Where are the trees? Extent, configuration, and drivers of poor forest recovery 30 years after the 1988 Yellowstone fires

Kiel, Nathan G.; Turner, Monica G.

doi:10.1016/j.foreco.2022.120536

Cited by 17 publications

(3 citation statements)

References 137 publications

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“…4a) despite vast ecological, economical, and cultural differences between the two vegetation types (Stine et al 2014). Researchers have attempted to differentiate between tree-dominated and shrub-dominated sites in post-fire mixed conifer forest landscapes by using multispectral data from fall, winter, and spring months to isolate conifer trees from deciduous trees and shrubs (Vanderhoof et al 2021;Kiel and Turner 2022), but these protocols are not expected to differentiate conifers and evergreen shrubs when assessing spectral recovery over short time periods (i.e., before the height of conifers exceeds the maximum expected height of evergreen shrubs). We recognize that studies using spectral recovery metrics often analyze postfire recovery over brief periods that do not reflect the expected period of recovery of mixed conifer forests following a stand-replacing, high severity fire.…”

Section: Fast Spectral Recovery Did Not Often Coincide With Forest Re...mentioning

confidence: 99%

A fast spectral recovery does not necessarily indicate post-fire forest recovery

Celebrezze,

Franz,

Andrus

et al. 2024

Preprint

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Background Climate change has increased wildfire activity in the western USA and limited the capacity for forests to recover post-fire, especially in areas burned at high severity. Land managers urgently need a better understanding of the spatiotemporal variability in natural postfire forest recovery to plan and implement active recovery projects. In burned areas, post-fire ‘spectral recovery’, determined by examining the trajectory of multispectral indices (e.g., normalized burn ratio) over time, generally corresponds with recovery of multiple post-fire vegetation types, including trees and shrubs. Field data are essential for deciphering the vegetation types reflected by spectral recovery, yet few studies validate spectral recovery metrics with field data or incorporate spectral recovery into spatial models of post-fire vegetation recovery. We investigated relationships between spectral recovery and field measurements of post-fire recovery (16 to 27 years post-fire) from 99 plots in mixed-conifer forests of the Blue Mountains, USA. Additionally, using generalized linear mixed effects models, we assessed the relative capacities of multispectral, climatic, and topographic data to predict field measurements of post-fire recovery. Results We found that spectral recovery did not necessarily coincide with field measurements of forest recovery (e.g., density of regenerating seedlings, saplings, and young trees and % juvenile conifer cover). Instead, rapid spectral recovery often coincided with increases in % shrub cover. We primarily attributed this relationship to the response of snowbrush ceanothus, an evergreen shrub that vigorously resprouts post-fire. However, in non-trailing edge forests – where it was cooler and wetter and fast-growing conifers were more common – rapid spectral recovery coincided with both increases in % shrub cover and forest recovery. Otherwise, spectral recovery showed potential to identify transitions to grasslands, as grass-dominated sites showcased distinctly slow spectral trajectories. Lastly, field measurements of post-fire forest recovery were best predicted when including post-fire climate and multispectral data in predictive models. Conclusions Despite a disconnect between a faster spectral recovery and forest recovery, our results suggest that including multispectral data improved models predicting the likelihood of post-fire forest recovery. Improving predictive models would aid land managers in identifying sites to implement active reforestation projects.

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Section: Fast Spectral Recovery Did Not Often Coincide With Forest Re...mentioning

confidence: 99%

A fast spectral recovery does not necessarily indicate post-fire forest recovery

Celebrezze,

Franz,

Andrus

et al. 2024

Preprint

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“…This baseline could subsequently be used to assess a recent wave of tree mortality-triggered by severe drought-in relation to the long-term variation of the system, giving a first indication that disturbance regimes have moved outside of their recent range of variability (Senf & Seidl 2021b). While the remote sensing of disturbances has made great advances in recent years, analyzing ecological responses to disturbance from space remains challenging, because it can take years or decades before satellite data can differentiate pathways of recovery (Kiel & Turner 2022). Novel analysis approaches, such as spectral unmixing (Viana-Soto et al 2022), can help to make inferences in this regard.…”

Section: Tools For Detecting Diagnosing and Projecting Disturbance Ch...mentioning

confidence: 99%

Novel Disturbance Regimes and Ecological Responses

Turner

Seidl

2023

Annu. Rev. Ecol. Evol. Syst.

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Many natural disturbances have a strong climate forcing, and concern is rising about how ecosystems will respond to disturbance regimes to which they are not adapted. Novelty can arise either as attributes of the disturbance regime (e.g., frequency, severity, duration) shift beyond their historical ranges of variation or as new disturbance agents not present historically emerge. How much novelty ecological systems can absorb and whether changing disturbance regimes will lead to novel outcomes is determined by the ecological responses of communities, which are also subject to change. Powerful conceptual frameworks exist for anticipating consequences of novel disturbance regimes, but these remain challenging to apply in real-world settings. Nonlinear relationships (e.g., tipping points, feedbacks) are of particular concern because of their disproportionate effects. Future research should quantify the rise of novelty in disturbance regimes and assess the capacity of ecosystems to respond to these changes. Novel disturbance regimes will be potent catalysts for ecological change. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…We used the presence/absence of Scots pine and European aspen regeneration across the 223 plots as response variable and a set of environmental drivers as covariates (Table 1). We used the existing literature to select the most important environmental variables guiding post-fire forest recovery (e. g., Marzano et al 2013;Perrault-Hébert et al 2017;Haffey et al 2018;Andrus et al 2022;Kiel and Turner 2022).…”

Section: Drivers Of Forest Regenerationmentioning

confidence: 99%

Modeling post-fire regeneration patterns under different restoration scenarios to improve forest recovery in degraded ecosystems

Mantero

Anselmetto

Morresi

et al. 2023

Preprint

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<p>Applied nucleation (AN) is a nature-based solution alternative to traditional regular plantations. It is a cost-effective technique that integrates artificial seedlings and natural regeneration dynamics to enhance forest recovery, mimicking successional processes. Given the current shift of disturbance regimes caused by global change, this technique will likely be a valuable active restoration approach for many forest ecosystems affected by extreme disturbance events. Indeed, AN is suitable for ecological restoration after stand-replacing events, improving seed availability and microsite conditions supporting natural regeneration. AN has been mostly applied in tropical forests, but its use in Mediterranean forests should be increasingly considered since a higher occurrence of large and severe fires has been observed over the last decades and further increases are expected in the future. These changes are raising concerns about regeneration recruitment, particularly for obligate seeders in mountain ecosystems. In these ecosystems, it is crucial to reconsider current post-fire policies to identify strategies that promote and maintain the ecosystem services of degraded forests, particularly when natural regeneration is ineffective.&#160;The main aim of this study was to define the best methodology for implementing AN in a mountain area affected by a large stand-replacing fire that occurred in 2005 in the Aosta Valley Region (North-Western Italy). After the fire salvage logging was performed, increasing ecosystem degradation, and, 16 years later, natural regeneration is still scarce and struggling to settle. Hotspots for AN were identified based on post-fire natural regeneration response to a series of site characteristics, such as topography, fire severity, and distance from seed trees. We assessed the drivers of post-fire regeneration through a machine learning correlative model (Bayesian Regression Tree, BART). The probability of regeneration presence across the landscape was then predicted under the current situation and a set of AN scenarios. Starting from the current scenario, we reclassified the prediction raster into three levels, according to the probability of presence and uncertainty. Polygons with low uncertainty and probability (< 0.3) were included in level one and considered hotspots for AN. From these predictions, it was possible to assess the most efficient active management scenario to speed up the regeneration process. Our results showed that AN could be a promising post-fire management technique for promoting natural regeneration while limiting anthropic interventions and their related economic and ecological costs. &#160;</p>

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Where are the trees? Extent, configuration, and drivers of poor forest recovery 30 years after the 1988 Yellowstone fires

Cited by 17 publications

References 137 publications

A fast spectral recovery does not necessarily indicate post-fire forest recovery

A fast spectral recovery does not necessarily indicate post-fire forest recovery

Novel Disturbance Regimes and Ecological Responses

Modeling post-fire regeneration patterns under different restoration scenarios to improve forest recovery in degraded ecosystems

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