Intraspecific population diversity (specifically, spatial asynchrony of population dynamics) is an essential component of metapopulation stability and persistence in nature. In 2D systems, theory predicts that metapopulation stability should increase with ecosystem size (or habitat network size): Larger ecosystems will harbor more diverse subpopulations with more stable aggregate dynamics. However, current theories developed in simplified landscapes may be inadequate to predict emergent properties of branching ecosystems, an overlooked but widespread habitat geometry. Here, we combine theory and analyses of a unique long-term dataset to show that a scale-invariant characteristic of fractal river networks, branching complexity (measured as branching probability), stabilizes watershed metapopulations. In riverine systems, each branch (i.e., tributary) exhibits distinctive ecological dynamics, and confluences serve as "merging" points of those branches. Hence, increased levels of branching complexity should confer a greater likelihood of integrating asynchronous dynamics over the landscape. We theoretically revealed that the stabilizing effect of branching complexity is a consequence of purely probabilistic processes in natural conditions, where within-branch synchrony exceeds among-branch synchrony. Contrary to current theories developed in 2D systems, metapopulation size (a variable closely related to ecosystem size) had vague effects on metapopulation stability. These theoretical predictions were supported by 18-y observations of fish populations across 31 watersheds: Our cross-watershed comparisons revealed consistent stabilizing effects of branching complexity on metapopulations of very different riverine fishes. A strong association between branching complexity and metapopulation stability is likely to be a pervasive feature of branching networks that strongly affects species persistence during rapid environmental changes.

Aim Although data collected by citizen scientists have received a great deal of attention for assessing species distributions over large extents, their sampling efforts are usually spatially biased. We assessed whether the bias of spatially varied sampling effort for opportunistic citizen data can be corrected using occupancy models that incorporate observation processes.Location Hokkaido Island, northern Japan.Methods We applied occupancy models for citizen data with spatially biased sampling effort to model and map large-scale distributions of 52 forest and 23 grassland/wetland bird species. We used estimated species richness (summed occupancy probabilities among the species) as the aggregated distributional patterns of each species group and compared them among two occupancy models (i.e. single-species and multispecies occupancy models), two conventional logistic regression models and Maxlike, which do not explicitly deal with observation processes.Results Conventional logistic regression models and Maxlike predicted inappropriate patterns, such as forest species preferring lowland non-forested areas where most of the data were collected. Occupancy models, however, showed more appropriate results, indicating that forest species preferred lowland forested areas. The prediction by logistic models was somewhat improved by the use of spatially biased non-detection data as the absence data; however, estimates of species richness were still much lower than those of occupancy models. Differences in model outputs were evident for the forest species but not for grassland/wetland species because citizen data covered virtually all environmental niches for grassland/wetland species. Results of the single-species and multispecies occupancy models were nearly identical, but in some cases, estimates from the single-species models were not converged or deviated notably from those of other species compared with estimates by the multispecies model. Main conclusionsWe found that citizen data with spatially biased sampling effort can be appropriately utilized for large-scale biodiversity distribution modelling with the use of occupancy models, which encourages data collection by citizen scientists.

In recent years, we have experienced mega-flood disasters in Japan due to climate change. In the last century, we have been building disaster prevention infrastructure (artificial levees and dams, referred to as "grey infrastructure") to protect human lives and assets from floods, but these hard protective measures will not function against mega-floods. Moreover, in a drastically depopulating society such as that in Japan, farmland abandonment prevails, and it will be more difficult to maintain grey infrastructure with a limited tax income. In this study, we propose the introduction of green infrastructure as an adaptation strategy for climate change. If we can use abandoned farmlands as green infrastructure, they may function to reduce disaster risks and provide habitats for various organisms that are adapted to wetland environments. First, we present a conceptual framework for disaster prevention using a hybrid of green infrastructure and conventional grey infrastructure. In this combination, the fundamental green infrastructure, composed of forests and wetlands in the catchment (GI-1), and additional multilevel green infrastructures such as flood control basins that function when floodwater exceeds the planning level (GI-2) are introduced. We evaluated the flood attenuation function (GI-1) of the Kushiro Wetland using a hydrological model and developed a methodology for selecting suitable locations of GI-2, considering flood risk, biodiversity, and the distribution of abandoned farmlands, which represent social and economic costs. The results indicated that the Kushiro Wetland acts as a large natural reservoir that attenuates the hydrological peak discharge during floods, and suitable locations for introducing GI-2 are concentrated in floodplain areas developing in the downstream reaches of large rivers. Finally, we discussed the network structure of GI-1 as a hub and GI-2 as a dispersal site for conservation of the Red-crowned Crane, one of the symbolic species of Japan.

A mixed-flow pump with an unshrouded impeller was computed by a one-way coupled fluid-structure simulation to evaluate a prediction accuracy of stress and analyze a flow pattern which caused the largest stress. The stress occurring around a blade root was predicted by a numerical simulation and compared with an experimental one. Five flow rates, Q/Q bep =0,40,70,100 and 120% were simulated and the predicted stresses at all flow rates agreed with the experimental ones within -11～+6% accuracy. The largest stress occurred around a blade root on a pressure side of blade surface at all flow rates. The stress became largest at 70% flow rate. A flow pattern around the blade was analyzed to investigate how the largest stress occurred at 70% flow rate. It was found in this study that a flow separation occurred around a leading edge on a suction side of blade surface at 70% flow rate and the largest load was acting on an outside region of blade.

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