Fictive swimming was elicited in low-spinal immobilized turtles by electrically stimulating the contralateral dorsolateral funiculus (cDLF) at the level of the third postcervical segment (D(3)). Fictive hindlimb motor output was recorded as electroneurograms (ENGs) from up to five peripheral nerves on the right side, including three knee extensors (KE; iliotibialis [IT]-KE, ambiens [AM]-KE, and femorotibialis [FT]-KE), a hip flexor (HF), and a hip extensor (HE). Quantitative analyses of burst amplitude, duty cycle and phase were used to demonstrate the close similarity of these cDLF-evoked fictive motor patterns with previous myographic recordings obtained from the corresponding hindlimb muscles during actual swimming. Fictive rostral scratching was elicited in the same animals by cutaneous stimulation of the shell bridge, anterior to the hindlimb. Fictive swim and rostral scratch motor patterns displayed similar phasing in hip and knee motor pools but differed in the relative amplitudes and durations of ENG bursts. Both motor patterns exhibited alternating HF and HE discharge, with monoarticular knee extensor (FT-KE) discharge during the late HF phase. The two motor patterns differed principally in the relative amplitudes and durations of HF and HE bursts. Swim cycles were dominated by large-amplitude, long-duration HE bursts, whereas rostral scratch cycles were dominated by large-amplitude, long-duration HF discharge. Small but significant differences were also observed during the two behaviors in the onset phase of biarticular knee extensor bursts (IT-KE and AM-KE) within each hip cycle. Finally, interactions between swim and scratch motor networks were investigated. Brief activation of the rostral scratch during an ongoing fictive swim episode could insert one or more scratch cycles into the swim motor pattern and permanently reset the burst rhythm. Similarly, brief swim stimulation could interrupt and reset an ongoing fictive rostral scratch. This shows that there are strong central interactions between swim and scratch neural networks and suggests that they may share key neural elements.