Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment

The recovery of European freshwater biodiversity has come to a halt

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.publishedVersio

Meta-analysis of multidecadal biodiversity trends in Europe

Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15-91 years) collected across Europe, using a comprehensive dataset comprising similar to 6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe. The global biodiversity decline might conceal complex local and group-specific trends. Here the authors report a quantitative synthesis of longterm biodiversity trends across Europe, showing how, despite overall increase in biodiversity metric and stability in abundance, trends differ between regions, ecosystem types, and taxa.peerReviewe

The recovery of European freshwater biodiversity has come to a halt

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.N. Kaffenberger helped with initial data compilation. Funding for authors and data collection and processing was provided by the EU Horizon 2020 project eLTER PLUS (grant agreement no. 871128); the German Federal Ministry of Education and Research (BMBF; 033W034A); the German Research Foundation (DFG FZT 118, 202548816); Czech Republic project no. P505-20-17305S; the Leibniz Competition (J45/2018, P74/2018); the Spanish Ministerio de Economía, Industria y Competitividad—Agencia Estatal de Investigación and the European Regional Development Fund (MECODISPER project CTM 2017-89295-P); Ramón y Cajal contracts and the project funded by the Spanish Ministry of Science and Innovation (RYC2019-027446-I, RYC2020-029829-I, PID2020-115830GB-100); the Danish Environment Agency; the Norwegian Environment Agency; SOMINCOR—Lundin mining & FCT—Fundação para a Ciência e Tecnologia, Portugal; the Swedish University of Agricultural Sciences; the Swiss National Science Foundation (grant PP00P3_179089); the EU LIFE programme (DIVAQUA project, LIFE18 NAT/ES/000121); the UK Natural Environment Research Council (GLiTRS project NE/V006886/1 and NE/R016429/1 as part of the UK-SCAPE programme); the Autonomous Province of Bolzano (Italy); and the Estonian Research Council (grant no. PRG1266), Estonian National Program ‘Humanitarian and natural science collections’. The Environment Agency of England, the Scottish Environmental Protection Agency and Natural Resources Wales provided publicly available data. We acknowledge the members of the Flanders Environment Agency for providing data. This article is a contribution of the Alliance for Freshwater Life (www.allianceforfreshwaterlife.org).Peer reviewe

Biodiversity through time:coherence, stability and species turnover in boreal stream communities

Abstract Describing how and why species composition of ecological communities varies across spatial and temporal scales is a primary objective for ecological research. A key challenge is to distinguish changes in community composition resulting from external factors from the natural background variability. In this thesis I aimed to study: 1) the level of temporal variation in community composition of stream macroinvertebrates, 2) the role of different environmental factors to temporal variability, 3) the effect of temporal variability on bioassessment outcomes, and 4) comparability of different approaches to study community variability through time. A majority of the studied macroinvertebrate communities showed lower level of inter-annual variation, i.e. temporal turnover, than expected by chance. The observation of high community stability was further supported by the low level of inter-annual variation in taxonomic completeness (quotient of observed and expected number of species, O/E). Despite the low absolute variation in O/E, ecological status assessments varied annually. Thus the use of one year data may bias management decisions. Macroinvertebrate communities experienced similar dynamics across several spatial extents, from riffles within a stream to streams among regions, suggesting that large-scale extrinsic factors are the major driver of community dynamics. Especially climatically exceptional years may have a strong imprint on community variability. However, at the within-stream scale, coherence was lower than expected, indicating that community dynamics may be driven by different processes at different spatial extents. Stream macroinvertebrate community dynamics were strongly related to in-stream vegetation, temporal variability decreasing with increasing macrophyte cover. Importantly, the effect of in-stream vegetation on temporal turnover of macroinvertebrate communities was masked by the stochastic effect of habitat connectivity, suggesting that unless stochastic effects are controlled for, the role of deterministic processes may be obscured, thus affecting our ability to understand and predict community changes through time. In addition, different approaches to study temporal variability may disagree on estimates for the level of temporal turnover and factors explaining it – a fact that should be taken into account when planning and comparing studies.Tiivistelmä Yksi ekologisen tutkimuksen keskeisistä tavoitteista on kuvata, miten ja miksi eliöyhteisöjen koostumus muuttuu paikasta ja ajankohdasta toiseen. On tärkeää pystyä erottamaan erilaisten ulkoisten tekijöiden aiheuttamat muutokset luonnollisesta taustavaihtelusta. Väitöskirjani tavoitteena oli selvittää 1) miten paljon virtavesien pohjaeläinyhteisöissä tapahtuu ajallista vaihtelua 2) mitkä ympäristötekijät vaikuttavat yhteisöjen ajalliseen vaihteluun 3) miten ajallinen vaihtelu vaikuttaa ympäristön tilan arviointiin ja 4) kuinka vertailukelpoisia ovat eri lähestymistavat ajallista vaihtelua tutkittaessa. Valtaosa tutkituista pohjaeläinyhteisöistä vaihteli vuosien välillä vähemmän kuin olisi sattumalta odotettavissa osoittaen varsin suurta ajallista pysyvyyttä. Käsitystä yhteisöjen pysyvyydestä tuki myös vähäinen vuosittainen vaihtelu ekologista tilaa kuvaavassa taksonomisessa eheydessä (=havaitun ja odotetun lajiston suhde O/E). Huolimatta näennäisen pienestä vaihtelusta O/E suhteessa paikkakohtaiset tilaluokka-arviot saattoivat vaihtua vuodesta toiseen. Yhden vuoden aineistoon perustuvat tilan arvioinnit voivat siis johtaa virheellisiin johtopäätöksiin. Pohjaeläinyhteisöjen ajallinen vaihtelu oli samankaltaista eri mittakaavoilla niin peräkkäisten koskipaikkojen kuin eri alueilla sijaitsevien purojen välillä. Suuren mittakaavan ympäristötekijät näyttävät siis säätelevän eliöyhteisöjen ajallista vaihtelua. Erityisesti ilmastotekijöiltään poikkeukselliset vuodet säätelevät eliöyhteisöjä, ja niiden vaikutus voi näkyä vielä useiden vuosien kuluttua. Vaihtelun samankaltaisuus peräkkäisten koskipaikkojen välillä oli kuitenkin odotettua pienempää. Yhteisöjä voivat siis säädellä osittain eri tekijät eri mittakaavoilla. Tutkittujen pohjaeläinyhteisöjen ajallisen vaihtelun voimakkuus liittyi erityisesti vesikasvillisuuden määrään: vaihtelu väheni kasvillisuuden lisääntyessä. Kasvillisuuden määrän vaikutus peittyi kuitenkin satunnaisten tekijöiden alle. Jos satunnaisia tekijöitä ei huomioida, deterministiset syy-seuraussuhteet voivat jäädä huomaamatta heikentäen mahdollisuuksiamme ymmärtää ja ennustaa eliöyhteisöjen vaihtelua. Lisäksi eri lähestymistavat ajallista vaihtelua tutkittaessa voivat johtaa erilaisiin arvioihin vaihtelun suuruudesta ja siihen vaikuttavista tekijöistä, mikä tulisi ottaa huomioon tutkimuksia suunnitellessa ja niiden tuloksia vertailtaessa

Convolutional neural network-based artificial intelligence for classification of protein localization patterns

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.publishedVersionPeer reviewe

Data from: Habitat connectivity and in-stream vegetation control temporal variability of benthic invertebrate communities

One of the key challenges to understanding patterns of β diversity is to disentangle deterministic patterns from stochastic ones. Stochastic processes may mask the influence of deterministic factors on community dynamics, hindering identification of the mechanisms causing variation in community composition. We studied temporal β diversity (among-year dissimilarity) of macroinvertebrate communities in near-pristine boreal streams across 14 years. To assess whether the observed β diversity deviates from that expected by chance, and to identify processes (deterministic vs. stochastic) through which different explanatory factors affect community variability, we used a null model approach. We observed that at the majority of sites temporal β diversity was low indicating high community stability. When stochastic variation was unaccounted for, connectivity was the only variable explaining temporal β diversity, with weakly connected sites exhibiting higher community variability through time. After accounting for stochastic effects, connectivity lost importance, suggesting that it was related to temporal β diversity via random colonization processes. Instead, β diversity was best explained by in-stream vegetation, community variability decreasing with increasing bryophyte cover. These results highlight the potential of stochastic factors to dampen the influence of deterministic processes, affecting our ability to understand and predict changes in biological communities through time

Drainage‐induced browning causes both loss and change of benthic biodiversity in headwater streams

Abstract Concentrations of dissolved organic carbon (DOC) have increased over the past few decades, causing freshwater browning. Impacts of browning on biodiversity have been little studied, despite many of the individual stressors associated with browning being known to control freshwater communities. We explored the responses of benthic invertebrates along a wide gradient of DOC concentrations (3.6–27 mg L−1) in 63 boreal streams variously impacted by peatland drainage or peat production. DOC was a prime determinant of macroinvertebrate diversity and abundance, with the strongest negative response in algal scrapers. Threshold indicator taxa analysis indicated an abrupt community change at 12–13 mg DOC L−1, with only four taxa increasing, while 13 taxa decreased along the gradient. Our findings of both a gradual loss and abrupt change of biodiversity along a browning gradient provide a benchmark against which changes to stream biodiversity relative to the predicted browning trend can be gauged

Estimates of benthic invertebrate community variability and its environmental determinants differ between snapshot and trajectory designs

Abstract Long-term data sets are essential for biodiversity research and monitoring. Researchers use 2 major approaches in the study of temporal variability of biological communities: 1) the trajectory approach (monitoring sites across several consecutive years) and 2) the snapshot approach (comparing sites among few sampling events several years apart). We used data on benthic macroinvertebrate communities in 23 near-pristine forested streams to compare these 2 approaches for different study periods ranging from 3 to 14 y. We asked whether the level of temporal turnover and the identity of the best explanatory variables underlying it were comparable across studies based on differing approaches, study periods, or total duration. The 2 approaches yielded partly different stories about the level of community variability and its environmental correlates. With the snapshot approach, variation in community similarity and factors explaining it reflected short-term (e.g., year-specific) conditions, which could be misinterpreted as long-term trends, the difference being most evident for periods that began or ended in an extreme drought year. Our results imply that snapshot studies may lead to ambiguous conclusions, whereas the trajectory approach yielded more consistent results. Trajectory data of differing length showed minor differences, apart from studies with the shortest durations. Overall, our results suggest that time sequences of ∼6 y of trajectory data (i.e., 6 generations for most benthic invertebrates in boreal streams) may be needed for the among-year similarity of macroinvertebrate communities in near-pristine streams to stabilize. If temporal replication is limited (snapshots/very short time sequences) the outcome depends strongly on the particular years included in a comparison. Based on our results, we advise caution when basing conclusions on a comparison of a few (e.g., just 2) occasions several years apart or on very short time sequences