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Abstract Mid-rotation silvicultural treatments (MRT) are commonly applied to loblolly pine (Pinus taeda L.) plantations in the southeastern United States to improve pine productivity. Competing vegetation is often present in operational plantations and limits site resource availability. The benefits of MRT for pine productivity are well known, but competing vegetation growth has not been extensively studied. Pine and competing vegetation growth within two regions of the southeastern United States was monitored for 8 years following a one-time post-thin application of either fertilization (224 kg ha-1 of nitrogen plus 28 kg ha-1 phosphorus), chemical herbicide (0.8 oz glyphosate and 0.8 oz triclopyr L-1 of water) or their combination. Fertilization significantly increased pine volume growth in the Lower Coastal Plain (LCP, 2.67-4.01 m3ha-1yr-1) and the Upper Coastal Plain/ Piedmont (UCPIE, 0.20-3.72 m3ha-1yr-1). Chemical herbicide application in both the LCP (0.34-4.87 m3 ha-1yr-1) and UCPIE (0.89-1.97 m3 ha-1yr-1) also significantly increased pine volume. Chemical herbicide application, individually and combined, did not result in significant decreases in herbaceous vegetation, but reduced woody vegetation by up to -2.40 m3 ha-1yr-1 in the LCP and -5.67 m3 ha-1yr-1 in the UCPIE. Consequently, we suggest that competing vegetation response should be considered within site-specific management plans aimed at maximizing pine productivity. 

Abstract Fish adaption behaviors in complex environments are of great importance in improving the performance of underwater vehicles. This work presents a numerical study of the adaption behaviors of self-propelled fish in complex environments by developing a numerical framework of deep learning and immersed boundary–lattice Boltzmann method (IB–LBM). In this framework, the fish swimming in a viscous incompressible flow is simulated with an IB–LBM which is validated by conducting two benchmark problems including a uniform flow over a stationary cylinder and a self-propelled anguilliform swimming in a quiescent flow. Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB–LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Kármán gaiting. Compared to existing learning models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action spaces as well as the historical effects. This framework makes use of the high computational efficiency of the IB–LBM which is of crucial importance for the effective coupling with learning algorithms. Applications of the proposed numerical framework in point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Kármán vortex street demonstrate the strategies used to adapt to different situations.