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Justin J. Van Ee

Publications and source records attributed to Justin J. Van Ee.

2 recordsLinked to original sources

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

Melded integrated population models

Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken ( Tympanuchus pallidicinctus ), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.

Colorado, Kansas, New Mexico, Oklahoma, Texas