Environmental DNA - An emerging tool in conservation for monitoring past and present biodiversity

The genomic revolution has fundamentally changed how we survey biodiversity on earth. High-throughput sequencing ("HTS") platforms now enable the rapid sequencing of DNA from diverse kinds of environmental samples (termed "environmental DNA" or "eDNA"). Coupling HTS with our ability to associate sequences from eDNA with a taxonomic name is called "eDNA metabarcoding" and offers a powerful molecular tool capable of noninvasively surveying species richness from many ecosystems. Here, we review the use of eDNA metabarcoding for surveying animal and plant richness, and the challenges in using eDNA approaches to estimate relative abundance. We highlight eDNA applications in freshwater, marine and terrestrial environments, and in this broad context, we distill what is known about the ability of different eDNA sample types to approximate richness in space and across time. We provide guiding questions for study design and discuss the eDNA metabarcoding workflow with a focus on primers and library preparation methods. We additionally discuss important criteria for consideration of bioinformatic filtering of data sets, with recommendations for increasing transparency. Finally, looking to the future, we discuss emerging applications of eDNA metabarcoding in ecology, conservation, invasion biology, biomonitoring, and how eDNA metabarcoding can empower citizen science and biodiversity education.

/pdf/environmental-dna-metabarcoding-transforming-how-we-survey-27b6nlb19x.pdf

Environmental DNA metabarcoding: transforming how we survey animal and plant communities

Extraction and identification of DNA from an environmental sample has proven noteworthy recently in detecting and monitoring not only common species, but also those that are endangered, invasive, or elusive. Particular attributes of so-called environmental DNA (eDNA) analysis render it a potent tool for elucidating mechanistic insights in ecological and evolutionary processes. Foremost among these is an improved ability to explore ecosystem-level processes, the generation of quantitative indices for analyses of species, community diversity, and dynamics, and novel opportunities through the use of time-serial samples and unprecedented sensitivity for detecting rare or difficult-to-sample taxa. Although technical challenges remain, here we examine the current frontiers of eDNA, outline key aspects requiring improvement, and suggest future developments and innovations for research.

https://www.cell.com/trends/ecology-evolution/pdf/S0169-5347(14)00086-X.pdf

Environmental DNA for wildlife biology and biodiversity monitoring

Ocean ambient noise results from both anthropogenic and natural sources. Different noise sources are dominant in each of 3 frequency bands: low (10 to 500 Hz), medium (500 Hz to 25 kHz) and high (>25 kHz). The low-frequency band is dominated by anthropogenic sources: pri- marily, commercial shipping and, secondarily, seismic exploration. Shipping and seismic sources con- tribute to ambient noise across ocean basins, since low-frequency sound experiences little attenua- tion, allowing for long-range propagation. Over the past few decades the shipping contribution to ambient noise has increased by as much as 12 dB, coincident with a significant increase in the num- ber and size of vessels comprising the world's commercial shipping fleet. During this time, oil explo- ration and construction activities along continental margins have moved into deeper water, and the long-range propagation of seismic signals has increased. Medium frequency sound cannot propagate over long ranges, owing to greater attenuation, and only local or regional (10s of km distant) sound sources contribute to the ambient noise field. Ambient noise in the mid-frequency band is primarily due to sea-surface agitation: breaking waves, spray, bubble formation and collapse, and rainfall. Var- ious sonars (e.g. military and mapping), as well as small vessels, contribute anthropogenic noise at mid-frequencies. At high frequencies, acoustic attenuation becomes extreme so that all noise sources are confined to an area close to the receiver. Thermal noise, the result of Brownian motion of water molecules near the hydrophone, is the dominant noise source above about 60 kHz.

/pdf/anthropogenic-and-natural-sources-of-ambient-noise-in-the-y12gqbl9jj.pdf

Anthropogenic and natural sources of ambient noise in the ocean

The underwater environment is filled with biotic and abiotic sounds, many of which can be important for the survival and reproduction of fish. Over the last century, human activities in and near the water have increasingly added artificial sounds to this environment. Very loud sounds of relatively short exposure, such as those produced during pile driving, can harm nearby fish. However, more moderate underwater noises of longer duration, such as those produced by vessels, could potentially impact much larger areas, and involve much larger numbers of fish. Here we call attention to the urgent need to study the role of sound in the lives of fish and to develop a better understanding of the ecological impact of anthropogenic noise.

/pdf/a-noisy-spring-the-impact-of-globally-rising-underwater-35qvvt0r0z.pdf

A noisy spring: the impact of globally rising underwater sound levels on fish

Throughout the past 10 years, there has been an increasing interest regarding the influence of man-made noise on life in the sea. Behavioral studies show that hearing in fish (and other animals) can be impaired, either temporarily or permanently, by exposure to intense sound (Smith et al. 2004). Also, physiological studies such as auditory brain stem response (ABR) measurements have shown that hearing thresholds shift when exposed to intense sounds (Kenyon et al. 1998). It is known that some fish use hearing for sound communication and auditory scene analyses (Popper et al. 2003), but little is known about how threshold shifts will affect their fitness through behavioral changes in, for example, predator/prey interactions.

Importance of hearing for survival of Danio rerio (Zebrafish) in an experimental predator/prey environment.

The Acoustic World of Harbor Porpoises

Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human-annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience. 

/pdf/animal-spot-enables-animal-independent-signal-detection-and-3u3p83wz.pdf

ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning

Understanding jellyfish ecology and roles in coastal ecosystems is challenging due to their patchy distribution. While standard net sampling or manned aircraft surveys are inefficient, Unmanned Aerial Vehicles (UAVs) or drones represent a promising alternative for data collection. In this technical report, we used pictures taken from a small drone to estimate the density of Aurelia sp. in a shallow fjord with a narrow entrance, where the population dynamic is well-known. We investigated the ability of an image processing software to count small and translucent jellyfish from the drone pictures at three locations with different environmental conditions (sun glare, waves or seagrass). Densities of Aurelia sp. estimated from semiautomated and manual counts from drone images were similar to densities estimated by netting. The semiautomated program was able to highlight the medusae from the background in order to discard false detections of items unlikely to be jellyfish. In spite of this, some objects (e.g., seagrass) were hardly distinguishable from jellyfish and resulted in a small number of false positives. This report presents a preview of the possible applications of drones to observe small and fragile jellyfishes, for which in situ sampling remains delicate. Drones may represent a noninvasive approach to monitoring jellyfish abundance over time, enabling the collection of a large amount of data in a short time. Software development may be useful for automatically measuring jellyfish size and even population biomass.

Magnus Wahlberg

Papers

Importance of hearing for survival of Danio rerio (Zebrafish) in an experimental predator/prey environment.

The Acoustic World of Harbor Porpoises

ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning

Using drones to measure jellyfish density in shallow estuaries

Is human underwater hearing mediated by bone conduction?