Assessing the potential of autonomous submarine gliders for ecosystem monitoring across multiple trophic levels (plankton to cetaceans) and pollutants in shallow shelf seas

A combination of scientific, economic, technological and policy drivers is behind a recent upsurge in the use of marine autonomous systems (and accompanying miniaturized sensors) for environmental mapping and monitoring. Increased spatial–temporal resolution and coverage of data, at reduced cost, is particularly vital for effective spatial management of highly dynamic and heterogeneous shelf environments. This proof-of-concept study involves integration of a novel combination of sensors onto buoyancy-driven submarine gliders, in order to assess their suitability for ecosystem monitoring in shelf waters at a variety of trophic levels. Two shallow-water Slocum gliders were equipped with CTD and fluorometer to measure physical properties and chlorophyll, respectively. One glider was also equipped with a single-frequency echosounder to collect information on zooplankton and fish distribution. The other glider carried a Passive Acoustic Monitoring system to detect and record cetacean vocalizations, and a passive sampler to detect chemical contaminants in the water column. The two gliders were deployed together off southwest UK in autumn 2013, and targeted a known tidal-mixing front west of the Isles of Scilly. The gliders’ mission took about 40 days, with each glider travelling distances of >1000 km and undertaking >2500 dives to depths of up to 100 m. Controlling glider flight and alignment of the two glider trajectories proved to be particularly challenging due to strong tidal flows. However, the gliders continued to collect data in poor weather when an accompanying research vessel was unable to operate. In addition, all glider sensors generated useful data, with particularly interesting initial results relating to subsurface chlorophyll maxima and numerous fish/cetacean detections within the water column. The broader implications of this study for marine ecosystem monitoring with submarine gliders are discussed

A scale-based framework to understand the promises, pitfalls and paradoxes of irrigation efficiency to meet major water challenges

An effective placement of irrigation efficiency in water management will contribute towards meeting the pre-eminent global water challenges of our time such as addressing water scarcity, boosting crop water productivity and reconciling competing water needs between sectors. However, although irrigation efficiency may appear to be a simple measure of performance and imply dramatic positive benefits, it is not straightforward to understand, measure or apply. For example, hydrological understanding that irrigation losses recycle back to surface and groundwater in river basins attempts to account for scale, but this generalisation cannot be readily translated from one location to another or be considered neutral for farmers sharing local irrigation networks. Because irrigation efficiency (IE) motives, measures, effects and technologies play out at different scales for different people, organisations and purposes, and losses differ from place to place and over time, IE is a contested term, highly changeable and subjective. This makes generalisations for science, management and policy difficult. Accordingly, we propose new definitions for IE and irrigation hydrology and introduce a framework, termed an ‘irrigation efficiency matrix’, comprising five spatial scales and ten dimensions to understand and critique the promises, pitfalls and paradoxes of IE and to unlock its utility for addressing contemporary water challenges

Preliminary results from the ECOCADIZ 2020-07 Spanish acoustic survey (01 – 14 August 2020)

The present working document summarises a part of the main results obtained from the Spanish (pelagic ecosystem-) acoustic survey conducted by IEO between 01st and 14th August 2020 in the Portuguese and Spanish shelf waters (20-200 m isobaths) off the Gulf of Cadiz (GoC) onboard the R/V Miguel Oliver. The 21 foreseen acoustic transects were sampled. A total of 26 valid fishing hauls were carried out for echo-trace ground-truthing purposes. Four additional night trawls were conducted to collect anchovy hydrated females (DEPM). This working document only provides abundance and biomass estimates for anchovy, sardine and chub mackerel, which are presented without age structure. The distribution of all the mid-sized and small pelagic fish species susceptible of being acoustically assessed is also shown from the mapping of their back-scattering energies. GoC anchovy acoustic estimates in summer 2020 were of 5153 million fish and 44 877 tones, with the bulk of the population occurring in the Spanish waters. The current biomass estimate becomes in the second historical maximum within the time-series. The estimates of sardine abundance and biomass in summer 2020 were 1923 million fish and 50 721 t, estimates close to the historical average, but lower than the values estimated last year and the most recent maxima reached in 2018. A total of 32 854 t and 448 million fish were estimated for Chub mackerel, estimates similar to the most recent ones and very close to the time-series average

A Review of the Tools Used for Marine Monitoring in the UK: Combining Historic and Contemporary Methods with Modeling and Socioeconomics to Fulfill Legislative Needs and Scientific Ambitions

Marine environmental monitoring is undertaken to provide evidence that environmental management targets are being met. Moreover, monitoring also provides context to marine science and over the last century has allowed development of a critical scientific understanding of the marine environment and the impacts that humans are having on it. The seas around the UK are currently monitored by targeted, impact-driven, programmes (e.g., fishery or pollution based monitoring) often using traditional techniques, many of which have not changed significantly since the early 1900s. The advent of a new wave of automated technology, in combination with changing political and economic circumstances, means that there is currently a strong drive to move toward a more refined, efficient, and effective way of monitoring. We describe the policy and scientific rationale for monitoring our seas, alongside a comprehensive description of the types of equipment and methodology currently used and the technologies that are likely to be used in the future. We contextualize the way new technologies and methodologies may impact monitoring and discuss how whole ecosystems models can give an integrated, comprehensive approach to impact assessment. Furthermore, we discuss how an understanding of the value of each data point is crucial to assess the true costs and benefits to society of a marine monitoring programme

Correction:How the COVID-19 pandemic highlights the necessity of animal research (vol 30, pg R1014, 2020)

(Current Biology 30, R1014–R1018; September 21, 2020) As a result of an author oversight in the originally published version of this article, a number of errors were introduced in the author list and affiliations. First, the middle initials were omitted from the names of several authors. Second, the surname of Dr. van Dam was mistakenly written as “Dam.” Third, the first name of author Bernhard Englitz was misspelled as “Bernard” and the surname of author B.J.A. Pollux was misspelled as “Pullox.” Finally, Dr. Keijer's first name was abbreviated rather than written in full. These errors, as well as various errors in the author affiliations, have now been corrected online

Data from: Visuomotor adaptation: how forgetting keeps us conservative

Even when provided with feedback after every movement, adaptation levels off before biases are completely removed. Incomplete adaptation has recently been attributed to forgetting: the adaptation is already partially forgotten by the time the next movement is made. Here we test whether this idea is correct. If so, the final level of adaptation is determined by a balance between learning and forgetting. Because we learn from perceived errors, scaling these errors by a magnification factor has the same effect as subjects increasing the amount by which they learn from each error. In contrast, there is no reason to expect scaling the errors to affect forgetting. The magnification factor should therefore influence the balance between learning and forgetting, and thereby the final level of adaptation. We found that adaptation was indeed more complete for larger magnification factors. This supports the idea that incomplete adaptation is caused by part of what has been learnt quickly being forgotten

Reward abundance interferes with error-based learning in a visuomotor adaptation task

The brain rapidly adapts reaching movements to changing circumstances by using visual feedback about errors. Providing reward in addition to error feedback facilitates the adaptation but the underlying mechanism is unknown. Here, we investigate whether the proportion of trials rewarded (the 'reward abundance') influences how much participants adapt to their errors. We used a 3D multi-target pointing task in which reward alone is insufficient for motor adaptation. Participants (N = 423) performed the pointing task with feedback based on a shifted hand-position. On a proportion of trials we gave them rewarding feedback that their hand hit the target. Half of the participants only received this reward feedback. The other half also received feedback about endpoint errors. In different groups, we varied the proportion of trials that was rewarded. As expected, participants who received feedback about their errors did adapt, but participants who only received reward-feedback did not. Critically, participants who received abundant rewards adapted less to their errors than participants who received less reward. Thus, reward abundance negatively influences how much participants learn from their errors. Probably participants used a mechanism that relied more on the reward feedback when the reward was abundant. Because participants could not adapt to the reward, this interfered with adaptation to errors

Results.

<p><b>(A)</b> Mean azimuthal errors as a function of trial number for the three magnification conditions (<i>m</i> = 2, <i>m</i> = 1, <i>m</i> = 0.5), together with a model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117901#pone.0117901.e005" target="_blank">eq. 3</a>) with the mean fit parameters of the 13 subjects. Gray background indicates the blocks without visual feedback. <b>(B)</b> Model comparison. Same data as in A, with a one-process model (<i>θ</i>_<i>i</i>+1 = <i>A*θ</i>_<i>i</i> + <i>B * e</i>_<i>i</i>) with mean fit parameters. <b>(C)</b> Comparison of the Akaike information Criterion for the two-process model (AIC₂) and one-process model (AIC₁). For all subjects, the AIC was lower for the two-process model, indicating that it was a better description of the data. <b>(D)</b> Fit learning fractions (<i>B</i>₁, dark grey bars; <i>B</i>₂, light grey bars) with 95% confidence intervals. <b>(E)</b> Fit retention fractions (<i>A</i>₁, dark grey bars; <i>A</i>₂, light grey bars) with 95% confidence intervals.</p

Adaptation data - movement paths

Data files with for each subject folder, for each magnification condition ('small' = m = 0.5; zero = m = 1; large = m = 2) the input files with target locations and feedback availability and the output files with movement paths towards each target position. The last number in the output_condition_number filename correspond to the trial numbers in the input file