Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making

Zooplankton are fundamental to aquatic ecosystem services such as carbon and nutrient cycling. Therefore, a robust evidence base of how zooplankton respond to changes in anthropogenic pressures, such as climate change and nutrient loading, is key to implementing effective policy-making and management measures. Currently, the data on which to base this evidence, such as long time-series and large-scale datasets of zooplankton distribution and community composition, are too sparse owing to practical limitations in traditional collection and analysis methods. The advance of in situ imaging technologies that can be deployed at large scales on autonomous platforms, coupled with artificial intelligence and machine learning (AI/ML) for image analysis, promises a solution. However, whether imaging could reasonably replace physical samples, and whether AI/ML can achieve a taxonomic resolution that scientists trust, is currently unclear. We here develop a roadmap for imaging and AI/ML for future zooplankton monitoring and research based on community consensus. To do so, we determined current perceptions of the zooplankton community with a focus on their experience and trust in the new technologies. Our survey revealed a clear consensus that traditional net sampling and taxonomy must be retained, yet imaging will play an important part in the future of zooplankton monitoring and research. A period of overlapping use of imaging and physical sampling systems is needed before imaging can reasonably replace physical sampling for widespread time-series zooplankton monitoring. In addition, comprehensive improvements in AI/ML and close collaboration between zooplankton researchers and AI developers are needed for AI-based taxonomy to be trusted and fully adopted. Encouragingly, the adoption of cutting-edge technologies for zooplankton research may provide a solution to maintaining the critical taxonomic and ecological knowledge needed for future zooplankton monitoring and robust evidence-based policy decision-making

RAPID : research on automated plankton identification

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.This material is based upon work supported by the National Science Foundation under Grants OCE-0325018, OCE-0324937, OCE-0325167 and OCE-9423471, and the European Union under grants Q5CR-2002-71699, MAS3-ct98-0188, and MAS2-ct92-0015

Combining motion analysis and microfluidics--a novel approach for detecting whole-animal responses to test substances.

Small, early life stages, such as zebrafish embryos are increasingly used to assess the biological effects of chemical compounds in vivo. However, behavioural screens of such organisms are challenging in terms of both data collection (culture techniques, drug delivery and imaging) and data evaluation (very large data sets), restricting the use of high throughput systems compared to in vitro assays. Here, we combine the use of a microfluidic flow-through culture system, or BioWell plate, with a novel motion analysis technique, (sparse optic flow - SOF) followed by spectral analysis (discrete Fourier transformation - DFT), as a first step towards automating data extraction and analysis for such screenings. Replicate zebrafish embryos housed in a BioWell plate within a custom-built imaging system were subject to a chemical exposure (1.5% ethanol). Embryo movement was videoed before (30 min), during (60 min) and after (60 min) exposure and SOF was then used to extract data on movement (angles of rotation and angular changes to the centre of mass of embryos). DFT was subsequently used to quantify the movement patterns exhibited during these periods and Multidimensional Scaling and ANOSIM were used to test for differences. Motion analysis revealed that zebrafish had significantly altered movements during both the second half of the alcohol exposure period and also the second half of the recovery period compared to their pre-treatment movements. Manual quantification of tail flicking revealed the same differences between exposure-periods as detected using the automated approach. However, the automated approach also incorporates other movements visible in the organism such as blood flow and heart beat, and has greater power to discern environmentally-driven changes in the behaviour and physiology of organisms. We suggest that combining these technologies could provide a highly efficient, high throughput assay, for assessing whole embryo responses to various drugs and chemicals

Robust motion segmentation by spectral clustering

Multibody motion segmentation is important in many computer vision tasks. One way to solve this problem is factorization. But practically segmentation is difficult since the shape interaction matrix is contaminated by noise. This paper presents a novel approach to robustly segment multiple moving objects by spectral clustering. We introduce two new affinity matrixes. One is based on the shape interaction matrix and the other one is based on the motion trajectory. By computing the sensitivities of the larger eigenvalues of a related Markov transition matrix with respect to perturbations in the affinity matrix, we improve the piecewise constant eigenvectors condition dramatically. The feature points are mapped into a low dimensional subspace and clustered in this subspace using a graph spectral approach. This makes clustering much more reliable and robust, which we confirm with experiments.

Femtosecond laser inscribed bragg sensor in terfenol-d coated optical fibre with ablated microslot for the detection of static magnetic fields

A novel device for the detection and characterisation of static magnetic fields is presented. It consists of a femtosecond laser inscribed fibre Bragg grating (FBG) that is incorporated into an optical fibre with a femtosecond laser micromachined slot. The symmetry of the fibre is broken by the micro-slot, producing non-uniform strain across the fibre cross section. The sensing region is coated with Terfenol-D making the device sensitive to static magnetic fields, whereas the symmetry breaking results in a vectorial sensor for the detection of magnetic fields as low as 0.046 mT with a resolution of ±0.3mT in transmission and ±0.7mT in reflection. The sensor output is directly wavelength encoded from the FBG filtering, leading to simple demodulation through the monitoring of wavelength shifts that result as the fibre structure changes shape in response to the external magnetic field. The use of a femtosecond laser to both inscribe the FBG and micro-machine the slot in a single stage, prior to coating the device, significantly simplifies the sensor fabrication.Oz Optics,Simbol Test Systems, Inc.,FISO Technologies, Inc.,CMC Microsystems Corporation,Innovative Economy: National Strategic Reference Framewor

Recommendations on methods for the detection and control of biological pollution in marine coastal waters

Adverse effects of invasive alien species (IAS), or biological pollution, is an increasing problem in marine coastal waters, which remains high on the environmental management agenda. All maritime countries need to assess the size of this problem and consider effective mechanisms to prevent introductions, and if necessary and where possible to monitor, contain, control or eradicate the introduced impacting organisms. Despite this, and in contrast to more enclosed water bodies, the openness of marine systems indicates that once species are in an area then eradication is usually impossible. Most institutions in countries are aware of the problem and have sufficient governance in place for management. However, there is still a general lack of commitment and concerted action plans are needed to address this problem. This paper provides recommendations resulting from an international workshop based upon a large amount of experience relating to the assessment and control of biopollution

DataSheet_1_Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making.pdf

Zooplankton are fundamental to aquatic ecosystem services such as carbon and nutrient cycling. Therefore, a robust evidence base of how zooplankton respond to changes in anthropogenic pressures, such as climate change and nutrient loading, is key to implementing effective policy-making and management measures. Currently, the data on which to base this evidence, such as long time-series and large-scale datasets of zooplankton distribution and community composition, are too sparse owing to practical limitations in traditional collection and analysis methods. The advance of in situ imaging technologies that can be deployed at large scales on autonomous platforms, coupled with artificial intelligence and machine learning (AI/ML) for image analysis, promises a solution. However, whether imaging could reasonably replace physical samples, and whether AI/ML can achieve a taxonomic resolution that scientists trust, is currently unclear. We here develop a roadmap for imaging and AI/ML for future zooplankton monitoring and research based on community consensus. To do so, we determined current perceptions of the zooplankton community with a focus on their experience and trust in the new technologies. Our survey revealed a clear consensus that traditional net sampling and taxonomy must be retained, yet imaging will play an important part in the future of zooplankton monitoring and research. A period of overlapping use of imaging and physical sampling systems is needed before imaging can reasonably replace physical sampling for widespread time-series zooplankton monitoring. In addition, comprehensive improvements in AI/ML and close collaboration between zooplankton researchers and AI developers are needed for AI-based taxonomy to be trusted and fully adopted. Encouragingly, the adoption of cutting-edge technologies for zooplankton research may provide a solution to maintaining the critical taxonomic and ecological knowledge needed for future zooplankton monitoring and robust evidence-based policy decision-making.</p

Formation and Characterization of Ultra-Sensitive Surface Plasmon Resonance Sensor Based Upon a Nano-Scale Corrugated Multi-Layered Coated D-Shaped Optical Fiber

We present experimental results on the performance of a series of coated, D-shaped optical fiber sensors that display high spectral sensitivities to external refractive index. Sensitivity to the chosen index regime and oupling of the fiber core mode to the surface plasmon resonance (SPR) is enhanced by using specific materials as part of a multi-layered coating. We present strong evidence that this effect is enhanced by post ultraviolet radiation of the lamellar coating that results in the formation of a nano-scale surface relief corrugation structure, which generates an index perturbation within the fiber core that in turn enhances the coupling. We have found reasonable agreement when we modeling the fiber device. It was found that the SPR devices operate in air with high coupling efficiency in excess of 40 dB with spectral sensitivities that outperform a typical long period grating, with one device yielding a wavelength spectral sensitivity of 12000 nm/RIU in the important aqueous index regime. The devices generate SPRs over a very large wavelength range, (visible to 2 µm) by alternating the polarization state of the illuminating light

Multidimensional scaling plot in two dimensions using Bray and Curtis similarities, performed on logarithmically transformed data consisting of 18 frequency bins for each of (i) negative angle, (ii) positive angle, (iii) centre of mass – rho, and (iv) centre of mass – theta.

<p>The mean values of each of the five groups (pre-exposure, ethanol 1 and 2, recovery 1 and 2) were used. Dissimilarities in the motion of embryos within the five experimental periods is signified by the distance between them. The bubble size reflects the average frequency of tail flicks of the 15 embryos for each group.</p