Biology subjects

Ryo, M.

Publications and source records attributed to Ryo, M..

8 recordsLinked to original sources

bioRxiv2025

Explainable AI shows climate impacts on wheat yields: insights from 30 years of field data.

Wheat (Triticum aestivum L.) is amongst the worlds most important staple crops and primary food source for an estimated 35% of the global population. Climate impacts have caused global yield stagnation and quantifying the climatic variables influencing wheat yield is critical to anticipate yield losses and design climate-resilient agricultural strategies. Here, we use a unique 30-year dataset on winter wheat variety trials in six sites across Switzerland, explainable artificial intelligence (XAI) and interpretable machine learning (IML) methods (i.e., decision trees and gradient boosting models combined with post hoc tests) to elucidate climate drivers on wheat yields. We showed based on 405 varieties and over 10,000 observations, that climatic variables such as cumulative solar radiation, precipitation from sowing to harvest and genotype makeup are significant yield drivers. Partial dependence plots and variable interaction analyses revealed, for example, a yield plateau above cumulative solar radiation levels of [~]3000 MJ m-{superscript 2}, suggesting complex genotype-by-environment interactions. These findings suggest that XAI adds important biological interpretability to predictive performance, and reveals the mechanisms how climate affects wheat yields. Our methodological framework and results can inform breeding activities, agronomic management, and adaptation strategies under climate change across environmental conditions in Switzerland and with global ramifications.

plant biology↗

bioRxiv2023

Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small data sets with class imbalance and hierarchical grouping

Deep learning applications in taxonomic classification for animals and plants from images have become popular, while those for microorganisms are still lagging behind. Our study investigated the potential of deep learning for the taxonomic classification of hundreds of filamentous fungi from colony images, which is typically a task that requires specialized knowledge. We isolated soil fungi, annotated their taxonomy using standard molecular barcode techniques, and took images of the fungal colonies grown in petri dishes (n = 606). We applied a convolutional neural network with multiple training approaches and model architectures to deal with some common issues in ecological datasets: small amounts of data, class imbalance, and hierarchically structured grouping. Model performance was overall low due mainly to the relatively small data set, class imbalance, and the high morphological plasticity exhibited by fungal colonies. However, our approach indicates that morphological features such as color, patchiness, and colony extension rate, could be used for the recognition of fungal colonies at higher taxonomic ranks (i.e., phylum, class, and order). Model explanation implies that image recognition characters appear at different positions within the colony (e.g., outer or inner hyphae), depending on the taxonomic resolution. Our study suggests the potential of deep learning applications for better understanding the taxonomy and ecology of filamentous fungi amenable to axenic culturing. Meanwhile, our study also highlights some technical challenges in deep learning image analysis in ecology, highlighting that the domain of applicability of these methods needs to be carefully considered.

microbiology↗

bioRxiv2022

From microbes to mammals: pond biodiversity homogenization across different land-use types in an agricultural landscape

Local biodiversity patterns are expected to strongly reflect variation in topography, land use, dispersal boundaries, nutrient supplies, contaminant spread, management practices and other anthropogenic influences. In contrast, studies focusing on specific taxa revealed a biodiversity homogenization effect in areas subjected to long-term intensive industrial agriculture. We investigated whether land use affects biodiversity and metacommunity structure in 67 kettle holes (KH) representing small aquatic islands embedded in the patchwork matrix of a largely agricultural landscape comprising grassland, forest, and arable fields. These KH, similar to millions of standing water bodies of glacial origin, spread across northern Europe, Asia, and North America, are physico-chemically diverse, differ in the degree of coupling with their surroundings. We assessed biodiversity patterns of eukaryotes, Bacteria and Archaea in relation to environmental features of the KH, using deep-amplicon-sequencing of eDNA. First, we asked whether deep sequencing of eDNA provides a representative picture of KH biodiversity across the three domains of life. Second, we investigated if and to what extent KH biodiversity is influenced by the surrounding land-use. Our data shows that deep eDNA amplicon sequencing is useful for in-depth assessments of cross-domain biodiversity comprising both micro- and macro-organisms, but, has limitations with respect to single-taxa conservation studies. Using this broad method, we show that sediment eDNA, integrating several years to decades, depicts the history of agricultural land-use intensification. The latter, coupled with landscape wide nutrient enrichment (including by atmospheric deposition), groundwater connectivity between KH and organismal movement in the tight network of ponds, resulted in a biodiversity homogenization in the KH water, levelling off todays detectable differences in KH biodiversity between land-use types.

ecology↗

bioRxiv2021

Tire abrasion particles negatively affect plant growth even at low concentrations and alter soil biogeochemical cycling

Tire particles (TPs) are a major source of microplastic on land, and considering their chemical composition, they represent a potential hazard for the terrestrial environment. We studied the effects of TPs at environmentally relevant concentrations along a wide concentration gradient (0 - 160 mg g-1) and tested the effects on plant growth, soil pH and the key ecosystem process of litter decomposition and soil respiration. The addition of TPs negatively affected shoot and root growth already at low concentrations. Tea litter decomposition slightly increased with lower additions of TPs but decreased later on. Soil pH increased until a TP concentration of 80 mg kg-1 and leveled off afterwards. Soil respiration clearly increased with increasing concentration of added TPs. Plant growth was likely reduced with starting contamination and stopped when contamination reached a certain level in the soil. The presence of TPs altered a number of biogeochemical soil parameters that can have further effects on plant performance. Considering the quantities of yearly produced TPs, their persistence, and toxic potential, we assume that these particles will eventually have a significant impact on terrestrial ecosystems.

ecology↗

bioRxiv2020

Dengue Disease Dynamics are Modulated by the Combined Influence of Precipitation and Landscapes: A Machine Learning-based Approach

BackgroundDengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors interact in time and space to affect mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain or flood water. ObjectivesThis study aimed to investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence. MethodsEntomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metro Manila, Philippines, from 2012 to 2014. Temperature, precipitation, and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterwards, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combinatory influences of landscape and climate factors on the ovitrap index and dengue incidence. ResultsThe MOB recursive partitioning for the ovitrap index indicated that mosquito occurrence was higher in high residential density areas, where industrial areas also exist and are well connected with roads. Precipitation was another key covariate modulating the effects of landscape factors, possibly by expanding breeding sites and activating mosquito reproduction. Moreover, the MOB recursive partitioning indicated that precipitation was the main predictor of dengue incidence, with a stronger effect in high residential density and commercial areas. DiscussionPrecipitation with floods has epidemiologically important implications by damaging shelters and causing population displacement, thus increasing exposure to dengue vectors. Our findings suggest that the intensification of vector control during the rainy season can be prioritized in residential and commercial areas to better control dengue disease dynamics.

ecology↗

bioRxiv2019

Diversity of responses of soil saprobic fungi to recurring heat events

As a consequence of ongoing climate change, the frequency of extreme heat events is expected to increase. Recurring heat pulses may disrupt functions supported by soil microorganisms, thus affecting the entire ecosystem. However, most perturbation experiments only test effects of single heat events, and therefore it remains largely unknown how soil microorganisms react to repeated pulse events. Here we present data from a lab experiment exposing 32 filamentous fungi, originally isolated from the same soil, to sequential heat perturbations. Soil saprobic fungi isolates were exposed to one or two heat pulses: mild (35{degrees}C/2h), strong (45{degrees}C/1h), or both in sequence (35{degrees}C/2h+45{degrees}C/1h), and we assessed growth rate. Out of the 32 isolates 13 isolates showed an antagonistic response, 3 isolates a synergistic response and 16 isolates responded in an additive manner. These differences in species responses to the thermal environment may contribute to species coexistence, and such dissimilarities in thermal perturbation responses may be a key aspect influencing ecosystem services that soil saprobic fungi support.

ecology↗

bioRxiv2019

Fungal traits important for soil aggregation

Soil health and sustainability is essential for ecosystem functioning and human well-being. Soil structure, the complex arrangement of soil into aggregates and pore spaces, is a key feature of soils under the influence of soil life. Soil biota, and among them filamentous saprobic fungi, have well-documented effects on soil aggregation. However, it is unclear what fungal properties, or traits, contribute to the overall positive effect on soil aggregation. So far, we lack a systematic investigation of a broad suite of fungal species for their trait expression and the relation of these traits to their soil aggregation capability.\n\nHere, 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.\n\nWe found a spectrum of soil aggregate formation capability ranging from neutral to positive and large differences in trait expression among strains. We identified biomass density (positive effects), leucine aminopeptidase activity (negative effects) and phylogeny as important modulators of fungal aggregate formation capability. Our results point to a typical suite of traits characterizing fungi that are good soil aggregators; this could inform screening for fungi to be used in biotechnological applications, and illustrates the power of employing a trait-based approach to unravel biological mechanisms of soil aggregation, which could now be extended to other organism groups.

ecology↗

bioRxiv2018

Growth rate trades off with enzymatic investment in soil filamentous fungi

Saprobic soil fungi drive many important ecosystem processes, including decomposition, and many of their effects are related to growth rate and enzymatic ability. In mycology, there has long been the implicit assumption of trade-off between growth and enzymatic investment, which we here test. Using a set of 31 filamentous fungi isolated from the same ecosystem, we measured growth rate (as colony radial extension) and enzymatic repertoire (activities of four enzymes: laccase, cellobiohydrolase, leucine aminopeptidase and acid phosphatase). Our results support the existence of a trade-off, however only for the enzymes representing a larger metabolic cost (laccase and cellobiohydrolase). Our study offers new insights into functional complementarity within the soil fungal community in a number of ecosystem processes, and experimentally supports an enzymatic investment/ growth rate tradeoff in explaining phenomena including substrate succession.

ecology↗