Motoaki Tojo’s research while affiliated with Osaka University and other places

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and influenced by surrounding ecological community members. How ecological communities influence the rice performance in the field has been underexplored despite the potential of ecological communities to establish an environment-friendly agricultural system. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously overlooked organisms for rice ( Oryza sativa ). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA metabarcoding in 2017 in Japan. We detected more than 1000 species (including microbes and macrobes such as insects) in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated under field conditions in 2019 by field manipulation experiments. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis , whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn -added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and influenced by surrounding ecological community members. How ecological communities influence the rice performance in the field has been underexplored despite the potential of ecological communities to establish an environment-friendly agricultural system. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously overlooked organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA metabarcoding in 2017 in Japan. We detected more than 1000 species (including microbes and macrobes such as insects) in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated under field conditions in 2019 by field manipulation experiments. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and inevitably influenced by surrounding ecological community members, and whether and how ecological communities influence the rice performance under field conditions has been underexplored. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously ignored organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA analysis in 2017 in Japan. We detected more than 1000 species in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated in 2019. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and inevitably influenced by surrounding ecological community members, and whether and how ecological communities influence the rice performance under field conditions has been underexplored. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously ignored organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA analysis in 2017 in Japan. We detected more than 1000 species in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated in 2019. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and inevitably influenced by surrounding ecological community members, and whether and how ecological communities influence the rice performance under field conditions has been underexplored. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously ignored organisms for rice ( Oryza sativa ). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA analysis in 2017 in Japan. We detected more than 1000 species in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated in 2019. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis , whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn -added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

For more than five decades, research has been conducted at Ny-Ålesund, in Svalbard, Norway, to understand the structure and functioning of High-Arctic ecosystems and the profound impacts on them of environmental change. Terrestrial, freshwater, glacial and marine ecosystems are accessible year-round from Ny-Ålesund, providing unique opportunities for interdisciplinary observational and experimental studies along physical, chemical, hydrological and climatic gradients. Here, we synthesize terrestrial and freshwater research at Ny-Ålesund and review current knowledge of biodiversity patterns, species population dynamics and interactions, ecosystem processes, biogeochemical cycles and anthropogenic impacts. There is now strong evidence of past and ongoing biotic changes caused by climate change, including negative effects on populations of many taxa and impacts of rain-on-snow events across multiple trophic levels. While species-level characteristics and responses are well understood for macro-organisms, major knowledge gaps exist for microbes, invertebrates and ecosystem-level processes. In order to fill current knowledge gaps, we recommend (1) maintaining monitoring efforts, while establishing a long-term ecosystem-based monitoring programme; (2) gaining a mechanistic understanding of environmental change impacts on processes and linkages in food webs; (3) identifying trophic interactions and cascades across ecosystems; and (4) integrating long-term data on microbial, invertebrate and freshwater communities, along with measurements of carbon and nutrient fluxes among soils, atmosphere, freshwaters and the marine environment. The synthesis here shows that the Ny-Ålesund study system has the characteristics needed to fill these gaps in knowledge, thereby enhancing our understanding of High-Arctic ecosystems and their responses to environmental variability and change.

To further classify the oomycete viruses that have been discovered in recent years, we investigated virus infection in the plant-parasitic oomycete Globisporangium ultimum in Japan. Double-stranded RNA detection, high-throughput sequencing, and RT-PCR revealed that the G. ultimum isolate UOP226 contained two viruses related to fusarivirus and totivirus, named Pythium ultimum RNA virus 1 (PuRV1) and Pythium ultimum RNA virus 2 (PuRV2), respectively. Phylogenetic analysis of the deduced amino acid sequence of the RNA-dependent RNA polymerase (RdRp) showed that fusari-like PuRV1 belonged to a different phylogenetic group than Plasmopara viticola lesion-associated fusari virus (PvlaFV) 1–3 from oomycete Plasmopara viticola. Codon usage bias of the PuRV1 RdRp gene was more similar to those of fungi than Globisporangium and Phytophthora, suggesting that the PuRV1 ancestor horizontally transmitted to G. ultimum ancestor from fungi. Phylogenetic analysis of the deduced amino acid sequence of the RdRp of toti-like PuRV2 showed a monophyletic group with the other toti-like oomycete viruses from Globisporangium, Phytophthora, and Pl. viticola. However, the nucleotide sequences of toti-like oomycete viruses were not so homologous, suggesting the possibility of convergent evolution of toti-like oomycete viruses.

In the Arctic, fungal mycelial growth takes place mainly during the cold season and beginning of growing season. Climate change induced increases of cold season temperatures may, hence, benefit fungal growth and increase their abundance. This is of particular importance for parasitic fungi, which may significantly shape Arctic vegetation composition. Here, we studied two contrasting plant parasitic fungi’s occurrences (biotrophic Exobasidium hypogenum Nannf. on the vascular plant Cassiope tetragona (L.) D. Don., and necrotrophic Pythium polare Tojo, van West & Hoshino on the moss Sanionia uncinata (Hedw.) Loeske) in response to increased snow depth, a method primarily used to increase cold season temperatures, after 7–13 years of snow manipulation in Adventdalen, Svalbard. We show that enhanced snow depth increased occurrences of both fungi tested here and indicate that increased fungal infections of host plants were at least partly responsible for decreases of host occurrences. Although bryophyte growth, in general, may be influenced by increased soil moisture and reduced competition from vascular plants, Pythium polare is likely enhanced by the combination of milder winter temperatures and moister environment provided by the snow. The relationships between host plants and fungal infection indicate ongoing processes involved in the dynamics of compositional adjustment to changing climate.

An isolate of a novel fungus and an isolate of a novel fungus-like microbe were obtained during field research conducted in Kuujjuarapik-Whapmagoostui, Quebec, in subarctic Canada, located on the southeastern coast of Hudson Bay, from August to September 2016. The isolates were identified as a Typhula sp. and Globisporangium sp. (syn. Pythium sp.), respectively. Mycelia with clamps connections were isolated from small sclerotia (0.2–0.7 × 0.5–1.2 mm in size) collected from dead leaves of sea pea (Lathyrus japonicus) on the banks of the Great Whale River in Kuujjuarapik-Whapmagoostui. The isolate was found to be closely related with T. japonica based on sclerotium morphology and hyphal growth under different temperatures. The Globisporangium sp. isolate was found to be most closely related to G. okanoganense based on phylogenetic analyses using rDNA-ITS. G. okanoganense is known as an important pathogen causing snow rot on wheat and other winter crops. Further investigation is needed to determine whether the novel fungus and fungus-like microbe found in Kuujjuarapik-Whapmagoostui are phytopathogenic like the snow rot pathogens to which they are closely related.