Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems.

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.

A high-resolution map of coastal vegetation for two Arctic Alaskan parklands: An object-oriented approach with point training data.

Bering Land Bridge National Preserve and Cape Krusenstern National Monument in northwest Alaska have approximately 1600 km of predominantly soft-sediment coastlines along the Chukchi Sea, a shallow bay of the Arctic Ocean. Over the past decade, marine vessel traffic through the Bering Strait has grown exponentially to take advantage of new ice-free summer shipping routes, increasing the risk of oil spills in these fragile ecosystems. We present a high-resolution coastal vegetation map to serve as a baseline for potential spill response, restoration, and change detection. We segmented 663 km2 of high-resolution multispectral satellite images by the mean-shift method and collected 40 spectral, topographic and spatial variables per segment. The segments were classified using photo-interpreted points as training data, and verified with field based plots. Digitizing points, rather than polygons, and intersecting them with the segmentation allows for rapid collection of training data. We classified the map segments using Random Forest because of its high accuracy, computational speed, and ability to incorporate non-normal, high-dimensional data. We found creating separate classification models by each satellite scene gave highly similar results to models combining the entire study area, and that reducing the number of variables had little impact on accuracy. A unified, study area-wide Random Forest model for both parklands produced the highest accuracy of various models attempted. We mapped 18 distinct classes, with an out-of-bag error of 11.6%, resulting in an improvement to the past per-pixel classification of this coast, and in higher spatial and vegetation classification resolution. The resulting map demonstrates the utility of our point-based method and provides baseline data for incident preparedness and change detection. Elevation is highly correlated with the ordination of the vegetation types, and was the most important variable in all tested classification models. The vegetation classification brings together the largest amount of vegetation data for the Chukchi Sea coast yet documented.

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity.

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.

Spatiotemporal remote sensing of ecosystem change and causation across Alaska.

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.

Environmental Limits of Tall Shrubs in Alaska's Arctic National Parks.

We sampled shrub canopy volume (height times area) and environmental factors (soil wetness, soil depth of thaw, soil pH, mean July air temperature, and typical date of spring snow loss) on 471 plots across five National Park Service units in northern Alaska. Our goal was to determine the environments where tall shrubs thrive and use this information to predict the location of future shrub expansion. The study area covers over 80,000 km2 and has mostly tundra vegetation. Large canopy volumes were uncommon, with volumes over 0.5 m3/m2 present on just 8% of plots. Shrub canopy volumes were highest where mean July temperatures were above 10.5°C and on weakly acid to neutral soils (pH of 6 to 7) with deep summer thaw (>80 cm) and good drainage. On many sites, flooding helped maintain favorable soil conditions for shrub growth. Canopy volumes were highest where the typical snow loss date was near 20 May; these represent sites that are neither strongly wind-scoured in the winter nor late to melt from deep snowdrifts. Individual species varied widely in the canopy volumes they attained and their response to the environmental factors. Betula sp. shrubs were the most common and quite tolerant of soil acidity, cold July temperatures, and shallow thaw depths, but they did not form high-volume canopies under these conditions. Alnus viridis formed the largest canopies and was tolerant of soil acidity down to about pH 5, but required more summer warmth (over 12°C) than the other species. The Salix species varied widely from S. pulchra, tolerant of wet and moderately acid soils, to S. alaxensis, requiring well-drained soils with near neutral pH. Nearly half of the land area in ARCN has mean July temperatures of 10.5 to 12.5°C, where 2°C of warming would bring temperatures into the range needed for all of the potential tall shrub species to form large canopies. However, limitations in the other environmental factors would probably prevent the formation of large shrub canopies on at least half of the land area with newly favorable temperatures after 2°C of warming.

Tissue temperature feedback control of power: the key to successful ablation.

Multiple ablation technologies are used to treat atrial fibrillation during cardiac operations. All such ablation technologies use locally induced temperature extremes (>50°C or <-20°C) to kill tissue and create a lesion pattern in the atria which blocks activation pathways that initiate and sustain atrial fibrillation. The technologies used to heat tissue have included radiofrequency (RF), microwave, high-intensity focused ultrasound, and infrared laser. RF accounts for more than 95% of the heating-based ablation technology used by cardiac surgeons. Energy delivery with RF is easier to control than with some other technologies, the heating produced by the energy source is well understood, and manufacturing costs are not excessive. Whichever heating technology is used, control of energy delivery is required to ensure both safe and effective heating of the targeted tissue. All targeted tissue needs to be heated above 50°C to achieve cell death. However, the targeted tissue should not be heated above 100°C, as this can cause perforation due to a steam pop. In addition, adjacent noncardiac tissues must not be damaged during the ablation procedure. The best method to achieve this control uses direct measurement of tissue temperature, because the tissue temperature defines both the safe and effective limits for the ablative process.