Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.Comment: To appear in ICRA 2017, Singapor

Tropical stream diatom communities – the importance of headwater streams for regional diversity

Peer reviewe

State tagging for improved Earth and environmental data quality assurance

Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established long-term monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily

Sustainable marine ecosystems: deep learning for water quality assessment and forecasting

An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.Postprint (published version

Book of Abstracts & Lead Articles The Second International Symposium Remote Sensing for Ecosystem Analysis and Fisheries

SAFARI (Societal Applications in Fisheries and Aquaculture using Remotely-Sensed Imagery) is an initiative which provides a forum for coordination, at the international level, of activities in global fisheries research and management. The forum is open to all interested parties, including policy makers, research scientists, government managers, and those involved in the fishing industries. SAFARI organizes international workshops and symposia as a platform to discuss the latest research in Earth observation and fisheries management, information sessions aimed at the fisheries industry, government officials and resource managers, representation at policy meetings, and producing publications relevant to the activities. SAFARI gains worldwide attention through collaboration with other international networks, such as ChloroGIN (Chlorophyll Global Integrated Network), IOCCG (International Ocean-Colour Coordinating Group), POGO (Partnership for Observation of the Global Oceans) and the oceans and society: Blue Planet Initiative of the intergovernmental organization, the Group on Earth Observations (GEO)

In the Margins: Reconsidering the Range and Contribution of Diazotrophs in Nearshore Environments

Dinitrogen (N2) fixation enables primary production and, consequently, carbon dioxide drawdown in nitrogen (N) limited marine systems, exerting a powerful influence over the coupled carbon and N cycles. Our understanding of the environmental factors regulating its distribution and magnitude are largely based on the range and sensitivity of one genus, Trichodesmium. However, recent work suggests that the niche preferences of distinct diazotrophic (N2 fixing) clades differ due to their metabolic and ecological diversity, hampering efforts to close the N budget and model N2 fixation accurately. Here, I explore the range of N2 fixation across physico-chemical gradients (e.g., light, nutrients, oxygen) in nearshore environments of significance in global biogeochemical cycling: the major pelagic oxygen deficient zones (ODZs) in the Eastern Tropical South (ETSP) and North (ETNP) Pacific Ocean, and the broad continental shelf of the Western North Atlantic Ocean (WNA). The ODZs are hypothesized to play an important role in N cycle homeostasis by generating conditions thought to promote diazotrophy; recent work suggests that broad continental shelf environments may contribute substantially to new reactive N inputs globally. N2 fixation rates were measured using a robust 15N tracer method that accounts for the slow dissolution of N2 gas. To explore niche partitioning and better characterize spatial heterogeneity on the WNA shelf, I built an empirical model of N2 fixation and investigated diazotroph identity using amplicon sequencing and qPCR. In the ETSP, N2 fixation was only detected in a subset of low-oxygen samples. N2 fixation within the ETNP ODZ was patchy and driven by organic carbon availability; however, significant rates were observed at coastal stations near the Gulf of California. Frontal mixing on the WNA shelf resulted in exceptionally high rates of N2 fixation, associated with high UCYN-A activity. My findings suggest that (1) diazotrophy is more energetically favorable (relative to dissolved inorganic N) in low-oxygen waters but may be carbon-limited, and (2) continental inputs and dynamic conditions at coastal margins can favor significant N inputs via diazotrophy

FRESHWATER RUNOFF FROM PACIFIC-DRAINING CONTINENTAL AND COASTAL BASINS IN PATAGONIA: CHARACTERIZING REGIONAL INPUTS TO CHILEAN FJORDS ASSOCIATED WITH CHANGES IN LAND USE/COVER

The region of west-southern Patagonia (WSP), characterized by its wild and remote landscapes, represents one of the last bastions of pristine natural environments on Earth. With recent colonization and limited human intervention, a significant portion of this territory retains its natural state. WSP boasts mighty rivers, expansive lakes, and the world's largest temperate icefields, making it a geographically distinct area. Unlike the northern Andes, it lacks a central valley, resulting in relatively short linear distances and steep gradients from source to sea. This topography shapes an intricate system of channels and fjords, contributing to one of the world's most extensive coastlines. The freshwater discharges into channels and fjords create a two-layer vertical structure, impacting various environmental parameters such as salinity, temperature, biogeochemistry, light availability, and biological communities. However, the variability of these discharges, influenced by factors like precipitation, temperature, landforms, vegetation, and land use, adds complexity to the system. While the importance of freshwater discharges in Patagonia's coastal ecosystems is recognized, understanding their magnitude, nutrient content, seasonal variations, and responses to climate change remains incomplete. Monitoring is limited to major rivers, with substantial gaps in the archipelago area, which receives higher precipitation. Besides, headwater streams, sensitive to land use and climate changes, have received less attention than larger rivers. This research aims to quantify freshwater coastal discharges in WSP and assess the impacts of land use and cover changes on runoff. Specific objectives include characterizing runoff in terrestrial basins, identifying bioindicators of land use change effects, and exploring relationships between hydrology indicators, land use, and climate variables. The study employs macro-scale and regional approaches across coastal basins and intensive micro-scale investigations in headwater streams. Chapter 1 focuses on estimating freshwater discharges in the entire study area using a hydrological model, considering factors such as soil, geology, land use, and climate. Chapter 2 investigates freshwater inputs into inner fjords, analyzing sources like precipitation, coastal runoff, and glacial ablation. Chapter 3 conducts a micro-scale analysis of headwater streams to detect early effects of land use changes and assess the impact of different hydrological regimes. Additional regional studies in appendices examine the influence of land use change on aquatic macroinvertebrate communities and identify bioindicators for forest degradation in Patagonia's evergreen forests. This research contributes essential insights into the hydrological and ecological dynamics of WSP, a region of global significance for its pristine nature and ongoing environmental changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes