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The 1988 Yellowstone fire altered the structure of the local forest ecosystem and left large non-recovery areas. This study assessed the pre-fire drivers and post-fire characteristics of the recovery and non-recovery areas and examined possible reasons driving non-recovery of the areas post-fire disturbance. Non-recovery and recovery areas were sampled with 44,629 points and 77,501 points, from which attribute values related to topography, climate, and subsequent soil conditions were extracted. We calculated the 1988 Yellowstone fire burn thresholds using the differenced Normalized Burn Ratio (dNBR) and official fire maps. We used a burn severity map from the US Forest Service to calculate the burn severity values. Spatial regressions and Chi-Square tests were applied to determine the statistically significant characteristics of a lack of recovery. The non-recovery areas were found to cover 1005.25 km2. Among 11 variables considered as potential factors driving recovery areas and 13 variables driving non-recovery areas, elevation and maximum temperature were found to have high Variance Inflation Factors (4.73 and 4.72). The results showed that non-recovery areas all experienced severe burns and were located at areas with steeper slopes (13.99°), more precipitation (871.73 mm), higher pre-fire vegetation density (NDVI = 0.38), higher bulk density (750.03 kg/m3), lower soil organic matter (165.61 g/kg), and lower total nitrogen (60.97 mg/L). Chi-square analyses revealed statistically different pre-fire forest species (p < 0.01) and soil order (p < 0.01) in the recovery and non-recovery areas. Although Inceptisols dominated in both recovery and non-recovery areas, however, the composition of Mollisols was higher in the non-recovery areas (14%) compared to the recovery areas (11%). This indicated the ecological memory of the non-recovery site reverting to grassland post-disturbance. Unlike conventional studies only focusing on recovery areas, this study analyzed the non-recovery areas and found the key characteristics that make a landscape not resilient to the 1988 Yellowstone fire. The significant effects of elevation, precipitation, and soil pH on recovery may be significant to the forest management and forest resilience in the post-fire period. 

Global warming and related disturbances, such as drought, water, and heat stress, are causing forest decline resulting in regime shifts. Conventional studies have combined tree-ring width (TRW) and the normalized difference vegetation index (NDVI) to reconstruct NDVI values and ignored the influences of mixed land covers. We built an integrated TRW-NDVI model and reconstructed the annual NDVI maps by using 622 Landsat satellite images and tree cores from 15 plots using point-by-point regression. Our model performed well in the study area, as demonstrated by significant reconstructions for 71.14% (p < 0.05) of the area with the exclusion of water and barren areas. The error rate between the reconstructed NDVI using the conventional approach and our approach could reach 10.36%. The 30 m resolution reconstructed NDVI images in the recent 100 years clearly displayed a decrease in vegetation density and detected decades-long regime shifts from 1906 to 2015. Our study site experienced five regime shifts, markedly the 1930s and 1950s, which were megadroughts across North America. With fine resolution maps, regime shifts could be observed annually at the centennial scale. They can also be used to understand how the Yellowstone ecosystem has gradually changed with its ecological legacies in the last century. 

The effects of anthropogenic climate change are apparent in the Greater Yellowstone Ecosystem (GYE), USA, with forest die-off, insect outbreaks, and wildfires impacting forest ecosystems. A long-term perspective would enable assessment of the historical range of variability in forest ecosystems and better determination of recent forest dynamics and historical thresholds. The objectives of this study were to (1) develop tree-ring chronologies for Engelmann spruce and Douglas fir growing at the study location, (2) correlate the annual ring widths of each species to monthly climate variables, (3) examine the instrumental climate data for regimes shifts in the mean state of variables, and (4) determine when ecological disturbances occurred through a quantification of growth releases. Finally, we discuss both climate-growth relationships and growth releases in the context of climate regime shifts and known forest disturbances. Engelmann spruce and Douglas fir showed some similar climate responses using moving correlation analysis including negative correlations between ring width and June –August current year temperature and previous growing season temperature. Regime shift analysis indicated significant ( p < 0.05) shifts in minimum and maximum GYE temperature in the latter half of the 20th century. Disturbance analysis indicated that both tree species responded to wildfire and insect outbreak events with growth releases in up to 25% of the trees. Disentangling the influence of climate regime shifts and forest disturbances on the climate- growth relationships can be difficult because climate and forest disturbances are intricately linked. Our evidence indicates that regime shifts in monthly climate variables and forest disturbances as recorded by growth releases can influence the ring width response to climate over time. Trees are key to providing a long-term perspective on climate and ecological health across the GYE because they integrate both climate and ecology in their annual ring widths.