Discriminating Tsuga canadensis Hemlock Forest Defoliation Using Remotely Sensed Change Detection

The eastern hemlock (Tsuga Canadensis) is declining in health and vigor in eastern North America due to infestation by an introduced insect, the hemlock woolly adelgid (Adelges isugue). Adelgid feeding activity results in the defoliation of hemlock forest canopy over several years. We investigated the application of Landsat satellite imagery and change-detection techniques to monitor the health of hemlock forest stands in northern New Jersey. We described methods used to correct effects due to atmospheric conditions and monitor the health status of hemlock stands over time. As hemlocks defoliate, changes occur in the spectral reflectance of the canopy in near infrared and red wavelengths—changes captured in the Normalized Difference Vegetation Index. By relating the differences in this index over time to hemlock defoliation on the ground, four classes of hemlock forest health were predicted across spatially heterogeneous landscapes with 82% accuracy. Using a time series of images, we are investigating temporal and spatial patterns in hemlock defoliation across the study area over the past decade. Based on the success of this methodology, we are no expanding out study to monitor hemlock health across the entire Mid-Atlantic region.     

Using Satellite Images to Classify and Analyze the Health of Hemlock Forests Infested by the Hemlock Woolly Adelgid

A method is described to classify stands of eastern hemlock by health condition, at the landscape level, using remote sensing. The hemlock woolly adelgid has been a major cause of hemlock decline in Connecticut since 1985, resulting in varying degrees of defoliation in the region. A 1985 Landsat Thematic Mapper (TM) image was classified to develop a base line of once healthy hemlock stands. Radiance normalization and non-hemlock masking techniques were used to pre-process a 1995 TM image. Several techniques were used to transform the 1995 TM image; each was followed by cluster analysis to separate hemlocks into four levels of tree vigor. We evaluated 600 trees at 150 sites across the study area using the USFS Crown Condition Rating Guide. These field data were used to measure the accuracy of various health classification techniques. The Modified Soil Adjusted Vegetation Index-2 (MSAVI₂) transform provided the best overall accuracy, 82.1%, for classifying hemlock according to tree vigor. Non-parametric statistics were used to determine if there were any significant variations in distribution of hemlock pixels by health class in association with features in the landscape. Several features were found to be statistically significant at a confidence level of 0.001. These were aspect of slope, hydrology group (infiltration rate), depth to bedrock, soil order, drainage class (hydraulic conductivity), and surface texture.     

Effects of an afforestation process on plant species richness: A retrogressive analysis

Effective conservation of biodiversity in the face of increasing human impacts and global environmental changes requires accurate measurement of key trends and alternative management actions at landscape scales. Past ecological conditions are certainly important key factors in determining the present species diversity patterns and the inclusion of such factors (e.g. by historical cartographic data) can dramatically improve the predictive power of ecological models. In this paper we applied a retrogressive approach with the aim of simulating secondary forest regrowth effects on plant species diversity using present field data and historic land-use maps. The field data from an extensive sample were here used to model the temporal species richness change among the forest areas in the last 60 years. In order to rebuild the past species pool matrix using present field data and historical land use map, we applied a nearest neighbour selection using spatial query. Species-based rarefaction curves were derived for the two dates (1954 and 2010); the two datasets have been interpolated using inverse distance weighted algorithm, obtaining two maps showing the distribution of plant species richness for the two dates.The results showed that the cessation of human pressure on semi-natural areas and the consequent forest recovery, resulted in a decrease of vascular plant as a woodland flora replaces the open habitats flora. This study also showed that secondary forest regrowth and its effect on plant species diversity may be revealed by a retrogressive analysis, which represent a valid support in case of high uncertainty or absence of historical data.     

Impacts of forest cover change on ecosystem services in high Andean mountains

Land conversion affects the delivery of ecosystem goods and services. In this study, we used a 50 years time series of land cover maps to assess the potential impacts of forest cover changes on ecosystem services. A multi-source data integration strategy was followed to reduce inconsistencies in land cover change detection that result from the comparison of historical aerial photographs and satellite images. Our forest cover change analysis highlighted a shift from net deforestation to net reforestation in the early 1990s, consistent with the forest transition theory. When taking the nature of forest cover changes into account, our data show that the areal increase of the forested area was not associated with an improvement in ecological conditions. The overall capacity of the landscape to deliver ecosystem services dropped steadily by 16% over the 50 years’ study period. Conversion of native forests to agricultural land was associated with the strongest decline in ecosystem services. Conversion of natural grasslands into pine plantations mostly led to negative and probably irreversible impacts on the delivery of ecosystem services. Conversion of degraded agricultural lands into pine plantations led to an improvement in ecological conditions. An effective spatial targeting of forestation programs has the potential to maximize the environmental benefits that forest plantations may offer while minimizing their environmental harm.     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

Historical land‐use and landscape change in southern Sweden and implications for present and future biodiversity

The two major aims of this study are (1) To test the performance of the Landscape Reconstruction Algorithm (LRA) to quantify past landscape changes using historical maps and related written sources, and (2) to use the LRA and map reconstructions for a better understanding of the origin of landscape diversity and the recent loss of species diversity. Southern Sweden, hemiboreal vegetation zone. The LRA was applied on pollen records from three small bogs for four time windows between AD 1700 and 2010. The LRA estimates of % cover for woodland/forest, grassland, wetland, and cultivated land were compared with those extracted from historical maps within 3‐km radius around each bog. Map‐extracted land‐use categories and pollen‐based LRA estimates (in % cover) of the same land‐use categories show a reasonable agreement in several cases; when they do not agree, the assumptions used in the data (maps)‐model (LRA) comparison are a better explanation of the discrepancies between the two than possible biases of the LRA modeling approach. Both the LRA reconstructions and the historical maps reveal between‐site differences in landscape characteristics through time, but they demonstrate comparable, profound transformations of the regional and local landscapes over time and space due to the agrarian reforms in southern Sweden during the 18th and 19th centuries. The LRA was found to be the most reasonable approach so far to reconstruct quantitatively past landscape changes from fossil pollen data. The existing landscape diversity in the region at the beginning of the 18th century had its origin in the long‐term regional and local vegetation and land‐use history over millennia. Agrarian reforms since the 18th century resulted in a dramatic loss of landscape diversity and evenness in both time and space over the last two centuries leading to a similarly dramatic loss of species (e.g., beetles).     

Fusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests

Remote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi-season images to classify tropical tree species is still poorly understood. In this study, we used RGB images from different seasons obtained by a low-cost UAS and convolutional neural networks (CNNs) to map tree species in an Amazonian forest. Individual tree crowns (ITC) were outlined in the UAS images and identified to the species level using forest inventory data. The CNN model was trained with images obtained in February, May, August, and November. The classification accuracy in the rainy season (November and February) was higher than in the dry season (May and August). Fusing images from multiple seasons improved the average accuracy of tree species classification by up to 21.1 percentage points, reaching 90.5%. The CNN model can learn species-specific phenological characteristics that impact the classification accuracy, such as leaf fall in the dry season, which highlights its potential to discriminate species in various conditions. We produced high-quality individual tree crown maps of the species using a post-processing procedure. The combination of multi-season UAS images and CNNs has the potential to map tree species in the Amazon, providing valuable insights for forest management and conservation initiatives.     

Mapping historical forest types in Baraga County Michigan,USA as fuzzy sets

Data on tree location and species in a portion of Northern Michigan were gathered from General Land Office (GLO) survey notes (ca. 1850), digitized, and generalized to represent forest types. Fuzzy membership values describing the degree of membership of each species in each forest type were derived from (a) semantic information in the forestry literature and (b) a fuzzy clustering routine applied to data from randomly placed circular plots. The fuzzy membership values assigned to each tree point for each forest type were interpolated to form continuous surfaces using kriging and co-kriging. Advantages of this method over traditional discrete mapping methods include: (a) multiple options are available for the display and analysis; (b) classification uncertainty and the continuity of natural vegetation can be represented; and (c) the classification scheme is applied systematically across the entire map area and can be altered to produce alternative maps. The subset of available display and analytical products presented include: discrete forest type maps; a surface representing the confusion between forest types; fuzzy logical overlays of forest types; and discrete class maps with color value altered within each class to indicate degree of confusion at each location.     

Past and present forest vegetation in NE Slovenia derived from old maps

Abstract. Using old military and cadastral maps and modern vegetation maps, the changes in land use over the past 230 yr were followed. The following maps were used: the military map from the second half of the 18th century, the cadastral map of the ‘Economic cadastral survey for regulation of land taxes’ from the first half of the 19th century, and a field map made in the 1980s. A vegetation map of the area was made on the basis of satellite images. We used basic classification techniques combined with extensive field inspections and aerial photographs. The output of this procedure was verified in the field. Additionally, a comparison of statistical data about land use categories is presented. It was established that the last remaining areas of inundated riverine forest disappeared 200 yr ago, and since then only minor changes in land use have occurred.     

Moist lower montane rainforest classification: a case study from Bwindi Impenetrable National Park,Uganda

Moist lower montane vegetation has rarely been classified beyond broad zonational belts over large altitudinal ranges due to highly diverse species composition and structure. This study shows it is possible to further classify such forest types within Bwindi‐Impenetrable National Park (BINP), and that these assemblages can be explained by a combination of environmental conditions and past management. Botanical and environmental data were collected along some 4000 m of linear transects from the area surrounding Mubwindi Swamp, BINP. Ordination using Nonmetric Multidimensional Scaling (NMDS) and classification using Two‐way Indicator Species Analysis (TWINSPAN) successfully identified four different species assemblages. These forest types were then named on the basis of the ecological characteristics of the species within the group, and the environmental conditions influencing the distribution and past disturbance of the forest. The techniques used were in agreement for three out of the four forest types identified. Analysis using an environmental overlay showed a significant association between forest type and altitude. The results of this study indicate that a regional classification of forest types within moist lower montane forest belt using only tree species is possible, and that the forest types identified can be explained by environmental conditions and past management.     

不同龄组的热带森林植被生物量与遥感地学数据之间的相关性分析

 在森林植被生物量遥感动态监测方面最基础性的研究是探讨生物量与遥感数据及其派生数据、地形数据和气象数据之间的相关性。为此,以我国云南省西双版纳的热带森林植被为例,分别对幼龄林、中龄林、近熟林和成过熟林的生物量与其对应的LANDSAT TM数据及其派生数据、气象数据和地形数据之间的相关性进行了分析。首先,利用森林资源连续清查的林业固定样地数据,通过各树种组的各器官生物量估算模型计算出各样地森林植被的生物量,并根据样地的坐标来建立样地GIS数据库。然后,利用地形图对遥感图像进行几何校正,并对遥感图像进行主成分变换、缨帽变换以及植被指数的计算来产生其派生数据。其次,将栅格样地数据、遥感数据（如LANDSAT TM数据）及其派生数据（如各种植被指数数据、主成分数据、缨帽变换的亮度、绿度和湿度数据）、栅格地形数据（如DEM和坡向）和栅格气象数据（包括年平均温度、大于0 ℃的积温、年平均降雨量和湿润度）统一到同一坐标系和投影下,并将所有的数据内插为30 m分辨率的格网数据,利用样地数据与遥感数据及其派生数据、地形数据和气象数据进行栅格空间叠加分析,从而得到各样地的样地数据、遥感数据及其派生数据、地形数据和气象数据。再次,根据各样地优势树种所属的龄组将所有的数据层化为幼龄林、中龄林、近熟林和成过熟林等几个不同龄组的样本数据。最后,分别对幼龄林、中龄林、近熟林和成过熟林的样地生物量与其对应的遥感数据和派生数据、气象数据和地形数据进行相关性分析。研究表明,在所有的因子中,幼龄林的生物量与LANDSAT 的TM1和TM6波段的亮度值在0.05的水平上呈显著相关,其相关系数均为-0.33;中龄林的生物量与降雨量在0.05的水平上呈显著相关,其相关系数为0.33;近熟林的生物量与LANDSAT TM的派生数据VI3、LANDSAT的TM4和缨帽变换的亮度值在0.05的水平上呈显著相关,其相关系数分别为0.50、-0.45和-0.45;成过熟林的生物量与主成分变换的第二主成分（PC2）在0.05的水平上呈显著相关,其相关系数为-0.46。在0.05的水平上,近熟林的生物量与LANDSAT TM的派生数据VI3的相关系数最高,达到0.50,其次是成过熟林的生物量与主成分变换的第二主成分的相关系数,为-0.46。     

Drivers of truffle biomass,community composition,and richness among forest types in the northeastern US

Truffle-producing fungi (hypogeous sporocarps) are important mycorrhizal symbionts and provide a key food source for many animals, including small mammals. To better understand truffle diversity and associations in the northeastern US, we surveyed for truffles and analyzed spores in eastern chipmunk (Tamias striatus) scat across hardwood (angiosperm-dominated), softwood (gymnosperm-dominated), and mixed forest at Bartlett Experimental Forest, New Hampshire. Truffle biomass ranged from 3.8 kg/ha in hardwood forest to 31.4 kg/ha in softwood forest and was up to 35 times greater than mushroom (epigeous sporocarp) production in softwood forest. Elaphomyces species were the most common truffle taxa in both field surveys and chipmunk scat. Scat analysis indicated that truffle richness increased over the summer and accurately reflected fruiting time, providing greater resolution of richness than field surveys alone. Basal area of eastern hemlock (Tsuga canadensis) was the primary driver of Elaphomyces biomass and was the best explanatory variable of truffle community composition. We discuss implications of hemlock loss, due to the introduced hemlock woolly adelgid (Adelges tsugae), on forest mycorrhizal communities and food webs.     

Land-Use History as Long-Term Broad-Scale Disturbance: Regional Forest Dynamics in Central New England

Human land-use activities differ from natural disturbance processes and may elicit novel biotic responses and disrupt existing biotic-environmental relationships. The widespread prevalence of land use requires that human activity be addressed as a fundamental ecological process and that lessons from investigations of land-use history be applied to landscape conservation and management. Changes in the intensity of land use and extent of forest cover in New England over the past 3 centuries provide the opportunity to evaluate the nature of forest response and reorganization to such broad-scale disturbance. Using a range of archival data and modern studies, we assessed historical changes in forest vegetation and land use from the Colonial period (early 17th century) to the present across a 5000 km² area in central Massachusetts in order to evaluate the effects of this novel disturbance regime on the structure, composition, and pattern of vegetation and its relationship to regional climatic gradients. At the time of European settlement, the distribution of tree taxa and forest assemblages showed pronounced regional variation and corresponded strongly to climate gradients, especially variation in growing degree days. The dominance of hemlock and northern hardwoods (maple, beech, and birch) in the cooler Central Uplands and oak and hickory at lower elevations in the Connecticut Valley and Eastern Lowlands is consistent with the regional distribution of these taxa and suggests a strong climatic control over broad-scale vegetation patterns. We infer from historical and paleoecological data that intensive natural or aboriginal disturbance was minimal in the Uplands, whereas infrequent surface fires in the Lowlands may have helped to maintain the abundance of central hardwoods and to restrict the abundance of hemlock, beech, and sugar maple in these areas. The modern vegetation is compositionally distinct from Colonial vegetation, exhibits less regional variation in the distribution of tree taxa or forest assemblages defined by tree taxa, and shows little relationship to broad climatic gradients. The homogenization of the vegetation, disruption of vegetation-environment relationships, and formation of new assemblages appear to be the result of (a) a massive, novel disturbance regime; (b) ongoing low-intensity human and natural disturbance throughout the reforestation period to the present; (c) permanent changes in some aspects of the biotic and abiotic environment; and (d) a relatively short period for forest recovery (100–150 years). These factors have maintained the regional abundance of shade intolerant and moderately tolerant taxa (for example, birch, red maple, oak, and pine) and restricted the spread and increase of shade-tolerant, long-lived taxa such as hemlock and beech. These results raise the possibility that historical land use has similarly altered vegetation-environment relationships across broader geographic regions and should be considered in all contemporary studies of global change. Received 5 May 1997; accepted 5 August 1997.     

Movement Activity Based Classification of Animal Behaviour with an Application to Data from Cheetah (Acinonyx jubatus)

We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal''s behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be , but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.     

Habitat-Net: Segmentation of habitat images using deep learning

Understanding environmental factors that influence forest health, as well as the occurrence and abundance of wildlife, is a central topic in forestry and ecology. However, the manual processing of field habitat data is time-consuming and months are often needed to progress from data collection to data interpretation. To shorten the time to process the data we propose here Habitat-Net: a novel deep learning application based on Convolutional Neural Networks (CNN) to segment habitat images of tropical rainforests. Habitat-Net takes color images as input and after multiple layers of convolution and deconvolution, produces a binary segmentation of the input image. We worked on two different types of habitat datasets that are widely used in ecological studies to characterize the forest conditions: canopy closure and understory vegetation. We trained the model with 800 canopy images and 700 understory images separately and then used 149 canopy and 172 understory images to test the performance of Habitat-Net. We compared the performance of Habitat-Net to the performance of a simple threshold based method, manual processing by a second researcher and a CNN approach called U-Net, upon which Habitat-Net is based. Habitat-Net, U-Net and simple thresholding reduced total processing time to milliseconds per image, compared to 45 s per image for manual processing. However, the higher mean Dice coefficient of Habitat-Net (0.94 for canopy and 0.95 for understory) indicates that accuracy of Habitat-Net is higher than that of both the simple thresholding (0.64, 0.83) and U-Net (0.89, 0.94). Habitat-Net will be of great relevance for ecologists and foresters, who need to monitor changes in their forest structures. The automated workflow not only reduces the time, it also standardizes the analytical pipeline and, thus, reduces the degree of uncertainty that would be introduced by manual processing of images by different people (either over time or between study sites).     

基于面向对象的QuickBird遥感影像林隙分割与分类

传统的实地调查和人工解译方法已经不能满足区域尺度的林隙获取,高空间分辨率遥感影像的出现为区域尺度的林隙获取提供了可能.本研究采用QuickBird高空间分辨率光学遥感影像,结合面向对象分类技术对福建省三明市将乐县将乐国有林场进行林隙分割与分类.在面向对象分类过程中,采用10种尺度(10~100,步长为10)对QuickBird遥感影像进行分割,应用参考对象相交面积(RA_or)和分割对象相交面积(RA_os)进行分割结果评价.对每个尺度分割结果应用16个光谱特征,采用向量机分类器(SVM)进行林隙、非林隙和其他类型分类.结果表明:通过RA_or和RA_os等值法获得最优分割尺度参数为40.不同尺度参数之间的分类总精度最高相差22%.在最优尺度下,应用SVM分类器对林隙、非林隙和其他类型分类的总精度高达88%(Kappa=0.82).采用高空间分辨率遥感数据并结合面向对象的方法,可以代替传统的实地调查和人工解译对区域尺度的林隙进行识别分类.     

Hemlock Declines Rapidly with Hemlock Woolly Adelgid Infestation: Impacts on the Carbon Cycle of Southern Appalachian Forests

The recent infestation of southern Appalachian eastern hemlock stands by hemlock woolly adelgid (HWA) is expected to have dramatic and lasting effects on forest structure and function. We studied the short-term changes to the carbon cycle in a mixed stand of hemlock and hardwoods, where hemlock was declining due to either girdling or HWA infestation. We expected that hemlock would decline more rapidly from girdling than from HWA infestation. Unexpectedly, in response to both girdling and HWA infestation, hemlock basal area increment (BAI) reduced substantially compared to reference hardwoods in 3 years. This decline was concurrent with moderate increases in the BAI of co-occurring hardwoods. Although the girdling treatment resulted in an initial pulse of hemlock needle inputs, cumulative litter inputs and O horizon mass did not differ between treatments over the study period. Following girdling and HWA infestation, very fine root biomass declined by 20–40% in 2 years, which suggests hemlock root mortality in the girdling treatment, and a reduction in hemlock root production in the HWA treatment. Soil CO₂ efflux (E _soil) declined by approximately 20% in 1 year after both girdling and HWA infestation, even after accounting for the intra-annual variability of soil temperature and moisture. The reduction in E _soil and the concurrent declines in BAI and standing very fine root biomass suggest rapid declines in hemlock productivity from HWA infestation. The accelerated inputs of detritus resulting from hemlock mortality are likely to influence carbon and nutrient fluxes, and dictate future patterns of species regeneration in these forest ecosystems. AEN performed research and analyzed data; NW performed research, analyzed data, and wrote the article; CRF contributed new methods, analyzed data, and wrote the article; RLH designed the study; JMV conceived of and designed the study; and BDK performed research.     

Using changes in basal area increments to map relative risk of HWA impacts on hemlock growth across the Northeastern U.S.A

Eastern hemlock (Tsuga canadensis) is a critical species in eastern North American forests, providing a multitude of ecological and societal benefits while also acting as a foundation species in many habitats. In recent decades, however, hemlock has become threatened by hemlock woolly adelgid (HWA; Adelges tsugae), an invasive sap-feeding insect from Asia. In addition to causing the more commonly assessed metrics of foliar damage, crown decline, and hemlock mortality, HWA also decreases hemlock growth and productivity. Dendrochronological methods provide a more nuanced assessment of HWA impacts on hemlock by quantifying variable rates of radial-growth decline that follow incipient infestation. This information is necessary to better understand the variable response of hemlock to HWA, and identify the characteristics of stands with the highest potential for tolerance and recovery. To quantify decline, we calculated changes in hemlock yearly radial growth using basal area increment (BAI) measurements to identify periods of growth decline from 41 hemlock stands across New England covering a range of infestation density, duration and hemlock vigor. The onset of growth decline periods were predominantly associated with either HWA infestation or drought. However, the magnitude of change in BAI values pre- and post-decline was significantly related to HWA infestation density and crown impacts, indicating that radial growth metrics can be used to identify locations where HWA infestations have incited significant reductions in hemlock health and productivity. Additional site characteristics (slope, hillshade, and January minimum temperatures), were also significantly associated with hemlock health and productivity decline rates. In order to develop a model to identify stands likely to tolerate HWA infestation, these metrics were used to build a logit model to differentiate high- and low-BAI-reduction stands with 78% accuracy. Independent validation of the model applied to 15 hemlock sites in Massachusetts classified high and low BAI reduction classes with 80% accuracy. The model was then applied to GIS layers for New England and eastern New York to produce a spatially-explicit model that predicts the likelihood of severe hemlock growth declines if/when HWA arrives. Currently 26% of the region’s hemlock stands fall into this high risk category. Under projected climate change, this could increase to 43%. This model, along with knowledge of current HWA infestation borders, can be used to direct management efforts of potentially tolerant hemlock stands in eastern North America, with the intention of minimizing HWA-induced hemlock mortality.     

Long‐term post‐fire changes in the northeastern boreal forest of Quebec

Abstract. Natural dynamics in the boreal forest is influenced by disturbances. Fire recurrence affects community development and landscape diversity. Forest development was studied in the northeastern boreal forest of Quebec. The objective was to describe succession following fire and to assess the factors related to the changes in forest composition and structure. The study area is located in northeastern Quebec, 50 km north of Baie‐Comeau. We used the forest inventory data gathered by the Ministère des Ressources naturelles du Québec (MRNQ). In circular plots of 400 m², the diameter at breast height (DBH) of all stems of tree species greater than 10 cm was recorded and in 40 m² subplots, stems smaller than 10 cm were measured. A total of 380 plots were sampled in an area of 6000 km². The fire history reconstruction was done based on historical maps, old aerial photographs and field sampling. A time‐since‐fire class, a deposit type, slope, slope aspect and altitude were attributed to each plot. Each plot was also described according to species richness and size structure characteristics. Traces of recent disturbance were also recorded in each plot. Changes in forest composition were described using ordination analyses (NMDS and CCA) and correlated with the explanatory variables. Two successional pathways were observed in the area and characterized by the early dominance of intolerant hardwood species or Picea mariana. With time elapsed since the last fire, composition converged towards either Picea mariana, Abies balsamea or a mixture of both species and the size structure of the coniferous dominated stands got more irregular. The environmental conditions varied between stands and explained part of the variability in composition. Their effect tended to decrease with increasing time elapsed since fire, as canopy composition was getting more similar. Gaps may be important to control forest dynamics in old successional communities.