Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a cost-effective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies

Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System

Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial x-ray diffraction datasets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of x-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition. By incorporating Gibbs’ phase rule into the algorithm, physically meaningful phase maps are obtained with unsupervised operation, and more refined solutions are attained by injecting expert knowledge of the system. The algorithm is demonstrated through investigation of the V-Mn-Nb oxide system where decomposition of eight oxide phases, including two with substantial alloying, provides the first phase map for this pseudo-ternary system. This phase map enables interpretation of high-throughput band gap data, leading to the discovery of new solar light absorbers and the alloying-based tuning of the direct-allowed band-gap energy of MnV2O6. The open-source family of AgileFD algorithms can be implemented into a broad range of high throughput workflows to accelerate materials discovery

Deeper habitats and cooler temperatures moderate a climate-driven seagrass disease

Eelgrass creates critical coastal habitats worldwide and fulfills essential ecosystem functions as a foundation seagrass. Climate warming and disease threaten eelgrass, causing mass mortalities and cascading ecological impacts. Subtidal meadows are deeper than intertidal and may also provide refuge from the temperature-sensitive seagrass wasting disease. From cross-boundary surveys of 5761 eelgrass leaves from Alaska to Washington and assisted with a machine-language algorithm, we measured outbreak conditions. Across summers 2017 and 2018, disease prevalence was 16% lower for subtidal than intertidal leaves; in both tidal zones, disease risk was lower for plants in cooler conditions. Even in subtidal meadows, which are more environmentally stable and sheltered from temperature and other stressors common for intertidal eelgrass, we observed high disease levels, with half of the sites exceeding 50% prevalence. Models predicted reduced disease prevalence and severity under cooler conditions, confirming a strong interaction between disease and temperature. At both tidal zones, prevalence was lower in more dense eelgrass meadows, suggesting disease is suppressed in healthy, higher density meadows. These results underscore the value of subtidal eelgrass and meadows in cooler locations as refugia, indicate that cooling can suppress disease, and have implications for eelgrass conservation and management under future climate change scenarios

Low-Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California

Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low-altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low-altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions

Disease surveillance by artificial intelligence links eelgrass wasting disease to ocean warming across latitudes

Ocean warming endangers coastal ecosystems through increased risk of infectious disease, yet detection, surveillance, and forecasting of marine diseases remain limited. Eelgrass (Zostera marina) meadows provide essential coastal habitat and are vulnerable to a temperature-sensitive wasting disease caused by the protist Labyrinthula zosterae. We assessed wasting disease sensitivity to warming temperatures across a 3500 km study range by combining long-term satellite remote sensing of ocean temperature with field surveys from 32 meadows along the Pacific coast of North America in 2019. Between 11% and 99% of plants were infected in individual meadows, with up to 35% of plant tissue damaged. Disease prevalence was 3× higher in locations with warm temperature anomalies in summer, indicating that the risk of wasting disease will increase with climate warming throughout the geographic range for eelgrass. Large-scale surveys were made possible for the first time by the Eelgrass Lesion Image Segmentation Application, an artificial intelligence (AI) system that quantifies eelgrass wasting disease 5000× faster and with comparable accuracy to a human expert. This study highlights the value of AI in marine biological observing specifically for detecting widespread climate-driven disease outbreaks.This work was supported by the National Science Foundation (awards OCE-1829921, OCE-1829922, OCE-1829992, OCE-1829890). This is contribution 104 from the Smithsonian's MarineGEO and Tennenbaum Marine Observatories Network.Peer reviewe

Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing

The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species -- invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability -- landcover classification -- and again find that it outperforms previous baselines

EeLISA: Combating Global Warming Through the Rapid Analysis of Eelgrass Wasting Disease

Global warming is the greatest threat facing our planet, and is causing environmental disturbance at an unprecedented scale. We are strongly positioned to leverage the advancements of Artificial Intelligence (AI) and Machine Learning (ML) which provide humanity, for the first time in history, an analysis and decision making tool at massive scale. Strong evidence supports that global warming is contributing to marine ecosystem decline, including eelgrass habitat. Eelgrass is affected by an opportunistic marine pathogen and infections are likely exacerbated by rising ocean temperatures. The necessary disease analysis required to inform conservation priorities is incredibly laborious, and acts as a significant bottleneck for research. To this end, we developed EeLISA (Eelgrass Lesion Image Segmentation Application). EeLISA enables ecologist experts to train a segmentation module to perform this crucial analysis at human level accuracy, while minimizing their labeling time and integrating into their existing workflow. EeLISA has been deployed for over 16 months, and has facilitated the preparation of four manuscripts including a critical eelgrass study ranging from Southern California to Alaska. These studies, utilizing EeLISA, have led to scientific insight and discovery in marine disease ecology

Confirmation and variability of the Allee effect in Dictyostelium discoideum cell populations, possible role of chemical signaling within cell clusters

In studies of the unicellular eukaryote Dictyostelium discoideum, many have anecdotally observed that cell dilution below a certain 'threshold density' causes cells to undergo a period of slow growth (lag). However, little is documented about the slow growth phase and the reason for different growth dynamics below and above this threshold density. In this paper, we extend and correct our earlier work to report an extensive set of experiments, including the use of new cell counting technology, that set this slow-to-fast growth transition on a much firmer biological basis. We show that dilution below a certain density (around 104cells ml-1) causes cells to grow slower on average and exhibit a large degree of variability: sometimes a sample does not lag at all, while sometimes it takes many moderate density cell cycle times to recover back to fast growth. We perform conditioned media experiments to demonstrate that a chemical signal mediates this endogenous phenomenon. Finally, we argue that while simple models involving fluid transport of signal molecules or cluster-based signaling explain typical behavior, they do not capture the high degree of variability between samples but nevertheless favor an intra-cluster mechanism.publishe

Phase Mapper: Accelerating Materials Discovery with AI

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery