Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.Peer reviewedPostprintPostprintPostprintPostprintPostprin

Assessing water quality for cropping management practices: A new approach for dissolved inorganic nitrogen discharged to the Great Barrier Reef

Applications of nitrogen (N) fertiliser to agricultural lands impact many marine and aquatic ecosystems, and improved N fertiliser management is needed to reduce these water quality impacts. Government policies need information on water quality and risk associated with improved practices to evaluate the benefits of their adoption. Policies protecting Great Barrier Reef (GBR) ecosystems are an example of this situation. We developed a simple metric for assessing the risk of N discharge from sugarcane cropping, the biggest contributor of dissolved inorganic N to the GBR. The metric, termed NiLRI, is the ratio of N fertiliser applied to crops and the cane yield achieved (i.e. kg N (t cane)−1). We defined seven classes of water quality risk using NiLRI values derived from first principles reasoning. NiLRI values calculated from (1) results of historical field experiments and (2) survey data on the management of 170,177 ha (or 53%) of commercial sugarcane cropping were compared to the classes. The NiLRI values in both the experiments and commercial crops fell into all seven classes, showing that the classes were both biophysically sensible (c.f. the experiments) and relevant to farmers’ experience. We then used machine learning to explore the association between crop management practices recorded in the surveys and associated NiLRI values. Practices that most influenced NiLRI values had little apparent direct impact on N management. They included improving fallow management and reducing tillage and compaction, practices that have been promoted for production rather than N discharge benefits. The study not only provides a metric for the change in N water quality risk resulting from adoption of improved practices, it also gives the first clear empirical evidence of the agronomic practices that could be promoted to reduce water quality risk while maintaining or improving yields of sugarcane crops grown in catchments adjacent to the GBR. Our approach has relevance to assessing the environmental risk of N fertiliser management in other countries and cropping systems

Applying Multi-Layer Artificial Neural Network and Mutual Information to the Prediction of Trends in Dissolved Oxygen

Predicting trends in water quality plays an essential role in the field of environmental modeling. Though artificial neural networks (ANN) have been involved in predicting water quality in many studies, the prediction performance is highly affected by the model's inputs and neural network structure. Many researchers selected water quality variables based on Pearson correlation. However, this kind of method can only capture linear dependencies. Moreover, when dealing with multivariate water quality data, ANN with the single layer and few numbers of units show difficulties in representing complex inner relationships between multiple water quality variables. Hence, in this paper we propose a novel model based on multi-layer artificial neural networks (MANN) and mutual information (MI) for predicting the trend of dissolved oxygen. MI is used to evaluate and choose water quality variables by taking into account the non-linear relationships between the variables. A MANN model is built to learn the levels of representations and approximate complex regression functions. Water quality data collected from Baffle Creek, Australia was used in the experiment. Our model had around 0.95 and 0.94 R2 scores for predicting 90 or 120 min ahead of the last observed data in the wet season, which are much higher than the typical ANN model, support vector regressor (SVR) and linear regression model (LRM). The results indicate that our MANN model can provide accurate predictions for the trend of DO in the upcoming hours and is a useful supportive tool for water quality management of the aquatic ecosystems

An integrative modelling framework for passive acoustic telemetry

The work was supported by a PhD Studentship at the University of St Andrews funded by NatureScot, via the Marine Alliance for Science and Technology for Scotland (MASTS), and the Centre for Research into Ecological and Environmental Modelling. Data were made available through the Movement Ecology of Flapper Skate project funded by NatureScot (project 015960) and Marine Scotland (projects SP004 and SP02B0). Jane Dodd, Ronnie Campbell, Roger Eaton, Francis Neat and Dmitry Aleynik supported this project. MASTS and Shark Guardian provided additional funding. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Passive acoustic telemetry is widely used to study the movements of aquatic animals. However, a holistic, mechanistic modelling framework that permits the reconstruction of fine-scale movements and emergent patterns of space use from detections at receivers remains lacking. Here, we introduce an integrative modelling framework that recapitulates the movement and detection processes that generate detections to reconstruct fine-scale movements and patterns of space use. This framework is supported by a new family of algorithms designed for detection and depth observations and can be flexibly extended to incorporate other data types. Using simulation, we illustrate applications of our framework and evaluate algorithm utility and sensitivity in different settings. As a case study, we analyse movement data collected from the Critically Endangered flapper skate (Dipturus intermedius) in Scotland. We show that our methods can be used to reconstruct fine-scale movement paths, patterns of space use and support habitat preference analyses. For reconstructing patterns of space use, simulations show that the methods are consistently more instructive than the most widely used alternative approach (the mean-position algorithm), particularly in clustered receiver arrays. For flapper skate, the reconstruction of movements reveals responses to disturbance, fine-scale spatial partitioning and patterns of space use with significant implications for marine management. We conclude that this framework represents a widely applicable methodological advance with applications to studies of pelagic, demersal and benthic species across multiple spatiotemporal scales.Publisher PDFPeer reviewe

Environmental cycles and individual variation in the vertical movements of a benthic elasmobranch

This research was supported by a PhD Studentship at the University of St Andrews, jointly funded by NatureScot via the Marine Alliance for Science and Technology for Scotland (MASTS), and the Centre for Research into Ecological and Environmental Modelling. The data were collected as part of research funded by NatureScot (project 015960) and Marine Scotland (projects SP004 and SP02B0) and the Movement Ecology of Flapper Skate (MEFS) project funded by the same organisations. Additional funding was provided from MASTS, in the form of a Small Research Grant, and Shark Guardian. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Trends in depth and vertical activity reflect the behaviour, habitat use and habitat preferences of marine organisms. However, among elasmobranchs, research has focused heavily on pelagic sharks, while the vertical movements of benthic elasmobranchs, such as skate (Rajidae), remain understudied. In this study, the vertical movements of the Critically Endangered flapper skate (Dipturus intermedius) were investigated using archival depth data collected at 2 min intervals from 21 individuals off the west coast of Scotland (56.5°N, −5.5°W) in 2016–17. Depth records comprised nearly four million observations and included eight time series longer than 1 year, forming one of the most comprehensive datasets collected on the movement of any skate to date. Additive modelling and functional data analysis were used to investigate vertical movements in relation to environmental cycles and individual characteristics. Vertical movements were dominated by individual variation but included prolonged periods of limited activity and more extensive movements that were associated with tidal, diel, lunar and seasonal cycles. Diel patterns were strongest, with irregular but frequent movements into shallower water at night, especially in autumn and winter. This research strengthens the evidence for vertical movements in relation to environmental cycles in benthic species and demonstrates a widely applicable flexible regression framework for movement research that recognises the importance of both individual-specific and group-level variation.Publisher PDFPeer reviewe

Understanding power, social capital and trust alongside near real-time water quality monitoring and technological development collaboration

We report on qualitative social research conducted with stakeholders in a local agricultural knowledge and advice network associated with a collaborative water quality monitoring project. These farmers, advisors and researchers allude to existing social dynamics, technological developments, and (more general) social evolution which is analysed against a novel analytical framework. This framework considers notions of power, social capital, and trust as related and dynamic, forming the basis of our contribution to knowledge. We then probe the data to understand perceived impacts of the collaborative project and social interaction associated with this research project, which involved cutting edge automated and frequent water quality monitoring that allowed for near real-time access to data visualisation displayed via a bespoke mobile or web ‘app’ (1622WQ). Our findings indicate that a multi-faceted approach to assessing and intervening based on consideration of multiple social dimensions holds promise in terms of creating conditions that allow for individual and group learning to encourage changes in thinking required to result in improved land management practice

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability

Grasping at digitalisation: turning imagination into fact in the sugarcane farming community

Nutrient runoff from catchments that drain into the Great Barrier Reef (GBR) is a significant source of stress for this World Heritage Area. An alliance of collaborative on-ground water quality monitoring (Project 25) and technologically driven digital application development (Digiscape GBR) projects were formulated to provide data that highlighted the contribution of a network of Australian sugar cane farmers, amongst other sources, to nutrient runoff. This environmental data and subsequent information were extended to the farming community through scientist-led feedback sessions and the development of specialised digital technology (1622 (TM) WQ) that help build an understanding of the nutrient movements, in this case nitrogen, such that farmers might think about and eventually act to alter their fertilizer application practices. This paper reflects on a socio-environmental sustainability challenge that emerged during this case study, by utilising the nascent concept of digi-grasping. We highlight the importance of the entire agricultural knowledge and advice network being part of an innovation journey to increase the utility of digital agricultural technologies developed to increase overall sustainability. We develop the digi-MAST analytical framework, which explores modes of being and doing in the digital world, ranging from 'the everyday mystery of the digital world (M)', through digital 'awareness (A)', digitally 'sparked' being/s (S), and finally the ability of individuals and/or groups to 'transform (T)' utilising digital technologies and human imaginations. Our digi-MAST framework allows us to compare agricultural actors, in this case, to understand present modes of digi-grasping to help determine the resources and actions likely to be required to achieve impact from the development of various forms of digital technological research outputs

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3%lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost