Ana Lara-Lopez

A Call for Deep-Ocean Stewardship

The precautionary approach and collaborative governance must balance deep-ocean use and protection. Covering more than half the planet, the deep ocean sequesters atmospheric CO2 and recycles major nutrients; is predicted to hold millions of yet-to-be-described species; and stores mind-boggling quantities of untapped energy resources, precious metals, and minerals (1). It is an immense, remote biome, critical to the health of the planet and human well-being. The deep ocean (defined here as below a typical continental shelf break, >200 m) faces mounting challenges as technological advances—including robotics, imaging, and structural engineering—greatly improve access. We recommend a move from a frontier mentality of exploitation and single-sector management to a precautionary system that balances use of living marine resources, energy, and minerals from the deep ocean with maintenance of a productive and healthy marine environment, while improving knowledge and collaboration.

Modeling what we sample and sampling what we model: challenges for zooplankton model assessment

Zooplankton are the intermediate trophic level between phytoplankton and fish, and are an important component of carbon and nutrient cycles, accounting for a large proportion of the energy transfer to pelagic fishes and the deep ocean. Given zooplankton’s importance, models need to adequately represent zooplankton dynamics. A major obstacle, though, is the lack of model assessment. Here we try and stimulate the assessment of zooplankton in models by filling three gaps. The first is that many zooplankton observationalists are unfamiliar with the biogeochemical, ecosystem, and size-based and individual-based models that have zooplankton functional groups, so we describe their primary uses and how each typically represents zooplankton. The second gap is that many modelers are unaware of the zooplankton data that are available, and are unaccustomed to the different zooplankton sampling systems, so we describe the main sampling platforms and discuss their strengths and weaknesses for model assessment. Filling these gaps in our understanding of models and observations provides the necessary context to address the last gap – a blueprint for model assessment of zooplankton. We detail two ways that zooplankton biomass/abundance observations can be used to assess models: data wrangling that transforms observations to be more similar to model output; and observation models that transform model outputs to be more like observations. We hope that this review will encourage greater assessment of zooplankton in models and ultimately improve the representation of their dynamics.

A database of marine larval fish assemblages in Australian temperate and subtropical waters

Larval fishes are a useful metric of marine ecosystem state and change, as well as species-specific patterns in phenology. The high level of taxonomic expertise required to identify larval fishes to species level, and the considerable effort required to collect samples, make these data very valuable. Here we collate 3178 samples of larval fish assemblages, from 12 research projects from 1983-present, from temperate and subtropical Australian pelagic waters. This forms a benchmark for the larval fish assemblage for the region, and includes recent monitoring of larval fishes at coastal oceanographic reference stations. Comparing larval fishes among projects can be problematic due to differences in taxonomic resolution, and identifying all taxa to species is challenging, so this study reports a standard taxonomic resolution (of 218 taxa) for this region to help guide future research. This larval fish database serves as a data repository for surveys of larval fish assemblages in the region, and can contribute to analysis of climate-driven changes in the location and timing of the spawning of marine fishes.

The use and efficacy of acoustic survey methods as applied to a mixed aquatic community: The deep scattering layer of the California Current.

Concurrent acoustic and trawl data were collected in 2008 near Point Conception to compare the abundance estimates of mesopelagic micronekton obtained with these two methods. Acoustic data were obtained with a calibrated EK‐60 equipped with four frequencies (38, 70, 120, and 200 kHz). Animals were collected with a midwater trawl. Target strength (TS) models were created of the animals collected by the trawl, and animals were assigned to acoustic groups based on the frequency spectrum of their modeled TS. The mean spectra of the acoustic groups were used with non‐negative least squares inverse methods to estimate abundance from the multi‐frequency volume scattering. The resulting acoustic abundances of each group were compared to the trawl catches, and capture efficiency of the net was estimated. The accuracy of the inverse method was tested with a Monte Carlo analysis of an artificial echogram. The accuracy of the forward modeling was tested via comparison to single targets recorded by the EK‐60. [Work su...