Spatial Patterns in Forest Understories: Relationships to Overstory Thinning Intensity and Understory Plant Diversity

Amount, spatial distribution, and species composition of understory plant communities have been shown to respond to changes in overstory structure. While response of the amount and composition of understory vegetation to thinning has been investigated in several ecosystems, spatial distributions have received less attention. We investigated spatial statistical techniques to examine associations of patch size of clonal shrubs and annual ruderals as they relate to overstory conditions after thinnings. We assessed the interpretation of empirical semivariograms in describing spatial pattern and whether semivariogram parameters can be useful when comparing impacts of different thinning regimes. We simulated vegetation patterns to test the ability of empirical semivariograms to describe patch sizes and suggest a nonparametric semivariogram range parameter as a metric of patch size. We applied results from the simulations to data from a long-term thinning study, in which intensity and spatial patterns of thinnings varied. We used range parameters from semivariograms of percent cover to compare response of patch sizes among thinning treatments and life forms. Initial results indicated that empirical semivariograms quantified both patch sizes and distance between patches. Nonparametric semivariogram estimates of patch size showed differences among thinning treatments, suggesting that spatial patterns of overstory conditions are influencing spatial distributions of understory vegetation. Patches of selected clonal shrubs were smallest in the treatment with spatially variable thinnings. Overall patch size of clonal shrubs was less strongly associated with thinning treatments than patch size of annual ruderals, likely reflecting differences in mobility between species that mainly regenerate by sprouting versus seeds. We conclude that spatial pattern of understory vegetation is responsive to thinning treatments and empirical semivariograms can provide useful information for developing silvicultural prescriptions

Globally weaker and topologically different: resting-state connectivity in youth with autism

Abstract Background There is a lack of agreement about functional connectivity differences in individuals with autism spectrum disorder (ASD). Studies using absolute strength have found reduced connectivity, while those using relative strength––a measure of system topology––reveal mostly enhanced connectivity. We hypothesized that mixed findings may be driven by the metric of functional connectivity. Methods Resting-state echo planar 3 T functional magnetic resonance imaging scans were acquired on a Siemens Verio Scanner from 6 to 17-year-old youth with ASD (n = 81) and a matched typically developing control group (n = 82). All functional time series data were preprocessed using a confound regression procedure that has been previously validated in large-scale developmental datasets. It has also been shown to be highly effective at reducing the influence of motion artifact on connectivity data. We extracted time series data from a 333-node parcellation scheme, which was previously mapped to 13 functional systems. A Pearson’s correlation was calculated and transformed to Fisher’s z between every pair of nodes to create a weighted 333 × 333 adjacency matrix. Mean absolute functional connectivity strength was the mean Fisher’s z of the matrix. Relative functional connectivity was corrected for individual differences in mean absolute functional connectivity (i.e., each connection in the matrix was divided by their mean z), and functional connectivity was evaluated within and across each of the functional networks in the parcellation scheme. Results Absolute functional connectivity strength was lower in ASD, and lower functional connectivity was correlated with greater ASD symptom severity. Relative functional connectivity was higher for the ASD group in the ventral attention and retrosplenial-temporal systems, with lower cross-system functional connectivity between the ventral attention and somatomotor-mouth systems. Functional connectivity within the ventral attention and retro-splenial systems correlated significantly with ASD symptom severity. Conclusions Within a context of globally weaker functional connectivity, youth with ASD have an atypical topology of brain systems that support social perception and communication. This study clarifies the mixed results reported previously and demonstrates that the functional connectivity metric influences the observed direction of functional connectivity differences for individuals with ASD

Potential for ecological nonlinearities and thresholds to inform Pacific salmon management

AbstractEcology is often governed by nonlinear dynamics. Nonlinear ecological relationships can include thresholds—incremental changes in drivers that provoke disproportionately large ecological responses. Among the species that experience nonlinear and threshold dynamics are Pacific salmon (Oncorhynchus spp.). These culturally, ecologically, and economically significant fishes are in many places declining and management focal points. Often, managers can influence or react to ecological conditions that salmon experience, suggesting that nonlinearities, especially thresholds, may provide opportunities to inform decisions. However, nonlinear dynamics are not always invoked in management decisions involving salmon. Here, we review reported nonlinearities and thresholds in salmon ecology, describe potential applications that scientists and managers could develop to leverage nonlinear dynamics, and offer a path toward decisions that account for ecological nonlinearities and thresholds to improve salmon outcomes. It appears that nonlinear dynamics are not uncommon in salmon ecology and that many management arenas may potentially leverage them to enable more effective or efficient decisions. Indeed, decisions guided by nonlinearities and thresholds may be particularly desirable considering salmon management arenas are often characterized by limited resources and mounting ecological stressors, practical constraints, and conservation challenges. More broadly, many salmon systems are data‐rich and there are an extensive range of ecological contexts in which salmon are sensitive to anthropogenic decisions. Approaches developed to leverage nonlinearities in salmon ecology may serve as examples that may inform analogous approaches in other systems and taxa

Use of Genetic Stock Identification Data for Comparison of the Ocean Spatial Distribution, Size at Age, and Fishery Exposure of an Untagged Stock and Its Indicator: California Coastal versus Klamath River Chinook Salmon

Managing weak stocks in mixed-stock fisheries often relies on proxies derived from data-rich indicator stocks. For example, full cohort reconstruction of tagged Klamath River fall run Chinook salmon (Oncorhynchus tshawytscha) of northern California, USA, enables the use of detailed models to inform management. Information gained from this stock is also used in the management of the untagged, threatened California Coastal Chinook (CCC) salmon stock, by capping Klamath harvest rates. To evaluate use of this proxy, we used genetic stock identification (GSI) data to compare the two stocks\u27 size-at-age and ocean distribution, two key factors influencing fishery exposure. We developed methods to account for both sampling and genetic assignment uncertainty in catch estimates. We found that, in 2010, the stocks were similar in size-at-age early in the year (age-3 and age-4), but CCC fish were larger later in the year. The stocks appeared similarly distributed early in the year (2010), but more concentrated near their respective source rivers later in the year (2010 and 2011). If these results are representative, relative fishery impacts on the two stocks might scale similarly early in the year but management changes later in the year might have differing impacts on the two stocks

Use of Genetic Stock Identification Data for Comparison of the Ocean Spatial Distribution, Size at Age, and Fishery Exposure of an Untagged Stock and Its Indicator: California Coastal versus Klamath River Chinook Salmon

Managing weak stocks in mixed-stock fisheries often relies on proxies derived from data-rich indicator stocks, although there have been limited tests of the appropriateness of such proxies. For example, full cohort reconstruction of tagged Klamath River fall-run Chinook Salmon Oncorhynchus tshawytscha of northern California enables the use of detailed models to inform management. Information gained from this stock is also used in the management of the untagged, threatened California Coastal Chinook Salmon (CCC) stock, where it is assumed that a cap on Klamath harvest rates effectively constrains impacts on CCC to acceptable levels. To evaluate use of this proxy, we used a novel approach based on genetic stock identification (GSI) data to compare the two stocks’ size at age and ocean distribution (as inferred from spatial variation in CPUE), two key factors influencing fishery exposure. We developed broadly applicable methods to account for both sampling and genetic assignment uncertainty in estimating total stock-specific catch from GSI data, and propagated this uncertainty into models quantifying variation in CPUE across space and time. We found that, in 2010, the stocks were similar in size at age early in the year (age 3 and age 4), but CCC fish were larger later in the year. The stocks appeared similarly distributed early in the year (2010) but more concentrated near their respective source rivers later in the year (2010 and 2011). If these results are representative, relative fishery impacts on the two stocks might scale similarly early in the year, but management changes later in the year could have differing impacts on the two stocks. This novel modeling approach is suited to evaluating the concordance between other data-limited stocks and their proxies, and can be broadly applied to estimate stock-specific harvest, and the uncertainty therein, using GSI in other systems

Estimating food production in an urban landscape

There is increasing interest in urban food production for reasons of food security, environmental sustainability, social and health benefits. In developed nations urban food growing is largely informal and localised, in gardens, allotments and public spaces, but we know little about the magnitude of this production. Here we couple own-grown crop yield data with garden and allotment areal surveys and urban fruit tree occurrence to provide one of the first estimates for current and potential food production in a UK urban setting. Current production is estimated to be sufficient to supply the urban population with fruit and vegetables for about 30 days per year, while the most optimistic model results suggest that existing land cultivated for food could supply over half of the annual demand. Our findings provide a baseline for current production whilst highlighting the potential for change under the scaling up of cultivation on existing land

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

A Practice Platform for Systematic Development of Microsurgical Instrument Technique

Abstract Many surgical disciplines, particularly those specializing in the pediatric age group, use microsurgical instruments with the assistance of either optical loupe or microscope magnification to perform high precision surgical procedures. We developed a course consisting of two parts: Part 1 consists of low fidelity, inanimate exercises or training and practice platform, and part 2 employed a rat model. In this report, we describe and provide templates for the first part of the course, namely the practice platform as an integral set of six low-fidelity exercises, each focusing on a specific aspect of instrument handling required to master the later use of the instruments during actual microsurgery. This platform is made to systematically and efficiently improve the microsurgical skills of junior as well as advanced surgical trainees

A practice platform for systematic development of microsurgical instrument technique

e1253Many surgical disciplines, particularly those specializing in the pediatric age group, use microsurgical instruments with the assistance of either optical loupe or microscope magnification to perform high precision surgical procedures. We developed a course consisting of two parts: Part 1 consists of low fidelity, inanimate exercises or training and practice platform, and part 2 employed a rat model. In this report, we describe and provide templates for the first part of the course, namely the practice platform as an integral set of six low-fidelity exercises, each focusing on a specific aspect of instrument handling required to master the later use of the instruments during actual microsurgery. This platform is made to systematically and efficiently improve the microsurgical skills of junior as well as advanced surgical trainees