Classical biomonitoring techniques have focused primarily on measures linked to various biodiversity metrics and indicator species. Next-generation biomonitoring (NGB) describes a suite of tools and approaches that allow the examination of a broader spectrum of organisational levels - from genes to entire ecosystems. Here, we frame ten key questions that we envisage will drive the field of NGB over the next decade. While not exhaustive, this list covers most of the key challenges facing NGB, and provides the basis of the next steps for research and implementation in this field. These questions have been grouped into current- and outlook-related categories, corresponding to the organization of this paper.
Benthic proliferations of the cyanobacteria Phormidium can cover many kilometres of riverbed. Phormidium can produce neurotoxic anatoxins and ingestion of benthic mats has resulted in numerous animal poisonings in the last decade. Despite this, there is a poor understanding of the environmental factors regulating growth and anatoxin production. In this study, the effects of nitrogen and phosphorus on the growth of two Phormidium strains (anatoxin-producing and non-anatoxin-producing) were examined in batch monocultures. Cell concentrations were significantly reduced under reduced nitrogen (ca. <0.100 mM) and phosphorus conditions (ca. <0.003 mM). Cell concentrations and maximum growth rates were higher for the non-anatoxin-producing strain in all treatments, suggesting there may be an energetic cost to toxin production. Cellular anatoxin concentrations were lowest (169 fg cell−1) under the high-nitrogen and high-phosphorus treatment. This supports the growth-differentiation balance hypothesis that suggests actively dividing and expanding cells are less likely to produce secondary-metabolites. Anatoxin quota was highest (>407 fg cell−1) in the reduced phosphorus treatments, possibly suggesting that it is produced as a stress response to growth limiting conditions. In all treatments there was a 4–5-fold increase in anatoxin quota in the lag growth phase, possibly indicating it may provide a physiological benefit during initial substrate colonization.
Abstract Freshwater cyanobacterial blooms have increased worldwide, channeling organic carbon into these systems, and threatening animal health through the production of cyanotoxins. Both toxic and nontoxic Microcoleus proliferations usually occur when there are moderate concentrations of dissolved inorganic nitrogen, but when phosphorus is scarce. In order to understand how Microcoleus establishes thick biofilms (or mats) on riverbeds under phosphorus-limiting conditions, we collected Microcoleus-dominated biofilms over a 19-day proliferation event for proteogenomics. A single pair of nitrogen-dependent Microcoleus species were consistently present in relatively high abundance, although each followed a unique metabolic trajectory. Neither possessed anatoxin gene clusters, and only very low concentrations of anatoxins (~2 µg kg−1) were detected, likely originating from rarer Microcoleus species also present. Proteome allocations were dominated by photosynthesizing cyanobacteria and diatoms, and data indicate biomass was actively recycled by Bacteroidetes and Myxococcales. Microcoleus likely acquired nutrients throughout the proliferation event by uptake of nitrate, urea, and inorganic and organic phosphorus. Both species also harbored genes that could be used for inorganic phosphate solubilization with pyrroloquinoline quinone cofactors produced by cohabiting Proteobacteria. Results indicate that Microcoleus are equipped with diverse mechanisms for nitrogen and phosphorus acquisition, enabling them to proliferate and out-compete others in low-phosphorus waters.
Abstract New records of planktonic cyanobacteria in New Zealand freshwaters are reported. Identified for the first time were one genus, Sphaerocavum, represented by one species, and one species of Apha‐nocapsa, five species of Microcystis, and three of Anabaena. Microcystis panniformis has previously been recorded only from tropical or subtropical regions. Descriptions, drawings, and photographs of species are presented.
There is growing interest in the potential for combining eDNA and artificial intelligence (machine learning) to detect and evaluate in real time changes in ecosystems at the global scale, in a more sensitive and cost-effective way than current biomonitoring methods. Machine learning might make better use of the eDNA census data that can currently be collected to evaluate the network of ecological interactions that are at the base of the services that ecosystems supply and that we wish to protect. To date, eDNA and machine learning developments have effectively progressed in parallel and in isolation in various spheres of ecosystem monitoring (disease, invasion, conservation, etc. in aerial, terrestrial, and aquatic systems).
The goal of this Research Topic is to explore the range of ongoing activities to build the next generation of biomonitoring tools and in doing so to make researchers in the different spheres aware of the breadth of work being undertaken, and to set a unifying research agenda (the key questions) for the development of global biomonitoring using eDNA and machine learning.
The scope of this Research Topic will be to explore:
1. eDNA approaches currently being used in case study systems from all spheres of monitoring;
2. Theoretical underpinnings of machine learning for biomonitoring;
3. What type of networks do we need to reconstruct for effective monitoring (co-occurrence, trophic, etc);
4. Examples of learning large scale, replicated networks from eDNA in the different spheres;
5. Statistical and analytical approaches to analysing large-scale, highly replicated networks;
6. Technological developments necessary to build a next-generation biomonitoring framework at the global scale;
7. A research agenda paper that develops “10 key questions for eDNA and machine learning in biomonitoring”.
Details for Authors: The Research Topic “A next-generation of global biomonitoring to detect ecosystem change” will publish conceptual, data, case study, technological and synthetic papers on eDNA and machine learning approaches for developing a unified next-generation biomonitoring framework. Paper length conforms to the guidelines of the journal Frontiers in Ecology and Evolution.
In lakes, benthic micro-algae and cyanobacteria (periphyton) can contribute significantly to total primary productivity and provide important food sources for benthic invertebrates. Despite recognition of their importance, few studies have explored the diversity of the algal and cyanobacterial composition of periphyton mats in temperate lakes. In this study, we sampled periphyton from three New Zealand lakes: Tikitapu (oligotrophic), Ōkāreka (mesotrophic) and Rotoiti (eutrophic). Statistical analysis of morphological data showed a clear delineation in community structure among lakes and highlighted the importance of cyanobacteria. Automated rRNA intergenic spacer analysis (ARISA) and 16S rRNA gene clone libraries were used to investigate cyanobacterial diversity. Despite the close geographic proximity of the lakes, cyanobacterial species differed markedly. The 16S rRNA gene sequence analysis identified eight cyanobacterial OTUs. A comparison with other known cyanobacterial sequences in GenBank showed relatively low similarities (91-97%). Cyanotoxin analysis identified nodularin in all mats from Lake Tikitapu. ndaF gene sequences from these samples had very low (≤ 89%) homology to sequences in other known nodularin producers. To our knowledge, this is the first detection of nodularin in a freshwater environment in the absence of Nodularia. Six cyanobacteria species were isolated from Lake Tikitapu mats. None were found to produce nodularin. Five of the species shared low (< 97%) 16S rRNA gene sequence similarities with other cultured cyanobacteria.
Scientists estimate that we share this planet with millions of other species! But how do we know which species are out there and how can we keep track of them? Unfortunately, humans are driving lots of species to extinction and disrupting important natural ecosystems. It is now more important than ever that we understand which species are present in different places and the roles they play in their ecosystems. With this knowledge, we can figure out how to protect important organisms and their habitats. Exciting new technology has made it possible to identify species using DNA that they have released into the environment—this is known as environmental DNA (eDNA). Scientists are now using eDNA to identify species in all kinds of ecosystems across the world. In this article, we explain how eDNA is used to detect species and describe the advantages and disadvantages of this method.
The Rosebank Peninsula is an industrial area in West Auckland, on the shores of the Waitemata Harbour. In 1988, the Auckland City Council commissioned an investigation to review the safety standards and the levels of risk in the Rosebank industrial area. For this investigation, a largely qualitative risk assessment approach was adopted which allowed the comparative evaluation of risks presented by existing hazardous industry, and the setting of targets for a risk reduction programme concerning these industries in the area. Recommendations were prepared to assist the Council with the future planning of hazardous industry and activities on the Rosebank Peninsula.
'/%3e%3cuse xlink:href='%23a' transform='rotate(72 0 0)'/%3e%3cuse xlink:href='%23a' transform='rotate(-72 0 0)'/%3e%3cuse xlink:href='%23a' transform='scale(-1 1) rotate(72)'/%3e%3c/g%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h1200v600H0z'/%3e%3cpath stroke='%23FFF' stroke-width='60' d='m0 0 600 300M0 300 600 0' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='40' d='m0 0 600 300M0 300 600 0' clip-path='url(%23c)'/%3e%3cpath stroke='%23FFF' stroke-width='100' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='60' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(45.4 0 0 45.4 900 120)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(30 0 0 30 900 120)'/%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 519.022 -457.666) scale(40.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 519.022 -457.666) scale(25)'/%3e%3c/g%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 668.57 -327.666) scale(45.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 668.57 -327.666) scale(30)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(50.4 0 0 50.4 900 480)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(35 0 0 35 900 480)'/%3e%3c/svg%3e)
