In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 12 ( 2021-12-29), p. e1009660-
Abstract:
Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse, and could be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit, allows for overlapping generations and a fluctuating population size, and includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, though only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning to identify the key parameters and processes that determine the population dynamics of a complex evolutionary system.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009660
DOI:
10.1371/journal.pcbi.1009660.g001
DOI:
10.1371/journal.pcbi.1009660.g002
DOI:
10.1371/journal.pcbi.1009660.g003
DOI:
10.1371/journal.pcbi.1009660.g004
DOI:
10.1371/journal.pcbi.1009660.g005
DOI:
10.1371/journal.pcbi.1009660.g006
DOI:
10.1371/journal.pcbi.1009660.g007
DOI:
10.1371/journal.pcbi.1009660.g008
DOI:
10.1371/journal.pcbi.1009660.g009
DOI:
10.1371/journal.pcbi.1009660.g010
DOI:
10.1371/journal.pcbi.1009660.g011
DOI:
10.1371/journal.pcbi.1009660.g012
DOI:
10.1371/journal.pcbi.1009660.g013
DOI:
10.1371/journal.pcbi.1009660.g014
DOI:
10.1371/journal.pcbi.1009660.t001
DOI:
10.1371/journal.pcbi.1009660.t002
DOI:
10.1371/journal.pcbi.1009660.t003
DOI:
10.1371/journal.pcbi.1009660.s001
DOI:
10.1371/journal.pcbi.1009660.s002
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10.1371/journal.pcbi.1009660.s003
DOI:
10.1371/journal.pcbi.1009660.s004
DOI:
10.1371/journal.pcbi.1009660.s005
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10.1371/journal.pcbi.1009660.s006
DOI:
10.1371/journal.pcbi.1009660.s007
DOI:
10.1371/journal.pcbi.1009660.s008
DOI:
10.1371/journal.pcbi.1009660.s009
DOI:
10.1371/journal.pcbi.1009660.s010
DOI:
10.1371/journal.pcbi.1009660.s011
DOI:
10.1371/journal.pcbi.1009660.s012
DOI:
10.1371/journal.pcbi.1009660.s013
DOI:
10.1371/journal.pcbi.1009660.s014
DOI:
10.1371/journal.pcbi.1009660.s015
DOI:
10.1371/journal.pcbi.1009660.r001
DOI:
10.1371/journal.pcbi.1009660.r002
DOI:
10.1371/journal.pcbi.1009660.r003
DOI:
10.1371/journal.pcbi.1009660.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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