Mesopelagic micronekton refers to organisms between 2 to 20 cm in size that inhabit the mesopelagic zone (200 – 1000 m) of the ocean. Mesopelagic micronekton are an extremely abundant and diverse group which plays a large role in ecosystem functioning and biochemical cycling due to their roles as meditators between oceanic zones and trophic levels. The mesopelagic zone’s extreme conditions and vast size make the scientific study of mesopelagic micronekton complex and expensive, leaving large knowledge gaps in their biology and ecology. Two primary sampling techniques used in the study of micronekton are active acoustics and net trawling, both of which offer distinct strengths and limitations. Trawl data while providing key information about species composition and size information, suffers from limited spatial and temporal resolution, while active acoustic data offers continuous, high-resolution spatial and temporal information but lack direct taxonomic information. Bridging these complementary approaches is a central challenge in answering key questions about mesopelagic micronekton such as estimates of their biomass and their spatial and temporal distributions. One such approach that attempts to combine these two sampling modalities is the “forward method,” which predicts acoustic backscatter from known species composition, size distributions, and abundance using theoretical scattering models. The present study’s aim is to interpret shipboard acoustics at 18, 38, and 70 kHz relative to trawl sampling data collected in the northern Gulf utilizing the forward method. This study utilized active acoustic and MOCNESS net catch data collected during the DEEPEND|RESTORE (Deep Pelagic Nekton Dynamics of the Gulf of Mexico) and DSB (Deep-Sea Benefits) research programs. Theoretical scattering models tuned to organismal traits, such as swim bladder parameters, body shape, and composition simulate expected backscatter. Preliminary results highlight the disproportionally large influence of swim bladdered fishes on modeled acoustic backscatter and highlight the inherent biases and challenges impacting the interpretability of active acoustic and net catch data.