Search Results (10)

The loss of mature forests is a known stressor of forest management on biodiversity. Mature forests provide unique habitat for forest birds. Here, we examine the capacity of mature forest stands embedded in an intensively managed landscape to provide habitat for landbird species that are associated with mature, unfragmented habitats. We carry this out by comparing bird communities in forest stands in three landscapes with a gradient of management activity. We examined community-level indicators (richness, diversity, abundance and community structure), and trait-level indicators (species groups associated with cavity nesting, mature forests, interior forests and area sensitivity). We found no obvious negative effects on bird communities, species and trait groups in forest stands in the most intensively managed landscape relative to the less intensively managed landscapes. Our ability to draw inferences about the influence of management intensity is limited due to lack of replication; however, these results do provide evidence that mature forest stands within intensively managed landscapes can provide valuable habitat to mature forest associates. There are often trade-offs between generating wood products from the forest and the provision of mature forest habitats. Research on forest birds can provide some of the necessary information for assessing the size and shape of those trade-offs and help to inform the conversation about the desired structure, function and composition of forests. Full article

Acadian forests of New Brunswick, Canada, are highly variable both in terms of species composition and quality. This is particularly accurate for hardwoods, for which value recovery is closely influenced by wood quality and a proper understanding of tree attributes. Therefore, based on several databases created between 2012 and 2021, the objectives of this study were to (1) characterize the stand-level distribution of species, size, form, and risk according to site factors and (2) determine the influence of different tree, stand, and site factors on the recovery of merchantable, sawlog, and veneer volumes. In total, 287,984 trees stemming from 9233 plots were analyzed for objective 1. For a subset of trees (743), tree, stand, and site attributes were also related to the product output of harvesting operations through a comprehensive inventory of standing trees and their associated products. Analyses were performed with linear and multinomial logistic regressions as well as factor analysis of mixed data (FAMD). Key results indicated that tree attributes and product recovery were significantly influenced by (1) environmental factors (soil, temperature, and precipitation), summarized by ecoregion in this study and (2) tree size and form. These findings suggest that the inclusion of site factors in supply planning could improve product recovery during forest operations. Full article

The capacity of forests to store carbon, combined with time-tested approaches to managing forests, make forests a useful tool for atmospheric carbon mitigation. The primary goals of this study are to determine the amount of unrealized mitigation available from Improved Forest Management (IFM) in the Acadian Forest of New England in the northeastern U.S., and to demonstrate how this mitigation can feasibly be attained. This study used the Forest Vegetation Simulator (FVS) to model the impacts of IFM practices articulated by the New England Forestry Foundation on carbon storage in the Acadian Forest. Our results, together with empirical data from well-managed forests, show that if the modeled improved management is employed on privately owned timberland across the Acadian Forest of New England, carbon storage could be increased by 488 Tg CO₂e. Our financial modeling shows that IFM could be funded in this region by combining income from carbon markets with the philanthropic funding of conservation easements, timber revenues, and capital investments from private investors who prioritize social and economic goals alongside financial returns. This study adds to the body of evidence from around the world that the potential for managed forests to contribute to climate change mitigation has not been fully realized. Full article

Sustainable forest management relies on practices ensuring vigorous post-harvest regeneration. Data on regeneration structure and composition are often collected through intensive field surveys. Remote sensing technologies (e.g., Light Detection and Ranging (LiDAR), satellite imagery) can cover a much larger spatial extent, but their ability to estimate regeneration characteristics is often challenged by the obstruction associated with canopy foliage. Here, we determined whether the integration of LiDAR and Sentinel-2 images can increase the accuracy of sapling density estimates and whether this accuracy decreased with canopy cover in the Acadian forest of New Brunswick, Canada. Using random forest regression, we compared the accuracy of three models (LiDAR and Sentinel-2 images alone or combined) to estimate sapling density for two species groups: saplings of all species or commercial species only. The integration of both sensors did not increase the accuracy of sapling density estimates, nor did it reduce the negative influence of canopy cover for either species group compared to LiDAR, but it increased the accuracy by approximately 15% relative to Sentinel-2 images. Under very high canopy cover, the accuracy of density estimates for all species combined was significantly lower with Sentinel-2 images only. We recommend using LiDAR and high-resolution satellite images acquired in the fall to obtain more accurate estimates of sapling density. Full article

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r² when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included. Full article

When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem in remote sensing applications, with few practical solutions. Attenuation is of particular concern for applications that are responsive to prediction patterns at the high end of observed data ranges, where systematic error is typically greatest. We addressed attenuation bias in models of tree species relative abundance (percent of total aboveground live biomass) based on multitemporal Landsat and topoclimatic predictor data. We developed a multi-objective support vector regression (MOSVR) algorithm that simultaneously minimizes total prediction error and systematic error caused by attenuation bias. Applied to 13 tree species in the Acadian Forest Region of the northeastern U.S., MOSVR performed well compared to other prediction methods including single-objective SVR (SOSVR) minimizing total error, Random Forest (RF), gradient nearest neighbor (GNN), and Random Forest nearest neighbor (RFNN) algorithms. SOSVR and RF yielded the lowest total prediction error but produced the greatest systematic error, consistent with strong attenuation bias. Underestimation at high relative abundance caused strong deviations between predicted patterns of species dominance/codominance and those observed at field plots. In contrast, GNN and RFNN produced dominance/codominance patterns that deviated little from observed patterns, but predicted species relative abundance with lower accuracy and substantial systematic error. MOSVR produced the least systematic error for all species with total error often comparable to SOSVR or RF. Predicted patterns of dominance/codominance matched observations well, though not quite as well as GNN or RFNN. Overall, MOSVR provides an effective machine learning approach to the reduction of systematic prediction error and should be fully generalizable to other remote sensing applications and prediction problems. Full article

Institutional greenspaces such as golf courses, cemeteries, military bases, hospitals, and university campuses are not generally revered for their ecological integrity. The existence of golf courses in particular has been heavily debated due to widespread perceptions of these spaces as environmentally degrading. Though much of the total area of golf courses is occupied by heavily manicured lawns, Canadian golf courses tend to be well treed and thus show significant potential to enhance forest coverage and contribute to the conservation of native tree species when established on previously unforested land. To explore this potential, a tree inventory was carried out on an inner-city golf course in Halifax, Nova Scotia, and findings compared to an earlier inventory of more naturalized (i.e., ingrowth) forest areas in the same city. Based in the Acadian Forest Region, this case study used the characteristics of a healthy and mature Acadian Forest as a model for ecological integrity. It was found that both the golf course and the ingrowth populations were largely representative of a mixedwood Acadian forest. Likewise, both populations were in a similar stage of regeneration and exhibited similar stresses. These results suggest that if improved forest management approaches are employed, golf courses will effectively strengthen the ecological integrity of urban forests. This is an especially important finding in the climate change era when tree populations are likely to be subjected to new environmental stressors which may be alleviated via the human intervention that is available on managed lands such as institutional greenspaces. Full article

Spruce budworm (SBW) is the most destructive forest pest in eastern forests of North America. Mapping annual current-year SBW defoliation is challenging because of the large landscape scale of infestations, high temporal/spatial variability, and the short period of time when detection is possible. We used Landsat-5 and Landsat-MSS data to develop a method to detect and map SBW defoliation, which can be used as ancillary or alternative information for aerial sketch maps (ASMs). Results indicated that Landsat-5 data were capable of detecting and classifying SBW defoliation into three levels comparable to ASMs. For SBW defoliation classification, a combination of three vegetation indices, including normalized difference moisture index (NDMI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI), were found to provide the highest accuracy (non-defoliated: 77%, light defoliation: 60%, moderate defoliation: 52%, and severe defoliation: 77%) compared to using only NDMI (non-defoliated: 76%, light defoliation: 40%, moderate defoliation: 43%, and severe defoliation: 67%). Detection of historical SBW defoliation was possible using Landsat-MSS NDVI data, and the produced maps were used to complement coarse-resolution aerial sketch maps of the past outbreak. The method developed for Landsat-5 data can be used for current SBW outbreak mapping in North America using Landsat-8 and Sentinel-2 imagery. Overall, the work highlights the potential of moderate resolution optical remote sensing data to detect and classify fine-scale patterns in tree defoliation. Full article

Determining the optimal timing and type of entry in mid-rotation, unthinned stands can be complicated by a variety of economic and biological factors. In this analysis, long-term data from the Commercial Thinning Research Network was used to project spruce-fir (Picea spp.—Abies balsamea (L.) Mill.) stand growth on six different sites across Maine following six alternative thinning treatments (33% and 50% relative density removal paired with low, crown, and dominant thinning methods). Results showed that the low-thinning treatment performed best in terms of maximum net present value, stand age at time of maximum net present value, and average merchantable stem size. Although the low-thinning resulted in a 10% mean reduction in maximum net present value when compared to the control, the average merchantable stem size more than doubled. Overall, results of this analysis indicate that it may be financially responsible to commercially thin these stands using a low-thinning method and a light removal intensity, as the average merchantable stems size was increased and a mid-rotation financial return provided. Full article

Climate change is having an impact on forest ecosystems around the world and is expected to alter the suitable habitat of individual tree species. Forest managers require resources about potential impacts of climate change at the regional scale to aid in climate mitigation efforts. By understanding the geographic distribution of changes in suitable habitat, migration corridors can be identified for conservation and active management. With the increased availability of climate projection data, ancillary Geographic Information Systems data, and field observations, modeling efforts at the regional scale are now possible. Here, we modeled and mapped the continuous distribution of Tsuga canadensis throughout the state of Maine at the regional scale(30 m) with high precision (89% of pixels had a coefficient of variation ≤ 4.0%). The random forest algorithm was used to create a strong prediction of suitable habitat for the years 2050 and 2100 from both high and low emission climate projections. The results clearly suggest a significant gain in suitable habitat for Tsuga canadensis range with a general northwest expansion. Full article