A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel

Fish population dynamics are affected by multiple ecosystem drivers, such as food-web interactions, exploitation, density-dependence and the wider environment. While tactical management is still dominated by single-species models that do not explicitly account for these drivers, more holistic ecosystem models are used in strategic management. One way forward in this regard is with individual-based models (IBMs), which provide a single framework in which these drivers can be represented explicitly. We present a generic marine fish IBM that incorporates spatial and temporal variation in food availability, temperature and exploitation. Key features of the model are that it (1) includes realistic energy budgets; (2) includes the full life cycle of fish; (3) is spatially-explicit and (4) incorporates satellite remote-sensing data to represent the environmental drivers. The rates at which individuals acquire and use energy depend on local food availability and temperature. Their state variables, including life stage, size and energy reserves, are updated daily, from which population structure and dynamics emerge. To demonstrate the use of the model we calibrate it for mackerel (Scomber scombrus) in the North East Atlantic. Most parameters are taken from the literature, except the background early mortality rate and the strength predator density dependence, which were estimated by fitting the model to data using Approximate Bayesian Computation. The calibrated model successfully matches the available data on mackerel population dynamics and structure. We demonstrate the use of the model for management purposes by simulating the population effects of opening and closing a sector of the North Sea to mackerel fishing. Our model uses basic principles of behavioural and physiological ecology to establish how spatial and temporal variations in ecosystem drivers affect the individuals in the population. Population dynamics and structure are calculated from the collective effects on individuals. Application to a test case shows the method can fit available data well. Individual-based approaches such as this study have potential for use in strategic management because they can account for spatial structuring, food-web interactions, density dependence, and environmental drivers within a single framework

Using Machine Vision to Estimate Fish Length from Images using Regional Convolutional Neural Networks

An image can encode date, time, location and camera information as metadata and implicitly encodes species information and data on human activity, for example the size distribution of fish removals. Accurate length estimates can be made from images using a fiducial marker; however, their manual extraction is time-consuming and estimates are inaccurate without control over the imaging system. This article presents a methodology which uses machine vision to estimate the total length (TL) of a fusiform fish (European sea bass). Three regional convolutional neural networks (R-CNN) were trained from public images. Images of European sea bass were captured with a fiducial marker with three non-specialist cameras. Images were undistorted using the intrinsic lens properties calculated for the camera in OpenCV; then TL was estimated using machine vision (MV) to detect both marker and subject. MV performance was evaluated for the three R-CNNs under downsampling and rotation of the captured images. Each R-CNN accurately predicted the location of fish in test images (mean intersection over union, 93%) and estimates of TL were accurate, with percent mean bias error (%MBE [95% CIs]) = 2.2% [2.0, 2.4]). Detections were robust to horizontal flipping and downsampling. TL estimates at absolute image rotations >20° became increasingly inaccurate but %MBE [95% CIs] was reduced to −0.1% [−0.2, 0.1] using machine learning to remove outliers and model bias. Machine vision can classify and derive measurements of species from images without specialist equipment. It is anticipated that ecological researchers and managers will make increasing use of MV where image data are collected (e.g. in remote electronic monitoring, virtual observations, wildlife surveys and morphometrics) and MV will be of particular utility where large volumes of image data are gathered

Accurate estimation of fish length in single camera photogrammetry with a fiducial marker

Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems employed are complex, in contrast to "consumer"digital cameras and smartphones carried by potential citizen scientists. However, using consumer cameras in photogrammetry will introduce unknown length estimation errors through both the image acquisition process and lens distortion. This study presents a methodology to achieve accurate 2-dimensional (2-D) total length (TL) estimates of fish without specialist equipment or proprietary software. Photographs of fish were captured with an action camera using a background fiducial marker, a foreground fiducial marker and a laser marker. The geometric properties of the lens were modelled with OpenCV to correct image distortion. TL estimates were corrected for parallax effects using an algorithm requiring only the initial length estimate and known fish morphometric relationships. Correcting image distortion decreased RMSE by 96% and the percentage mean bias error (%MBE) by 50%. Correcting for parallax effects achieved a %MBE of -0.6%. This study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for complex camera equipment, making it particularly suitable for deployment in citizen science and other volunteer-based data collection endeavours

Observing and modelling phytoplankton community structure in the North Sea

© Author(s) 2017. CC Attribution 3.0 License. Phytoplankton form the base of the marine food chain, and knowledge of phytoplankton community structure is fundamental when assessing marine biodiversity. Policy makers and other users require information on marine biodiversity and other aspects of the marine environment for the North Sea, a highly productive European shelf sea. This information must come from a combination of observations and models, but currently the coastal ocean is greatly under-sampled for phytoplankton data, and outputs of phytoplankton community structure from models are therefore not yet frequently validated. This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea using accessory pigment analysis. The observations allow a good understanding of the patterns of surface phytoplankton biomass and community structure in the North Sea for the observed months of August 2010 and 2011. Two physical-biogeochemical ocean models, the biogeochemical components of which are different variants of the widely used European Regional Seas Ecosystem Model (ERSEM), were then validated against these and other observations. Both models were a good match for sea surface temperature observations, and a reasonable match for remotely sensed ocean colour observations. However, the two models displayed very different phytoplankton community structures, with one better matching the in situ observations than the other. Nonetheless, both models shared some similarities with the observations in terms of spatial features and inter-annual variability. An initial comparison of the formulations and parameterizations of the two models suggests that diversity between the parameter settings of model phytoplankton functional types, along with formulations which promote a greater sensitivity to changes in light and nutrients, is key to capturing the observed phytoplankton community structure. These findings will help inform future model development, which should be coupled with detailed validation studies, in order to help facilitate the wider application of marine biogeochemical modelling to user and policy needs

Contact structures in the poultry industry in Great Britain: Exploring transmission routes for a potential avian influenza virus epidemic

Background: The commercial poultry industry in United Kingdom (UK) is worth an estimated £3.4 billion at retail value, producing over 174 million birds for consumption per year. An epidemic of any poultry disease with high mortality or which is zoonotic, such as avian influenza virus (AIV), would result in the culling of significant numbers of birds, as seen in the Netherlands in 2003 and Italy in 2000. Such an epidemic would cost the UK government millions of pounds in compensation costs, with further economic losses through reduction of international and UK consumption of British poultry. In order to better inform policy advisers and makers on the potential for a large epidemic in GB, we investigate the role that interactions amongst premises within the British commercial poultry industry could play in promoting an AIV epidemic, given an introduction of the virus in a specific part of poultry industry in Great Britain (GB). Results: Poultry premises using multiple slaughterhouses lead to a large number of premises being potentially connected, with the resultant potential for large and sometimes widespread epidemics. Catching companies can also potentially link a large proportion of the poultry population. Critical to this is the maximum distance traveled by catching companies between premises and whether or not between-species transmission could occur within individual premises. Premises closely linked by proximity may result in connections being formed between different species and or sectors within the industry. Conclusion: Even quite well-contained epidemics have the potential for geographically widespread dissemination, potentially resulting in severe logistical problems for epidemic control, and with economic impact on a large part of the country. Premises sending birds to multiple slaughterhouses or housing multiple species may act as a bridge between otherwise separate sectors of the industry, resulting in the potential for large epidemics. Investment into further data collection and analyses on the importance of industry structure as a determinant for spread of AIV would enable us to use the results from this study to contribute to policy on disease control

Spatial distribution of the active surveillance of sheep scrapie in Great Britain: an exploratory analysis

<p>Abstract</p> <p>Background</p> <p>This paper explores the spatial distribution of sampling within the active surveillance of sheep scrapie in Great Britain. We investigated the geographic distribution of the birth holdings of sheep sampled for scrapie during 2002 – 2005, including samples taken in abattoir surveys (c. 83,100) and from sheep that died in the field ("fallen stock", c. 14,600). We mapped the birth holdings by county and calculated the sampling rate, defined as the proportion of the holdings in each county sampled by the surveys. The Moran index was used to estimate the global spatial autocorrelation across Great Britain. The contributions of each county to the global Moran index were analysed by a local indicator of spatial autocorrelation (LISA).</p> <p>Results</p> <p>The sampling rate differed among counties in both surveys, which affected the distribution of detected cases of scrapie. Within each survey, the county sampling rates in different years were positively correlated during 2002–2005, with the abattoir survey being more strongly autocorrelated through time than the fallen stock survey. In the abattoir survey, spatial indices indicated that sampling rates in neighbouring counties tended to be similar, with few significant contrasts. Sampling rates were strongly correlated with sheep density, being highest in Wales, Southwest England and Northern England. This relationship with sheep density accounted for over 80% of the variation in sampling rate among counties. In the fallen stock survey, sampling rates in neighbouring counties tended to be different, with more statistically significant contrasts. The fallen stock survey also included a larger proportion of holdings providing many samples.</p> <p>Conclusion</p> <p>Sampling will continue to be uneven unless action is taken to make it more uniform, if more uniform sampling becomes a target. Alternatively, analyses of scrapie occurrence in these datasets can take account of the distribution of sampling. Combining the surveys only partially reduces uneven sampling. Adjusting the distribution of sampling between abattoirs to reduce the bias in favour of regions with high sheep densities could probably achieve more even sampling. However, any adjustment of sampling should take account of the current understanding of the distribution of scrapie cases, which will be improved by further analysis of this dataset.</p

Factors affecting fisher decisions: the case of the inshore fishery for European sea bass (Dicentrarchus labrax)

Fishery management relies on forecasts of fish abundance over time and space, on scales of months and kilometres. While much research has focussed on the drivers of fish populations, there has been less investigation of the decisions made day-to-day by fishers and their subsequent impact on fishing pressure. Studies that focus on the fisher decisions of smaller vessels may be particularly important due to the prevalence of smaller vessels in many fisheries and their potential vulnerability to bad weather and economic change. Here we outline a methodology with which to identify the factors affecting fisher decisions and success as well as quantifying their effects. We analyse first the decision of when to leave port, and then the success of the fishing trip. Fisher behaviour is here analysed in terms of the decisions taken by fishers in response to bio-physical and socio-economic changes and to illustrate our method, we describe its application to the under 10-meter fleet targeting sea bass in the UK. We document the effects of wave height and show with increasing wave height fewer vessels left port to go fishing. The decision to leave port was only substantially affected by time of high tide at one of the four ports investigated. We measured the success of fishing trips by the landings of sea bass (kg) per metre of vessel length. Fishing success was lower when wave height was greater and when fish price had increased relative to the previous trip. Fuel price was unimportant, but a large proportion of the variation in success was explained by variation between individual vessels, presumably due to variation in skipper ability or technical restrictions due to vessel characteristics. The results are discussed in the context of management of sea bass and other small-scale inshore fisheries

Balancing biological and economic goals in commercial and recreational fisheries:Systems modelling of sea bass fisheries

The importance of social and economic factors, in addition to biological factors, in fisheries management is being increasingly recognised. However, exploration of trade-offs between biological, social, and economic factors under different sustainable catch limits for recreational and commercial fisheries is limited, especially in Europe. The European sea bass (Dicentrarchus labrax) is valuable and important for both commercial and recreational fisheries. Stocks have rapidly declined and management measures have been implemented, but trade-offs between social, biological, and economic factors have not been explicitly considered. In this study, a system dynamics model framework capturing biological and economic elements of the European sea bass fishery was developed and refined to incorporate a catch limit reflecting sustainable fishing with adjustable partition between recreational and commercial sectors, under low, medium, or high recruitment. Model outputs were used to explore the relative impact of different catch allocations on trade-offs between biological sustainability and economic impact when recruitment was limiting or not. Recruitment had a large impact on the fish population dynamics and the viability of the sectors. At high and moderate recruitment, management contributed to stock sustainability and sector economic impact, but recruitment is important in determining the balance between sectors

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

BACKGROUND: New and emerging diseases of livestock may impact animal welfare, trade and public health. Early detection of outbreaks can reduce the impact of these diseases by triggering control measures that limit the number of cases that occur. The aim of this study was to investigate whether prospective spatiotemporal methods could be used to identify outbreaks of new and emerging diseases in scanning surveillance data. SaTScan was used to identify clusters of unusually high levels of submissions where a diagnosis could not be reached (DNR) using different probability models and baselines. The clusters detected were subjected to a further selection process to reduce the number of false positives and a more detailed epidemiological analysis to ascertain whether they were likely to represent real outbreaks. RESULTS: 187,925 submissions of clinical material from cattle were made to the Regional Laboratory of the Veterinary Laboratories Agency (VLA) between 2002 and 2007, and the results were stored on the VLA FarmFile database. 16,925 of these were classified as DNRs and included in the analyses. Variation in the number and proportion of DNRs was found between syndromes and regions, so a spatiotemporal analysis for each DNR syndrome was done. Six clusters were identified using the Bernoulli model after applying selection criteria (e.g. size of cluster). The further epidemiological analysis revealed that one of the systemic clusters could plausibly have been due to Johne's disease. The remainder were either due to misclassification or not consistent with a single diagnosis. CONCLUSIONS: Our analyses have demonstrated that spatiotemporal methods can be used to detect clusters of new or emerging diseases, identify clusters of known diseases that may not have been diagnosed and identify misclassification in the data, and highlighted the impact of data quality on the ability to detect outbreaks. Spatiotemporal methods should be used alongside current temporal methods for analysis of scanning surveillance data. These statistical analyses should be followed by further investigation of possible outbreaks to determine whether cases have common features suggesting that these are likely to represent real outbreaks, or whether issues with the collection or processing of information have resulted in false positives

Citizen scientists’ dive computers resolve seasonal and interannual temperature variations in the Red Sea

Dive computers have the potential to provide depth resolved temperature data that is often lacking especially in close to shore, but spatiotemporal assessment of the robustness of this citizen science approach has not been done. In this study, we provide this assessment for the Red Sea, one of the most dived areas in the world. A comparison was conducted between 17 years of minimum water temperatures collected from SCUBA dive computers in the northern Red Sea (23–30° N, 32–39.4° E), satellite-derived sea surface temperatures from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) optimal interpolation product, and depth-banded monthly mean in-situ temperature from the TEMPERSEA dataset, which incorporates data originating from several in-situ recording platforms (including Argo floats, ships and gliders). We show that dive computer temperature data clearly resolve seasonal patterns, which are in good agreement in both phase and amplitude with OSTIA and TEMPERSEA. On average, dive computer temperatures had an overall negative bias of (–0.5 ± 1.1) °C compared with OSTIA and (–0.2 ± 1.4) °C compared with TEMPERSEA. As may be expected, increased depth-related biases were found to be associated with stratified periods and shallower mixed layer depths, i.e., stronger vertical temperature gradients. A south-north temperature gradient consistent with values reported in the literature was also identifiable. Bias remains consistent even when subsampling just 1% of the total 9310 dive computer datapoints. We conclude that dive computers offer potential as an alternative source of depth-resolved temperatures to complement existing in situ and satellite SST data sources