Fire catalyzed rapid ecological change in lowland coniferous forests of the Pacific Northwest over the past 14,000 years

Crausbay, Shelley D.; Higuera, Philip E.; Sprugel, Douglas G.; Brubaker, Linda B.

doi:10.1002/ecy.1897

Cited by 52 publications

(57 citation statements)

References 56 publications

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“…As fires become more extensive, they will likely catalyze ecosystem changes that would otherwise unfold over decades to centuries (Crausbay et al. ), through increased fire‐induced tree mortality and decreased post‐fire regeneration under warmer climate conditions.…”

Section: Introductionmentioning

confidence: 99%

Climate will increasingly determine post‐fire tree regeneration success in low‐elevation forests, Northern Rockies,USA

et al. 2019

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Climate change is expected to cause widespread shifts in the distribution and abundance of plant species through direct impacts on mortality, regeneration, and survival. At landscape scales, climate impacts will be strongly mediated by disturbances, such as wildfire, which catalyze shifts in species distributions through widespread mortality and by shaping the post-disturbance environment. We examined the potential for regional shifts in low-elevation tree species in response to wildfire and climate warming in low-elevation, dry mixed-conifer forests of the northern Rocky Mountains, USA. We analyzed interactions among climate and wildfire on post-fire tree seedling regeneration 5-13 yr post-fire at 177 sites burned in 21 large wildfires during two years with widespread regional burning. We used generalized additive mixed models to quantify how the density of Douglas-fir and ponderosa pine seedlings varied as a function of climate normals (30-yr mean temperature, precipitation, soil moisture, and evapotranspiration) and fire (tree survivorship, burn severity, and seed source availability). Mean summer temperature was the most important predictor of post-fire seedling densities for both ponderosa pine and Douglas-fir. Seed availability was also important in determining Douglas-fir regeneration. As mean summer temperature continues to increase, however, seed availability will become less important for determining post-fire regeneration. Above a mean summer temperature of 17°C, Douglas-fir regeneration is predicted to be minimal regardless of how close a seed source is to a site. The majority (82%) of our sampled sites are predicted to exceed a mean summer temperature of 17°C by mid-century, suggesting significant declines in seedling densities and potential forest loss. Our results highlight mechanisms linking climate change to shifts in the distribution of two widely dominant tree species in western North America. Under a warming climate, we expect post-fire tree regeneration in these low-elevation forests to become increasingly unsuccessful. Such widespread regeneration failures would have important implications for ecosystem processes and forest resilience, particularly as wildfires increase in response to climate warming.

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Section: Introductionmentioning

confidence: 99%

Climate will increasingly determine post‐fire tree regeneration success in low‐elevation forests, Northern Rockies,USA

et al. 2019

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“…In this study, we use the original accumulation rates, reconstructed in Cuffey and Clow (1997), produced using an ice-flow model adapted to the Greenland Ice Sheet Project Two (GISP2) location but adapted to the GICC05 chronology (Rasmussen et al, 2008;Seierstad et al, 2014). A detailed description of the adaption procedure can be found in Sect.…”

Section: Accumulation-rate Datamentioning

confidence: 99%

“…Except for these technical adjustments, the accumulation-rate input remains unmodified, assuming high reliability of these data during the Holocene. The accumulation data were reconstructed using annual layer counting, and a thinning model which should lead to maximum relative uncertainty of 10 % for the first 1500 of the 3000 m ice core (Cuffey and Clow, 1997). From the three accumulationrate scenarios reconstructed in Cuffey and Clow (1997) and adapted here to the GICC05 chronology, the intermediate one is chosen (red curves in Fig.…”

Section: Accumulation-rate Datamentioning

confidence: 99%

“…Mayewski et al (2004) used these data as the base of a multiproxy study identifying rapid climate changes (so-called RCCs) globally distributed over the whole Holocene time period. Although not all proxy data showed an equal behaviour in timing and extent during the quasi-periodic RCC patterns, the authors found evidence for a highly variable Holocene climate controlled by multiple mechanisms, which significantly affects ecosystems (de Beaulieu et al, 2017;Crausbay et al, 2017;Pál et al, 764 M. Döring and M. C. Leuenberger: Novel automated inversion algorithm for temperature reconstruction 2016) and human societies (Holmgren et al, 2016;Lespez et al, 2016). Precise high-resolution temperature estimates can contribute significantly to the understanding of these mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…Ice-core proxy data offer multiple paths for reconstructing past climate and temperature variability. The studies of Cuffey et al (1995), Cuffey and Clow (1997) and DahlJensen et al (1998) demonstrate the usefulness of inverting the measured borehole-temperature profile for surfacetemperature-history estimates for the investigated drilling site using a coupled heat-and ice-flow model. Because of smoothing effects due to heat diffusion within an ice sheet, this method is unable to resolve fast temperature oscillations and leads to a rapid reduction of the time resolution towards the past.…”

Section: Introductionmentioning

confidence: 99%

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Novel automated inversion algorithm for temperature reconstruction using gas isotopes from ice cores

Döring

Leuenberger

2018

Clim. Past

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Abstract. Greenland past temperature history can be reconstructed by forcing the output of a firn-densification and heat-diffusion model to fit multiple gas-isotope data (δ 15 N or δ 40 Ar or δ 15 N excess ) extracted from ancient air in Greenland ice cores using published accumulation-rate (Acc) datasets. We present here a novel methodology to solve this inverse problem, by designing a fully automated algorithm. To demonstrate the performance of this novel approach, we begin by intentionally constructing synthetic temperature histories and associated δ 15 N datasets, mimicking real Holocene data that we use as "true values" (targets) to be compared to the output of the algorithm. This allows us to quantify uncertainties originating from the algorithm itself. The presented approach is completely automated and therefore minimizes the "subjective" impact of manual parameter tuning, leading to reproducible temperature estimates. In contrast to many other ice-core-based temperature reconstruction methods, the presented approach is completely independent from ice-core stable-water isotopes, providing the opportunity to validate water-isotope-based reconstructions or reconstructions where water isotopes are used together with δ 15 N or δ 40 Ar. We solve the inverse problem T (δ 15 N, Acc) by using a combination of a Monte Carlo based iterative approach and the analysis of remaining mismatches between modelled and target data, based on cubic-spline filtering of random numbers and the laboratory-determined temperature sensitivity for nitrogen isotopes. Additionally, the presented reconstruction approach was tested by fitting measured δ 40 Ar and δ 15 N excess data, which led as well to a robust agreement between modelled and measured data. The obtained final mismatches follow a symmetric standard-distribution function. For the study on synthetic data, 95 % of the mismatches compared to the synthetic target data are in an envelope between 3.0 to 6.3 permeg for δ 15 N and 0.23 to 0.51 K for temperature (2σ , respectively). In addition to Holocene temperature reconstructions, the fitting approach can also be used for glacial temperature reconstructions. This is shown by fitting of the North Greenland Ice Core Project (NGRIP) δ 15 N data for two Dansgaard-Oeschger events using the presented approach, leading to results comparable to other studies.

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Fuel treatment effectiveness in the context of landform, vegetation, and large, wind‐driven wildfires

Prichard

Povak

Kennedy

et al. 2020

Ecological Applications

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Large wildfires (>50,000 ha) are becoming increasingly common in semiarid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind‐driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record‐setting complex of wildfires in north‐central Washington State. Across varied topography, vegetation, and distinct fire progressions, we used a combination of simultaneous autoregression (SAR) and random forest (RF) approaches to model drivers of fire severity and evaluated how fuel treatments mitigated fire severity. Predictor variables included fuel treatment type, time since treatment, topographic indices, vegetation and fuels, and weather summarized by progression interval. We found that the two spatial regression methods are generally complementary and are instructive as a combined approach for landscape analyses of fire severity. Simultaneous autoregression improves upon traditional linear models by incorporating information about neighboring pixel burn severity, which avoids type I errors in coefficient estimates and incorrect inferences. Random forest modeling provides a flexible modeling environment capable of capturing complex interactions and nonlinearities while still accounting for spatial autocorrelation through the use of spatially explicit predictor variables. All treatment areas burned with higher proportions of moderate and high‐severity fire during early fire progressions, but thin and underburn, underburn only, and past wildfires were more effective than thin‐only and thin and pile burn treatments. Treatment units had much greater percentages of unburned and low severity area in later progressions that burned under milder fire weather conditions, and differences between treatments were less pronounced. Our results provide evidence that strategic placement of fuels reduction treatments can effectively reduce localized fire spread and severity even under severe fire weather. During wind‐driven fire spread progressions, fuel treatments that were located on leeward slopes tended to have lower fire severity than treatments located on windward slopes. As fire and fuels managers evaluate options for increasing landscape resilience to future climate change and wildfires, strategic placement of fuel treatments may be guided by retrospective studies of past large wildfire events.

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Fire catalyzed rapid ecological change in lowland coniferous forests of the Pacific Northwest over the past 14,000 years

Cited by 52 publications

References 56 publications

Climate will increasingly determine post‐fire tree regeneration success in low‐elevation forests, Northern Rockies,USA

Climate will increasingly determine post‐fire tree regeneration success in low‐elevation forests, Northern Rockies,USA

Novel automated inversion algorithm for temperature reconstruction using gas isotopes from ice cores

Fuel treatment effectiveness in the context of landform, vegetation, and large, wind‐driven wildfires

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