Bimodality and alternative equilibria do not help explain long-term patterns in shallow lake chlorophyll-a

Since its inception, the theory of alternative equilibria in shallow lakes has evolved and been applied to an ever wider range of ecological and socioecological systems. The theory posits the existence of two alternative stable states or equilibria, which in shallow lakes are characterised by either clear water with abundant plants or turbid water where phytoplankton dominate. Here, we used data simulations and real-world data sets from Denmark and north-eastern USA (902 lakes in total) to examine the relationship between shallow lake phytoplankton biomass (chlorophyll-a) and nutrient concentrations across a range of timescales. The data simulations demonstrated that three diagnostic tests could reliably identify the presence or absence of alternative equilibria. The real-world data accorded with data simulations where alternative equilibria were absent. Crucially, it was only as the temporal scale of observation increased (>3 years) that a predictable linear relationship between nutrient concentration and chlorophyll-a was evident. Thus, when a longer term perspective is taken, the notion of alternative equilibria is not required to explain the response of chlorophyll-a to nutrient enrichment which questions the utility of the theory for explaining shallow lake response to, and recovery from, eutrophication.C.D.S. and T.A.D. would like to thank June and Derek Sayer for extraordinary support over many years. The authors of this work have been supported by a number of projects over the elephantine gestation period of this manuscript. These include support from the Poul Due Jensen Fonden, Danmarks Frie Forskningsfond Natur og Univers project GREENLAKES (No. 9040-00195B) and the UFM-funded project LTER_DK for Long Term Ecosystem Research in Denmark. In addition, support was provided by The European Union’s Horizon 2020 research and innovation programmes under grant agreement No 869296—The PONDERFUL Project”, TREICLAKE under grant agreement No 951963, and the AQUACOSM project and by the European Commission EU H2020- INFRAIA-project (No. 731065) and AQUACOSMplus (No. 871081). E.J. was also supported by the TÜBITAK outstanding researcher programme2232 (project 118C250) and AnaEE, Denmark. The work of D.G. was funded by the Fourth Period of Programme-oriented Funding, Helmholtz Association of German ResearchCentres, Research Field Earth and Environment.C.D.S. and T.A.D. would like to thank June and Derek Sayer for extraordinary support over many years. The authors of this work have been supported by a number of projects over the elephantine gestation period of this manuscript. These include support from the Poul Due Jensen Fonden, Danmarks Frie Forskningsfond Natur og Univers project GREENLAKES (No. 9040-00195B) and the UFM-funded project LTER_DK for Long Term Ecosystem Research in Denmark. In addition, support was provided by The European Union’s Horizon 2020 research and innovation programmes under grant agreement No 869296—The PONDERFUL Project”, TREICLAKE under grant agreement No 951963, and the AQUACOSM project and by the European Commission EU H2020- INFRAIA-project (No. 731065) and AQUACOSMplus (No. 871081). E.J. was also supported by the TÜBITAK outstanding researcher programme2232 (project 118C250) and AnaEE, Denmark. The work of D.G. was funded by the Fourth Period of Programme-oriented Funding, Helmholtz Association of German ResearchCentres, Research Field Earth and Environment

Danish Fungi 2020 - Not Just Another Image Recognition Dataset

We introduce a novel fine-grained dataset and bench-mark, the Danish Fungi 2020 (DF20). The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy. DF20 has zero overlap with ImageNet, al-lowing unbiased comparison of models fine-tuned from publicly available ImageNet checkpoints. The proposed evaluation protocol enables testing the ability to improve classification using metadata - e.g. precise geographic location, habitat, and substrate, facilitates classifier calibration testing, and finally allows to study the impact of the device settings on the classification performance. Experiments using Convolutional Neural Networks (CNN) and the recent Vision Transformers (ViT) show that DF20 presents a challenging task. Interestingly, ViT achieves results su-perior to CNN baselines with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and 12% respectively. A simple procedure for including metadata into the decision process improves the classification accuracy by more than 2.95 percentage points, reducing the error rate by 15%. The source code for all methods and experiments is available at https://sites.google.com/view/danish-fungi-dataset

Density-dependent effects as key drivers of intraspecific size structure of six abundant fish species in lakes across Europe

Fish size structure has traditionally been used for elucidating trophic interactions and patterns of energy transfer through trophic levels(Trebilco et al.2013). We analysed the siz estructure of six common freshwater fish species in several hundred European lakes. We found little effect on the strength of the environmental gradients of size structure. The intraspecific density-dependent effect was the strongest and most consistent predictor

Automatic Fungi Recognition: Deep Learning Meets Mycology

The article presents an AI-based fungi species recognition system for a citizen-science community. The system’s real-time identification too — FungiVision — with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset — Danish Fungi 2020 (DF20) — with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities

The UNITE database for molecular identification of fungi : handling dark taxa and parallel taxonomic classifications

Alfred P. Sloan Foundation [G-2015-14062]; Swedish Research Council of Environment, Agricultural Sciences, and Spatial Planning [FORMAS, 215-2011-498]; European Regional Development Fund (Centre of Excellence EcolChange) [TK131]; Estonian Research Council [IUT20-30]. Funding for open access charge: Swedish Research Council of Environment, Agricultural Sciences and Spatial Planning.Peer reviewedPublisher PD

Investigation of pre-structured GaAs surfaces for subsequent site-selective InAs quantum dot growth

In this study, we investigated pre-structured (100) GaAs sample surfaces with respect to subsequent site-selective quantum dot growth. Defects occurring in the GaAs buffer layer grown after pre-structuring are attributed to insufficient cleaning of the samples prior to regrowth. Successive cleaning steps were analyzed and optimized. A UV-ozone cleaning is performed at the end of sample preparation in order to get rid of remaining organic contamination

Response of Submerged Macrophyte Communities to External and Internal Restoration Measures in North Temperate Shallow Lakes

Submerged macrophytes play a key role in north temperate shallow lakes by stabilising clear-water conditions. Eutrophication has resulted in macrophyte loss and shifts to turbid conditions in many lakes. Considerable efforts have been devoted to shallow lake restoration in many countries, but long-term success depends on a stable recovery of submerged macrophytes. However, recovery patterns vary widely and remain to be fully understood. We hypothesize that reduced external nutrient loading leads to an intermediate recovery state with clear spring and turbid summer conditions similar to the pattern described for eutrophication. In contrast, lake internal restoration measures can result in transient clear-water conditions both in spring and summer and reversals to turbid conditions. Furthermore, we hypothesize that these contrasting restoration measures result in different macrophyte species composition, with added implications for seasonal dynamics due to differences in plant traits. To test these hypotheses, we analysed data on water quality and submerged macrophytes from 49 north temperate shallow lakes that were in a turbid state and subjected to restoration measures. To study the dynamics of macrophytes during nutrient load reduction, we adapted the ecosystem model PCLake. Our survey and model simulations revealed the existence of an intermediate recovery state upon reduced external nutrient loading, characterised by spring clear-water phases and turbid summers, whereas internal lake restoration measures often resulted in clear-water conditions in spring and summer with returns to turbid conditions after some years. External and internal lake restoration measures resulted in different macrophyte communities. The intermediate recovery state following reduced nutrient loading is characterised by a few macrophyte species (mainly pondweeds) that can resist wave action allowing survival in shallow areas, germinate early in spring, have energy-rich vegetative propagules facilitating rapid initial growth and that can complete their life cycle by early summer. Later in the growing season these plants are, according to our simulations, outcompeted by periphyton, leading to late-summer phytoplankton blooms. Internal lake restoration measures often coincide with a rapid but transient colonisation by hornworts, waterweeds or charophytes. Stable clear-water conditions and a diverse macrophyte flora only occurred decades after external nutrient load reduction or when measures were combined.Additional co-authors: Wolf M. Mooij, Ruurd Noordhuis, Geoff Phillips, Jacqueline Rücker, Hans-Heinrich Schuster, Martin Søndergaard, Sven Teurlincx, Klaus van de Weyer, Ellen van Donk, Arno Waterstraat and Carl D. Saye

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Habitual Physical Activity in Mitochondrial Disease

Mitochondrial disease is the most common neuromuscular disease and has a profound impact upon daily life, disease and longevity. Exercise therapy has been shown to improve mitochondrial function in patients with mitochondrial disease. However, no information exists about the level of habitual physical activity of people with mitochondrial disease and its relationship with clinical phenotype.Habitual physical activity, genotype and clinical presentations were assessed in 100 patients with mitochondrial disease. Comparisons were made with a control group individually matched by age, gender and BMI. = −0.49; 95% CI −0.33, −0.63, P<0.01). There were no systematic differences in physical activity between different genotypes mitochondrial disease.These results demonstrate for the first time that low levels of physical activity are prominent in mitochondrial disease. Combined with a high prevalence of obesity, physical activity may constitute a significant and potentially modifiable risk factor in mitochondrial disease

Benefits of a ball and chain: simple environmental enrichments improve welfare and reproductive success in farmed American mink (Neovison vison)

Can simple enrichments enhance caged mink welfare? Pilot data from 756 sub-adults spanning three colour-types (strains) identified potentially practical enrichments, and suggested beneficial effects on temperament and fur-chewing. Our main experiment started with 2032 Black mink on three farms: from each of 508 families, one juvenile male-female pair was enriched (E) with two balls and a hanging plastic chain or length of hose, while a second pair was left as a non-enriched (NE) control. At 8 months, more than half the subjects were killed for pelts, and 302 new females were recruited (half enriched: ‘late E’). Several signs of improved welfare or productivity emerged. Access to enrichment increased play in juveniles. E mink were calmer (less aggressive in temperament tests; quieter when handled; less fearful, if male), and less likely to fur-chew, although other stereotypic behaviours were not reduced. On one farm, E females had lower cortisol (inferred from faecal metabolites). E males tended to copulate for longer. E females also weaned more offspring: about 10% more juveniles per E female, primarily caused by reduced rates of barrenness (‘late E’ females also giving birth to bigger litters on one farm), effects that our data cautiously suggest were partly mediated by reduced inactivity and changes in temperament. Pelt quality seemed unaffected, but E animals had cleaner cages. In a subsidiary side-study using 368 mink of a second colour-type (‘Demis’), similar temperament effects emerged, and while E did not reduce fur-chewing or improve reproductive success in this colour-type, E animals were judged to have better pelts. Overall, simple enrichments were thus beneficial. These findings should encourage welfare improvements on fur farms (which house 60-70 million mink p.a.) and in breeding centres where endangered mustelids (e.g. black-footed ferrets) often reproduce poorly. They should also stimulate future research into more effective practical enrichments