trackdem : Automated particle tracking to obtain population counts and size distributions from videos in r

A free and user-friendly software protocol for the quantification of microfauna swimming behavior

Characterizing swimming behavior can provide a holistic assessment of the health, physiology and ecology of microfaunal species when done in conjunction with measuring other biological parameters. However, tracking and quantifying microfauna swimming behavior using existing automated tools is often difficult due to the animals' small size or transparency, or because of the high cost, expertise, or labor needed for the analysis. To address these issues, we created a cost-effective, user-friendly protocol for behavior analysis that employs the free software packages HitFilm and ToxTrac along with the R package 'trajr' and used the method to quantify the behavior of rotifers. This protocol can be used for other microfaunal species for which investigators may face similar issues in obtaining measurements of swimming behavior.

The Amphibian Short-Term Assay: Evaluation of a New Ecotoxicological Method for Amphibians Using Two Organophosphate Pesticides Commonly Found in Nature-Assessment of Behavioral Traits

Neurotoxic pesticides are used worldwide to protect crops from insects; they are recognized to impact nontarget organisms that live in areas surrounded by treated crops. Many biochemical and cell-based solutions have been developed for testing insecticide neurotoxicity. Nevertheless, such solutions provide a partial assessment of the impact of neurotoxicity, neglecting important phenotypic components such as behavior. Behavior is the apical endpoint altered by neurotoxicity, and scientists are increasingly recommending including behavioral endpoints in available tests or developing new methods for assessing contaminant-induced behavioral changes. In the present study, we extended an existing protocol (the amphibian short-term assay) with a behavioral test. To this purpose, we developed a homemade device along with an open-source computing solution for tracking trajectories of Xenopus laevis tadpoles exposed to two organophosphates insecticides (OPIs), diazinon (DZN) and chlorpyrifos (CPF). The data resulting from the tracking were then analyzed, and the impact of exposure to DZN and CPF was tested on speed- and direction-related components. Our results demonstrate weak impacts of DZN on the behavioral components, while CPF demonstrated strong effects, notably on speed-related components. Our results also suggest a time-dependent alteration of behavior by CPF, with the highest impacts at day 6 and an absence of impact at day 8. Although only two OPIs were tested, we argue that our solution coupled with biochemical biomarkers is promising for testing the neurotoxicity of this pesticide group on amphibians. Environ Toxicol Chem 2023;42:1595-1606. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Zoobooth: A portable, open-source and affordable approach for repeated size measurements of live individual zooplankton

Repeated size measurements of individual animals are valuable data for many research questions, but it is often hard to obtain without causing stress or damage to the animal. We developed a video-based approach called Zoobooth to size individual zooplankton, which involves a low risk of handling accidents and stress. Here we describe the process of assembling the instrument we used to acquire video recordings of single zooplankton and the procedure to obtain size estimates from the recorded videos. Our setup produces accurate size estimates for Daphnia magna (correlation to manual measurements = 0.97), and was also tested with other zooplankton species. Zoobooth is especially advantageous when one needs size measurements of live, individual mesozooplankton. The device is small, portable, and comprised of very affordable and readily available components. It can easily be modified for other purposes, such as studies of coloration or behavior of micro-and macro-plankton. We share all the files to build and use Zoobooth.

New machine learning-based automatic high-throughput video tracking system for assessing water toxicity using Daphnia Magna locomotory responses

Daphnia magna is an important organism in ecotoxicity studies because it is sensitive to toxic substances and easy to culture in laboratory conditions. Its locomotory responses as a biomarker are highlighted in many studies. Over the last several years, multiple high-throughput video tracking systems have been developed to measure the locomotory responses of Daphnia magna. These high-throughput systems, used for high-speed analysis of multiple organisms, are essential for efficiently testing ecotoxicity. However, existing systems are lacking in speed and accuracy. Specifically, speed is affected in the biomarker detection stage. This study aimed to develop a faster and better high-throughput video tracking system using machine learning methods. The video tracking system consisted of a constant temperature module, natural pseudo-light, multi-flow cell, and an imaging camera for recording videos. To measure Daphnia magna movements, we developed a tracking algorithm for automatic background subtraction using k-means clustering, Daphnia classification using machine learning methods (random forest and support vector machine), and tracking each Daphnia magna location using the simple online real-time tracking algorithm. The proposed tracking system with random forest performed the best in terms of identification (ID) precision, ID recall, ID F1 measure, and ID switches, with scores of 79.64%, 80.63%, 78.73%, and 16, respectively. Moreover, it was faster than existing tracking systems such as Lolitrack and Ctrax. We conducted an experiment to observe the impact of toxicants on behavioral responses. Toxicity was measured manually in the laboratory and automatically using the high-throughput video tracking system. The median effective concentration of Potassium dichromate measured in the laboratory and using the device was 1.519 and 1.414, respectively. Both measurements conformed to the guideline provided by the Environmental Protection Agency of the United States; therefore, our method can be used for water quality monitoring. Finally, we observed Daphnia magna behavioral responses in different concentrations after 0, 12, 18, and 24 h and found that there was a difference in movement according to the concentration at all hours.

Evolution of population dynamics following invasion by a non-native predator

Invasive predatory species are frequently observed to cause evolutionary responses in prey phenotypes, which in turn may lead to evolutionary shifts in the population dynamics of prey. Research has provided a link between rates of predation and the evolution of prey population growth in the lab, but studies from natural populations are rare. Here, we tested for evolutionary changes in population dynamics parameters of zooplankton Daphnia pulicaria following invasion by the predator Bythotrephes longimanus into Lake Kegonsa, Wisconsin, US. We used a resurrection ecological approach, whereby clones from pre- and post-invasive periods were hatched from eggs obtained in sediment cores and were used in a 3-month growth experiment. Based on these data, we estimated intrinsic population growth rates (r), the shape of density dependence (θ) and carrying capacities (K) using theta-logistic models. We found that post-invasion Daphnia maintained a higher r and K under these controlled, predation-free laboratory conditions. Evidence for changes in θ was weaker. Whereas previous experimental evolution studies of predator-prey interactions have demonstrated that genotypes that have evolved under predation have inferior competitive ability when the predator is absent, this was not the case for the Daphnia. Given that our study was conducted in a laboratory environment and the possibility for genotype-by-environment interactions, extrapolating these apparent counterintuitive results to the wild should be done with caution. However, barring such complications, we discuss how selection for reduced predator exposure, either temporally or spatially, may have led to the observed changes. This scenario suggests that complexities in ecological interactions represents a challenge when predicting the evolutionary responses of population dynamics to changes in predation pressure in natural systems.

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Contrasting Manual and Automated Assessment of Thermal Stress Responses and Larval Body Size in Black Soldier Flies and Houseflies

Within ecophysiological and genetic studies on insects, morphological and physiological traits are commonly assessed and phenotypes are typically obtained from manual measurements on numerous individuals. Manual observations are, however, time consuming, can introduce observer bias and are prone to human error. Here, we contrast results obtained from manual assessment of larval size and thermal tolerance traits in black soldier flies (Hermetia illucens) and houseflies (Musca domestica) that have been acclimated under three different temperature regimes with those obtained automatically using an image analysis software (Noldus EthoVision XT). We found that (i) larval size estimates of both species, obtained by manual weighing or by using the software, were highly correlated, (ii) measures of heat and cold tolerance using manual and automated approaches provided qualitatively similar results, and (iii) by using the software we obtained quantifiable information on stress responses and acclimation effects of potentially higher ecological relevance than the endpoint traits that are typically assessed when manual assessments are used. Based on these findings, we argue that automated assessment of insect stress responses and largescale phenotyping of morphological traits such as size will provide new opportunities within many disciplines where accurate and largescale phenotyping of insects is required.

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

Population dynamics and resting egg production in Daphnia: Interactive effects of mercury, population density and temperature

Toxicity studies on freshwater organisms are commonly conducted by quantifying effects on asexual (clonal) reproductive rates in Daphnia, whereas studies of effects on sexual reproductive rates remain relatively rare. Sexual reproduction in Daphnia and the associated production of resting eggs allows them to survive unfavorable environmental conditions and is thus a crucial component of their long-term fitness. It also maintains genetic diversity within Daphnia populations and hence their potential for adaptation to new environmental conditions. This aspect of their biology may therefore be important to consider in toxicity studies. The aim of this study was to investigate for the first time how mercury (Hg) affects sexual versus asexual reproduction in Daphnia under varying environmental conditions. Specifically, we experimentally tested the interactive effects of Hg and temperature on the population dynamics of Daphnia magna. For this purpose, we exposed D. magna to environmentally relevant concentrations (0 μg/L, 0.5 μg/L and 2 μg/L) of Hg (in the form of mercury (II) chloride) found in stream water and measured biomass growth rate resulting from asexual reproduction, and resting egg production resulting from sexual reproduction. This was done at both 17 °C and 24 °C. Biomass growth rate did not vary across Hg treatments and depended mainly on temperature and population density. Density dependence of biomass growth rate was indeed more pronounced at 24 °C than at 17 °C, as resource limitation from intraspecific competition was further exacerbated by the rise in feeding rates with temperature. Density dependence of resting egg production was unaffected by Hg and temperature, but resting egg production was higher under Hg exposure at low temperature. These findings show that depending on environmental conditions, rates of sexual reproduction in D. magna may respond to metal exposure at lower concentrations than those impacting population growth during the asexual phase.

Analysis of the Interaction Between Plasmodium falciparum-Infected Erythrocytes and Human Endothelial Cells Using a Laminar Flow System, Bioinformatic Tracking and Transcriptome Analysis

During malaria infection, the endothelial lining of the small blood vessels of the brain and other vital organs is strongly stimulated. This leads to fatal complications and poor prognosis of the infection. It is believed that two main reasons are responsible for this pathology, namely the cytoadhesion of Plasmodium falciparum-infected erythrocytes (IEs) on the one hand and the proinflammatory products released by the IEs which activate the endothelial cells (ECs) on the other hand. Until recently, most of the studies that characterized the activation of ECs were performed under static conditions, which do not reflect the real sequelae in vivo. In this chapter, we present a system, which allows authentic simulation of the IEs-ECs interactions during P. falciparum infection.The main idea of the system is to provide an adequate shear stress over the ECs during the cytoadhesion and stimulation with IEs, which provides a better basis for the investigation of the cytoadhesion pathology through analyzing the ECs' transcriptome after stimulation. On the other hand, analyzing the transcriptome of the IEs might also give deeper analysis of their response to shear stress. Deep understanding of these events might help in the development of novel treatment strategies that interfere with this cell-cell interaction.

Single-Nanoparticle Orientation Sensing by Deep Learning

This paper describes a computational imaging platform to determine the orientation of anisotropic optical probes under differential interference contrast (DIC) microscopy. We established a deep-learning model based on data sets of DIC images collected from metal nanoparticle optical probes at different orientations. This model predicted the in-plane angle of gold nanorods with an error below 20°, the inherent limit of the DIC method. Using low-symmetry gold nanostars as optical probes, we demonstrated the detection of in-plane particle orientation in the full 0-360° range. We also showed that orientation predictions of the same particle were consistent even with variations in the imaging background. Finally, the deep-learning model was extended to enable simultaneous prediction of in-plane and out-of-plane rotation angles for a multibranched nanostar by concurrent analysis of DIC images measured at multiple wavelengths.