Climate structures global associations between human footprint and arthropod diversity
- 1Harvard T.H. Chan School of Public Health
Abstract
Understanding global biodiversity patterns and their drivers is a prerequisite for countering the biodiversity crisis. In this paper, we introduce a novel generalized linear model, Hubbell regression, to estimate a key biodiversity descriptor, the fundamental biodiversity number. This can be converted into a set of biodiversity descriptors, including Shannon and Simpson indices, and more. Hence, quantifying the impact of environmental conditions on the fundamental biodiversity number allows us to predict the general properties of local biodiversity in any setting. In addition to having a strong mathematical foundation, Hubbell regression consistently outperformed current state-of-the-art models in predicting global biodiversity. We apply the method to arthropods, which account for the majority of terrestrial biodiversity. By parameterizing the models using samples of 1.78 million arthropods from 2415 samples collected at 135 sites spanning all continents, we pinpoint the drivers of arthropod biodiversity and its features at the global scale. We find that actual evapotranspiration is the single largest predictor of arthropod diversity and explains nearly 30% of the variation in richness. Moreover, we infer that high human activity has led to a 21.3% and 29.2% decrease in potential insect richness in tropical and dry zones, respectively, but increased insect richness in polar regions. These insights bring a new foundation for biodiversity research and action.
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BibTeX
@misc{zito2026climate,
title = {{Climate structures global associations between human footprint and arthropod diversity}},
author = {A. Zito and T. Rigon and T. Roslin and P. Niittynen and P. D. N. Hebert and E. Zakharov and S. Ratnasingham and iBOL Consortium and O. Ovaskainen and D. B. Dunson},
howpublished = {bioRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.29.728852},
url = {https://www.biorxiv.org/content/10.64898/2026.05.29.728852v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Climate structures global associations between human footprint and arthropod diversity
AU - A. Zito
AU - T. Rigon
AU - T. Roslin
AU - P. Niittynen
AU - P. D. N. Hebert
AU - E. Zakharov
AU - S. Ratnasingham
AU - iBOL Consortium
AU - O. Ovaskainen
AU - D. B. Dunson
PY - 2026
DA - 2026/05/29
PB - bioRxiv
DO - 10.64898/2026.05.29.728852
UR - https://www.biorxiv.org/content/10.64898/2026.05.29.728852v2
AB - Understanding global biodiversity patterns and their drivers is a prerequisite for countering the biodiversity crisis. In this paper, we introduce a novel generalized linear model, Hubbell regression, to estimate a key biodiversity descriptor, the fundamental biodiversity number. This can be converted into a set of biodiversity descriptors, including Shannon and Simpson indices, and more. Hence, quantifying the impact of environmental conditions on the fundamental biodiversity number allows us to predict the general properties of local biodiversity in any setting. In addition to having a strong mathematical foundation, Hubbell regression consistently outperformed current state-of-the-art models in predicting global biodiversity. We apply the method to arthropods, which account for the majority of terrestrial biodiversity. By parameterizing the models using samples of 1.78 million arthropods from 2415 samples collected at 135 sites spanning all continents, we pinpoint the drivers of arthropod biodiversity and its features at the global scale. We find that actual evapotranspiration is the single largest predictor of arthropod diversity and explains nearly 30% of the variation in richness. Moreover, we infer that high human activity has led to a 21.3% and 29.2% decrease in potential insect richness in tropical and dry zones, respectively, but increased insect richness in polar regions. These insights bring a new foundation for biodiversity research and action.
ER -