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Jesse R. Lasky

Publications and source records attributed to Jesse R. Lasky.

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Local adaptation to climate has facilitated the global invasion of cheatgrass

Local adaptation may facilitate range expansion during invasions, but the mechanisms promoting destructive invasions remain unclear. Cheatgrass ( Bromus tectorum ), native to Eurasia and Africa, has invaded globally, with particularly severe impacts in western North America. We aimed to identify mechanisms and consequences of local adaptation in the North American cheatgrass invasion. We sequenced 307 range-wide genotypes and conducted controlled experiments. We found that diverse lineages invaded North America, where long-distance gene flow is common. Nearly half of North American cheatgrass is comprised of a mosaic of ~19 locally adapted near clonal genotypes, each seemingly very successful in a different part of its range. Additionally, ancestry- and phenotype- environment clines in the native range predicted those in the invaded range, indicating pre-adapted genotypes colonized different regions. Common gardens showed directional selection on flowering time that reversed between warm and cold sites, potentially maintaining clines. In the Great Basin, genomic predictions of strong local adaptation identified sites where cheatgrass is most dominant. Our results indicate that multiple introductions and ongoing migration within the invaded range likely fueled pre-adaptation and subsequent dominance of cheatgrass in western North America. Understanding how environment and gene flow shape invasive adaptation is critical for managing ongoing invasions.

Nature Communications

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

Proceedings of the Royal Society B: Biological Sci