Using Great Basin cottonwoods to study climate change and evolution

The mountains of Great Basin National Park represent important “natural laboratories” for studying the ecological and evolutionary consequences of past climate change. The Cottonwood Ecology group at Northern Arizona University is interested in examining whether genetic changes in Nevada’s cottonwood populations have resulted in the adaptation of dependent organisms to new hosts

Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks

Fremont cottonwood (Populus fremonti) is a foundation riparian tree species that drives community structure and ecosystem processes in southwestern U.S. ecosystems. Despite its ecological importance, little is known about the ecological and environmental processes that shape its genetic diversity, structure, and landscape connectivity. Here, we combined molecular analyses of 82 populations including 1312 individual trees dispersed over the species’ geographical distribution. We reduced the data set to 40 populations and 743 individuals to eliminate admixture with a sibling species, and used multivariate restricted optimization and reciprocal causal modeling to evaluate the effects of river network connectivity and climatic gradients on gene flow. Our results confirmed the following: First, gene flow of Fremont cottonwood is jointly controlled by the connectivity of the river network and gradients of seasonal precipitation. Second, gene flow is facilitated by mid-sized to large rivers, and is resisted by small streams and terrestrial uplands, with resistance to gene flow decreasing with river size. Third, genetic differentiation increases with cumulative differences in winter and spring precipitation. Our results suggest that ongoing fragmentation of riparian habitats will lead to a loss of landscape-level genetic connectivity, leading to increased inbreeding and the concomitant loss of genetic diversity in a foundation species. These genetic effects will cascade to a much larger community of organisms, some of which are threatened and endangered

Arthropod Community Similarity in Clonal Stands of Aspen: A Test of the Genetic Similarity Rule

Understanding the factors that affect community composition is essential for community ecology. The genetic similarity rule (GSR) identifies 3 variables (host genetic composition, phytochemistry, and the environment) that could affect community composition. Few studies have determined the relative influence of these variables on community composition. Using path analysis, we found that arthropod community similarity was better explained by geographic (56%) and environmental (32%) distance than genetic distance in clonal aspen (Populus tremuloides). Comparing our data with data from similar studies of poplars (P. fremontii and P. fremontii × P. angustifolia hybrids), we found that hybrid poplar stands had higher levels of genetic and arthropod diversity than did clonal aspen stands. We found a significant relationship between genetic and arthropod diversity only in hybrid stands and across Populus systems. Our findings agree with the GSR expectations that the importance of the genetic composition of the host in structuring communities depends in part on the amount of genetic variation present in the study system. This is relevant for management and restoration strategies of geographically restricted species and of disjunct populations of otherwise widespread species, as these tend to have lower effective population sizes and reduced levels of genetic diversity

Appendix B. Figures showing the results of the intermediate steps of the reciprocal causal modeling analysis to optimize relative resistance to gene flow presented by rivers and climate gradients.

Figures showing the results of the intermediate steps of the reciprocal causal modeling analysis to optimize relative resistance to gene flow presented by rivers and climate gradients

Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys

Ecologists are improving predictive capability using near-term ecological forecasts, in which predictions are made iteratively and publically to increase transparency, rate of learning, and maximize utility. Ongoing ecological forecasting efforts focus mostly on long-term datasets of continuous variables, such as CO2 fluxes, or more abrupt variables, such as phenological events or algal blooms. Generally lacking from these forecasting efforts is the integration of short-term, opportunistic data concurrent with developing climate extremes such as drought. We posit that incorporating targeted experiments and regional surveys, implemented rapidly during developing extreme events, into current forecasting efforts will ultimately enhance our ability to forecast ecological responses to climate extremes, which are projected to increase in both frequency and intensity. We highlight a project, "chasing tree die-off," in which we coupled an experiment with regional-scale observational field surveys during a developing severe drought to test and improve forecasts of tree die-off. General insights to consider in incorporating this approach include: (1) tracking developing climate extremes in near-real time to efficiently ramp up measurements rapidly and, if feasible, initiate an experiment quickly-including funding and site selection challenges; (2) accepting uncertainty in projected extreme climatic events and adjusting sampling design over-time as needed, especially given the spatially heterogeneous nature of many ecological disturbances; and (3) producing timely and iterative output. In summary, targeted experiments and regional surveys implemented rapidly during developing extreme climatic events offer promise to efficiently (both financially and logistically) improve our ability to forecast ecological responses to climate extremes.Division of Environmental BiologyOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]