Differential species responses to compounded perturbations and implications for landscape heterogeneity and resilience

Disturbance interactions are of great interest in ecology due to their potential to cause non-linear, unexpected results. Increases in disturbance frequency and intensity as a result of climate change increase the need for better conceptual and mechanistic understanding of ecosystem response to compounded perturbations. Impacts on structural elements of ecosystems, such as tree species, are particularly important, as changes in these species’ populations, frequencies, and distributions may influence landscape functioning for extended periods of time. This study investigated the impact of three overlapping disturbances common to western US forests (wind, logging, and fire) on three dominant tree species: Lodgepole pine, Engelmann spruce, and quaking aspen. Ninety-nine study plots were examined across a gradient of interaction severities from a 1997 blowdown, subsequent salvage logging, and a 2002 fire in a Rocky Mountain subalpine forest. Regeneration of dominant species was analyzed in the context of disturbance history and species-specific disturbance response strategies. Results indicated that species are differentially affected by disturbance interactions. Lodgepole pine is highly sensitive to both previous disturbances and their severities, whereas spruce and aspen are insensitive to disturbance history, although both showed higher recruitment levels in three-disturbance environments. Disturbance types, combinations, and specific resilience mechanisms appear to be more important than number of disturbances. Disturbance interactions were not necessarily additive, and in some cases, three disturbances were less severe than two. As a result of long-distance dispersal, aspen seems likely to greatly increase in dominance across the landscape. Species-specific responses are generalized through their individual response strategies, with specialized responses being less resilient to multiple disturbances than generic seed dispersal strategies. Differential responses by structural tree species will likely drive an increase in future landscape heterogeneity and potential decreases in future landscape resilience to fire

Cyberinfrastructure deployments on public research clouds enable accessible Environmental Data Science education

Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline. © 2023 Owner/Author.Open access articleThis 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]

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale*

To further develop the methods to remotely sense the biochemical content of plant canopies, we report the results of an experiment to estimate the concentrations of three biochemical variables of corn, i.e., nitrogen (N), crude fat (EE) and crude fiber (CF) concentrations, by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed, and a set of estimation models were established using curve-fitting analyses. Coefficient of determination (R 2), root mean square error (RMSE) and relative error of prediction (REP) of estimation models were calculated for the model quality evaluations, and the possible optimum estimation models of three biochemical variables were proposed, with R 2 being 0.891, 0.698 and 0.480 for the estimation models of N, EE and CF concentrations, respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation, and that the first derivative reflectances at 759 nm, 1954 nm and 2370 nm were most suitable to develop the estimation models of N, EE and CF concentrations, respectively. In addition, the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained, especially for nitrogen (r=0.948)

Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes

10.1038/s41467-019-12438-5Nature Communications111253

Characterising the loss-of-function impact of 5’ untranslated region variants in 15,708 individuals

10.1038/s41467-019-10717-9Nature Communications111252