Tree Mortality Models Research Articles

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.

Prefire Drought Intensity Drives Postfire Recovery and Mortality in Pinus monticola and Pseudotsuga menziesii Saplings

Abstract Increasing frequency of droughts and wildfire are sparking concerns that these compounded disturbance events are pushing forested ecosystems beyond recovery. An improved understanding of how compounded events affect tree physiology and mortality is needed given the reliance of fire management planning on accurate estimates of postfire tree mortality. In this study, we use a toxicological dose-response approach to quantify the impact of variable-intensity drought and fire on the physiology and mortality of Pinus monticola and Pseudotsuga menziesii saplings. We show that the dose-response relationship between fire intensity and mortality shifts toward increased vulnerability under drought, indicating higher mortality with increasing drought at any fire intensity. The trajectory we observed in postfire chlorophyll fluorescence, an indicator of photosynthetic efficiency and stress, was an effective early warning sign of impending tree death. Postfire mortality modeling shows that accurate mortality classification can be achieved using prefire physiology and morphology metrics combined with fire intensity. Variable importance measures indicate that physiological condition and fire intensity have greater influence on the classification accuracy than morphological metrics. The wide range in drought and fire responses observed between this study and others highlights the need for more research on compound disturbance effects. Study Implications: An improved understanding of how drought and fire affect tree physiology and mortality is needed by natural resource managers looking to predict postfire tree mortality. This study advances our compound disturbance understanding by subjecting conifer saplings to variable drought and fire intensities and quantifying and modeling moderate-term recovery and mortality. The results show reduced physiological recovery and amplified mortality in saplings exposed to greater drought and fire intensity. Overall, this study highlights the importance of physiological condition when modeling tree mortality and could potentially be used to inform current postfire tree mortality models.

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.

Nonstructural carbohydrates explain post-fire tree mortality and recovery patterns.

Trees use nonstructural carbohydrates (NSCs) to support many functions, including recovery from disturbances. However, NSC's importance for recovery following fire and whether NSC depletion contributes to post-fire delayed mortality are largely unknown. We investigated how fire affects NSCs based on fire-caused injury from a prescribed fire in a young Pinus ponderosa (Lawson & C. Lawson) stand. We assessed crown injury (needle scorch and bud kill) and measured NSCs of needles and inner bark (i.e., secondary phloem) of branches and main stems of trees subject to fire and at an adjacent unburned site. We measured NSCs pre-fire and at six timesteps post-fire (4days-16months). While all trees initially survived the fire, NSC concentrations declined quickly in burned trees relative to unburned controls over the same post-fire period. This decline was strongest for trees that eventually died, but those that survived recovered to unburned levels within 14months post-fire. Two months post-fire, the relationship between crown scorch and NSCs of the main stem inner bark was strongly negative (Adj-R2=0.83). Our results support the importance of NSCs for tree survival and recovery post-fire and suggest that post-fire NSC depletion is in part related to reduced photosynthetic leaf area that subsequently limits carbohydrate availability for maintaining tree function. Crown scorch is a commonly measured metric of tree-level fire severity and is often linked to post-fire tree outcome (i.e., recovery or mortality). Thus, our finding that NSC depletion may be the mechanistic link between the fire-caused injury and tree outcome will help improve models of post-fire tree mortality and forest recovery.

Competition‐induced tree mortality across Europe is driven by shade tolerance, proportion of conspecifics and drought

Abstract Forest stand densities are increasing in the boreal and temperate biomes, suggesting that tree‐tree competition is intensifying. Anticipating the consequences of this intensified competition is difficult because competition‐induced mortality may depend not only on the occurrence of extreme climatic events such as drought, but also on stand composition, since tree species differ in their ability to compete and tolerate competition. A better understanding of the effects of stand composition and drought on competition‐induced mortality would help to anticipate future changes in forest ecosystems. We studied the tree‐level probability of competition‐induced mortality using National Forest Inventory data from three European countries (Finland, France and Germany), covering a latitudinal gradient from the Mediterranean to the Arctic. We investigated how (i) the proportion of conspecifics, (ii) the shade tolerance (ST) of the focal tree and its competitors and (iii) drought events modify the effect of competition on tree mortality. We used a generalized linear mixed model on a dataset of 461,109 trees representing 39 species on 48,088 individual plots. Competition, measured as the basal area of larger trees, was a stronger driver of background mortality (BM) than tree size and climate. A higher proportion of conspecifics increased the competition effect on mortality, showing that conspecific individuals had a higher competitive effect compared to heterospecific individuals. The competition effect on mortality also increased as a function of the ST of neighbouring trees, suggesting an increased shading effect. A higher ST of a focal tree decreased the competition effect on mortality. Drought anomalies increased the competition effect, resulting in a higher mortality probability for the most suppressed trees. Synthesis. Competition was the main driver of background mortality. Increasing stand density increased competition‐induced tree mortality in both monospecific and mixed stands, but to different extents depending on the proportion of conspecifics and tree species shade tolerance (ST). Drought periods increase mortality, especially among the most suppressed trees, suggesting an interaction between competitive status and drought. Incorporating more detailed information on stand composition and tree species ST into tree mortality models will improve our understanding of forest dynamics in a changing climate.

Stress disorders of woody plants as a consequence of damage from high temperatures and fires

Plants are sessile organisms that are under the constant influence of the environmental conditions in which they grow. Any change in "inanimate" factors that have a significant impact on plant growth and development is collectively referred to as "abiotic stress". Extreme temperatures, especially in changed climatic conditions, are one of the most harmful abiotic factors, which cause heat stress in plants. The effects of high temperatures can be manifested through various morphological, physiological and genetic changes in the plant organism. Generally speaking, plants can be divided into three groups according to temperature requirements: psychrophilic plants, mesophilic plants and thermophilic plants. Most woody species belong to the group of mesophilic plants, which require temperatures between 10 and 30 °C for successful growth and development. As the temperature changes on a daily and seasonal basis in relation to the optimal range, certain changes occur in the plant organism that are necessary to maintain cell growth and homeostasis. Regardless of their ability to adapt to temperature oscillations, plants that have been exposed to temperatures above the optimal level for a long time can show symptoms of irreversible damage, which manifest as heat stress. Generally speaking, this type of stress is considered to occur when the temperature is 10- 15 °C higher than usual at some time of the year, and lasts long enough to cause irreversible damage or disturbances in plant growth and development. As the intensity, duration and degree of temperature change change, so do the various effects of heat stress on plants. In order to survive, plants must adapt to changes in environmental conditions through a specific response that depends on the physiology and morphology of a species. According to their level of tolerance to high temperatures, plants can be divided into three categories: heat-sensitive, relatively heat-sensitive, and heattolerant. The shape and strength of tolerance to high temperatures mostly depend on the plant species, tissue type and cells that are exposed to negative influences. The increase in ambient temperature can also jeopardize the productivity of agricultural crops and forest trees, which has been visible in recent decades in light of climate change and projections regarding the security of supply of the growing population on Earth. Injuries resulting from high temperatures during a fire can initiate a cascade of complex mechanisms that affect the physiology of trees after a fire. The discovery of exact physiological mechanisms and corresponding specific injuries that occur on individual trees, as well as in forest ecosystems, are the focus of intensive modern research. Recent studies have made critical strides in understanding the physiological processes in trees that manifest after fire injuries, and these injuries can affect the tree in combination with some other stressful conditions, such as drought and insect and pathogen attacks. The paper presents a conceptual framework that combines all these processes, their mutual interactions and possible responses, and puts these plant responses in the context of existing hypotheses about the impacts of specific disturbances on plants and ecosystems. By focusing on carbon and water as the main factors in the functioning of the plant organism, this paper presents cambium/phloem necrosis and xylem damage as the main effects of fire injuries. The resulting lack of carbon and hydraulic dysfunction of plants are associated with drought and insect attack. Assessing the precise relationships of the processes presented will be crucial to fully understanding how fires can affect tree functionality and will help improve fire risk assessment and predict tree mortality models. Knowledge of the physiological responses of trees is important for a better assessment of ecosystem dynamics after a fire and their interaction with climate disturbances, and especially taking into account the predicted increase in the frequency and intensity of fires.

Too hot, too cold, or just right: Can wildfire restore dry forests of the interior Pacific Northwest?

As contemporary wildfire activity intensifies across the western United States, there is increasing recognition that a variety of forest management activities are necessary to restore ecosystem function and reduce wildfire hazard in dry forests. However, the pace and scale of current, active forest management is insufficient to address restoration needs. Managed wildfire and landscape-scale prescribed burns hold potential to achieve broad-scale goals but may not achieve desired outcomes where fire severity is too high or too low. To explore the potential for fire alone to restore dry forests, we developed a novel method to predict the range of fire severities most likely to restore historical forest basal area, density, and species composition in forests across eastern Oregon. First, we developed probabilistic tree mortality models for 24 species based on tree characteristics and remotely sensed fire severity from burned field plots. We applied these estimates to unburned stands in four national forests to predict post-fire conditions using multi-scale modeling in a Monte Carlo framework. We compared these results to historical reconstructions to identify fire severities with the highest restoration potential. Generally, we found basal area and density targets could be achieved by a relatively narrow range of moderate-severity fire (roughly 365-560 RdNBR). However, single fire events did not restore species composition in forests that were historically maintained by frequent, low-severity fire. Restorative fire severity ranges for stand basal area and density were strikingly similar for ponderosa pine (Pinus ponderosa) and dry mixed-conifer forests across a broad geographic range, in part due to relatively high fire tolerance of large grand (Abies grandis) and white fir (Abies concolor). Our results suggest historical forest conditions created by recurrent fire are not readily restored by single fires and landscapes have likely passed thresholds that preclude the effectiveness of managed wildfire alone as a restoration tool.

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.

Predictive accuracy of post-fire conifer death declines over time in models based on crown and bole injury.

A key uncertainty of empirical models of post-fire tree mortality is understanding the drivers of elevated post-fire mortality several years following fire, known as delayed mortality. Delayed mortality can represent a substantial fraction of mortality, particularly for large trees that are a conservation focus in western US coniferous forests. Current post-fire tree mortality models have undergone limited evaluation of how injury level and time since fire interact to influence model accuracy and predictor variable importance. Less severe injuries potentially serve as an indicator for vulnerability to additional stressors such as bark beetle attack or moisture stress. We used a collection of 164,293 individual tree records to examine post-fire tree mortality in eight western USA conifers: Abies concolor, Abies grandis, Calocedrus decurrens, Larix occidentalis, Pinus contorta, Pinus lambertiana, Pinus ponderosa, and Pseudotsuga menziesii. We evaluated the importance of fire injury predictors on discriminating between surviving trees versus immediate and delayed post-fire mortality. We fit balanced random forest models for each species using cumulative tree mortality from 1 to 5-years post-fire. We compared these results to multi-class random forest models using first-year mortality, 2-5-year mortality, and survival 5-years post-fire as a response variable. Crown volume scorched, diameter at breast height, and relative bark char height, were used as predictor variables. The cumulative mortality models all predicted trees that died within 1-year of fire with high accuracy but failed to predict 2-5-year mortality. The multi-class models were an improvement but had lower accuracy for predicting 2-5-year mortality. Multi-class model accuracies ranged from 85% to 95% across all species for predicting 1-year post-fire mortality, 42%-71% for predicting 2-5-year mortality, and 64%-85% for predicting trees that lived past 5-years. Our study highlights the differences in tree species tolerance to fire injury and suggests that including second-order predictors such as beetle attack or climatic water stress before and after fire will be critical to improve accuracy and better understand the mechanisms and patterns of fire-caused tree death. Random forest models have potential for management applications such as post-fire harvesting and simulating future stand dynamics.

Multi-scale quantification of anthropogenic, fire, and drought-associated forest disturbances across the continental U.S., 2000–2014

Our understanding of broad-scale forest disturbances under climatic extremes remains incomplete. Drought, as a typical extreme event, is a key driver of forest mortality but there have been no reports on continental-scale quantification of its impact on forest mortality or how it compares to other natural or anthropogenic drivers. Thus, our ability to understand and predict broad-scale carbon cycling in response to changing climate and extreme events is limited. In this study, we applied an attribution approach based on different sources of data to quantify the area and potential carbon loss/transfer in continental U.S. (CONUS) from four types of disturbance: (1) anthropogenic (especially timber harvest); (2) fire; (3) drought-associated; and (4) other from 2000 to 2014. Our results showed that anthropogenic disturbances, fire, drought-associated disturbances, and other disturbances accounted for 54.3, 10.7, 12.7, and 22.3% of total canopy area loss, respectively. Anthropogenic disturbance was the most important driver contributing to 58.1% potential carbon loss/transfer in CONUS for 2000–2014. The potential carbon loss/transfer from natural disturbances (fire, drought, and other) for the same study period accounted for approximately 41.9% of the total loss/transfer from all agents, suggesting that natural disturbances also played a very important role in forest carbon turnover. Potential carbon loss/transfer associated with drought accounted for approximately 11.6% of the total loss/transfer in CONUS, which was of similar magnitude to potential carbon loss/transfer from fire (∼11.0%). The other natural disturbance accounted for 19.3% of potential carbon loss/transfer. Our results demonstrated the importance of the impacts of various disturbances on forest carbon stocks at the continental scale, and the drought-associated carbon loss/transfer data developed here could be used for evaluating the performance of predictive models of tree mortality under droughts.

ForestexplorR: an R package for the exploration and analysis of stem‐mapped forest stand data

Stem‐mapped forest stands offer important opportunities for investigating the fine‐scale spatial processes occurring in forest ecosystems. These stands are areas of the forest where the precise locations and repeated size measurements of each tree are recorded, thereby enabling the calculation of spatially‐explicit metrics of individual growth rates and of the entire tree community. The most common use of these datasets is to investigate the drivers of variation in forest processes by modeling tree growth rate or mortality as a function of these neighborhood metrics. However, neighborhood metrics could also serve as important covariates of many other spatially variable forest processes, including seedling recruitment, herbivory and soil microbial community composition. Widespread use of stem‐mapped forest stand datasets is currently hampered by the lack of standardized, efficient and easy‐to‐use tools to calculate tree dynamics (e.g. growth, mortality) and the neighborhood metrics that impact them. We present the forestexplorR package that facilitates the munging, exploration, visualization and analysis of stem‐mapped forest stands. By providing flexible, user‐friendly functions that calculate neighborhood metrics and implement a recently‐developed rapid‐fitting tree growth and mortality model, forestexplorR broadens the accessibility of stem‐mapped forest stand data. We demonstrate the functionality of forestexplorR by using it to investigate how the species identity of neighboring trees influences the growth rates of three common tree species in Mt Rainier National Park, WA, USA. forestexplorR is designed to facilitate researchers to incorporate spatially‐explicit descriptions of tree communities in their studies and we expect this increased diversity of contributors to develop exciting new ways of using stem‐mapped forest stand data.

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.

Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp. principis-rupprechtii in Northern China

Tree mortality models play an important role in predicting tree growth and yield, but existing mortality models for Larix gmelinii subsp. principis-rupprechtii, an important species used for regeneration and afforestation in northern China, have overlooked potential regional influences on tree mortality. This study used data acquired from 102 temporary sample plots (TSPs) in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest (n = 67) and state-owned Boqiang Forest (n = 35) in northern China. To model stand-level tree mortality, we compared seven model forms of county data. Three continuous (dominant height, plot mean diameter, and basal area per hectare) and one dummy variable with two levels (region) were used as fixed effects variables. Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models. Results showed that tree mortality significantly positively correlated with stand basal area and dominant height, but negatively correlated with stand mean diameter. Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements, and Hurdle Poisson mixed-effects model showed the most attractive fit statistics (largest R2 and smallest RMSE) when employing leave-one-out cross-validation. These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.

Postfire tree mortality and fire resistance patterns in pine forests of Ukraine

Abstract The study was conducted in pure Scots pine (Pinus sylvestris L.) stands within the forest steppe physiographic region of Ukraine damaged by surface fires with different intensity. The aim of the research is to determine the effect of different fire intensity on pine stand and individual trees, considering tree morphometric parameters and type of damage. The intensity and duration of fire-related tree mortality was different in stands with different age. We found that tree fire resistance is driven by tree diameter, height of the rough bark, and natural degree of thickness. The proportion of dead trees one year after the spring fires in the middle-aged pine stands was 5 times lower and in mature pine stands even 10 times lower than after the summer fires. The critical damage to tree crowns in young pine trees causing their death is 80% of the needles burned. In the middle-aged pine trees, critical damage depended on the size of trees. The death of large, mature trees after smoldering summer fires was associated with the accumulation of a large stock of forest litter and duff near the tree-base, which contributed to the increased intensity of fire and its localization near the base part of the trees. Based on our findings, postfire tree mortality models have been developed for different age groups of pine stands.

Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate

Self-thinning dynamics are often considered when managing stand density in forests and are used to constrain forest growth models. However, self-thinning relationships are often quantified using only data at a conceptualised self-thinning line, even though self-thinning can begin before the stand actually reaches a self-thinning line. Also, few self-thinning relationships account for the effects of species composition in mixed-species forests, and stand structure such as relative height of species (in mixtures), and/or size or age cohorts in uneven-aged forests. Such considerations may be important given the effects of global climate change and interest in mixed-species and uneven-aged forests.The objective of this study was to develop self-thinning relationships based on changes in the tree density relative to mean tree diameter, instead of focusing only on data for state variables (e.g. tree density) at the self-thinning line. This was done while also considering how the change in tree density is influenced by site quality and stand structure (species composition and relative height).The relationships were modelled using data from temperate Australian Eucalyptus plantations (436 plots), subtropical forests in China (88 plots), and temperate forests in Switzerland (1055 plots). Zero-inflated and hurdle generalized linear models with Poisson and negative binomial distributions were fit for several species, as well as for all-species equations.The intercepts and slopes of the self-thinning lines were higher than many published studies which may have resulted from both the less restrictive equation form and data selection. The rates of self-thinning often decreased as the proportion of the object species increased, as relative height increased (species or size cohort became more dominant), and as site (quality) index increased. The effects of aridity varied between species, with self-thinning increasing with aridity index for Abies alba, Pinus sylvestris, Quercus petraea and Quercus robur, but decreasing with aridity index for Eucalyptus nitens, Fagus sylvatica and Picea abies as sites became wetter and cooler. Self-thinning model parameters were not correlated with species traits, including specific leaf area, wood basic density or crown diameter – stem diameter allometry. All-species self-thinning relationships based on all data could be adjusted using a correction factor for rarer species where there were insufficient data to develop species-specific equations.The approach and equations developed could be used in forest growth models to calculate how the tree density declines as mean tree size increases, as height changes relative to other cohorts or species, as species proportions change, and as climatic and edaphic conditions change.

A Matter of Life and Death: Alternative Stable States in Trees, From Xylem to Ecosystems

Global forests are experiencing widespread climate-induced mortality. Predicting this phenomenon has proven difficult, despite recent advances in understanding physiological mechanisms of mortality in individual trees along with environmental drivers of mortality at broad scales. With heat and drought as primary climatic drivers, and convergence on hydraulic failure as a primary physiological mechanism, new models are needed to improve our predictions of Earth’s forests under future climate conditions. While much of ecology focuses on equilibrium states, transitions from one stable state to another are often described with alternative stable state theory (ASST), where systems can settle to more than one stable condition. Recent studies have identified threshold responses of hydraulic failure during tree mortality, indicating that alternative stable states may be present. Here, I demonstrate that the xylem of trees has characteristics indicative of alternative stable states. Through empirical evidence, I identify a catastrophic shift during hydraulic failure which prevents trees from returning to pre-droughted physiological states after environmental stressors (e.g., drought, heat) are relieved. Thus, the legacy of climate-induced hydraulic failure likely contributes to reduced resilience of forests under future climate. I discuss the implications and future directions for including ASST in models of tree mortality.

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

BackgroundPredictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016.ResultsFor each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower.ConclusionsApproximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.

A climate-sensitive transition matrix growth model for uneven-aged mixed-species oak forests in North China

AbstractOak-dominated forests, economically and ecologically valuable ecosystems, are widely distributed in China. These oak-dominated forests are now generally degraded coppice forests, and are of relatively low quality. Climate change has been shown to affect forest growth, tree mortality, and recruitment, but available forest growth models are lacking to study climate effects. In this study, a climate-sensitive, transition-matrix growth model (CM) was developed for uneven-aged, mixed-species oak forests using data collected from 253 sample plots from the 8th (2010) and 9th (2015) Chinese National Forest Inventory in Shanxi Province, China. To investigate robustness of the model, we also produced a variable transition model that did not consider climate change (NCM), and fixed parameter transition matrix model (FM), using the same data. Short-term and long-term predictive performance of CM, NCM, and FM were compared. Results indicated that for short-term prediction (5 years), there was almost no significant difference among the three predictive models, though CM exhibited slightly better performance. In contrast, for long-term prediction (100 years), CM, under the three representative concentration pathways (RCPs), i.e. RCP2.6, RCP4.5 and RCP8.5, indicated rather different dynamics that were more reliable because climate factors were considered which could significantly influence forest dynamics, especially in long-term prediction intervals. The CM model provides a framework for the management of mixed-species oak forests in the context of climate change.

Прогнозування постпірогенного відпаду в сосняках, пошкоджених літніми низовими пожежами, у Лівобережному Лісостепу України

Пожежі є одними із найнебезпечніших чинників дестабілізації лісів. У зв’язку зі зростанням посушливості клімату ризик збільшення частоти й масштабів лісових пожеж залишається високим, тому актуальним є розроблення науково обґрунтованих заходів із пом’якшення екологічних, економічних та соціальних наслідків лісових пожеж. Удосконалено та уточнено показники, що характеризують візуальні прояви пошкодження дерев після низових пожеж, та показники вогнестійкості середньовікових дерев сосни звичайної (Pinus sylvestris L.). Виявлено, що в міру збільшення середньої висоти нагару на стовбурах зростає частка відпаду в досліджуваних групах дерев (r = 0,87, tф = 5,80, t0,01 = 3,17). Встановлено, що вогнестійкість дерев збільшується зі збільшенням природного ступеня товщини дерева в насадженні (виявлено обернений достовірний кореляційний зв’язок між природним ступенем товщини (ПСТ) і категорією санітарного стану пошкоджених дерев: (r = -0,54; р = 0,05). Розроблено прогностичну модель для встановлення ймовірності відпаду дерев, пошкоджених низовими стійкими літніми пожежами, яка як предиктори включає середню висоту нагару на стовбурах та ПСТ (точність прогностичної моделі становить понад 78 %).