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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.

 

Permafrost formation and degradation creates a highly patchy mosaic of boreal peatland ecosystems in Alaska driven by climate, fire, and ecological changes. To assess the biophysical factors affecting permafrost dynamics, we monitored permafrost and ecological conditions in central Alaska from 2005 to 2021 by measuring weather, land cover, topography, thaw depths, hydrology, soil properties, soil thermal regimes, and vegetation cover between burned (1990 fire) and unburned terrain. Climate data show large variations among years with occasional, extremely warm–wet summers and cold–snowless winters that affect permafrost stability. Microtopography and thaw depth surveys revealed both permafrost degradation and aggradation. Thaw depths were deeper in post-fire scrub compared to unburned black spruce and increased moderately during the last year, but analysis of historical imagery (1954–2019) revealed no increase in thermokarst rates due to fire. Recent permafrost formation was observed in older bogs due to an extremely cold–snowless winter in 2007. Soil sampling found peat extended to depths of 1.5–2.8 m with basal radiocarbon dates of ~5–7 ka bp, newly accumulating post-thermokarst peat, and evidence of repeated episodes of permafrost formation and degradation. Soil surface temperatures in post-fire scrub bogs were ~1 °C warmer than in undisturbed black spruce bogs, and thermokarst bogs and lakes were 3–5 °C warmer than black spruce bogs. Vegetation showed modest change after fire and large transformations after thermokarst. We conclude that extreme seasonal weather, ecological succession, fire, and a legacy of earlier geomorphic processes all affect the repeated formation and degradation of permafrost, and thus create a highly patchy mosaic of ecotypes resulting from widely varying ecological trajectories within boreal peatland ecosystems. 

Wildfire frequency and extent is increasing throughout the boreal forest-tundra ecotone as climate warms. Understanding the impacts of wildfire throughout this ecotone is required to make predictions of the rate and magnitude of changes in boreal-tundra landcover, its future flammability, and associated feedbacks to the global carbon (C) cycle and climate. We studied 48 sites spanning a gradient from tundra to low-density spruce stands that were burned in an extensive 2013 wildfire on the north slope of the Alaska Range in Denali National Park and Preserve, central Alaska. We assessed wildfire severity and C emissions, and determined the impacts of severity on understory vegetation composition, conifer tree recruitment, and active layer thickness (ALT). We also assessed conifer seed rain and used a seeding experiment to determine factors controlling post-fire tree regeneration. We found that an average of 2.18 ± 1.13 Kg C m -2 was emitted from this fire, almost 95% of which came from burning of the organic soil. On average, burn depth of the organic soil was 10.6 ± 4.5 cm and both burn depth and total C combusted increased with pre-fire conifer density. Sites with higher pre-fire conifer density were also located at warmer and drier landscape positions and associated with increased ALT post-fire, greater changes in pre- and post-fire understory vegetation communities, and higher post-fire boreal tree recruitment. Our seed rain observations and seeding experiment indicate that the recruitment potential of conifer trees is limited by seed availability in this forest-tundra ecotone. We conclude that the expected climate-induced forest infilling (i.e. increased density) at the forest-tundra ecotone could increase fire severity, but this infilling is unlikely to occur without increases in the availability of viable seed. 

Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In boreal North America, black spruce shapes forest flammability and depends on fire for regeneration. This relationship has helped black spruce maintain its dominance through much of the Holocene. However, with climate change and more frequent and severe fires, shifts away from black spruce dominance to broadleaf or pine species are emerging, with implications for ecosystem functions including carbon sequestration, water and energy fluxes, and wildlife habitat. Here, we predict that such reductions in black spruce after fire may already be widespread given current trends in climate and fire. To test this, we synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014). While black spruce was resilient following most fires (62%), loss of resilience was common, and spruce regeneration failed completely in 18% of 1,140 black spruce sites. In contrast, postfire regeneration never failed in forests dominated by jack pine, which also possesses an aerial seed bank, or broad-leaved trees. More complete combustion of the soil organic layer, which often occurs in better-drained landscape positions and in dryer duff, promoted compositional changes throughout boreal North America. Forests in western North America, however, were more vulnerable to change due to greater long-term climate moisture deficits. While we find considerable remaining resilience in black spruce forests, predicted increases in climate moisture deficits and fire activity will erode this resilience, pushing the system toward a tipping point that has not been crossed in several thousand years. 

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree‐ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea marianaandPicea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced—UVAFME. Through this process, we provide preliminary ecological inference about the long‐term competitive dynamics between slow‐growingP. marianaand relatively faster‐growingP. glauca. Incorporating tree‐ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values forP. marianaminimum growth under stress andP. glaucamaximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster‐growingP. glaucamay outcompeteP. marianaunder climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.

 

The negative growth response of North American boreal forest trees to warm summers is well documented and the constraint of competition on tree growth widely reported, but the potential interaction between climate and competition in the boreal forest is not well studied. Because competition may amplify or mute tree climate‐growth responses, understanding the role current forest structure plays in tree growth responses to climate is critical in assessing and managing future forest productivity in a warming climate. Using white spruce tree ring and carbon isotope data from a long‐term vegetation monitoring program in Denali National Park and Preserve, we investigated the hypotheses that (a) competition and site moisture characteristics mediate white spruce radial growth response to climate and (b) moisture limitation is the mechanism for reduced growth. We further examined the impact of large reproductive events (mast years) on white spruce radial growth and stomatal regulation. We found that competition and site moisture characteristics mediated white spruce climate‐growth response. The negative radial growth response to warm and dry early‐ to mid‐summer and dry late summer conditions intensified in high competition stands and in areas receiving high potential solar radiation. Discrimination against 13C was reduced in warm, dry summers and further diminished on south‐facing hillslopes and in high competition stands, but was unaffected by climate in open floodplain stands, supporting the hypothesis that competition for moisture limits growth. Finally, during mast years, we found a shift in current year's carbon resources from radial growth to reproduction, reduced 13C discrimination, and increased intrinsic water‐use efficiency. Our findings highlight the importance of temporally variable and confounded factors, such as forest structure and climate, on the observed climate‐growth response of white spruce. Thus, white spruce growth trends and productivity in a warming climate will likely depend on landscape position and current forest structure. 

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.