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Plant available soil nitrogen (N) may affect deep root growth and soil N depletion by catch crops. We investigated the influence of topsoil N availability on root growth and uptake by fodder radish.We conducted field and greenhouse experiments of root growth and late autumn N uptake at medium and high soil N availabilities, and root N inflow at medium and deep soil depths ( 15 N injection) in sandy loam and loamy sand, using the minirhizotron method in the field and rhizotrons in the greenhouse.High soil N availability resulted in lower root intensity in the field, but higher root intensity in the greenhouse experiment under both soil types. Radish had deeper roots and higher root intensity in sandy loam than in loamy sand. High soil N availability caused lower 15 N uptake at both soil depths in the field and lower N inflow rates at both soil depths in field and greenhouse. At medium soil N availability in the field, N inflow was twice as high in the deep than in the medium depth.Higher soil N availability affects root growth and decrease N inflow, thus lowering late autumn N uptake and hampering deep N exploitation by fodder radish. At medium soil N availability, the potential for N uptake in deep soil is higher probably due to younger roots than at a medium soil depth. The shallower and less dense root growth in loamy sand is probably due to its lower clay and higher P contents.

Most research on aposematism has focused on chemically defended prey, but the signalling difficulty of capture remains poorly explored. Similar to classical Batesian and Müllerian mimicry related to distastefulness, such ‘evasive aposematism’ may also lead to convergence in warning colours, known as evasive mimicry. A prime candidate group for evasive mimicry are Adelpha butterflies, which are agile insects and show remarkable colour pattern convergence. We tested the ability of naive blue tits to learn to avoid and generalize Adelpha wing patterns associated with the difficulty of capture and compared their response to that of birds that learned to associate the same wing patterns with distastefulness. Birds learned to avoid all wing patterns tested and generalized their aversion to other prey to some extent, but learning was faster with evasive prey compared to distasteful prey. Our results on generalization agree with longstanding observations of striking convergence in wing colour patterns among Adelpha species, since, in our experiments, perfect mimics of evasive and distasteful models were always protected during generalization and suffered the lowest attack rate. Moreover, generalization on evasive prey was broader compared to that on distasteful prey. Our results suggest that being hard to catch may deter predators at least as effectively as distastefulness. This study provides empirical evidence for evasive mimicry, a potentially widespread but poorly understood form of morphological convergence driven by predator selection.

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Fishery catch forecasting is a crucial aspect of aquatic research because of its relevance to establishing effective fishery management and resource allocation systems. In this study, we aim to forecast and analyze fish catch by collaboratively processing data using methods at multiple scales. To this end, we propose two computational fishery catch forecasting models. A neural network model based on the multi-timescale features of a convolutional neural network and a long short-term memory neural network is proposed and implemented to forecast short-term measures for the daily catch in the eastern ports of Hokkaido, Japan. Similarly, we propose a long-term catch forecasting and analysis model combining the autoregressive integrated moving average (ARIMA) method and a neural network to explore short-term water temperature and long-term catch dependence in the case of sparse data; we implement this method to investigate the total monthly catch in Hokkaido. The experimental results demonstrate that the proposed methods were able to effectively forecast and analyze fishery catch based on different data scales, volumes, and other complex situations. This is also the first work in the field that considers multiple perspectives.