Coupling state‐of‐the‐art modelling tools for better informed Red List assessments of marine fishes

Journal of Applied Ecology, Journal Year: 2024, Volume and Issue: 61(4), P. 647 - 657

Published: Feb. 14, 2024

Abstract In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk guide conservation efforts. The International Union for Conservation Nature (IUCN)'s Red List considered global standard evaluating risks. IUCN criteria also inform national assessments. Bayesian models, including state‐of‐the‐art JARA (‘Just Another Assessment’) tool, deliver probabilistic statements about species falling into categories, thereby enabling characterisation and communication uncertainty in We coupled VAST (‘Vector Autoregressive Spatio‐Temporal’) modelling tool JARA, better informed assessments marine fishes. this framework, fitted scientific survey catch rate data provide indices whose propagated outcomes suggesting categories (under population reduction criterion). addition, delivers a valuable habitat assessment understand what may be driving study region. Here, we demonstrate VAST‐JARA framework by applying five contrasting North Sea species, with or without quantitative stock different statuses according latest application previous studies suggest that, among three elasmobranchs, starry ray most need urgent research (and actions where appropriate), followed spurdog, while lesser‐spotted dogfish increasing biomass. Moreover, both indicate European plaice not concern, cod has likely met being listed as Endangered recently. Synthesis applications . predictions output assessment, constitute supporting information make interpretations based on guidelines, which will help decision‐makers their next assessment. foresee assist numerous fishes worldwide. Our many potential advantageous uses, informing resource management climate change impacts

Language: Английский

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Impacts of different types of data integration on the predictions of spatio-temporal models: A fishery application and simulation experiment

Fisheries Research, Journal Year: 2025, Volume and Issue: 284, P. 107321 - 107321

Published: March 11, 2025

Language: Английский

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Identifying the spatio-temporal distribution patterns of mixed fisheries to inform multispecies management in the Yellow and Bohai Seas

Fisheries Research, Journal Year: 2025, Volume and Issue: 285, P. 107351 - 107351

Published: April 3, 2025

Language: Английский

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Comparing fishery-independent and fishery-dependent data to characterize West Coast groundfish populations

Marine and Coastal Fisheries, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 1, 2025

ABSTRACT Objective Fisheries-independent data from the California Collaborative Fisheries Research Program (CCFRP), a long-term marine protected area (MPA) and fishery monitoring study, have been incorporated as source into stock assessments of U.S. West Coast rockfish Sebastes spp. conducted in past 5 years. We investigate specific population information that is being gained this fisheries-independent compared to fisheries-dependent onboard observer for three representative species. Methods used length distributions abundance indices Blue Rockfish S. mystinus, Vermilion miniatus, Gopher carnatus characterize differences provided by each source. evaluated what new was using inside MPAs, whether an objectively designed sampling method gathered different about than method, could be applied across breadth species’ depth distribution. Results found species dependent. exhibited similar size structure trends abundance, showing high congruency between CCFRP (fisheries-independent) Observer (fisheries-dependent) data. In contrast, rockfishes demonstrated differing MPAs areas open recreational fishing methods, indicating are introducing juveniles small adults both nearshore waters large adult MPAs. Conclusions Our results demonstrate sources may augment sets. Understanding bring analyses assists with development uses best practices management analyses, leading better informed thus conservation populations.

Language: Английский

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Applications of species distribution modeling and future needs to support marine resource management

ICES Journal of Marine Science, Journal Year: 2025, Volume and Issue: 82(3)

Published: Feb. 25, 2025

Abstract Fisheries science agencies are responsible for informing fisheries management and ocean planning worldwide, often requiring scientific analysis actions across multiple spatial scales. For example, catch limits typically defined annually over regional scales, fishery bycatch rules at fine scales on daily to annual time aquaculture energy lease areas decades subregional permitting intermediate Similarly, these activities require synthesizing monitoring data mechanistic knowledge operating different resolutions domains. These needs drive a growing role models that predict animal presence or densities including daily, seasonal, interannual variation, called species distribution/density (SDMs). SDMs can inform many needs; however, their development usage haphazard. In this paper we discuss various ways have been used in stock, habitat, protected species, ecosystem as well marine planning, survey optimization, an interface with climate models. We conclude discussion of future directions, focusing information current development, highlight avenues furthering the community practice around SDM use.

Language: Английский

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Integrating counts from rigorous surveys and participatory science to better understand spatiotemporal variation in population processes

Methods in Ecology and Evolution, Journal Year: 2024, Volume and Issue: 15(8), P. 1380 - 1393

Published: June 18, 2024

Abstract Knowledge of variation in population processes (e.g. growth) across broad spatiotemporal scales is fundamental to ecology and critical for conservation decision‐making. Count data from rigorous surveys with probabilistic sampling design distance information) can inform but are often limited space time. Participatory science cover broader extents prone bias due no lack information, hindering their capability informing processes. Here, we developed an integrated dynamic N‐mixture model that jointly analyses survey participatory growth at extents. The contains a flexible scaling parameter allows fixed random effects account biases errors data. We conducted simulations evaluate the inference performance this range spatial temporal overlap between also case study Baird's Sparrow ( Centronyx bairdii ), species concern, illustrate application Integrated Monitoring Bird Conservation Regions programme North American Breeding Survey eBird Simulations showed improved precision without biasing estimates, comparison informed by alone. further demonstrated utility quantifying range‐wide, long‐term environmental drivers despite extent In particular, found rate peaked under medium temperature, which were only apparent model. useful understanding wildlife count structure model, particular parameter, makes it highly adaptable ecological systems procedures. These properties make modelling approach relevant both practice.

Language: Английский

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The challenges of modelling and assessing fisheries resources

ICES Journal of Marine Science, Journal Year: 2023, Volume and Issue: 80(10), P. 2563 - 2566

Published: Nov. 14, 2023

Abstract Many years after the publication of Beverton and Holt’s on Dynamics Exploited Fish Populations in 1957, many problems that they highlighted remain unsolved. For example, accurately estimating critical biological fishing processes such as growth, natural mortality, recruitment, selectivity continues to be challenging. Despite lack progress some areas, there have been advances our understanding other areas fisheries modelling analysis, including development integrative studies marine resources for application an ecosystem-based management. In this context, ICES Journal Marine Science solicited contributions themed article set (TS) “Advances challenges assessment fishery resources”. Here, we summarize included TS provide examples emerging tools are able account different issues a stocks’ assessments integrated analyses.

Language: Английский

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