Social as much as environmental: the drivers of tree biomass in smallholder forest landscape restoration programmes

A major challenge for forest landscape restoration initiatives is the lack of quantitative evidence on how social factors drive environmental outcomes. Here we conduct an interdisciplinary quantitative analysis of the environmental and social drivers of tree biomass accumulation across 639 smallholder farms restoring native tree species in Mexico, Uganda and Mozambique. We use environmental and social data to assess the relative effects of key hypothesised drivers on aboveground biomass accumulation at the farm-level over ten years. We supplement this with a qualitative analysis of perspectives from local farmers and agroforestry technicians on the potential causal mechanisms of the observed social effects. We find that the material wellbeing of farmers (e.g. assets) and access to agroforestry knowledge explain as much variation in biomass as water availability. Local perspectives suggest that this is caused by the higher adaptive capacity of some farmers and their associated ability to respond to social-ecological shocks and stresses. Additionally, the variation in biomass between farms increased over time. Local perspectives suggested that this was caused by emergent exogenous and stochastic influences which cannot be reliably predicted in technical analyses and guidance. To deal with this persistent uncertainty, local perspectives emphasised the need for flexible and adaptive processes at the farm- and village-levels. The consistency of these findings across three countries suggests these findings are relevant to similar forest restoration interventions. Our findings provide novel quantitative evidence of a social-ecological pathway where the adaptive capacity of local land users can improve ecological processes. Our findings emphasize the need for forest restoration programmes to prioritise investment in the capabilities of local land users, and to ensure that rules support, rather than hinder, adaptive management

Confronting deep uncertainty in the forest carbon industry

Global momentum on carbon markets has the potential to direct substantial capital toward protecting the world’s forests. Yet the billion-dollar forest-carbon-offsetting industry is attracting criticism, in part from doubts about the methods used to measure and causally attribute changes in tree cover and biomass (1). Many actors in the industry are thus pursuing increasingly detailed measurement and monitoring of carbon outcomes and risks, under the assumption that this will improve accuracy and offset integrity (2). However, mounting scientific evidence (3, 4) implies that many forest landscapes are subject to “deep uncertainty” (5), such that claims of high accuracy in assessing carbon change are likely to remain inherently contestable, regardless of the technology or methodology deployed. Further, demands for such accuracy are likely to perpetuate inefficiencies and injustices among carbon suppliers (6–9). Approaches from other sectors may offer alternative ways forward in the absence of highly accurate measurements of outcomes

Automated Pattern Identification and Classification: Anomaly Detection Case Study

In this study, the efficacy of the Automated Pattern Identification and Classification (APIC) Machine Learning (ML) pipeline method was evaluated as an Anomaly Intrusion Detection (AID) system to determine if using an ML-pipeline method could reduce false positive rates compared to similar methods using the same data set

Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR) models

Background The Conditional Autoregressive (CAR) model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. Methods We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW), Australia within 198 Statistical Local Areas. Between the years 1995–2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995–2000 and their fit evaluated within the following time period: 2001–2003. Results We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age). In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model') was able to correctly classify 35/47 high-risk areas (sensitivity 74%) with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. Conclusion We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori based on decision-theoretic considerations including loss, cost and inferential aims

Automating Network Protocol Identification

The proliferation of computer network users has, in recent years, placed a strain on network resources, such as bandwidth and number allocations. This issue is more apparent where connectivity is limited, such as in developing countries. The provisioning of services over these congested resources needs to be managed, ensuring a fair quality of experience (QoE) to consumers and producers alike. Quality of service (QoS) techniques used to manage such resources require constant revision, catering for new application protocols introduced to the network on a daily basis. This research proposes an efficient, autonomous method for distinguishing application protocols through the use of a dynamic protocol classification system (DPCS). Using this method, the burden of signature creation is reduced, while the accuracy achieved in application protocol identification increases

The Impact of Depression on Patient Outcomes in Hip Arthroscopic Surgery.

Background: Mental health impairments have been shown to negatively affect preoperative self-reported function in patients with various musculoskeletal disorders, including those with femoroacetabular impingement. Hypothesis: Those with symptoms of depression will have lower self-reported function, more pain, and less satisfaction on initial assessment and at 2-year follow-up than those without symptoms of depression. Study Design: Cohort study; Level of evidence, 3. Methods: Patients who were enrolled in a multicenter hip arthroscopic surgery registry and had 2-year outcome data available were included in the study. Patients completed the 12-item International Hip Outcome Tool (iHOT-12), visual analog scale (VAS) for pain, and 12-item Short-Form Health Survey (SF-12) when consenting for surgery. At 2-year follow-up, patients were emailed the iHOT, the VAS, and a rating scale of surgical satisfaction. Initial SF-12 mental component summary (MCS) scores Results: A total of 781 patients achieved the approximate 2-year milestone (mean follow-up, 735 ± 68 days), with 651 (83%) having 2-year outcome data available. There were 434 (67%) female and 217 (33%) male patients, with a mean age of 35.8 ± 13.0 years and a mean body mass index of 25.4 ± 8.8 kg/m Conclusion: A large number of patients who underwent hip arthroscopic surgery presented with symptoms of depression, which negatively affected self-reported function, pain levels, and satisfaction on initial assessment and at 2-year follow-up. Surgeons who perform hip arthroscopic surgery may need to identify the symptoms of depression and be aware of the impact that depression can have on surgical outcomes

Low dose influenza virus challenge in the ferret leads to increased virus shedding and greater sensitivity to oseltamivir

Ferrets are widely used to study human influenza virus infection. Their airway physiology and cell receptor distribution makes them ideal for the analysis of pathogenesis and virus transmission, and for testing the efficacy of anti-influenza interventions and vaccines. The 2009 pandemic influenza virus (H1N1pdm09) induces mild to moderate respiratory disease in infected ferrets, following inoculation with 106 plaque-forming units (pfu) of virus. We have demonstrated that reducing the challenge dose to 102 pfu delays the onset of clinical signs by 1 day, and results in a modest reduction in clinical signs, and a less rapid nasal cavity innate immune response. There was also a delay in virus production in the upper respiratory tract, this was up to 9-fold greater and virus shedding was prolonged. Progression of infection to the lower respiratory tract was not noticeably delayed by the reduction in virus challenge. A dose of 104 pfu gave an infection that was intermediate between those of the 106 pfu and 102 pfu doses. To address the hypothesis that using a more authentic low challenge dose would facilitate a more sensitive model for antiviral efficacy, we used the well-known neuraminidase inhibitor, oseltamivir. Oseltamivir-treated and untreated ferrets were challenged with high (106 pfu) and low (102 pfu) doses of influenza H1N1pdm09 virus. The low dose treated ferrets showed significant delays in innate immune response and virus shedding, delayed onset of pathological changes in the nasal cavity, and reduced pathological changes and viral RNA load in the lung, relative to untreated ferrets. Importantly, these observations were not seen in treated animals when the high dose challenge was used. In summary, low dose challenge gives a disease that more closely parallels the disease parameters of human influenza infection, and provides an improved pre-clinical model for the assessment of influenza therapeutics, and potentially, influenza vaccines

Motivations of Sport Volunteers in England: A review for Sport England

This review is the first to combine the findings of commercial reports and academic research into the motivations of sports volunteers with general theory understanding volunteers and volunteering. This provides a broader understanding of volunteering in sport. It provides a useful resource for anyone in the planning, management and delivery of sports volunteering and a stepping stone for further research

Pattern of activation of pelvic floor muscles in men differs with verbal instructions

AimsTo investigate the effect of instruction on activation of pelvic floor muscles (PFM) in men as quantified by transperineal ultrasound imaging (US) and to validate these measures with invasive EMG recordings