Threshold-dependent gene drives in the wild: Spread, controllability, and ecological uncertainty
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Threshold-dependent gene drives are those that spread when they exist in a wild population above some critical frequency. If there aren't many in the population, the gene drive should be lost over time. However, if there are many, the gene drive spreads until most of the population carries it. Because of this, they are often considered to be local gene drives as they can are not expected to spread into a new population if only a few migrating individuals reach an unintended location. The threshold frequency that determines whether the gene drive can spread depends on several factors, including inherent genetic characteristics, ecological dynamics, and behavioral dynamics. Each of these factors are veiled in some uncertainty and the relative ecological fitness of organisms carrying the gene drive is very difficult to predict without extensive field experiments. We describe how ignoring this ecological uncertainty could lead to unexpected spreading behavior. Unlike many simpler gene drives, threshold-dependent drives do show a lot of promise in allowing managers to control spreading dynamics, but a considerable amount of ecological uncertainty suggests that local spreading dynamics are context-dependent.
Find the 2019 paper [Free access] in BioScience or email me for a copy. I also talk about this paper on the BioScience Talks podcast with my co-author Jason Delborne. If you're interested in learning more, you should also consider these papers from other brilliant scientists.
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Identifying robust strategies for assisted migration given risks and uncertainties in a stochastic metacommunity
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We created a multi-species mathematical model that simulates how species move with changing climates. Because some of the simulated species cannot keep up with climate change, we relocate a species to its optimal climate if it falls below a critical, low-population threshold. We found that assisted migration performs best when we relocated a fraction (around 50%) of the total species’ population, as temporarily retaining the population in the original location could allow managers to repeat relocation in case the species fails to establish the first time. Also, competition from other species could limit the practicality of assisted migration. We found that assisted migration was less successful when relocating a species into areas already occupied by its competitors, even if that area would have been an optimal climate in a single-species context. Additionally, assisted migration did not appear to affect non-target species, implying that the risk of invasion is unlikely to arise from competition as compared to other species interactions (e.g., predation, disease spread).
This paper is currently under review, but a preprint is currently available on bioRxiv or you can email me for a copy. If you are also interested in learning more about decision making for managed relocation models, you should also consider these papers from other researchers
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A structured decision making approach to managed relocation
As experts are trying to make sense of the risks of managed relocation, the practice is already underway for many species. In response, we outline how employing a structured decision making framework could allow the conservation community to carefully move forward while addressing key knowledge gaps that impede progress in understanding the risks and benefits. This work is largely based on topics that were discussed during at 2017 international symposium at the University of California, Davis entitled “Managed Relocation Under a Changing Climate: An Interdisciplinary Perspective Symposium”. In particular, we describe how specifying clear and quantifiable conditions for success and failure could allow decision makers to reduce the unprecedented scale of managed relocation and its associated risks. Next, we describe how placing assisted migration alongside alternative conservation strategies, instead of treating it as a last-resort strategy, can open up opportunities to test managed relocation in lower risk scenarios. We discuss how we can leverage expert elicitation and previous translocation experiments across environmental gradients to estimate risks and benefits of relocations across these unprecedented scales. Finally, after outlining a variety of risks on genetic, population, community, and social scales, we consider how adaptive management with explicit exit strategies could limit negative outcomes while learning how to avoid risks in the future.
This paper is currently under review, but if you're interested in reading a preprint, please email me for a copy. If you are also interested in learning more about decision making for managed relocation, you should also consider these papers from other researchers |