Individual Tree Mortality Research Articles

Individual tree mortality: Risks of climate change in the eastern Brazilian Amazon region

The mortality of trees in humid tropical forests plays a fundamental role in understanding forest development, particularly after disturbances such as those caused by logging and extreme weather events. The aim of this study was to evaluate estimates of individual tree mortality following Reduced Impact Logging (RIL) in the Eastern Brazilian Amazon at biennial intervals from 2005 to 2012. RIL is based on operations planning, personnel training, and investments in forest management, and harvesting through RIL must: (a) minimize environmental damage, (b) diminish operation cost by increasing work efficiency, and (c) reduce operational waste. A mortality model was constructed based on the estimation of three distance-independent competition-indices (DII) and five models for predicting the probability of individual tree mortality. The Kolmogorov-Smirnov statistical test was used to determine the most representative model, from which a Neural Network Autoregressive (NNAR) model was constructed to forecast mortality after RIL. Mortality data was correlated with the El Niño–Southern Oscillation (ENSO) and climate (Rainfall, Maximum, Minimum, and Average air temperature). The tested models showed similar and accurate estimates with R2 exceeding 0.90, although underestimation and overestimation trends were observed. The NNAR satisfactorily represented species mortality over the simulated years. The period from 2012 to 2014 was characterized by a Neutral and Weak El Niño event, and exhibited the highest mortality value for a 25 cm DBH (diameter at breast height), the smallest DBH class measured in this study. In the correlation matrix analysis, maximum air temperature showed the highest positive correlation with trees mortality. Despite the challenges in estimating individual tree mortality in tropical forests after selective logging, accurate estimates were achieved using traditional regression techniques and NNAR. These results can support technical and silvicultural decisions regarding forest management in the Eastern Amazon region of Brazil.

A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate.

Combined 2D- and 3D ERT monitoring as a geophysical tool for investigating spatial and temporal soil moisture fluctuations in a pine-beech forest

As climate change continues, forests are increasingly suffering from drought stress, which is leading to widespread forest dieback, but also to increased mortality of individual trees. In this regard, the impact of small-scale differences in water availability on individual trees has not yet been sufficiently studied to determine possible responses of different tree species to future droughts. Since conventional soil moisture monitoring and sampling methods only consider single points or small volumes, Electrical Resistivity Tomography (ERT) is becoming increasingly important to cover a larger survey area and to detect small-scale heterogeneities with regard to soil properties and soil moisture.The current study describes the application of a combined two- and three-dimensional geoelectrical monitoring approach with daily measurements over two years (May 2021 – April 2023) in a forest ecosystem in Lower Franconia (northwestern Bavaria, Germany), which is strongly affected by climate change. Soil water content, soil matric potential, throughfall, and stem flow are also measured at the forest site as well as precipitation at an adjacent forest clearing.The seasonally (long-term) and precipitation-driven (short-term) temporal change of soil resistivity is correlated strongly with the measured soil water content and matric potential. The applied 3D-ERT approach also allowed a first three-dimensional monitoring of the subsurface below a European beech located in the middle of the measuring grid with a daily resolution. The corresponding results also provide first indications that besides soil moisture changes also chemical processes in the subsurface may influence temporal resistivity changes in the soil of forest sites.The results of this study show that daily 3D-ERT is very suitable for investigating small-scale as well as short- and long-term variations in soil moisture, which is becoming increasingly important for understanding the causal relationships considering tree mortality and the subsurface.

Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation.

A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data

ABSTRACT Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.

Future impacts of climate change on black spruce growth and mortality: review and challenges

Black spruce ( Picea mariana (Mill.) B.S.P.) is the dominant conifer species across a large part of North American boreal forests, providing many goods and services essential to human activities, and playing a major climatic role through the global carbon cycle. However, a comprehensive synthesis of the effects of climate change on black spruce has not yet been undertaken. The dynamics of black spruce are influenced by various living (biotic) and non-living (abiotic) factors, as well as their combined effects, which are particularly responsive to changes in climate. Climate change predictions suggest that northern ecosystems will experience the world's most significant impact. Therefore, black spruce is likely to undergo profound disruptions in its growth and mortality rate in the next few decades, resulting in significant changes in forestry and carbon storage. However, these changes will not be uniform throughout the entire distribution of the species. Future changes in temperature and precipitation will create more stress for water availability in the boreal forests of western and central North America than in their eastern counterparts. Thus, significant longitudinal and latitudinal gradients in tree growth and mortality variability are expected throughout the range of the species. This literature review aims to summarise the impacts of climate change on individual tree growth and mortality of this major species. While enhanced black spruce productivity could occur through both increased air temperature and nitrogen mineralisation in the soil, moisture limitation in central and western North America will result in significant growth reduction and mortality events across these regions. Conversely, under the expected climate change scenarios, black spruce forests may be more resilient in eastern North America, where climatic conditions appear more suitable, particularly in their northernmost range. In this review, we identify current research gaps for some disturbances, which should be addressed to better understand the impact of climate change on black spruce. Finally, we identify issues associated with sustainable forest management and the maintenance of black spruce under projected future climate changes.

Scattered tree death contributes to substantial forest loss in California

In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.

Enhancing individual tree mortality mapping: The impact of models, data modalities, and classification taxonomy

Tree mortality is rapidly increasing as a result of more frequent and extensive droughts and forest fires across the globe. The increasing pace and scale of disturbance and resulting mortality necessitates the study of tree mortality at the scale at which it occurs (individual trees) to develop effective management strategies. Achieving this objective requires high-resolution aerial or satellite remote sensing data supported by field validation and efficient modeling. However, the optimal remote sensing data modalities and models for mapping overstory tree mortality remain uncertain and there is a lack of benchmark datasets for reproducible research, comparison, and model improvement. In this research, we propose a framework to generate tree mortality maps at the scale of individual trees and a labeled benchmark dataset using open-source high-resolution remote sensing data. Our benchmark dataset consists of over 15,000 image crops that have been manually labeled via multi-annotator majority for live, dead, mixed, and other classes in National Ecological Observatory Network (NEON) RGB, NEON hyperspectral, and National Agriculture Imagery Program (NAIP) RGBNIR imagery. Specifically, we present a multi-class classification framework that categorizes lidar-derived tree segments as live, dead, mixed, or other. We compared the performance of different machine learning models, remote sensing data modalities and their associated spatial resolution, spectral depth, and orthorectification using our benchmark dataset. Among various models, we found Convolutional Neural Network (CNN) models outperformed all other models with the highest accuracies and F1 scores across all data modalities. Similarly, among remote sensing data modalities, NEON RGB data at 0.1 m resolution performed best due to its high spatial resolution and high quality orthorectification. The overall accuracy of 87.27% with a macro F1 score of 0.76 was achieved using the RGB data with the CNN model. Our framework can be applied to any forest ecosystem in which lidar and raster imagery are available. We applied the framework using the best performing model and data modality to produce an individual tree mortality map over the Teakettle Experimental Forest in the southern Sierra Nevada. The framework and high-resolution mortality map can be valuable resources for forest managers and provide an invaluable basis for studying potential mortality drivers of dominant trees, forest inventory, selective logging, fire disturbance, and succession modeling.

Mapping temperate forest stands using mobile terrestrial LiDAR shows the influence of forest management regimes on tree mortality

With global change, forest trees will be exposed to increasing stress in the coming decades with various studies demonstrating that stress-related mortality will increase in forests. While tree death can be triggered by a single factor, it is often caused by the accumulation and the complex interaction of various stressors. Several silvicultural strategies have been developed to cope with global change but very few studies have addressed the ways in which silvicultural regimes interact with various stressors to influence tree mortality. This lack of research on the effects of forest management regimes on tree mortality may be due to the challenge of acquiring large (or long-term) datasets to assess tree mortality in forests. Within this context and using a mobile terrestrial LiDAR approach for rapid 3D-mapping of forest stands, we aimed to (i) compare recent tree mortality patterns in temperate forests among contrasting forest management regimes (even-aged silviculture, uneven-aged silviculture and unmanaged forests), and (ii) evaluate the relative influence of regeneration harvest severity on tree mortality compared to other spatially explicit factors (i.e., localized competition and slope position) and non-spatially explicit factors (i.e. tree DBH -diameter at breast height- and tree species group). In a permanent sample plot network, we mapped 15 508 dead and living trees (>9.1 cm DBH) within 37 sugar maple-dominated stands: 14 even-aged, 16 uneven-aged and 7 unmanaged stands. We separated the relative role of forest management on individual tree mortality from other factors such as size, species, slope position, and localized competition by modeling the probability of mortality for each tree. Localized competition or slope position were not significant factors describing tree mortality. Results showed that tree mortality was influenced by tree species, DBH and forest management regime. Models indicated that forest management regime, by itself, had a strong effect on tree mortality proportion. Results also indicated that trees in uneven-aged stands had a higher probability of dying than the those in even-aged or unmanaged stands. However, we do not advocate for the replacement of uneven-aged regimes in favour of even-aged ones. Instead, we believe that, in the context of global change, adjustments should be made to uneven-aged regimes to reduce risk of mortality. Moreover, our study reinforces the idea that silviculture can be applied in such a way as to enhance stand resistance and resilience to cope with global change; for example, by influencing size structure and species composition, which greatly influence tree mortality.

Estimating Individual Tree Mortality in the Sierra Nevada Using Lidar and Multispectral Reflectance Data

AbstractWidespread tree mortality events occur during periods of severe drought in temperate conifer forests and are expected to become more frequent in many areas due to climate change. Improved mapping of individual tree mortality is needed to identify risk factors and design effective conservation strategies. In this study, we used National Ecological Observatory Network (NEON) lidar and multispectral reflectance airborne observations to map individual tree mortality over a 160 km2 area during and after the 2012–2016 drought for two sites in California's Sierra National Forest. We used NEON lidar to derive tree locations and crown perimeters and multispectral data to map tree mortality for more than 1 million trees. We found that 25.4% of the trees in our study area died between 2013 and 2017, with considerably higher mortality at the lower‐elevation Soaproot Saddle site. Between 2017 and 2019, an additional 2.0%–2.8% of the trees died each year. Two wildfires in 2020 and 2021 increased tree mortality within burned area perimeters by 49%–89% between 2019 and 2021. Consistent with previous work, we found that tree mortality risk increased as a function of tree height. Tree mortality was positively associated with distance from rivers, trees per hectare, and decreasing slope at the lower elevation site. In contrast, increasing slope was positively associated with tree mortality at the higher elevation site. Our approach and dataset provide a means to study the combined effects of drought and wildfire on tree mortality and may improve projections of forest resilience under a changing climate.

Vitality loss of beech: a serious threat to Fagus sylvatica in Germany in the context of global warming

In the light of ongoing climate change, European beech trees face a loss of vitality due to an expected increase in the frequency of droughts, exacerbated by prolonged periods of high temperatures and solar radiation. Such conditions increase the predisposition to European beech splendour beetle attacks, and trigger sunburn, fungal infections and complex diseases. As a result, European beech forests are seriously threatened by climate change. Following prolonged and repeated periods of exceptional drought since 2018, European beech decline have been observed in several regions of Germany and Europe. Vitality losses, in some cases severe, of due to the extraordinarily warm, dry summers of 2018 and 2019 have been observed and investigated in several regions of Germany. The first symptoms of vitality loss of European beech associated with severe signs of crown dieback were recorded in midsummer 2018. Mortality of branches and individual trees was often associated with sunburn, bark necrosis and bleeding spots of sap flux. The first signs of mortality were observed locally in predisposed stands in autumn 2018. The stem stability and break resistance of affected trees were significantly reduced. To elucidate the complex interactions involved, the occurrence of vitality loss of European beech and associated causative agents were examined in north-western Germany. Disease symptoms of individual trees were studied, associated fungi (endophytes, plant pathogens and saprobes) were isolated and identified. Frequent potentially severe pathogens included Neonectria coccinea, Diplodia corticola and D. mutila. The presented cases of disease were the first, where D. corticola was recorded on beech. In addition, inoculation tests were used to assess the potential roles of Botryosphaeria species in the observed symptomatology.

Profiling overstory survival trends following varying thinning and burning disturbance regimes in a mixed pine-hardwood forest in the US South

Prescribed burning as a silvicultural tool may be effective for achieving various management objectives, yet some practitioners have concerns about inadvertently increasing overstory tree mortality. Using extensive data from a long-term, ongoing study in northcentral Alabama, USA, that systematically applies a variety of thinning and prescribed burning disturbances on mixed pine-hardwood stands, we assess survival trends for different groups of trees. The primary research question is whether more frequent prescribed burns adversely affects overstory survival versus infrequent or no burns; secondary questions explore whether overstory survival trends differ based on thinning level, species group, or size class. Interest is in broad groups of trees, not individual tree survival or mortality; consequently, survival analysis methods are used, including nonparametric Kaplan-Meier (KM) techniques for examining single grouping factors, and parametric accelerated failure time models for analyzing simultaneous effects of multiple covariates. Leveraging relatively recent methodological advances, the statistical techniques are adjusted for interval censored data. KM survival curves showed that more frequent prescribed fires did not result in differing survival trends. Also, trees in unthinned stands had the lowest 14-year survival compared to thinned stands, oaks had the highest survival compared to pines and the others species group, and the smallest size class of overstory trees had the lowest survival. These results support managing transitional mixed pine-hardwoods using thinning and multiple prescribed fires to restore specific species composition and structure.

Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems, yet it is one of the most difficult phenomena to accurately predict. Various modeling strategies have been developed to improve individual tree mortality predictions. One less explored strategy is the use of a multistage modeling approach. Potential improvements from this approach have remained largely unknown. In this study, we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation. Extensive permanent plot data (n ​= ​9442) covering the Acadian Region of North America and over multiple decades (1965–2014) were used in this study. Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach. The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach. In addition, tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations, whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data. The new multistage approach also predictions of zero mortality in individual plots, a result not possible in conventional models. Finally, the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values. Overall, this new multistage approach deserves to be considered and tested in future studies.

A spectral three-dimensional color space model of tree crown health.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m2) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n = 122 NAIP state x year combinations; median overall accuracy 0.89–0.90; median Kappa 0.85–0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.

Post-fire regeneration of cork oak (Quercus suber) in Kiadi forest (Akfadou- Algeria)

Cork oak (Quercus suber) as a West Mediterranean species is known for its ecological, economic and social values. Wildfires are one of the most serious problems threatening Quercus suber, endangering its occurrence in its area of distribution. Therefore, knowing the behavior of the species after fire and the factors influencing its responses are particularly important for forest management. In this study we assessed the post fire vegetative recovery in 730 trees affected by wildfires on 2014 in Kiadi cork oak forest, located in the Western side of Akfadou Mountains in Algeria. Few months after the fire, individual tree mortality was very low (7.53%), and nearly, all the trees sampled survived the fire since almost all trees resprouted from canopy and some of them showed basal resprouts. Moreover, those two modes of post fire vegetative recovery were not correlated to each other. The performed redundancy analyzes (RDA) revealed that the cork oak post-fire response was highly correlated with individual characteristics and with the environmental data. The main variables influencing the likelihood of good or poor vegetative recovery were the understory height and cover, soil characteristics, fire severity, tree status (alive/dead trees), tree diameter and tree exploitation. Our results confirmed the fire resistance of cork oak species; which is also the only Algerian tree to resprouts. Hence, this makes the species a good candidate for reforestation programs in fire prone ecosystems.

Predicting individual tree mortality in tropical rain forest using decision tree with improved particle swarm optimization

Predicting individual tree mortality in tropical rain forest using decision tree with improved particle swarm optimization

Developing Tree Mortality Models Using Bayesian Modeling Approach

The forest mortality models developed so far have ignored the effects of spatial correlations and climate, which lead to the substantial bias in the mortality prediction. This study thus developed the tree mortality models for Prince Rupprecht larch (Larix gmelinii subsp. principis-rupprechtii), one of the most important tree species in northern China, by taking those effects into account. In addition to these factors, our models include both the tree—and stand—level variables, the information of which was collated from the temporary sample plots laid out across the larch forests. We applied the Bayesian modeling, which is the novel approach to build the multi-level tree mortality models. We compared the performance of the models constructed through the combination of selected predictor variables and explored their corresponding effects on the individual tree mortality. The models precisely predicted mortality at the three ecological scales (individual, stand, and region). The model at the levels of both the sample plot and stand with different site condition (block) outperformed the other model forms (model at block level alone and fixed effects model), describing significantly larger mortality variations, and accounted for multiple sources of the unobserved heterogeneities. Results showed that the sum of the squared diameter was larger than the estimated diameter, and the mean annual precipitation significantly positively correlated with tree mortality, while the ratio of the diameter to the average of the squared diameter, the stand arithmetic mean diameter, and the mean of the difference of temperature was significantly negatively correlated. Our results will have significant implications in identifying various factors, including climate, that could have large influence on tree mortality and precisely predict tree mortality at different scales.

Stand-Age Derived Competition Indices Influence Individual Tree Mortality Model Prediction for Naturally Occurring Even-Aged Shortleaf Pine Stands

To understand the influence of competition indices and post-thinning effects in predicting individual tree mortality, we developed two models, one without the effect of thinning and another, with the effect of thinning for naturally occurring even-aged natural shortleaf pine (Pinus echinata Mill.) stands in the Ozark and Ouachita National Forests in Oklahoma and Arkansas, USA. Over the period of 25 years, six re-measurements of an individual tree from each plot were collected between 1985 and 2014. The logistic function was used to model the probability of mortality for which the binary response variable was, ‘0’ for living and ‘1’ for dead trees, using iteratively weighted regression and mixed-effects model. Stand-age derived competition indices such as, the ratio of stand basal area to stand age (SBAG), ratio of individual diameter to stand age (DAG), and the quadratic mean diameter (QMD), were found significant in predicting the probability of mortality. These variables were also found to be effective in the thinning effect model. However, excluding the thinning variable resulted in better performance with the chi-square test based on mortality within mid-diameter classes. Thus, the mortality model suggests that over time, individual tree mortality is influenced more greatly by competition modified by stand age rather than by a post-thinning effect in even-aged naturally occurring shortleaf pine trees.

Multiyear defoliations in southern New England increases oak mortality

After decades of multiyear defoliation episodes in southern New England, Lymantria dispar dispar (previously gypsy moth) populations diminished with the appearance of the L. dispar fungus in 1989. Multiyear defoliations did not occur again until 2015–2018. To assess the impact of the return of multiyear defoliations, we examined 3095 oaks on 29 permanent study areas in Connecticut and Rhode Island that were established at least 11 years before the latest outbreaks. Pre-defoliation stand-level oak mortality averaged 2% (3-year basis). Post-defoliation mortality did not differ between managed and unmanaged stands but was much higher in severely defoliated stands (36%) than in stands with moderate (7%) or low to no defoliation (1%). Pre-defoliation mortality of individual trees differed among species, was lower for larger diameter trees and on unmanaged than managed stands. Post-defoliation mortality on plots with no to moderate defoliation was similar to pre-defoliation mortality levels. Following multiyear defoliations, white oak (Quercus alba L.) mortality was higher than for northern red oak (Quercus rubra L.) and black oak (Q. velutina Lam.). There was weak evidence that mortality was elevated on stands with higher basal area following severe defoliation. Natural resource managers should not assume that oaks that survived earlier multiyear defoliations episodes will survive future multiyear outbreaks, possibly because trees are older.

Forest Resistance to Extended Drought Enhanced by Prescribed Fire in Low Elevation Forests of the Sierra Nevada

Prescribed fire reduces fire hazards by removing dead and live fuels (small trees and shrubs). Reductions in forest density following prescribed fire treatments (often in concert with mechanical treatments) may also lessen competition so that residual trees might be more likely to survive when confronted with additional stressors, such as drought. The current evidence for these effects is mixed and additional study is needed. Previous work found increased tree survivorship in low elevation forests with a recent history of fire during the early years of an intense drought (2012 to 2014) in national parks in the southern Sierra Nevada. We extend these observations through additional years of intense drought and continuing elevated tree mortality through 2017 at Sequoia and Kings Canyon National Parks. Relative to unburned sites, we found that burned sites had lower stem density and had lower proportions of recently dead trees (for stems ≤47.5 cm dbh) that presumably died during the drought. Differences in recent tree mortality among burned and unburned sites held for both fir (white fir and red fir) and pine (sugar pine and ponderosa pine) species. Unlike earlier results, models of individual tree mortality probability supported an interaction between plot burn status and tree size, suggesting the effect of prescribed fire was limited to small trees. We consider differences with other recent results and discuss potential management implications including trade-offs between large tree mortality following prescribed fire and increased drought resistance.