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Tire-wear particles (TWPs) are being released into the environment by wearing down during car driving, and are considered an important microplastic pollution source. The chemical additive leaching from these polymer-based materials and its potential effects are likely temporally dynamic, since amounts of potentially toxic compounds can gradually increase with contact time of plastic particles with surrounding media. In the present study, we conducted soil toxicity tests using the soil nematode Caenorhabditis elegans with different soil pre-incubation (30 and 75 days) and exposure (short-term exposure, 2 days; lifetime exposure, 10 days) times. Soil pre-incubation increased toxicity of TWPs, and the effective concentrations after the pre-incubation were much lower than environmentally relevant concentrations. The lifetime of C. elegans was reduced faster in the TWP treatment groups, and the effective concentration for lifetime exposure tests were 100- to 1,000-fold lower than those of short-term exposure tests. Water-extractable metal concentrations (Cr, Cu, Ni, Pb, and Zn) in the TWP-soils showed no correlation with nominal TWP concentrations or pre-incubation times, and the incorporated metals in the TWPs may be not the main reason of toxicity in this study. Our results show that toxic effects of TWPs can be time-dependent, both in terms of the microplastic particles themselves and their interactions in the soil matrix, but also because of susceptibility of target organisms depending on developmental stage. It is vital that future works consider these aspects, since otherwise effects of microplastics and TWPs could be underestimated.
1. Microplastics in soils have become an important threat for terrestrial systems as they may potentially alter the geochemical/biophysical soil environment and can interact with drought. As microplastics may affect soil water content, this could exacerbate the well-known negative effects of drought on ecosystem functionality. Thus, functions including litter decomposition, soil aggregation or those related with nutrient cycling can be altered. Despite this potential interaction, we know relatively little about how microplastics, under different soil water conditions, affect ecosystem functions and multifunctionality.
2. To address this gap, we performed an experiment using grassland plant communities growing in microcosms. Microplastic fibres (absent, present) and soil water conditions (well-watered, drought) were applied in a fully factorial design. At harvest, we measured soil ecosystem functions related to nutrient cycling (beta-glucosaminidase, beta-D-cellobiosidase, phosphatase, beta-glucosidase enzymes), respiration, nutrient retention, pH, litter decomposition and soil aggregation (water stable aggregates). As terrestrial systems provide these functions simultaneously, we also assessed ecosystem multifunctionality, an index that encompasses the array of ecosystem functions measured here.
3. We found that the interaction between microplastic fibres and drought affected ecosystem functions and multifunctionality. Drought had negatively affected nutrient cycling by decreasing enzymatic activities by up to similar to 39%, while microplastics increased soil aggregation by similar to 18%, soil pH by similar to 4% and nutrient retention by up to similar to 70% by diminishing nutrient leaching. Microplastic fibres also impacted soil enzymes, respiration and ecosystem multifunctionality, but importantly, the direction of these effects depended on soil water status. That is, under well-watered conditions, these functions decreased with microplastic fibres by up to similar to 34% while under drought they had similar values irrespective of the microplastic presence, or tended to increase with microplastics. Litter decomposition had a contrary pattern increasing with microplastics by similar to 6% under well-watered conditions while decreasing to a similar percentage under drought.
4. Synthesis and applications. Single ecosystem functions can be positively or negatively affected by microplastics fibres depending on soil water status. However, our results suggest that microplastic fibres may cause negative effects on ecosystem soil multifunctionality of a similar magnitude as drought. Thus, strategies to counteract this new global change factor are necessary.
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
Soil structure, the complex arrangement of soil into aggregates and pore spaces, is a key feature of soils and soil biota. Among them, filamentous saprobic fungi have well-documented effects on soil aggregation. However, it is unclear what properties, or traits, determine the overall positive effect of fungi on soil aggregation. To achieve progress, it would be helpful to systematically investigate a broad suite of fungal species for their trait expression and the relation of these traits to soil aggregation. Here, we apply a trait-based approach to a set of 15 traits measured under standardized conditions on 31 fungal strains including Ascomycota, Basidiomycota, and Mucoromycota, all isolated from the same soil. We find large differences among these fungi in their ability to aggregate soil, including neutral to positive effects, and we document large differences in trait expression among strains. We identify biomass density, i.e., the density with which a mycelium grows (positive effects), leucine aminopeptidase activity (negative effects) and phylogeny as important factors explaining differences in soil aggregate formation (SAF) among fungal strains; importantly, growth rate was not among the important traits. Our results point to a typical suite of traits characterizing fungi that are good soil aggregators, and our findings illustrate the power of employing a trait-based approach to unravel biological mechanisms underpinning soil aggregation. Such an approach could now be extended also to other soil biota groups. In an applied context of restoration and agriculture, such trait information can inform management, for example to prioritize practices that favor the expression of more desirable fungal traits.
When running a lab we do not think about calamities, since they are rare events for which we cannot plan while we are busy with the day-to-day management and intellectual challenges of a research lab. No lab team can be prepared for something like a pandemic such as COVID-19, which has led to shuttered labs around the globe. But many other types of crises can also arise that labs may have to weather during their lifetime. What can researchers do to make a lab more resilient in the face of such exterior forces? What systems or behaviors could we adjust in 'normal' times that promote lab success, and increase the chances that the lab will stay on its trajectory? We offer 10 rules, based on our current experiences as a lab group adapting to crisis.
Movement ecology aims to provide common terminology and an integrative framework of movement research across all groups of organisms. Yet such work has focused on unitary organisms so far, and thus the important group of filamentous fungi has not been considered in this context. With the exception of spore dispersal, movement in filamentous fungi has not been integrated into the movement ecology field. At the same time, the field of fungal ecology has been advancing research on topics like informed growth, mycelial translocations, or fungal highways using its own terminology and frameworks, overlooking the theoretical developments within movement ecology. We provide a conceptual and terminological framework for interdisciplinary collaboration between these two disciplines, and show how both can benefit from closer links: We show how placing the knowledge from fungal biology and ecology into the framework of movement ecology can inspire both theoretical and empirical developments, eventually leading towards a better understanding of fungal ecology and community assembly. Conversely, by a greater focus on movement specificities of filamentous fungi, movement ecology stands to benefit from the challenge to evolve its concepts and terminology towards even greater universality. We show how our concept can be applied for other modular organisms (such as clonal plants and slime molds), and how this can lead towards comparative studies with the relationship between organismal movement and ecosystems in the focus.
Biochar is being discussed as a soil amendment to improve soil fertility and mitigate climate change. While biochar interactions with soil microbial biota have been frequently studied, interactions with soil mesofauna are understudied. We here present an experiment in which we tested if the collembolan Folsomia candida I) can transport biochar particles, II) if yes, how far the particles are distributed within 10 days, and III) if it shows a preference among biochars made from different feedstocks, i.e. pine wood, pine bark and spelt husks. In general, biochar particles based on pine bark and pine wood were consistently distributed significantly more than those made of spelt husks, but all types were transported more than 4cm within 10 days. Additionally, we provide evidence that biochar particles can become readily attached to the cuticle of collembolans and hence be transported, potentially even over large distances. Our study shows that the soil mesofauna can indeed act as a vector for the transport of biochar particles and show clear preferences depending on the respective feedstock, which would need to be studied in more detail in the future.
Biochar is being discussed as a soil amendment to improve soil fertility and mitigate climate change. While biochar interactions with soil microbial biota have been frequently studied, interactions with soil mesofauna are understudied. We here present an experiment in which we tested if the collembolan Folsomia candida I) can transport biochar particles, II) if yes, how far the particles are distributed within 10 days, and III) if it shows a preference among biochars made from different feedstocks, i.e. pine wood, pine bark and spelt husks. In general, biochar particles based on pine bark and pine wood were consistently distributed significantly more than those made of spelt husks, but all types were transported more than 4cm within 10 days. Additionally, we provide evidence that biochar particles can become readily attached to the cuticle of collembolans and hence be transported, potentially even over large distances. Our study shows that the soil mesofauna can indeed act as a vector for the transport of biochar particles and show clear preferences depending on the respective feedstock, which would need to be studied in more detail in the future.
Global change has complex eco-evolutionary consequences for organisms and ecosystems, but related concepts (e.g., novel ecosystems) do not cover their full range. Here we propose an umbrella concept of "ecological novelty" comprising (1) a site-specific and (2) an organism-centered, eco-evolutionary perspective. Under this umbrella, complementary options for studying and communicating effects of global change on organisms, ecosystems, and landscapes can be included in a toolbox. This allows researchers to address ecological novelty from different perspectives, e.g., by defining it based on (a) categorical or continuous measures, (b) reference conditions related to sites or organisms, and (c) types of human activities. We suggest striving for a descriptive, non-normative usage of the term "ecological novelty" in science. Normative evaluations and decisions about conservation policies or management are important, but require additional societal processes and engagement with multiple stakeholders.