ALBERT

Description: Since plastics degrade very slowly, they remain in the environment on much longer timescales than most natural organic substrates and provide a novel habitat for colonization by bacterial communities. The spectrum of relationships between plastics and bacteria, however, is little understood. The first objective of this study was to examine plastics as substrates for communities of Bacteria in estuarine surface waters. We used next-generation sequencing of the 16S rRNA gene to characterize communities from plastics collected in the field, and over the course of two colonization experiments, from biofilms that developed on plastic (low-density polyethylene, high-density polyethylene, polypropylene, polycarbonate, polystyrene) and glass substrates placed in the environment. Both field sampling and colonization experiments were conducted in estuarine tributaries of the lower Chesapeake Bay. As a second objective, we concomitantly analyzed biofilms on plastic substrates to ascertain the presence and abundance of Vibrio spp. bacteria, then isolated three human pathogens, V. cholerae, V. parahaemolyticus, and V. vulnificus, and determined their antibiotic-resistant profiles. In both components of this study, we compared our results with analyses conducted on paired samples of estuarine water. This research adds to a nascent literature that suggests environmental factors govern the development of bacterial communities on plastics, more so than the characteristics of the plastic substrates themselves. In addition, this study is the first to culture three pathogenic vibrios from plastics in estuaries, reinforcing and expanding upon earlier reports of plastic pollution as a habitat for Vibrio species. The antibiotic resistance detected among the isolates, coupled with the longevity of plastics in the aqueous environment, suggests biofilms on plastics have potential to persist and serve as focal points of potential pathogens and horizontal gene transfer.

Description: Multiomics approaches need to be applied in the central Arctic Ocean to benchmark biodiversity change and to identify novel species and their genes. As part of MOSAiC, EcoOmics will therefore be essential for conservation and sustainable bioprospecting in one of the least explored ecosystems on Earth.

Description: Due to its involvement in numerous feedbacks, sea ice plays a crucial role not only for polar climate but also at global scale. We analyse state-of-the-art observed, reconstructed, and modelled sea-ice concentration (SIC) together with sea surface temperature (SST) to disentangle the influence of different forcing factors on the variability of these coupled fields. Canonical Correlation Analysis provides distinct pairs of coupled Arctic SIC–Atlantic SST variability which are linked to prominent oceanic and atmospheric modes of variability over the period 1854–2017. The first pair captures the behaviour of the Atlantic meridional overturning circulation (AMOC) while the third and can be associated with the North Atlantic Oscillation (NAO) in a physically consistent manner. The dominant global SIC–Atlantic SST coupled mode highlights the contrast between the responses of Arctic and Antarctic sea ice to changes in AMOC over the 1959–2021 period. Model results indicate that coupled SST–SIC patterns can be associated with changes in ocean circulation. We conclude that a correct representation of AMOC-induced coupled SST–SIC variability in climate models is essential to understand the past, present and future sea-ice evolution.

Description: While the output of a team is evident, the productivity of each team member is typically not readily identifiable. In this paper we consider the problem of measuring the productivity of team members. We propose a new concept of coworker productivity, which we refer to as eigenvalue productivity (EVP). We demonstrate the existence and uniqueness of our concept and show that it possesses several desirable properties. Also, we suggest a procedure for specifying the required productivity matrix of a team, and illustrate the operational practicability of EVP by means of three examples representing different types of available data.

Description: Euphausia superba is a key species of the Southern Ocean, impacted by climate change and human exploitation. Understanding how these changes affect the distribution and abundance of krill is crucial for generating projections of change for Southern Ocean ecosystems. Krill growth is an important indicator of habitat suitability and a series of models have been developed and used to examine krill growth potential at different spatial and temporal scales. The available models have been developed using a range of empirical and mechanistic approaches, providing alternative perspectives and comparative analyses of the key processes influencing krill growth. Here we undertake an intercomparison of a suite of the available models to understand their sensitivities to major driving variables. This illustrates that the results are strongly determined by the model structure and technical characteristics, and the data on which they were developed and validated. Our results emphasize the importance of assessing the constraints and requirements of individual krill growth models to ensure their appropriate application. The study also demonstrates the value of the development of alternative modelling approaches to identify key processes affecting the dynamics of krill. Of critical importance for modelling the growth of krill is appropriately assessing and accounting for differences in estimates of food availability resulting from alternative methods of observation. We suggest that an intercomparison approach is particularly valuable in the development and application of models for the assessment of krill growth potential at circumpolar scales and for future projections. As another result of the intercomparison, the implementations of the models used in this study are now publicly available for future use and analyses.

Description: The superfamily Orthalicoidea comprises approximately 2,000 species of terrestrial gastropods, mostly concentrated in the Neotropics but also present in southern Africa and Oceania. We provide a multi-marker molecular phylogeny of this superfamily, reassessing its \nfamily- and genus-level classification. We exclude two families from the group, Odontostomidae and Vidaliellidae, transferring them to Rhytidoidea based on their phylogenetic relationships as recovered herein. Two new families are recognized herein as members of \nOrthalicoidea, Tomogeridae and Cyclodontinidae fam. nov. The family Megaspiridae and \nthe subfamily Prestonellinae are paraphyletic but are retained herein for taxonomic stability. \nThe subfamily Placostylinae is synonymized with Bothriembryontinae. The new genera \nAlterorhinus gen. nov. and Sanniostracus gen. nov. containing some Brazilian species are \ndescribed here to better reflect the phylogeny. The fossil record and paleobiogeographic \nhistory of the group is explored under the new phylogenetic framework.

Description: Insect population numbers and biodiversity have been rapidly declining with time, and monitoring \nthese trends has become increasingly important for conservation measures to be \neffectively implemented. But monitoring methods are often invasive, time and resource \nintense, and prone to various biases. Many insect species produce characteristic sounds \nthat can easily be detected and recorded without large cost or effort. Using deep learning \nmethods, insect sounds from field recordings could be automatically detected and classified \nto monitor biodiversity and species distribution ranges. We implement this using recently \npublished datasets of insect sounds (up to 66 species of Orthoptera and Cicadidae) and \nmachine learning methods and evaluate their potential for acoustic insect monitoring. We \ncompare the performance of the conventional spectrogram-based audio representation \nagainst LEAF, a new adaptive and waveform-based frontend. LEAF achieved better classification \nperformance than the mel-spectrogram frontend by adapting its feature extraction \nparameters during training. This result is encouraging for future implementations of deep \nlearning technology for automatic insect sound recognition, especially as larger datasets \nbecome available.

Description: Biodiversity loss is a major global challenge and minimizing extinction rates is the goal of several multilateral environmental agreements. Policy decisions require comprehensive, spatially explicit information on species’ distributions and threats. We present an analysis of the conservation status of 14,669 European terrestrial, freshwater and marine species (ca. 10% of the continental fauna and flora), including all vertebrates and selected groups of invertebrates and plants. Our results reveal that 19% of European species are threatened with extinction, with higher extinction risks for plants (27%) and invertebrates (24%) compared to vertebrates (18%). These numbers exceed recent IPBES (Intergovernmental Platform on Biodiversity and Ecosystem Services) assumptions of extinction risk. Changes in agricultural practices and associated habitat loss, overharvesting, pollution and development are major threats to biodiversity. Maintaining and restoring sustainable land and water use practices is crucial to minimize future biodiversity declines.

Description: Using the climate model CLIMBER-X, we present an efficient method for assimilating the temporal evolution of surface temperatures for the last deglaciation covering the period 22000 to 6500 years before the present. The data assimilation methodology combines the data and the underlying dynamical principles governing the climate system to provide a state estimate of the system, which is better than that which could be obtained using just the data or the model alone. In applying an ensemble Kalman filter approach, we make use of the advances in the parallel data assimilation framework (PDAF), which provides parallel data assimilation functionality with a relatively small increase in computation time. We find that the data assimilation solution depends strongly on the background evolution of the decaying ice sheets rather than the assimilated temperatures. Two different ice sheet reconstructions result in a different deglacial meltwater history, affecting the large-scale ocean circulation and, consequently, the surface temperature. We find that the influence of data assimilation is more pronounced on regional scales than on the global mean. In particular, data assimilation has a stronger effect during millennial warming and cooling phases, such as the Bølling-Allerød and Younger Dryas, especially at high latitudes with heterogeneous temperature patterns. Our approach is a step toward a comprehensive paleo-reanalysis on multi-millennial time scales, including incorporating available paleoclimate data and accounting for their uncertainties in representing regional climates.