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Home range size and metabolic rate of animals are expected to scale with body mass at similar rates; with home ranges expanding to meet increased metabolic requirements. This expectation has widely been tested using lab-derived estimates of basal metabolic rate as proxies for field energy requirements, however, it is unclear if existing theory aligns with patterns of home range scaling observed in the field. Here, we conduct the first direct field test of the relationship between home range and metabolic rate allometry. Using acoustic telemetry, we simultaneously measured the individual home range size and field metabolic rate of lemon sharks (Negaprion brevirostris) spanning one order of magnitude in body mass. Although scaling rates of field metabolic rate were consistent with standard metabolic rate, home range size scaled at shallower rates than metabolic rates. This is evidence for strong top-down controls on home range scaling rates, likely a result of predation pressure placing constraints on home range expansions. Consequently, direct resource competition can lead to decreased home range scaling rates. We highlight inconsistencies with theory on the effects of population density and competition on home range scaling and propose that the influence of diverse types of competition should be examined.

Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pretraining strategy for a data-deficient domain using coral reef bioacoustics. We assemble ReefSet, a large annotated library of reef sounds, though modest compared to bird libraries at 2% of the sample count. Through testing few-shot transfer learning performance, we observe that pretraining on bird audio provides notably superior generalizability compared to pretraining on ReefSet or unrelated audio alone. However, our key findings show that cross-domain mixing which leverages bird, reef and unrelated audio during pretraining maximizes reef generalizability. SurfPerch, our pretrained network, provides a strong foundation for automated analysis of marine PAM data with minimal annotation and compute costs.