The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable

Hierarchical modelling of temperature and habitat size effects on population dynamics of North Atlantic cod

Understanding how temperature affects cod (Gadus morhua) ecology is important for forecasting how populations will develop as climate changes in future. The effects of spawning-season temperature and habitat size on cod recruitment dynamics have been investigated across the North Atlantic. Ricker and Beverton and Holt stock–recruitment (SR) models were extended by applying hierarchical methods, mixed-effects models, and Bayesian inference to incorporate the influence of these ecosystem factors on model parameters representing cod maximum reproductive rate and carrying capacity. We identified the pattern of temperature effects on cod productivity at the species level and estimated SR model parameters with increased precision. Temperature impacts vary geographically, being positive in areas where temperatures are <5°C, and negative for higher temperatures. Using the relationship derived, it is possible to predict expected changes in population-specific reproductive rates and carrying capacities resulting from temperature increases. Further, carrying capacity covaries with available habitat size, explaining at least half its variability across stocks. These patterns improve our understanding of environmental impacts on key population parameters, which is required for an ecosystem approach to cod management, particularly under ocean-warming scenarios. Key words: carrying capacity , cod , hierarchical models , North Atlantic , temperature , uncertaint

Cognitive processing biases in those experiencing frequent or chronic headaches or migraines: a meta-analysis and systematic review (protocol)

The aim of this review is to explore the role of cognitive biases in those experiencing frequent or chronic headaches and migraines (based on selection criteria outlined across studies – e.g. criteria stated in the International Classification of Headache Disorders). More specifically, we are interested in attentional, interpretation and memory biases. We present here the protocol for this review

Time-Varying Arrival Rates of Informed and Uninformed Trades

In this paper we extend the model of Easley and O'Hara (1992) to allow the arrival rates of informed and uninformed trades to be time-varying and forecastable. We specify a generalized autoregressive bivariate process for the arrival rates of informed and uninformed trades and estimate the model on 16 actively traded stocks on the New York Stock Exchange over 15 years of transaction data. Our results show that uninformed trades are highly persistent. Uninformed order arrivals clump together, with high uninformed volume days likely to follow high uninformed volume days, and conversely. This behavior is consistent with the passive characterization of the uninformed found in the literature. But we do find an important difference in how the uninformed behave; they avoid trading when the informed are forecasted to be present. Informed trades also exhibit complex patterns, but these patterns are not consistent with the strategic behavior posited in the literature. The informed do not appear to hide in order flow, but instead they trade persistently. We also investigate the correlation between the arrival rates of trades and trade composition on market volatility, liquidity and depth. We find that although volatility increases with the forecasted arrival rates of total trades, it is relatively independent of the forecasted composition of the trade. We use the opening bid-ask spread as a measure of market liquidity. We find that as the number of trades increases over time, the relative proportion of informed trades decreases and hence, spreads become narrower and the market becomes more liquid. Finally, we compute the price impact curve of consecutive buy orders and report the half life of the price impact as a measure of market depth. We find a positive correlation between the half life and total trades indicating that the market is deeper in presence of more trades.Arrival rates; informed trades; uninformed trades; autoregressive process; market depth; liquidity; volatility.

Vehicle Path Optimization of Emergency Lane Change Maneuvers for Vehicle Simulation

Driver-based handling tests, such as the Double Lane Change (DLC) maneuver are subjective in nature and depend largely on driver skill and road conditions. They also suffer from poor repeatability. Implementation of these tests on hardware-in-the-loop simulators can also produce subjective results if the steer profiles are not systematically generated. This research produced a vehicle path optimization model that generated optimal paths for handling tests based on minimizing the maximum curvature during the maneuver. This approach lessened the dynamics of the vehicle and increased the chances of successful test results at given speeds. Excel's Solver was used for the optimization. The model results were compared to field test and hardware-in-the-loop test results, showing potential for reductions in lateral acceleration and vehicle side-slip

A concise and scalable strategy for the total synthesis of dictyodendrin B based on sequential C-H functionalization.

A sequential CH functionalization strategy for the synthesis of the marine alkaloid dictyodendrin B is reported. Our synthesis begins from commercially available 4-bromoindole and involves six direct functionalizations around the heteroarene core as part of a gram-scale strategy towards the natural product.We are grateful to AstraZeneca, Pfizer, and EPSRC (A.K.P., F.O. and R.H.S.) and the ERC and EPSRC for fellowships (M.J.G.).This is the author accepted manuscript. The final version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1002/anie.201500067/abstract

Accounting for Misclassification in Multispecies Distribution Models

1. Species identification errors may have severe implications for the inference of species distributions. Accounting for misclassification in species distributions is an important topic of biodiversity research. With an increasing amount of biodiversity that comes from Citizen Science projects, where identification is not verified by preserved specimens, this issue is becoming more important. This has often been dealt with by accounting for false positives in species distribution models. However, the problem should account for misclassifications in general. 2. Here we present a flexible framework that accounts for misclassification in the distribution models and provides estimates of uncertainty around these estimates. The model was applied to data on viceroy, queen and monarch butterflies in the United States. The data were obtained from the iNaturalist database in the period 2019 to 2020. 3. Simulations and analysis of butterfly data showed that the proposed model was able to correct the reported abundance distribution for misclassification and also predict the true state for misclassified state

On the setting of environmental noise and the performance of population dynamical models

Background Environmental noise is ubiquitous in population growth processes, with a well acknowledged potential to affect populations regardless of their sizes. It therefore deserves consideration in population dynamics modelling. The usual approach to incorporating noise into population dynamical models is to make some model parameter(s) (typically the growth rate, the carrying capacity, or both) stochastic and responsive to environment fluctuations. It is however still unclear whether including noise in one or/and another parameter makes a difference to the model performance. Here we investigated this issue with a focus on model fit and predictive accuracy. To do this, we developed three population dynamical models of the Ricker type with the noise included in the growth rate (Model 1), in the carrying capacity (Model 2), and in both (Model 3). We generated several population time series under each model, and used a Bayesian approach to fit the three models to the simulated data. We then compared the model performances in fitting to the data and in forecasting future observations. Results When the mean intrinsic growth rate, r, in the data was low, the three models had roughly comparable performances, irrespective of the true model and the level of noise. As r increased, Models 1 performed best on data generated from it, and Model 3 tended to perform best on data generated from either Models 2 or Model 3. Model 2 was uniformly outcompeted by the other two models, regardless of the true model and the level of noise. The correlation between the deviance information criterion (DIC) and the mean square error (MSE) used respectively as measure of fit and predictive accuracy was broadly positive. Conclusion Our results suggested that the way environmental noise is incorporated into a population dynamical model may profoundly affect its performance. Overall, we found that including noise in one or/and another parameter does not matter as long as the mean intrinsic growth rate, r, is low. As r increased, however, the three models performed differently. Models 1 and 3 broadly outperformed Model 2, the first having the advantage of being simple and more computationally tractable. A comforting result emerging from our analysis is the broad positive correlation between MSEs and DICs, suggesting that the latter may also be informative about the predictive performance of a model.Peer reviewe

Recognizability bias in citizen science photographs

Citizen science and automated collection methods increasinglydepend on image recognition to provide the amountsof observational data research and management needs.Recognition models, meanwhile, also require large amounts ofdata from these sources, creating a feedback loop between themethods and tools. Species that are harder to recognize, bothfor humans and machine learning algorithms, are likely to beunder-reported, and thus be less prevalent in the trainingdata. As a result, the feedback loop may hamper trainingmostly for species that already pose the greatest challenge. Inthis study, we trained recognition models for various taxa, andfound evidence for a‘recognizability bias’, where species thatare more readily identified by humans and recognitionmodels alike are more prevalent in the available image data.This pattern is present across multiple taxa, and does notappear to relate to differences in picture quality, biologicaltraits or data collection metrics other than recognizability. Thishas implications for the expected performance of futuremodels trained with more data, including such challenging species. citizen science, image recognition, machinelearning, recognizability, artificial intelligence/environmental science/ecology, Ecology, conservation and global change biologypublishedVersio