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Using macroecological constraints on spatial biodiversity predictions under climate change: the modelling method matters

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  • Di Febbraro, Mirko
  • D’Amen, Manuela
  • Raia, Pasquale
  • De Rosa, Davide
  • Loy, Anna
  • Guisan, Antoine

Abstract

The prevailing method for estimating the potential impact of future climate change on biological communities is to stack binary predictions from species distribution models (binary stacked species distribution models, bS-SDM). However, it has been argued that bS-SDM may overestimate species richness and, hence, community composition. Alternative approaches, such as SESAM (‘Spatially Explicit Species Assemblage Modelling’), explicitly incorporate limits to species richness, preventing overestimation. We compared richness and taxonomic composition estimates as predicted by SESAM and bS-SDM for Mediterranean bird communities both in the present day and as projected in the future under simulated climate change scenarios. We trained single-species distribution models (S-SDM) and direct macroecological richness models (MEM) for 81 bird species, using climate, topographic, land-use and human-pressure indicators as predictors. Then, we compared and evaluated the models’ predictions. Species richness as predicted by bS-SDM was more accurate than under SESAM for present-day communities. Taxonomic composition was well predicted under both methods. However, we detected significant differences in future projections. Under bS-SDM, increased suitable area for a number of species leads to important changes in community composition and predicts higher levels of diversity in the future. In stark contrast, SESAM predicts lower species richness in the future and strong homogenization of bird communities across space. This study shows how the choice of the modelling approach drives substantially different expectations about future community composition under climate change. We therefore recommend contrasting predictions generated under different modelling approaches to gain better understanding of possible future scenario of biodiversity change. In addition, trasferability tests (i.e. hindcasting to past or predicting from the past to the present) should be used to effectively compare S-SDM and SESAM predictive abilities.

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  • Di Febbraro, Mirko & D’Amen, Manuela & Raia, Pasquale & De Rosa, Davide & Loy, Anna & Guisan, Antoine, 2018. "Using macroecological constraints on spatial biodiversity predictions under climate change: the modelling method matters," Ecological Modelling, Elsevier, vol. 390(C), pages 79-87.
  • Handle: RePEc:eee:ecomod:v:390:y:2018:i:c:p:79-87
    DOI: 10.1016/j.ecolmodel.2018.10.023
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    References listed on IDEAS

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    1. Rubén G Mateo & Ángel M Felicísimo & Julien Pottier & Antoine Guisan & Jesús Muñoz, 2012. "Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    2. Maggini, Ramona & Lehmann, Anthony & Kéry, Marc & Schmid, Hans & Beniston, Martin & Jenni, Lukas & Zbinden, Niklaus, 2011. "Are Swiss birds tracking climate change?," Ecological Modelling, Elsevier, vol. 222(1), pages 21-32.
    3. Steinmann, K. & Linder, H.P. & Zimmermann, N.E., 2009. "Modelling plant species richness using functional groups," Ecological Modelling, Elsevier, vol. 220(7), pages 962-967.
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    1. Bazzato, Erika & Rosati, Leonardo & Canu, Simona & Fiori, Michele & Farris, Emmanuele & Marignani, Michela, 2021. "High spatial resolution bioclimatic variables to support ecological modelling in a Mediterranean biodiversity hotspot," Ecological Modelling, Elsevier, vol. 441(C).
    2. 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).
    3. Mirko Di Febbraro & Ludovico Frate & Maria Carla de Francesco & Angela Stanisci & Francesco Pio Tozzi & Marco Varricchione & Maria Laura Carranza, 2021. "Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models," Land, MDPI, vol. 10(1), pages 1-17, January.
    4. He, Pinglin & Zhang, Shuhao & Wang, Lei & Ning, Jing, 2023. "Will environmental taxes help to mitigate climate change? A comparative study based on OECD countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1440-1464.

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