Relationship of site and stand characteristics to Armillaria root disease incidence on ponderosa pine in the Black Hills,South Dakota

Ponderosa pine (Pinus ponderosa) in the Black Hills National Forest, SD, USA, was surveyed for Armillaria root disease (ARD). The root pathogen Armillaria ostoyae occurred on ponderosa pine seedlings, saplings, pole‐size trees and large‐diameter trees. The mean incidence of aboveground disease symptoms by stem count was low (0.2%), but in certain areas, the incidence was higher, affecting the regeneration success and tree longevity. Symptomatic ponderosa pine were in areas characterized by having higher elevation, greater annual precipitation, more seedlings, bigger large‐diameter trees and greater odds of past harvesting activity than in areas without root disease. Stump density was positively spatially correlated with root disease incidence. No particular soil type was related to disease occurrence; though, in areas with symptomatic trees, soil available water holding capacity (AWC) was greater and soil permeability was less where root disease was present. Spatial analysis confirmed the relationships found in linear correlations, with soil AWC and stump density positively and soil permeability negatively correlated with per cent infected stems ha^?1 and basal area infected.     

Models for prediction of mortality in even‐aged forest

Models for predicting mortality in even‐aged stands were developed. The models rely on data from the Norwegian National Forest Inventory, and were designed for use in large‐scale forestry scenario models. A two‐step modelling strategy was applied: (1) logistic regression models predicting the probability of complete survival occurring,” and (2) multiplicative regression models for stem number reduction and diameter calibration. A joint model for all species predicting the probability of survival occurring on a plot was developed. Separate models for forests dominated by spruce, pine and broadleaved trees were developed for stem number reduction, while no appropriate models for diameter calibration were found. The phenomenon mortality is a stochastic, rare and irregular event, and this was reflected as low R ² in the models. However, the model performance appeared logical and the results of validations based on independent data were reasonably good, i.e. the presented models may be applied to large‐scale forestry scenario analyses. With new rotations of permanent sample plot measurements, the models should be evaluated and, if necessary, revised.     

Multi-scale nest-site selection by black-backed woodpeckers in outbreaks of mountain pine beetles

Areas of mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreaks in the Black Hills can provide habitat for black-backed woodpeckers (Picoides arcticus), a U.S. Forest Service, Region 2 Sensitive Species. These outbreaks are managed through removal of trees infested with mountain pine beetles to control mountain pine beetle populations and salvage timber resources. To minimize impacts to black-backed woodpeckers while meeting management objectives, there is a need to identify characteristics of these areas that support black-backed woodpeckers. We examined the habitat associations of this species nesting in areas of beetle outbreaks in the Black Hills, South Dakota in 2004 and 2005. We used an information theoretic approach and discrete choice models to evaluate nest-site selection of 42 woodpecker nests at 3 spatial scales—territory, nest area, and nest tree. At the territory scale (250 m around nest), availability and distribution of food best explained black-backed woodpecker selection of beetle outbreaks versus the surrounding forest. Selection at the territory scale was positively associated with densities of trees currently infested by mountain pine beetles and indices of wood borer (Cerambycidae and Buprestidae) abundance, and was greatest at distances of 50–100 m from the nearest patch of infestation. At the nest-area scale (12.5 m radius around the nest), densities of snags positively influenced nest-area selection. Finally, at the nest-tree scale, aspen (Populus tremuloides) and 3–5-year-old ponderosa pine (Pinus ponderosa) snags were important resources. The association between abundant wood-boring insects and black-backed woodpeckers creates a difficult challenge for forest managers. In the absence of fire, areas of beetle outbreak might serve as the only substantial source of habitat in the Black Hills. Regulating insect populations via salvage logging will reduce key food resources to black-backed woodpeckers during nesting. Therefore, given the relatively infrequent occurrence of large-scale fire in the Black Hills, management should recognize the importance of beetle-killed forests to the long-term viability of the black-backed woodpecker population in the Black Hills.     

Characterizing Spatial Distributions of Insect Pests Across Alaskan Forested Landscape: A Case Study Using Aspen Leaf Miner (Phyllocnistis populiella Chambers)

Insects are ectotherms that cannot regulate their own temperature, and thus rely on and are at the disposal of the surrounding environment. In this study, long-term climatic data are used to stratify forested regions of Alaska into climatic zones based on temperature and precipitation. Temperature and precipitation are shown to be important ecological drivers in determining the distribution of aspen leaf minor (Phyllocnistis populiella Chambers) and the aspen (Populus tremuloides Michx.) host in the state of Alaska. Climatic regions based on temperatures and precipitation accounted for 83 to 97% of the variability in the probability of observing aspen and the aspen leaf minor (ALM). The frequency of observing aspen was highest throughout the central region of the state, which represents a climate with low to moderate levels of precipitation and cold to mild temperatures. The highest probability of observing aspen was in the mild-very cold region of the state. The probability of observing ALM in a given climate zone followed a pattern similar to aspen. Differences were in the colder and drier climate zones where the probability of observing ALM decreased to near zero. The derived climatic models could be used to provide a basis for the analysis of climatic impacts on the distribution of forest insects throughout the state.     

Susceptibility of ponderosa pine, Pinus ponderosa (Dougl. ex Laws.), to mountain pine beetle, Dendroctonus ponderosae Hopkins,attack in uneven-aged stands in the Black Hills of South Dakota and Wyoming USA

Mountain pine beetle, Dendroctonus ponderosae Hopkins can cause extensive tree mortality in ponderosa pine, Pinus ponderosa Dougl. ex Laws., forests in the Black Hills of South Dakota and Wyoming. Most studies that have examined stand susceptibility to mountain pine beetle have been conducted in even-aged stands. Land managers increasingly practice uneven-aged management. We established 84 clusters of four plots, one where bark beetle-caused mortality was present and three uninfested plots. For all plot trees we recorded species, tree diameter, and crown position and for ponderosa pine whether they were killed or infested by mountain pine beetle. Elevation, slope, and aspect were also recorded. We used classification trees to model the likelihood of bark beetle attack based on plot and site variables. The probability of individual tree attack within the infested plots was estimated using logistic regression. Basal area of ponderosa pine in trees ≥25.4 cm in diameter at breast height (dbh) and ponderosa pine stand density index were correlated with mountain pine beetle attack. Regression trees and linear regression indicated that the amount of observed tree mortality was associated with initial ponderosa pine basal area and ponderosa pine stand density index. Infested stands had higher total and ponderosa pine basal area, total and ponderosa pine stand density index, and ponderosa pine basal area in trees ≥25.4 cm dbh. The probability of individual tree attack within infested plots was positively correlated with tree diameter with ponderosa pine stand density index modifying the relationship. A tree of a given size was more likely to be attacked in a denser stand. We conclude that stands with higher ponderosa pine basal area in trees >25.4 cm and ponderosa pine stand density index are correlated with an increased likelihood of mountain pine beetle bark beetle attack. Information form this study will help forest managers in the identification of uneven-aged stands with a higher likelihood of bark beetle attack and expected levels of tree mortality.     

Using multi-source data to map and model the predisposition of forests to wind disturbance

Natural disturbances such as wind are known to cause threats to ecosystem services as well as sustainable forest ecosystem management. The objective of this research was to better understand and quantify drivers of predisposition to wind disturbance, and to model and map the probability of wind-induced forest disturbances (P_DIS) in order to support forest management planning. To accomplish this, we used open-access airborne light detection and ranging (LiDAR) data as well as multi-source National Forest Inventory (NFI) data to model P_DIS in southern Finland. A strong winter storm occurred in the study area in December 2011. High spatial resolution aerial images, acquired after the disturbance event, were used as reference data. Potential drivers associated with P_DIS were examined using a multivariate logistic regression model. The model based on LiDAR provided good agreement with detected areas susceptible to wind disturbance (73%); however, when LiDAR was combined with multi-source NFI data, the results were more promising: prediction accuracy increased to 81%. The strongest predictors in the model were mean canopy height, mean elevation, and stem volume of the main tree species (Norway spruce and Scots pine). Our results indicate that open-access LiDAR data can be used to model and map the probability of predisposition to wind disturbance, providing spatially detailed, valuable information for planning and mitigation purposes.     

Structure of mixed Picea abies (L) Karst,and Betula pendula Roth and Betula pubescens Ehrh. stands in South and middle Sweden

Observed diameter distributions of forest stands are adapted to the Johnson S _b probability function. The stands investigated are untreated mixed stands of Norway spruce (Picea abies (L.) Karst.) and birch (Betula pendula Roth and Betula pubescens Ehrh.) aged between 20 and 32 years. The adaptation of the Johnson S_b probability function is made both on mixed spruce and birch, and on each species separately. Altogether 156 observations were tested with observed distributions against calculated distributions in the Kolmo‐gorov‐Smirnov test. The fractiles are predicted with multiple regression and two multivari‐ate techniques, simultaneous‐equation models (multivariate regression) and partial least squares with latent variables. The independent variables are characteristics of site and stand. Both multivariate methods predict diameter distribution well when tested.     

Relative size and stand age determine Pinus banksiana mortality

Tree mortality is a poorly understood process in the boreal forest. While large disturbances reset succession by killing all or most trees, background tree mortality was hypothesized to be affected by competition, ageing, and stand composition. We tested these hypotheses on jack pine (Pinus banksiana Lamb.) mortality using data from long-term repeatedly measured permanent sample plots collected between 1952 and 1989 in Ontario, Canada. The probability of mortality over a 5-year period was modeled using logistic regression with the maximum likelihood estimation employed for parameter estimation. Relative competitiveness measured as the ratio of individual tree diameter at breast height (DBH) to mean stand DBH explained more variation in mortality than stand age did. Mortality increased rapidly with decreasing DBH ratio. A U-shaped mortality pattern with stand age was found while stand composition had no effect on mortality. Developed by using a residual sequential regression approach, our final mixed-effects model with a 81% model correctness of mortality prediction conclusively demonstrated that relative competitiveness is the key determinant for jack pine mortality.     

Risk of white pine blister rust to limber pine in Colorado and Wyoming,USA

Cronartium ribicola, the introduced pathogen that causes white pine blister rust (WPBR), continues to spread to additional limber pine populations in the Southern Rocky Mountains of Colorado and Wyoming. Because WPBR can severely impact ecosystems, forecasts of its potential distribution and incidence would be useful to land managers. Site and climate data from long infested study areas in Wyoming were fit with two regression models [logistic and classification and regression trees (CART)] to determine the environmental conditions associated with the distribution of WPBR. These models were then used to map limber pine stands at risk of infestation by C. ribicola throughout Wyoming (where it has long occurred) and Colorado (where it is just becoming established). Although variables representing vegetation and landform could identify infested plots, 1‐km‐scale climate variables for monthly temperature and moisture were better predictors of current WPBR distribution and were available for mapping expected future distribution across the region. Of 280 485 ha where limber pine was projected to occur in Colorado, 41% was forecast by the logistic model to be at risk of infestation, and 53%, by the CART model. Of an estimated 782 229 ha in Wyoming with limber pine, the logistic model projected 61% to be at risk; CART projected 79%. Additional regression models were fit with site and climate data to predict WPBR incidence (per cent of trees infected) and intensification (incidence/age of the oldest canker). Nearly one half of the plot‐to‐plot variation in incidence was explained using environmental variables readily available to land managers. Although mean plot incidence increased over time, mean intensification decreased 50% per decade. This work provides managers with several tools to reduce uncertainty over the expected distribution and incidence of WPBR, but surveillance and monitoring remain prudent activities for supplementing forecasts of WPBR epidemics.     

Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data

About 90% of the wildland fires occurred in Southern Europe are caused by human activities. In spite of these figures, the human factor hardly ever appears in the definition of operational fire risk systems due to the difficulty of characterising it. This paper describes two spatially explicit models that predict the probability of fire occurrence due to human causes for their integration into a comprehensive fire risk–mapping methodology. A logistic regression technique at 1 × 1 km grid resolution has been used to obtain these models in the region of Madrid, a highly populated area in the centre of Spain. Socio-economic data were used as predictive variables to spatially represent anthropogenic factors related to fire risk. Historical fire occurrence from 2000 to 2005 was used as the response variable. In order to analyse the effects of the spatial accuracy of the response variable on the model performance (significant variables and classification accuracy), two different models were defined. In the first model, fire ignition points (x, y coordinates) were used as response variable. This model was compared with another one (Kernel model) where the response variable was the density of ignition points and was obtained through a kernel density interpolation technique from fire ignition points randomly located within a 10 × 10 km grid, which is the standard spatial reference unit established by the Spanish Ministry of Environment, Rural and Marine Affairs to report fire location in the national official statistics. Validation of both models was accomplished using an independent set of fire ignition points (years 2006–2007). For the validation, we used the area under the curve (AUC) obtained by a receiver-operating system. The first model performs slightly better with a value of AUC of 0.70 as opposed to 0.67 for the Kernel model. Wildland–urban interface was selected by both models with high relative importance.     

Involvement of Phytophthora spp. in chestnut decline in the Black Sea region of Turkey

Chestnut blight caused by Cryphonectria parasitica is a serious disease of Castanea sativa in the Black Sea region of Turkey. During disease surveys, dieback and decline symptoms were observed on trees without apparent blight and ink disease symptoms. Black necroses, similar to those caused by Phytophthora infections, were noted on some of the chestnut coppices and saplings in one nursery in Ordu and led to an investigation into this disease complex. Only symptomatic plants showing dieback symptoms were investigated. Soil samples together with fine roots were collected from two directions, north and north‐east, approximately 150 cm away from the main stems. Phytophthora spp. were baited with young chestnut leaves. Three Phytophthora spp., P. cambivora, P. cinnamomi and P. plurivora, were identified from 12 soil samples collected from 73 locations, while from the nurseries, only P. cinnamomi was obtained. Phytophthora cinnamomi was the most common species, obtained from seven locations in five provinces and from four nurseries having similar symptoms mentioned above in different locations. Phytophthora cambivora and P. plurivora were less frequently obtained, from three to two stands, respectively. Phytophthora cinnamomi and P. cambivora were the most aggressive species when inoculated at the stem base on 3‐year‐old chestnut saplings, killing six saplings of eight inoculated in 2 months. The three Phytophthora species were first recorded on chestnut in Black sea region of Turkey with the limited samples investigated in a large area about 150 000 ha chestnut forest.     

Ingrowth model for pyrenean oak stands in north-western Spain using continuous forest inventory data

In this paper, a two stage ingrowth model is presented for predicting periodic, 10 years ingrowth for pyrenean oak (Quercus pyrenaica Willd.) grown in medium to fully stocked coppice stands in north-western Spain. Data from the Spanish National Forest Inventory was used to develop the model, extracting the information from two inventories taken in 222 permanent plots. The first stage of the model predicts the probability of ingrowth occurrence, and in the second stage, the number of recruits is predicted using a conditional model. Both models were biologically realistic and presented logical behaviour. The ingrowth occurrence probability model was dependent on quadratic mean diameter and average height. The recruitment quantification model included stand density and average diameter as explanatory variables. Although the occurrence probability of ingrowth was predicted correctly in 71.7% of cases, the predictions of the number of recruitment are poorer, yielding a coefficient of determination of 0.358. The evaluation criteria included qualitative and quantitative examinations and a testing with independent data from another region. The proposed ingrowth model is the first to be developed for mediterranean oak species in Spain and is an essential feature in any stand growth system.     

Spatio-temporal analysis of forest fire events in the Margalla Hills,Islamabad,Pakistan using socio-economic and environmental variable data with machine learning methods

Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study consid-ers both environmental (altitude,precipitation,forest type,terrain and humidity index) and socioeconomic (popula-tion density,distance from roads and urban areas) factors to analyze how human behavior affects the risk of forest fires.Maximum entropy (Maxent) modelling and random forest (RF) machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC) curve and the area under the ROC curve (AUC) were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the prob-ability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999s,2009s,and 2019s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to acces-sibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by consid-ering socio-economic and environmental conditions.     

Forest inventory inference with spatial model strata

ABSTRACT

In design-based model assisted inference from data gathered in a large area forest inventory under a probability sampling design, one should anticipate spatial heterogeneity in the regression coefficients of an assisting model. The consequence of such heterogeneity is that a global estimate of a root mean squared error (RMSE) becomes unsuited for local predictions. With data from the Danish National Forest Inventory, we demonstrate how to: obtain an assisting model with the lasso method; test for spatial stationarity in regression coefficients of the assisting model; and identify spatial model strata for a post-stratification with either a finite mixture modeling or a lasso spatial clustered coefficients method. Spatial model strata apply to any domain and small area estimation problem without the need for complex modeling when domains or small area changes with shifting user needs. One should not à priori expect a spatial model stratification to improve design-based population and strata estimates of precision, but the reliability of domain and small area RMSEs will improve in presence of statistically significant spatial model strata.     

The maturation of Pinus sylvestris seeds in relation to temperature climate in Northern Finland

The probability of achieving an anatomically mature seed crop of Scots pine (Pinus sylvestris L.) in Northern Finland has been studied on the basis of earlier data and meteorological observations. The meteorological data over the period of 1951–1980 was used to calculate the probability of having a mature seed crop in various parts of Northern Finland. The climatic variables used were: (a) June‐August mean temperature (b) June—September mean temperature, and (c) annual temperature sum (+5°C base temperature). A non‐linear relationship between the percentage of mature seeds in a seed crop and the annual temperature sum was detected, indicating that 50% mature seed crop was achieved at appr. 890 degree days. The results suggest that the probability of having 50% mature seed crop is approximately 0.02–0.03 near the polar timber line. The probability of the occurrence of such years is, however, considerably higher in lowlands and lake districts even near the timber line.     

Competition as a predisposing factor of crown dieback in a secondary forest of <Emphasis Type="Italic">Betula maximowicziana</Emphasis> in Hokkaido,northern Japan

We analyzed the probability that Betula maximowicziana Regel (monarch birch) would suffer crown dieback (crown-dieback probability) and the basal area growth rate (GB), which was found to be a predisposing stress factor making birch trees susceptible to crown dieback. First, we analyzed the relationship between the probability that birch trees would suffer from crown dieback in 1999 and GB from a period prior to the occurrence of crown dieback (1985–1987), using a data set of repeated measurements on 217 trees. Logistic regression analysis revealed that monarch birch had a larger crown-dieback probability when GB was low in the preceding period. Hence, there were predisposing stress factors that reduced GB and continued to affect trees for at least a decade. Next, we analyzed GB in the same period in relation to symmetrical and asymmetrical competition between trees and found that GB was reduced by symmetrical competition, suggesting that this was one of the predisposing factors for crown dieback. Based on these results, we used selected models for crown-dieback probability and GB to calculate crown-dieback probabilities for individuals with different initial basal areas and experiencing different intensities of symmetrical competition. The predicted crown-dieback probability decreased with decreasing symmetrical competition between trees. We discuss a possible process of crown dieback to death for monarch birch and the use of thinning as a method to reduce the risk of crown dieback.     

A reconstruction of Turkey's potential natural vegetation using climate indicators

Turkey, containing three of the world's biodiversity hotspots, is a hub for genetic biodiversity. However,the vegetation cover has drastically changed in recent decades as a result of substantial transformations in landuse practices. A map of the potential natural vegetation can be used to represent the biodiversity of a country, and therefore a reference to effectively develop conservation strategies. The multinomial logistic regression is used to simulate the probability of different biomes occurring in the country using elevation, climatological data and natural vegetation data. A correlation test was applied to the climatological data to determine which predictors influence vegetation the most. These were temperature, precipitation,relative humidity and cloudiness. The Ordinary Kriging method was employed to transform the data into the format for the multinomial logistic regression model. The model showed that temperature was the most influencing factor with respect to Turkey's vegetation and distribution follows a similar distribution as the various macroclimates.Broadleaf forests are mostly found in the Black Sea region,which is also the wettest region of the country. The Marmara region is the only other region where there are broadleaf forests. Mixed forests and shrublands are mostly located in Central Anatolia due to the region's low humidity which favours herbaceous flora. Coniferous forests were dominant in the Aegean and Mediterranean regions, attributed to high temperatures.     

Comparison of the potential spread of pinewood nematode (Bursaphelenchus xylophilus) in Finland and Iberia simulated with a cellular automaton model

Pinewood nematode (PWN) is one of the most threatening invasive pests in the pine forests of Europe, and it has recently spread to the Iberian Peninsula via import of timber and wooden packaging material from East Asia. A cellular automaton (CA) model was developed to simulate and compare the potential spread of PWN by transportation and its vectors, Monochamus beetles in the pine forests of Finland and Iberian Peninsula. The model assumes that all pines are equally sensitive to PWN. The CA is a spatio‐temporal grid‐based model, which can easily be applied on different geographical scales. The effects of climate warming and number of entries from ports on the spread of PWN were studied. A sensitivity analysis was conducted on the most uncertain model parameters. Twenty years after hypothetical entries, the predicted area of symptomatic PWN infection (pine wilt disease, PWD) was very low in Finland compared to Iberia. This was because of the low probability of warm July in Finland. The increase in the mean July temperature increased the area of PWD‐infected pine forest relatively more in Finland than in Iberia. An increase in the number of entries also increased the area of PWD‐infected pine forest relatively more in Finland than in Iberia. The probability of PWD infection was the highest in pine forests that were close to entry points and in areas with low elevation and high human population density.