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Reversible phenotypic flexibility allows organisms to better match phenotypes to prevailing environmental conditions and may produce fitness benefits. Costs and constraints of phenotypic flexibility may limit the capacity for flexible responses but are not well understood nor documented. Costs could include expenses associated with maintaining the flexible system or with generating the flexible response. One potential cost of maintaining a flexible system is an energetic cost reflected in the basal metabolic rate (BMR), with elevated BMR in individuals with more flexible metabolic responses. We accessed data from thermal acclimation studies of birds where BMR and/or M_sum(maximum cold-induced metabolic rate) were measured before and after acclimation, as a measure of metabolic flexibility, to test the hypothesis that flexibility in BMR (ΔBMR), M_sum(ΔM_sum), or metabolic scope (M_sum − BMR; ΔScope) is positively correlated with BMR. When temperature treatments lasted at least three weeks, three of six species showed significant positive correlations between ΔBMR and BMR, one species showed a significant negative correlation, and two species showed no significant correlation. ΔM_sumand BMR were not significantly correlated for any species and ΔScope and BMR were significantly positively correlated for only one species. These data suggest that support costs exist for maintaining high BMR flexibility for some bird species, but high flexibility in M_sumor metabolic scope does not generally incur elevated maintenance costs.

 

The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio-temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi-scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya–Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios.

 

Abstract Achieving food security is a critical challenge of the Anthropocene that may conflict with environmental and societal goals such as increased energy access. The “fuel versus food” debate coupled with climate mitigation efforts has given rise to next-generation biofuels. Findings of this systematic review indicate just over half of the studies (56% of 224 publications) reported a negative impact of bioenergy production on food security. However, no relationship was found between bioenergy feedstocks that are edible versus inedible and food security ( P value = 0.15). A strong relationship was found between bioenergy and type of food security parameter ( P value < 0.001), sociodemographic index of study location ( P value = 0.001), spatial scale ( P value < 0.001), and temporal scale ( P value = 0.017). Programs and policies focused on bioenergy and climate mitigation should monitor multiple food security parameters at various scales over the long term toward achieving diverse sustainability goals. 

This report summarizes the discussions from a workshop convened at NSF on May 30-31, 2018 in Alexandria, VA. The overarching objective of the workshop was to rethink the nature and composition of the NSF-supported computational ecosystem given changing application requirements and resources and technology landscapes. The workshop included roughly 50 participants, drawn from high-performance computing (HPC) centers, campus computing facilities, cloud service providers (academic and commercial), and distributed resource providers. Participants spanned both large research institutions and smaller universities. Organized by Daniel Reed (University of Utah, chair), David Lifka (Cornell University), David Swanson (University of Nebraska), Rommie Amaro (UCSD), and Nancy Wilkins-Diehr (UCSD/SDSC), the workshop was motivated by the following observations. First, there have been dramatic changes in the number and nature of applications using NSF-funded resources, as well as their resource needs. As a result, there are new demands on the type (e.g., data centric) and location (e.g., close to the data or the users) of the resources as well as new usage modes (e.g., on-demand and elastic). Second, there have been dramatic changes in the landscape of technologies, resources, and delivery mechanisms, spanning large scientific instruments, ubiquitous sensors, and cloud services, among others. 

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long‐term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster‐specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site‐level cluster labels associated with assemblage‐level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

 

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km2) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.