Hector M. Lozano-Montes

Impacts of Fishing Low-Trophic Level Species on Marine Ecosystems

High harvest levels of low–trophic level fishes may have cascading marine ecosystem effects. Low–trophic level species account for more than 30% of global fisheries production and contribute substantially to global food security. We used a range of ecosystem models to explore the effects of fishing low–trophic level species on marine ecosystems, including marine mammals and seabirds, and on other commercially important species. In five well-studied ecosystems, we found that fishing these species at conventional maximum sustainable yield (MSY) levels can have large impacts on other parts of the ecosystem, particularly when they constitute a high proportion of the biomass in the ecosystem or are highly connected in the food web. Halving exploitation rates would result in much lower impacts on marine ecosystems while still achieving 80% of MSY.

EDITOR'S CHOICE: Evaluating marine spatial closures with conflicting fisheries and conservation objectives

Summary Spatial management is used extensively in natural resource management to address sustainability and biodiversity issues, for example through declaration of terrestrial National Parks and marine protected areas (MPAs). Spatial management is used also to optimize yields or protect key parts of the life cycle of species that are utilized (hunted, farmed or fished), for example through rotational harvesting. To evaluate the effectiveness of marine spatial closures with conflicting fisheries and conservation objectives, a series of marine fisheries closures are here analysed using an integrative modelling tool known as management strategy evaluation (MSE). This modelling framework combines a food web model of a tropical ecosystem fished by a prawn (shrimp) fishery that emulates the resource being managed, together with the present management system and risk-based tools of fishing the prawn species at maximum economic yield. A series of spatial closures are designed and tested with the aim of investigating trade-offs among biodiversity (MPA), benthic impacts, ecosystem function, key species at risk to fishing, economic and sustainability objectives. Synthesis and applications. This paper illustrates that existing tools often available in actively managed fisheries can be linked together into an effective management strategy evaluation framework. Spatial closures tended to succeed with respect to their specific design objective, but this benefit did not necessarily flow to other broad-scale objectives. This demonstrates that there is no single management tool which satisfies all objectives, and that a suite of management tools is needed.

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.

Challenges of transferring models of fish abundance between coral reefs

Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1% <R2 < 50.6% for Acanthuridae) compared to total fish abundance (9% <R2 < 18.6%). However, in contrast with previous transferability obtained for similar models for fish species richness from the GBR to NR, transferability for these fish abundance models was poor. When compared with observations of fish abundance collected in NR, our transferability results had low validation scores (R2 < 6%, p > 0.05). High spatio-temporal variability of patterns in fish abundance at the family and population levels in both reef systems likely affected the transferability of these models. Inclusion of additional predictors with potential direct effects on abundance, such as local fishing effort or topographic complexity, may improve transferability of fish abundance models. However, observations of these local-scale predictors are often not available, and might thereby hinder studies on model transferability and its usefulness for conservation planning and management.