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Managing biological invasions: the cost of inaction

journal contribution
posted on 22.06.2022, 10:02 authored by Danish A Ahmed, Emma J Hudgins, Ross N Cuthbert, Melina Kourantidou, Christophe Diagne, Phillip J Haubrock, Brian Leung, Chunlong Liu, Boris Leroy, Sergei Petrovskii, Ayah Beidas, Franck Courchamp
Ecological and socioeconomic impacts from biological invasions are rapidly escalating worldwide. While effective management underpins impact mitigation, such actions are often delayed, insufficient or entirely absent. Presently, management delays emanate from a lack of monetary rationale to invest at early invasion stages, which precludes effective prevention and eradication. Here, we provide such rationale by developing a conceptual model to quantify the cost of inaction, i.e., the additional expenditure due to delayed management, under varying time delays and management efficiencies. Further, we apply the model to management and damage cost data from a relatively data-rich genus (Aedes mosquitoes). Our model demonstrates that rapid management interventions following invasion drastically minimise costs. We also identify key points in time that differentiate among scenarios of timely, delayed and severely delayed management intervention. Any management action during the severely delayed phase results in substantial losses (> 50 % of the potential maximum loss). For Aedes spp., we estimate that the existing management delay of 55 years led to an additional total cost of approximately $ 4.57 billion (14% of the maximum cost), compared to a scenario with management action only seven years prior (< 1% of the maximum cost). Moreover, we estimate that in the absence of management action, long-term losses would have accumulated to US$ 32.31 billion, or more than seven times the observed inaction cost. These results highlight the need for more timely management of invasive alien species—either pre-invasion, or as soon as possible after detection—by demonstrating how early investments rapidly reduce long-term economic impacts.

Funding

The authors acknowledge the French National Research Agency (ANR-14-CE02-0021) and the BNP-Paribas Foundation Climate Initiative for funding the InvaCost project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the AXA Research Fund Chair of Invasion Biology and is part of the AlienScenarios project funded by BiodivERsA and Belmont-Forum call 2018 on biodiversity scenarios. DAA is funded by the Kuwait Foundation for the Advancement of Sciences (KFAS), grant no. PR1914SM-01 and the Gulf University for Science and Technology (GUST) internal seed fund, grant no. 234597. EJH is supported by a Fonds de recherche du Québec—nature et téchnologies B3X fellowship. RNC acknowledges funding from the Alexander von Humboldt Foundation. CL was sponsored by the PRIME programme of the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF)

History

Citation

Biol Invasions (2022). https://doi.org/10.1007/s10530-022-02755-0

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

VoR (Version of Record)

Published in

Biological Invasions

Publisher

Springer

issn

1387-3547

eissn

1573-1464

Acceptance date

10/02/2022

Copyright date

2022

Available date

22/06/2022

Notes

Correction to: Biol Invasions https://doi.org/10.1007/s10530-022-02755-0 In this article the caption to Fig. 7 was inaccurate. The amended caption is given in this correction. Fig. 7 a Damage impacts plotted according to the first record in InvaCost for each region based on the rworldmap package, where applicable. b Management impacts plotted according to the first record in InvaCost for each region based on the rworldmap package, where applicable. The authors have created this map for illustrative purposes and do not make any political claims regarding the status of the regions shown on the map. Correction to: Managing biological invasions: the cost of inaction. Biol Invasions (2022). https://doi.org/10.1007/s10530-022-02799-2

Language

English