Test-retest reliability of structural brain networks from diffusion MRI

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability

Using Functional Traits to Model Annual Plant Community Dynamics

Predicting the response of biological communities to changes in the environment or management is a fundamental pursuit of community ecology. Meeting this challenge requires the integration of multiple processes: habitat filtering, niche differentiation, biotic interactions, competitive exclusion, and stochastic demographic events. Most approaches to this long-standing problem focus either on the role of the environment, using trait-based filtering approaches, or on quantifying biotic interactions with process-based community dynamics models. We introduce a novel approach that uses functional traits to parametrise a process-based model. By combining the two approaches we make use of the extensive literature on traits and community filtering as a convenient means of reducing the parametrisation requirements of a complex population dynamics model whilst retaining the power to capture the processes underlying community assembly. Using arable weed communities as a case study, we demonstrate that this approach results in predictions that show realistic distributions of traits and that trait selection predicted by our simulations is consistent with in-field observations. We demonstrate that trait-based filtering approaches can be combined with process-based models to derive the emergent distribution of traits. While initially developed to predict the impact of crop management on functional shifts in weed communities, our approach has the potential to be applied to other annual plant communities if the generality of relationships between traits and model parameters can be confirmed

Modelling the effect of spatially variable soil properties on the distribution of weeds

The patch spraying of weeds is an area of precision agriculture that has had limited uptake. This is in part due to the perceived risks associated with not controlling individual weeds. Nevertheless, the inherent patchiness of weeds makes them ideal targets for site-specific management. We propose using a mechanistic model to identify areas of a field vulnerable to invasion by weeds, allowing the creation of treatment maps that are risk averse. We developed a spatially-explicit mechanistic model of the life-cycle of Alopecurus myosuroides, a particularly problematic weed of cereal crops in the UK. In the model, soil conditions which vary across the field, affect the life-cycle of A. myosuroides. The model was validated using data on the within-field distribution of A. myosuroides on commercial farms and its co-location with soil properties. We demonstrate the important role played by soil properties in determining the within-field distribution of A. myosuroides. We also show that scale-dependent correlations between A. myosuroides and soil properties observed in the field are an emergent property of the modelled dynamics of the A. myosuroides life-cycle. Our model could therefore support effective site-specific management of A. myosuroides within fields by predicting areas that are vulnerable to A. myosuroides. The usefulness of this model in its ability to predict patch locations for A. myosuroides highlights the possibility of using similar models for other species where data are available on the response of the species to various soil properties

Defining Integrated Weed Management: A Novel Conceptual Framework for Models

Weed population dynamics models are an important tool for predicting the outcome of alternative Integrated Weed Management (IWM) scenarios. The growing problem of herbicide resistance has increased the urgency for these tools in the design of sustainable IWM solutions. We developed a conceptual framework for defining IWM as a standardised input template to allow output from different models to be compared and to design IWM scenarios. The framework could also be used as a quantitative metric to determine whether more diverse systems are more sustainable and less vulnerable to herbicide resistance using empirical data. Using the logic of object-oriented programming, we defined four classes of weed management options based on the stage in the weed life cycle that they impact and processes that mediate their effects. Objects in the same class share a common set of properties that determine their behaviour in weed population dynamics models. Any weed control “event” in a system is associated with an object, meaning alternative management scenarios can be built by systematically adding events to a model either to compare existing systems or design novel approaches. Our framework is designed to be generic, allowing IWM systems from different cropping systems and countries to be compared

The implications of spatially variable pre-emergence herbicide efficacy for weed management

BACKGROUND The efficacy of pre-emergence herbicides within fields is spatially variable due to soil heterogeneity. We quantified the effect of soil organic matter on the efficacy of two pre-emergence herbicides; flufenacet and pendimethalin, against A. myosuroides and investigated the implications of variation in organic matter for weed management using a crop-weed competition model. RESULTS Soil organic matter played a critical role in determining the level of control achieved. The high organic matter soil had more surviving weeds with higher biomass than the low organic matter soil. In the absence of competition, surviving plants recovered to produce the same amount of seed as if no herbicide were applied. The competition model predicted that weeds surviving pre-emergence herbicides could compensate for sub-lethal effects even when competing with the crop. The ED50 was higher for weed seed production than seedling mortality or biomass. This difference was greatest on high organic matter soil. CONCLUSION These results show that the application rate of herbicides should be adjusted to account for within-field variation in soil organic matter. The results from the modelling emphasised the importance of crop competition in limiting the capacity of weeds surviving pre-emergence herbicides to compensate and replenish the seedbank

Communicating Carabids: Engaging farmers to encourage uptake of Integrated Pest Management.

BACKGROUND: Natural enemy pest control (NPC) is becoming more desirable as restrictions increase on pesticide use. Carabid beetles are proven agents of NPC, controlling pests and weeds in crop areas. Agro-ecological measures can be effective for boosting carabid abundance and associated NPC, however the benefits of specific interventions to production are seldom communicated to farmers. We explore pathways to improved NPC by engaging farmers and increasing knowledge about Farm Management Practices (FMPs) beneficial to carabids using engagement materials. We used a questionnaire to measure awareness, beliefs, and attitudes to carabids and analysed these within a framework of the Theory of Planned Behaviour (TPB), relative to a control group. RESULTS: We found awareness of carabid predation to be associated with beliefs of pest and weed control efficacy. Within the framework of TPB, we found that current implementation of FMPs was higher if farmers perceived them to be both important for carabids, and easy to implement. This was also true for future intention to implement, yet the perceived importance was influenced by engagement materials. Field margins/buffer strips and beetle banks (16% and 13% of responses) were the most favoured by farmers as interventions for carabids. CONCLUSION: The TPB is a valuable tool with which to examine internal elements of farmer behaviour. In this study self-selected participants were influenced by online engagement in a single intervention, proving this approach has the potential to change behaviour. Our results are evidence for the effectiveness of raising awareness of NPC to change attitudes and increase uptake of sustainable practices

Above‐ and below‐ground assessment of carabid community responses to crop type and tillage

Carabid beetles are major predators in agro‐ecosystems. The composition of their communities within crop environments governs the pest control services they provide. An understudied aspect is the distribution of predacious carabid larvae in the soil. We used novel subterranean trapping with standard pitfall trapping, within a multi‐crop rotation experiment, to assess the responses of above‐ and below‐ground carabid communities to management practices. Crop and trap type significantly affected pooled carabid abundance with an interaction of the two, the highest numbers of carabids were caught in subterranean traps in barley under sown with grass. Trap type accounted for the most variance observed in carabid community composition, followed by crop. Tillage responses were only apparent at the species level for three of the eight species modelled. Responses to crop type varied by species. Most species had higher abundance in under‐sown barley, than grass, wheat and barley. Crop differences were greater in the subterranean trap data. For predaceous larvae, standard pitfalls showed lowest abundances in under‐sown barley, yet subterranean traps revealed abundances to be highest in this crop. Comprehensive estimation of ecosystem services should incorporate both above‐ and below‐ground community appraisal, to inform appropriate management

Adaptive thresholding for reliable topological inference in single subject fMRI analysis

Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined

Species matter when considering landscape effects on carabid distributions

Increasing the abundance and diversity of carabid beetles is a common objective of farm habitat management to deliver sustainable pest control. Carabid spatial distributions in relation to crop areas are important to the delivery of this ecosystem service. We used pitfall count data at distances from edge habitats into crop centres, from farm sites across the UK, to determine the effects of in-field and adjacent environmental features on carabid abundance and diversity. Overall carabid abundance increased towards the crop centre, whilst species richness and diversity decreased. The analyses of carabid abundance based on all the species pooled together strongly reflected the behaviour of the most abundant species. Species preferences varied by crop, soil type, and environmental features. For instance, some species were positively associated with habitats such as margins, while others responded negatively. This contrast in individual species models highlights the limitations on pooled models in elucidating responses. Studies informing farm-habitat design should consider individual species’ preferences for effective enhancement of pest control services. Diverse cropping and landscape heterogeneity at the farm scale can benefit the varied preferences of individual species, help build diverse communities and, potentially increase service resilience and stability over time

Legume based plant mixtures for delivery of multiple ecosystem services: An overview of benefits

As costs for mineral fertilizers rise, legume-based leys are recognised as a potential alternative nitrogen source for crops. Here we demonstrate that including species-rich legume-based leys in the rotation helps to maximize synergies between agricultural productivity and other ecosystem services. By using functionally diverse plant species mixtures these services can be optimised and fine-tuned to regional and farm-specific needs. Field experiments run over three years at multiple locations showed that the stability of ley performance was greater in multi-species mixtures than in legume monocultures. In addition, mixing different legume species in the ley helps to suppress both early and late weeds. Further, combining complementary phenologies of different legume species extended forage availability for key pollinator species. Finally, widening the range of legume species increases opportunities to build short term leys into rotations on conventional farms via cover cropping or undersowing