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1. It’s elemental, my dear Watson: validating seasonal patterns in otolith chemical chronologies

   https://doi.org/10.1139/cjfas-2020-0388  

   Hüssy, Karin; Krüger-Johnsen, Maria; Thomsen, Tonny Bernt; Heredia, Benjamin Dominguez; Næraa, Tomas; Limburg, Karin E.; Heimbrand, Yvette; McQueen, Kate; Haase, Stefanie; Krumme, Uwe; et al (May 2021, Canadian Journal of Fisheries and Aquatic Sciences)

   null (Ed.)

   Accurate age data are essential for reliable fish stock assessment. Yet many stocks suffer from inconsistencies in age interpretation. A new approach to obtain age makes use of the chemical composition of otoliths. This study validates the periodicity of recurrent patterns in 25 Mg, 31 P, 34 K, 55 Mn, 63 Cu, 64 Zn, 66 Zn, 85 Rb, 88 Sr, 138 Ba, and 208 Pb in Baltic cod (Gadus morhua) otoliths from tag–recapture and known-age samples. Otolith P concentrations showed the highest consistency in seasonality over the years, with minima co-occurring with otolith winter zones in the known-age otoliths and in late winter – early spring when water temperatures are coldest in tagged cod . The timing of minima differs between stocks, occurring around February in western Baltic cod and 1 month later in eastern Baltic cod; seasonal maxima are also stock-specific, occurring in August and October, respectively. The amplitude in P is larger in faster-growing western compared with eastern Baltic cod. Seasonal patterns with minima in winter – late spring were also evident in Mg and Mn, but less consistent over time and fish size than P. Chronological patterns in P, and to a lesser extent Mg and Mn, may have the potential to supplement traditional age estimation or to guide the visual identification of translucent and opaque otolith patterns used in traditional age estimation. 

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2. Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution

   https://doi.org/10.1038/s41598-022-14224-8  

   Pierson, Derek; Lohse, Kathleen A.; Wieder, William R.; Patton, Nicholas R.; Facer, Jeremy; de Graaff, Marie-Anne; Georgiou, Katerina; Seyfried, Mark S.; Flerchinger, Gerald; Will, Ryan (December 2022, Scientific Reports)

   Abstract From hillslope to small catchment scales (< 50 km 2 ), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m 2 ) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics. 

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3. "So, where do you come from?" The impact of assumed spatial population structure on estimates of recruitment

   https://doi.org/https://doi:10.1016/j.fishres.2018.11.030  

   Cadrin, Steven X. (January 2019, Fisheries research)

   Understanding population dynamics is essential for achieving sustainable and productive fisheries. However, estimating recruitment in a stock assessment model involves the challenging task of identifying a self-sustaining population, which often includes representing complex geographic structure. A review of several case studies demonstrated that alternative stock assessment models can influence estimates of recruitment. Incorporating spatial population structure and connectivity into stock assessment models changed the perception of recruit- ment events for a wide diversity of fisheries, but the degree to which estimates were impacted depended on movement rates and relative stock sizes. For multiple population components, estimates of strong recruitment events and the productivity of smaller population units were often more sensitive to connectivity assumptions. Simulation testing, conditioned on these case studies, suggested that accurately accounting for population structure, either in management unit definitions or stock assessment model structure, improved recruitment estimates. An understanding of movement dynamics improved estimation of connected sub-populations. The challenge of representing geographic structure in stock assessment emphasizes the importance of defining self- sustaining management units to justify a unit-stock assumption. 

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4. Rapid approach for assessing an unregulated fishery using a series of data-limited tools

   https://doi.org/10.1002/mcf2.10270  

   Gervasi, Carissa_L; Karnauskas, Mandy; Rios, Adyan; Santos, Rolando_O; James, W_Ryan; Rezek, Ryan_J; Rehage, Jennifer_S (October 2023, Marine and Coastal Fisheries)

   Abstract ObjectiveFisheries provide countless benefits to human populations but face many threats ranging from climate change to overfishing. Despite these threats and an increase in fishing pressure globally, most stocks remain unassessed and data limited. An abundance of data-limited assessment methods exists, but each has different data requirements, caveats, and limitations. Furthermore, developing informative model priors can be difficult when little is known about the stock, and uncertain model parameters could create misleading results about stock status. Our research illustrates an approach for rapidly creating robust initial assessments of unregulated and data-limited fisheries without the need for additional data collection. MethodsOur method uses stakeholder knowledge combined with a series of data-limited tools to identify an appropriate stock assessment method, conduct an assessment, and examine how model uncertainty influences the results. Our approach was applied to the unregulated and data-limited fishery for Crevalle Jack Caranx hippos in Florida. ResultResults suggested a steady increase in exploitation and a decline in stock biomass over time, with the stock currently overfished and undergoing overfishing. These findings highlight a need for management action to prevent continued stock depletion. ConclusionOur approach can help to streamline the initial assessment and management process for unregulated and data-limited stocks and serves as an additional tool for combating the many threats facing global fisheries. 

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5. A Non-Parametric Approach for Setting Safety Stock Levels

   https://doi.org/10.2139/ssrn.3624998  

   Saldanha, John; Price, Brad; Thomas, Douglas J. (June 2020, SSRN Electronic Journal)

   In practice, lead time demand (LTD) can be non-standard: skewed, multi-modal or highly variable; factors that compromise the validity of the classic approaches for setting safety stock levels. Motivated by encountering this problem at our industry partner, we develop an approach for setting safety stock levels using the bootstrap, a widely-used statistical procedure. We extend prior research that has used the bootstrap for quantile estimation to address the multi-parameter estimation of safety stocks. We develop a multivariate central limit theorem for the bootstrap mean and bootstrap quantile -- components of the safety stock calculation -- highlighting why the generalization of these bootstrap methods is critical for inventory management. These results provide a theoretical underpinning for the bootstrap estimator of safety stock and permit the construction of confidence intervals for safety stock estimates, allowing decision makers to understand the reliability with which the desired service level will be achieved. Building on our theoretical results, and supported by numerical experiments, we provide insights on the behavior of the bootstrap for various LTD distributions, which our results demonstrate are critical when employing the bootstrap method. Implementation results with our industry partner indicate our approach is quite effective in setting safety stock levels. 

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