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The Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut (74°55′N, 109°34′W) was established in 2003 to examine Arctic ecosystem processes that would be impacted by climate warming and permafrost degradation. This paper provides a synthesis of how remote sensing has contributed to biogeophysical modelling and monitoring at the CBAWO from 2003 to 2023. Given the location and isolated nature of the CBAWO in the Canadian High Arctic, remote sensing data and derivatives have been instrumental for studies examining ecosystem structure and function at local and landscape scales. In combination with field measurements, remote sensing data facilitated mapping and modelling of vegetation types, % vegetation cover and aboveground phytomass, soil moisture, carbon exchange rates, and permafrost degradation and disturbance. It has been demonstrated that even in an environment with limited vegetation cover and phytomass, spectral vegetation indices (e.g., the normalized difference vegetation index) are able to model various biogeophysical variables. These applications are feasible for research sites such as the CBAWO using high spatial resolution remote sensing data across the visible, infrared, and microwave regions of the electromagnetic spectrum. Furthermore, as the satellite record continues to expand, we will gain a greater understanding of the impacts arising from the expected continued warming at northern latitudes. Although the logistics for research in the Arctic remain challenging, today's technologies (e.g., high spatial resolution satellite remote sensing, automated in situ sensors and data loggers, and wireless communication systems) can support a host of scientific endeavours in the Arctic (and other remote sites) through modelling and monitoring of biogeophysical variables and Earth surface processes with limited but critical field campaigns. The research synthesized here for the CBAWO highlights the essential role of remote sensing of terrestrial ecosystems in the Canadian Arctic.

Increased soil nutrient availability, and associated increases in vegetation productivity, could create a negative feedback between Arctic ecosystems and the climate system, thereby reducing the contribution of Arctic ecosystems to future climate change. To predict whether this feedback will develop, it is important to understand the environmental controls over nutrient cycling in High Arctic ecosystems and their impact on carbon cycling processes. Here, we examined the environmental controls over soil nitrogen availability in a High Arctic wet sedge meadow and how abiotic factors and soil nitrogen influenced carbon dioxide exchange processes. The importance of environmental variables was consistent over the 3 years, but the magnitudes of their effect varied depending on climate conditions. Ammonium availability was higher in warmer years and wetter conditions, while drier areas within the wetland had higher nitrate availability. Carbon uptake was driven by soil moisture, active layer depth, and variability between sampling sites and years (R2 = 0.753), while ecosystem respiration was influenced by nitrogen availability, soil temperature, active layer depth, and sampling year (R2 = 0.848). Considered together, the future carbon dioxide source or sink potential of high latitude wetlands will largely depend on climate-induced changes in moisture and subsequent impacts on nutrient availability.

ABSTRACTThe Caenorhabditis elegans natural microbiota isolates Pseudomonas lurida MYb11 and Pseudomonas fluorescens MYb115 protect the host against pathogens through distinct mechanisms. While P. lurida produces an antimicrobial compound and directly inhibits pathogen growth, P. fluorescens MYb115 protects the host without affecting pathogen growth. It is unknown how these two protective microbes affect host biological processes. We used a proteomics approach to elucidate the C. elegans response to MYb11 and MYb115. We found that both Pseudomonas isolates increase vitellogenin protein production in young adults, which confirms previous findings on the effect of microbiota on C. elegans reproductive timing. Moreover, the C. elegans responses to MYb11 and MYb115 exhibit common signatures with the response to other vitamin B12-producing bacteria, emphasizing the importance of vitamin B12 in C. elegans-microbe metabolic interactions. We further analyzed signatures in the C. elegans response specific to MYb11 or MYb115. We provide evidence for distinct modifications in lipid metabolism by both symbiotic microbes. We could identify the activation of host-pathogen defense responses as an MYb11-specific proteome signature and provide evidence that the intermediate filament protein IFB-2 is required for MYb115-mediated protection. These results indicate that MYb11 not only produces an antimicrobial compound but also activates host antimicrobial defenses, which together might increase resistance to infection. In contrast, MYb115 affects host processes such as lipid metabolism and cytoskeleton dynamics, which might increase host tolerance to infection. Overall, this study pinpoints proteins of interest that form the basis for additional exploration into the mechanisms underlying C. elegans microbiota-mediated protection from pathogen infection and other microbiota-mediated traits.IMPORTANCESymbiotic bacteria can defend their host against pathogen infection. While some protective symbionts directly interact with pathogenic bacteria, other protective symbionts elicit a response in the host that improves its own pathogen defenses. To better understand how a host responds to protective symbionts, we examined which host proteins are affected by two protective Pseudomonas bacteria in the model nematode Caenorhabditis elegans. We found that the C. elegans response to its protective symbionts is manifold, which was reflected in changes in proteins that are involved in metabolism, the immune system, and cell structure. This study provides a foundation for exploring the contribution of the host response to symbiont-mediated protection from pathogen infection.

The use of tungsten fiber-reinforced tungsten composites (Wf/W) has been demonstrated to significantly enhance the mechanical properties of tungsten (W) by incorporating W-fibers into the W-matrix. However, prior research has been restricted by the usage of single fiber-based textile fabrics, consisting of 150 µm warp and 50 µm weft filaments, with limited homogeneity, reproducibility, and mechanical properties in bulk structures due to the rigidity of the 150 µm W-fibers. To overcome this limitation, two novel textile preforms were developed utilizing radial braided W-yarns with 7 core and 16 sleeve filaments (R.B. 16 + 7), with a diameter of 25 µm each, as the warp material. In this study, bulk composites of two different fabric types were produced via a layer-by-layer CVD process, utilizing single 50 µm filaments (type 1) and R.B. 16 + 7 yarns (type 2) as weft materials. The produced composites were sectioned into KLST-type specimens based on DIN EN ISO 179-1:2000 using electrical discharge machining (EDM) and subjected to three-point bending tests. Both composites demonstrated enhanced mechanical properties with pseudo-ductile behavior at room temperature and withstood over 10,000 load cycles between 50–90% of their respective maximum load without sample fracture in three-point cyclic loading tests. Furthermore, a novel approach to predict the fatigue behavior of the material under cyclic loading was developed based on the high reproducibility of the composites produced, especially for the composite based on type 1. This approach provides a new benchmark for upscaling endeavors and may enable a better prediction of the service life of the produced components made of Wf/W in the future. In comparison, the composite based on fabric type 1 demonstrated superior results in manufacturing performance and mechanical properties. With a high relative average density (>97%), a high fiber volume fraction (14–17%), and a very homogeneous fiber distribution in the CVD-W matrix, type 1 shows a promising option to be further tested in high heat flux tests and to be potentially used as an alternative to currently used materials for the most stressed components of nuclear fusion reactors or other potential application fields such as concentrated solar power (CSP), aircraft turbines, the steel industry, quantum computing, or welding tools. Type 2 composites have a higher layer spacing compared to type 1, resulting in gaps within the matrix and less homogeneous material properties. While type 2 composites have demonstrated a notable enhancement over 150 µm fiber-based composites, they are not viable for industrial scale-up unlike type 1 composites.

ABSTRACTArctic warming and changing precipitation patterns are altering soil nutrient availability and other processes that control the greenhouse gas balance of high-latitude ecosystems. Changes to these biogeochemical processes will ultimately determine whether the Arctic will enhance or dampen future climate change. At the Cape Bounty Arctic Watershed Observatory, a full-factorial International Tundra Experiment site was established in 2008, allowing for the investigation of ten years of experimental warming and increased snow depth on nutrient availability and trace gas exchange in a mesic heath tundra across two growing seasons (2017 and 2018). Plots with open-top chambers (OTCs) had drier soils (p

The aim of our three consecutive years (2017-2019) field trial was to obtain information as to the effect of weather conditions of the actual year as well as to assess the impact of some technological parameters such as fertilisation, the choice on the hybrid type on the yield parameters, phytosanitary conditions and mycotoxin contamination of maize. According to our results, the climatic characteristics of the years, the examined hybrid characters (FAO 310 and 490) and the fact of N-fertilisation had significant effects on yield parameters and grain moisture content. The additional N-supply did not affect the development or severity of stem rot in any of the hybrid effects. In this respect, the year effect appeared to be the decisive factor since much higher stem rot values were recorded in the plots of the longer growing season hybrids. Among the mycotoxins examined, only zearalenone and fumonisin found in the harvest were significantly influenced by the effect of the year, the length of the growing season as well as nutrient replenishment. It can be stated that the applied technological parameters have a major effect on the expression of this toxin load in maize. Dry maize stocks that have lost their water in the vegetation are predisposing factors for toxin accumulation. N-content of soil and that of plants can play a different role in mycotoxin accumulation in maize plants.

Thematic maps developed from remote sensing data are extremely useful for designing intensive field studies, particularly for large areas that are logistically challenging to access. The integrated watershed studies at the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut, rely heavily on land cover for establishing sampling locations regardless of the type of research being conducted (e.g., permafrost degradation, greenhouse gas exchange, surface water chemistry, etc.). Here, we present an environmental land-cover classification of the CBAWO that was developed through an iterative process employing parametric and non-parametric classification algorithms applied to WorldView-2 satellite data and topographic variables. The support vector machine classification of eight-band WorldView-2 spectral data and a topographic wetness index produced the highest classification accuracy for eight land-cover classes (overall classification accuracy: 90.7%; Kappa coefficient (κ): 0.89). This analysis also provided a more precise classification scheme, particularly in the context of the relationship between vegetation type and moisture regime. The environmental land-cover classification derived will better inform future integrated studies of the watershed and allow for upscaling of site-level characteristics to the watershed-scale using the updated vegetation classes.

Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004–2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 °C; GDD5) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. There was no significant change in NDVI nor in GDD5 over time. However, there were statistically significant (p

The ability to expand the use of predictive Airborne Laser Scanning (ALS)-derived Forest Resource Inventory (FRI) models to broader regional scales is crucial for supporting large scale sustainable forest management. This research examined the transferability of ALS-based FRI attributes between two forest estates located in the eastern and western boreal forest regions of Canada. The sites were structurally diverse due to a strong east-to-west gradient in climate conditions and disturbance regimes. We first examined the ALS–FRI attribute relationships between the sites. Second, we applied Ordinary Least Squares regressions and Random Forest, to predict four FRI attributes. Third, we tested if the inclusion of calibration data from the target location improved the performance of the transferred models. As the sites were located on opposing sides of a bioclimatic gradient, inclusion of target calibration data improved transferred model performance. However, attribute prediction accuracy varied with modeling approach, attribute, and site. The best transferability models fell within a ± 5% relative RMSE of the local predictive models but increased up to 10% in relative bias. These results have implications for forest researchers and managers on both the number, and location, of FRI plots when considering undertaking forest inventories over large disparate areas.

Climate warming is affecting terrestrial ecosystems in the Canadian Arctic, potentially altering the carbon balance of the landscape and contributing additional CO2 to the atmosphere. High spatial resolution remote sensing data can enhance models of net ecosystem exchange (NEE) and its component fluxes, gross ecosystem exchange (GEE), and ecosystem respiration (ER) by quantifying vegetation structure and function over time. In this study, we explored the variability of daytime CO2 exchange rates for three vegetation types along a natural moisture gradient at ecologically distinct mid- and high Arctic sites. We demonstrated that for the two sites studied, there was no statistically significant variation in CO2 exchange rates for the vegetation types through the peak growing season. Hence, the capacity to model these rates with a limited number of satellite data acquisitions is feasible. Simple bivariate models relating the Normalized Difference Vegetation Index (NDVI) to CO2 exchange processes (GEE, ER, and NEE) were developed independent of vegetation type and geographic location and validated using independent data. The spectral models explain between 33 and 94 percent of the variation in CO2 exchange rates at each site, indicating a high level of functional convergence in ecosystem-level structure and function within Arctic landscapes.

Differential interferometry of synthetic aperture radar (DInSAR) can be used to generate high-precision surface displacement maps in continuous permafrost environments, capturing isotropic surface subsidence and uplift associated with the seasonal freeze and thaw cycle. We generated seasonal displacement maps using DInSAR with ultrafine-beam Radarsat-2 data for the summers of 2013, 2015, and 2019 at Cape Bounty, Melville Island, and examined them in combination with a land-cover classification, meteorological data, topographic data, optical satellite imagery, and in situ measures of soil moisture, soil temperature, and depth to the frost table. Over the three years studied, displacement magnitudes (estimated uncertainty ± 1 cm) of up to 10 cm per 48-day DInSAR stack were detected. However, generally, the displacement was far smaller (up to 4 cm). Surface displacement was found to be most extensive and of the greatest magnitude in low-lying, wet, and steeply sloping areas. The few areas where large vertical displacements (>2.5 cm) were detected in multiple years were clustered in wet, low lying areas, on steep slopes or ridges, or close to the coast. DInSAR also captured the expansion of two medium-sized retrogressive thaw slumps (RTS), exhibiting widespread negative surface change in the slump floor.

Warming of the Arctic in recent years has led to changes in the active layer and uppermost permafrost. In particular, thick active layer formation results in more frequent thaw of the ice-rich transient layer. This addition of moisture, as well as infiltration from late season precipitation, results in high pore-water pressures (PWPs) at the base of the active layer and can potentially result in landscape degradation. To predict areas that have the potential for subsurface pressurization, we use susceptibility maps generated using a generalized additive model (GAM). As model response variables, we used active layer detachments (ALDs) and mud ejections (MEs), both formed by high PWP conditions at the Cape Bounty Arctic Watershed Observatory, Melville Island, Canada. As explanatory variables, we used the terrain characteristics elevation, slope, distance to water, topographic position index (TPI), potential incoming solar radiation (PISR), distance to water, normalized difference vegetation index (NDVI; ME model only), geology, and topographic wetness index (TWI). ALDs and MEs were accurately modelled in terms of susceptibility to disturbance across the study area. The susceptibility models demonstrate that ALDs are most probable on hill slopes with gradual to steep slopes and relatively low PISR, whereas MEs are associated with higher elevation areas, lower slope angles, and areas relatively far from water. Based on these results, this method identifies areas that may be sensitive to high PWPs and helps improve our understanding of geomorphic sensitivity to permafrost degradation.

Two types of nonparametric modeling techniques and various metrics derived from airborne laser scanning (ALS) data were examined in terms of their utility for modeling stem diameter distributions in an uneven-aged tolerant hardwood forest in Ontario, Canada. Using an area-based approach (ABA), the frequency distribution of trees across 6 size classes was predicted using k-nearest neighbor (k-NN) imputation and Random Forest (RF) regression. Predictor variables derived from ALS height and intensity data were divided into 3 groups: height only, intensity only, and all metrics. Prediction results demonstrated that the first 2 groups of predictor variables exhibited similar predictive accuracy, whereas the synergy of both resulted in enhanced performance. The utility of intensity-based metrics was corroborated by an importance measure obtained from RF. The size class-specific stem density estimation approach based on RF was more accurate and flexible than the simultaneous estimation approach based on k-NN models. After the predicted diameter distributions were grouped into 9 structural groups, heterogeneous accuracy scores revealed the challenges for predicting select diameter distributions. Although successes were observed for certain size classes, there remains additional research (e.g., development of additional metrics or data types) to be done to accurately predict a complete range of size classes.

Spatial clustering of enzymes has proven an elegant approach to optimize metabolite transfer between enzymes in synthetic metabolic pathways. Among the multiple methods used to promote colocalisation, enzyme fusion is probably the simplest. Inspired by natural systems, we have explored the metabolic consequences of spatial reorganizations of the catalytic domains of Xanthophyllomyces dendrorhous carotenoid enzymes produced in Saccharomyces cerevisiae. Synthetic genes encoding bidomain enzymes composed of CrtI and CrtB domains from the natural CrtYB fusion were connected in the two possible orientations, using natural and synthetic linkers. A tridomain enzyme (CrtB, CrtI, CrtY) harboring the full β-carotene producing pathway was also constructed. Our results demonstrate that domain order and linker properties considerably impact both the expression and/or stability of the constructed proteins and the functionality of the catalytic domains, all concurring to either diminish or boost specific enzymatic steps of the metabolic pathway. Remarkably, the yield of β-carotene production doubled with the tridomain fusion while precursor accumulation decreased, leading to an improvement of the pathway efficiency, when compared to the natural system. Our data strengthen the idea that fusion of enzymatic domains is an appropriate technique not only to achieve spatial confinement and enhance the metabolic flux but also to produce molecules not easily attainable with natural enzymatic configurations, even with membrane bound enzymes. Keywords: Metabolic engineering, Synthetic biology, Metabolic flux, Enzyme spatial proximity, Carotenoids, Multidomain enzymes

Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.

Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012−2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p < 0.05), while LiDAR-based G&Y models were not. None of the models were equivalent to validation data for accumulation (p > 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p < 0.05). Using a constant mortality rate, we also found statistical equivalency of inventory and photo-interpreted accumulation models for all size classes ≥17 cm. These results suggest that more precise information is needed on tree characteristics than we could derive from LiDAR, but that plot-level species information is not as critical for predictions of carbon accumulation in mixed-species forests. Further work is needed on the use of LiDAR to quantify stand properties before this technique can be used to replace recurring plot measurements to quantify carbon accumulation.

Various methods are used to determine soil moisture information from synthetic aperture radar (SAR) data, but none specific to High Arctic regions and their unique physical characteristics. This research presents a method for determining, at high spatial and temporal resolutions, surface soil moisture and its changes through time in the Canadian High Arctic. An artificial neural network (ANN) is implemented using input variables derived from RADARSAT-2 SAR data and previously modelled surface roughness information. The model is applied to SAR data collected at various incidence angles and acquisition dates across two study sites on Melville Island, Nunavut. The model results in absolute soil moisture errors of approximately 15% (r2 = 0.46) for the primary study sites and 12% (r2 = 0.26) for the verification study area. The ANN model is accurate for modelling (i) the spatial distribution of soil moisture and (ii) the changes in moisture through time across the study areas, two characteristics that are very important for inputs to hydrologic or climate models. In addition, the models appear to be scalable when applied at coarser spatial resolutions, showing potential for large-area mapping or modelling.

Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

The ecosite unit in Ontario’s boreal forest ecological land classification system is a polygon of common vegetation type and soil conditions intended to provide a standardized provincial framework to inform meso-scale forestry and planning applications. To determine whether the physical factors used for ecosite classification relate to patterns in ecological function over finer spatial scales, we examined 14 soil properties in replicate boreal forest plots representing eight mineral soil ecosite classes and three organic soil ecosite classes in the Hearst Forest. Despite large differences in vegetation composition, we found few statistically significant differences in properties when compared for individual classes or for more general groupings based on vegetation type and soil texture or expected fertility status. However, some properties (soil organic carbon, total nitrogen, and C:N ratio) were approaching significance in the 0–10 cm depth increment, and there were distinct differences between organic soil and mineral soil sites. Overall, these results suggest few explicit links between ecosystem function and ecosite class at this scale of measurement, highlighting the potential importance of non-steady-state relationships between vegetation species and soil properties in disturbed forests and the potential need for finer-scale characterization to capture patterns in ecosystem function.

Over the last decade, spatially-explicit modeling of landscape-scale forest attributes for forest inventories has greatly benefitted from airborne laser scanning (ALS) and the area-based approach (ABA) to derive wall-to-wall maps of these forest attributes. Which ALS-derived metrics to include when modeling forest inventory attributes, and how prediction accuracies vary over forest types depends largely on the structural complexity of the forest(s) being studied. Hence, the purpose of this study was to (i) examine the usefulness of adding texture and intensity metrics to height-based ALS metrics for the prediction of several forest resource inventory (FRI) attributes in one boreal and two Great Lakes, St. Lawrence (GLSL) forest region sites in Ontario and (ii) quantify and compare the site and forest type variability within the context of the FRI prediction accuracies. Basal area (BA), quadratic mean diameter-at-breast height (QMD), and stem density (S) were predicted using the ABA and a nonparametric Random Forests (RF) regression model. At the site level, prediction accuracies (i.e., expressed as RMSE (Root Mean Square Error), bias, and R2) improved at the three sites when texture and intensity metrics were included in the predictor set, even though no significant differences (p > 0.05) could be detected using the nonparametric RMANOVA test. Stem density benefitted the most from the inclusion of texture and intensity, particularly in the GLSL sites (% RMSE improved up to 6%). Combining site and forest type results indicated that improvements in site level predictions, due to the addition of texture and intensity metrics to the ALS predictor set, were the result of changes in prediction accuracy in some but not all forest types present at a site and that these changes in prediction accuracy were site and FRI attribute specific. The nonparametric Kruskal–Wallis test indicated that prediction errors between the different forest types were significantly different (p ≤ 0.01). In the boreal site, prediction accuracies for conifer forest types were higher than for deciduous and mixedwoods. Such patterns in prediction accuracy among forest types and FRI attributes could not be observed in the GLSL sites. In the Petawawa Research Forest (PRF), we did detect the impact of silvicultural treatments especially on QMD and S predictions.

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers for tactical planning. This paper focuses on estimating LAI from: (i) height and density metrics derived from Light Detection and Ranging (LiDAR); (ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and (iii) a combination of these methods. For the Hearst Forest of Northern Ontario, in situ measurements of LAI were derived from digital hemispherical photographs (DHPs) while remote sensing variables were derived from low density LiDAR (i.e., 1 m−2) and high spatial resolution WorldView-2 data (2 m). Multiple Linear Regression (MLR) models were generated using these variables. Results from these analyses demonstrate: (i) moderate explanatory power (i.e., R2 = 0.53) for LiDAR height and density metrics that have proven to be related to canopy structure; (ii) no relationship when using SVIs; and (iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with narrow ranges of LAI for model calibration. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at high spatial resolution for decision makers in the forestry community. This method can be easily incorporated into simultaneous modeling efforts for forest inventory variables using LiDAR.

As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes.

Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m−2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m−2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R2(adj) = 0.75–0.95); (2) top height (R2(adj) = 0.74–0.98); (3) quadratic mean diameter (R2(adj) = 0.55–0.85); (4) basal area (R2(adj) = 0.22–0.93); (5) gross total volume (R2(adj) = 0.42–0.94); (6) gross merchantable volume (R2(adj) = 0.35–0.93); (7) total aboveground biomass (R2(adj) = 0.23–0.93); and (8) stem density (R2(adj) = 0.17–0.86). Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m−2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.

The main objective of this study was to investigate the process of dry matter accumulation (DMA) in achenes during the grain-filling period of fifty sunflower genotypes by using the functional method of growth analysis in a field trial at Bicsérd, Hungary. The Hunt-formula of lnY= P0 + P1*X + P2*X2 was fitted to data. Maximum yield (Ymax), the average of the absolute growth rate (AGRavg), maximum growth rate (AGRmax), date of the maximum growth rate (Xagrmax), and the average of the relative growth rate (RGRavg) were calculated from growth curves for hybrids and replications. Significant differences among hybrids and their interaction with sampling dates indicate hybrid differences in the intensity of DMA accumulation. The strongest correlation was observed between the parameters of Ymax and AGRmax.

Abstract Background Schizophrenia is the collective term for an exclusively clinically diagnosed, heterogeneous group of mental disorders with still obscure biological roots. Based on the assumption that valuable information about relevant genetic and environmental disease mechanisms can be obtained by association studies on patient cohorts of ≥ 1000 patients, if performed on detailed clinical datasets and quantifiable biological readouts, we generated a new schizophrenia data base, the GRAS (Göttingen Research Association for Schizophrenia) data collection. GRAS is the necessary ground to study genetic causes of the schizophrenic phenotype in a 'phenotype-based genetic association study' (PGAS). This approach is different from and complementary to the genome-wide association studies (GWAS) on schizophrenia. Methods For this purpose, 1085 patients were recruited between 2005 and 2010 by an invariable team of traveling investigators in a cross-sectional field study that comprised 23 German psychiatric hospitals. Additionally, chart records and discharge letters of all patients were collected. Results The corresponding dataset extracted and presented in form of an overview here, comprises biographic information, disease history, medication including side effects, and results of comprehensive cross-sectional psychopathological, neuropsychological, and neurological examinations. With >3000 data points per schizophrenic subject, this data base of living patients, who are also accessible for follow-up studies, provides a wide-ranging and standardized phenotype characterization of as yet unprecedented detail. Conclusions The GRAS data base will serve as prerequisite for PGAS, a novel approach to better understanding 'the schizophrenias' through exploring the contribution of genetic variation to the schizophrenic phenotypes.

The paper presents an automated screwing application inside a robot cell with human-robot-cooperation. It describes the use case and how the design challenges for process reliability and safety have been addressed from concept idea to the real set up. A focus is thereby set on the safety implementation, which is enabled by the development of a balanced decoupling unit, which enables force limiting at the end-effector during physical contact. The decoupling unit is implemented for the application with the screwing tool and is designed according to functional safety requirements. Its usability is validated in appropriate force measurements (according to ISO/TS 15066) and evaluated on its performance characteristics for the productivity of the robot application. [ABSTRACT FROM AUTHOR]

The distribution of laser pulses within conifer forest trees and canopies are examined by varying the rate of laser pulse emission and the inherent laser pulse properties (laser pulse energy, pulse width, pulse length, and roll-over or trigger time). In this study, an Optech, Inc. ALTM 3100 airborne lidar is used, emitting pulses at 50 kHz and 100 kHz, allowing for changes in laser pulse characteristics while also keeping all other survey parameters equal. We found that: 1. Pulses and associated characteristics emitted at 50 kHz penetrated further into the canopy than 100 kHz for a significant number of individual trees. 2. At tall tree plots with no understory, pulses emitted at 50 kHz penetrated further into the canopy than 100 kHz for a significant number of plots. 3. For plots with significant understory and shorter trees, pulses emitted at 100 kHz penetrated further into the canopy than 50 kHz. We suspect that this may be due, in part, to canopy openness. Laser pulse energy and character differences associated with different laser pulse emission frequencies are likely a contributing factor in laser pulse penetration through the canopy to the ground surface. Efforts to understand laser pulse character influences on canopy returns are important as biomass and vegetation structure models derived from lidar are increasingly adopted. [ABSTRACT FROM AUTHOR]

An evaluation of airborne lidar (Light Detection And Ranging) technology for snow depth mapping beneath different forest canopy covers (deciduous, coniferous, and mixed) is presented. Airborne lidar data were collected for a forested study site both prior to and during peak snowpack accumulation. Manual field measurements of snow depth were collected coincident with the peak snowpack lidar survey, and a comparison between field and lidar depth estimates was made. It was found that (1) snow depth distribution patterns can be mapped by subtracting a ‘bare-earth’ DEM from a ‘peak snowpack’ DEM, (2) snow depth estimates derived from lidar data are strongly related to manual field measures of snow depth, and (3) snow depth estimates are most accurate in areas of minimal understory. It has been demonstrated that airborne lidar data provide accurate snow depth data for the purpose of mapping spatial snowpack distribution for volume estimations, even under forest canopy conditions. [ABSTRACT FROM AUTHOR]

Sets of spectral, spectral-spatial, textural, and geomorphometric variables derived from high spatial resolution Compact Airborne Spectrographic Imager (CASI) and elevation data are tested to determine their ability to discriminate landscape-scale forest ecosystem classes for a study area in northern Ontario, Canada. First, linear discriminant analysis for various spectral and spectral-spatial variables indicated that a spatial resolution of approximately 6 m was optimal for discriminating six landscape-scale forest ecosystem classes. Second, texture features, using second-order spatial statistics, significantly improved discrimination of the classes over the original reflectance data. Finally, addition of terrain descriptors improved discrimination of the six forest ecosystem classes. It has been demonstrated that,in a low- to moderate-relief boreal environment, addition of textural and terrain variables to high-resolution CASI reflectance data provides improved discrimination of forest ecosystem classes. [ABSTRACT FROM AUTHOR]