distributions, it was higher with intermediate complexity when predicting mid-Holocene distributions. Hence, models of intermediate complexity resulted in the best trade-off to predict species distributions across time. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. Consequently, species-specific model tuning should be used to find the best modeling settings to control for complexity, notably with paleoecological data to independently validate model projections. For cross-temporal projections of species distributions for which paleoecological data is not available, models of intermediate complexity should be selected.

Suggested Citation

  • Moreno-Amat, Elena & Mateo, Rubén G. & Nieto-Lugilde, Diego & Morueta-Holme, Naia & Svenning, Jens-Christian & García-Amorena, Ignacio, 2015. "Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data," Ecological Modelling, Elsevier, vol. 312(C), pages 308-317.
  • Handle: RePEc:eee:ecomod:v:312:y:2015:i:c:p:308-317
    DOI: 10.1016/j.ecolmodel.2015.05.035
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    References listed on IDEAS

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    1. Cao, Yong & DeWalt, R. Edward & Robinson, Jason L. & Tweddale, Tari & Hinz, Leon & Pessino, Massimo, 2013. "Using Maxent to model the historic distributions of stonefly species in Illinois streams: The effects of regularization and threshold selections," Ecological Modelling, Elsevier, vol. 259(C), pages 30-39.
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    6. Worthington, Thomas A. & Zhang, Tianjiao & Logue, Daniel R. & Mittelstet, Aaron R. & Brewer, Shannon K., 2016. "Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability," Ecological Modelling, Elsevier, vol. 342(C), pages 1-18.
    7. Beaumont, Linda J. & Graham, Erin & Duursma, Daisy Englert & Wilson, Peter D. & Cabrelli, Abigail & Baumgartner, John B. & Hallgren, Willow & Esperón-Rodríguez, Manuel & Nipperess, David A. & Warren, , 2016. "Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?," Ecological Modelling, Elsevier, vol. 342(C), pages 135-146.
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    9. Mateo, Rubén G. & Arellano, Gabriel & Gómez-Rubio, Virgilio & Tello, J. Sebastián & Fuentes, Alfredo F. & Cayola, Leslie & Loza, M. Isabel & Cala, Victoria & Macía, Manuel J., 2022. "Insights on biodiversity drivers to predict species richness in tropical forests at the local scale," Ecological Modelling, Elsevier, vol. 473(C).
    10. Duque-Lazo, J. & van Gils, H. & Groen, T.A. & Navarro-Cerrillo, R.M., 2016. "Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia," Ecological Modelling, Elsevier, vol. 320(C), pages 62-70.
    11. Zeng, Yiwen & Low, Bi Wei & Yeo, Darren C.J., 2016. "Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish," Ecological Modelling, Elsevier, vol. 341(C), pages 5-13.
    12. Ignacio García-Amorena & Elena Moreno-Amat & María Encina Aulló-Maestro & María Cruz Mateo-Sánchez & Silvia Merino-De-Miguel & Jaime Ribalaygua & Robert Marchant, 2021. "Combining Remote Sensing and Species Distribution Modelling to Assess Pinus hartwegii Response to Climate Change and Land Use from Izta-Popo National Park, Mexico," Land, MDPI, vol. 10(10), pages 1-20, October.
    13. Uzma Ashraf & Hassan Ali & Muhammad Nawaz Chaudry & Irfan Ashraf & Adila Batool & Zafeer Saqib, 2016. "Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model," Sustainability, MDPI, vol. 8(8), pages 1-11, July.

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