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Biodiversity of Tropical Forests

A. Tovo, S. Suweis, M. Formentin, M. Favretti, Jayanth R. Banavar, S. Azaele, A. Maritan
doi: https://doi.org/10.1101/088534
A. Tovo
1Dipartimento di Matematica ‘Tullio Levi-Civita’, Università di Padova, Via Trieste 63, 35121 Padova, IT.
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S. Suweis
2Dipartimento di Fisica e Astronomia, ‘G. Galilei’ & CNISM, INFN, Università di Padova, Via Marzolo 8, 35131 Padova, IT.
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M. Formentin
1Dipartimento di Matematica ‘Tullio Levi-Civita’, Università di Padova, Via Trieste 63, 35121 Padova, IT.
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M. Favretti
1Dipartimento di Matematica ‘Tullio Levi-Civita’, Università di Padova, Via Trieste 63, 35121 Padova, IT.
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Jayanth R. Banavar
3Department of Physics, University of Maryland, College Park, Maryland 20742, USA.
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S. Azaele
4Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK
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A. Maritan
2Dipartimento di Fisica e Astronomia, ‘G. Galilei’ & CNISM, INFN, Università di Padova, Via Marzolo 8, 35131 Padova, IT.
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Abstract

The quantification of tropical tree biodiversity worldwide remains an open and challenging problem. In fact, more than two-fifths of the global tree population can be found either in tropical or sub-tropical forests1, but species identities are known only for ≈ 0.000067% of the individuals in all tropical forests2. For practical reasons, biodiversity is typically measured or monitored at fine spatial scales. However, important drivers of ecological change tend to act at large scales. Conservation issues, for example, apply to diversity at global, national or regional scales. Extrapolating species richness from the local to the global scale is not straightforward. Indeed, a vast number of different biodiversity estimators have been developed under different statistical sampling frameworks3–7, but most of them have been designed for local/regional-scale extrapolations, and they tend to be sensitive to the spatial distribution of trees8, sample coverage and sampling methods9. Here, we introduce an analytical framework that provides robust and accurate estimates of species richness and abundances in biodiversity-rich ecosystems, as confirmed by tests performed on various in silico-generated forests. The new framework quantifies the minimum percentage cover that should be sampled to achieve a given average confidence in the upscaled estimate of biodiversity. Our analysis of 15 empirical forest plots shows that previous methods10,11 have systematically overestimated the total number of species and leads to new estimates of hyper-rarity10 at the global scale11, known as Fisher’s paradox2. We show that hyper-rarity is a signature of critical-like behavior12 in tropical forests13–15, and it provides a buffer against mass extinctions16. When biotic factors or environmental conditions change, some of these rare species are more able than others to maintain the ecosystem’s functions, thus underscoring the importance of rare species.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 15, 2016.
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Biodiversity of Tropical Forests
A. Tovo, S. Suweis, M. Formentin, M. Favretti, Jayanth R. Banavar, S. Azaele, A. Maritan
bioRxiv 088534; doi: https://doi.org/10.1101/088534
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Biodiversity of Tropical Forests
A. Tovo, S. Suweis, M. Formentin, M. Favretti, Jayanth R. Banavar, S. Azaele, A. Maritan
bioRxiv 088534; doi: https://doi.org/10.1101/088534

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