Browsing by Author "Donovan, Carl R."

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    A pilot tagging program on southern rays bream (Brama australis): methodology and preliminary recaptures
    (2023) Wiff, Rodrigo; Flores, Andres; Gacitua, Santiago; Donovan, Carl R.; Canales, T. Mariella; Ahumada, Mauricio; Queirolo, Dante
    The southern rays bream (Brama australis) is a highly migratory, epi-mesopelagic species supporting an important artisanal fishery off central-southern Chile. Despite its importance, several questions exist about this species's demography and migratory routes. The first step in understanding the migratory behavior of B. australis is to test the feasibility of a conventional tagging program, a standard mark-recapture method, to infer migration in fish. Between February 2020 and December 2021, conventional tagging was conducted during 21 fishing trips on board artisanal vessels off Lebu harbor ( Biobio Region, Chile) using gillnets, longlines, and handlines. Three thousand nine hundred forty-six individuals of B. australis between 30 and 55 cm fork length were tagged using external T-anchor bar labels (commonly known as "spaghetti"). Approximately 100 and 200 fish were tagged per fishing trip using longlines and gillnets, respectively. The size distribution of the tagged individuals was consistent with those retained in the catch, with 90% of tagged fish being longer than the fork length at 50% maturity. Eight tags have been recovered off the coast of Lebu up to May 2022. With times at liberty between 50 and 537 days. These preliminary recaptures are also analyzed in the context of the conceptual model for demography and migration proposed for this species in Chile. The main conclusion of this research is that a conventional tagging program is feasible for B. australis in Chile.
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    Applying machine learning to predict reproductive condition in fish
    (2024) Flores, Andres; Wiff, Rodrigo; Donovan, Carl R.; Galvez, Patricio
    Knowledge of reproductive traits in exploited marine populations is crucial for their management and conservation. The maturity status in fish is usually assigned by traditional methods such as macroscopy and histology. Macroscopic analysis is the assessing of maturity stages by naked eye and usually introduces large amount of error. In contrast, histology is the most accurate method for maturity staging but is expensive and unavailable for many stocks worldwide. Here, we use the Random Forest (RF) machine learning method for classification of reproductive condition in fish, using the extensive data from Chilean hake (Merluccius gayi gayi). Gonads randomly collected from commercial industrial and acoustic surveys were classified as immature, mature-active and mature-inactive. A classifier for these three maturity classes was fitted using RFs, with the continuous covariates total length (TL), gonadosomatic index (GSI), condition factor (Krel), latitude, longitude, and depth, along with month as a factor variable. The RF model showed high accuracy (>82%) and high proportion of agreement (>71%) compared to histology, with an OOB error rate lower than 15%. GSI and TL were the most important variables for predicting the reproductive condition in Chilean hake, and to lesser extent, depth when using survey data. The application of the RF shows a promising tool for assigning maturity stages in fishes when covariates are available, and also to improve the accuracy of maturity classification when only macroscopic staging is available.
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    Biphasic growth modelling in elasmobranchs based on asymmetric and heavy-tailed errors
    (2021) Contreras-Reyes, Javier E.; Wiff, Rodrigo; Soto, Javier; Donovan, Carl R.; Araya, Miguel
    Growth in fishes is usually modelled by a function encapsulating a common growth mechanism across ages. However, several theoretical works suggest growth may comprise two distinct mechanistic phases arising from changes in reproductive investment, diet, or habitat. These models are termed two-state or biphasic, where acceleration in growth typically changes around some transition age. Such biphasic models have already been successfully applied in elasmobranch species, where such transitions are detectable from length-at-age data alone, but where estimation has assumed normally distributed errors, which is inappropriate for such slow-growing and long-lived fishes. Using recent advances in growth parameter estimation, we implement a biphasic growth model with asymmetric and heavy-tailed errors. We use data from six datasets, encompassing four species of elasmobranchs, to compare the performance of the von Bertalanffy and biphasic models under normal, skew-normal, and Student-t error distributions. Conditional expectation maximization estimation proves both effective and efficient in this context. Most datasets analysed here supported asymmetric and heavy-tailed errors and biphasic growth, producing parameter estimates different from previous studies.
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    Sensitivity of the stock assessment for the Antarctic krill fishery to time-varying natural and fishing mortality
    (2024) Johannessen, Elling Deehr; Krafft, Bjorn A.; Donovan, Carl R.; Wiff, Rodrigo; Caneco, Bruno; Lowther, Andrew
    The stock assessment model for the Antarctic krill fishery is a population model operating on daily timesteps, which permits modeling within-year patterns of some population dynamics. We explored the effects of including within-year patterns in natural and fishing mortality on catch limits of krill, by incorporating temporal presence of key predator species and contemporary temporal trends of the fishing fleet. We found that inclusion of within-year variation in natural and fishing mortalities increased catch limits. Fishing mortality had a greater effect than natural mortality despite differences in top-down predation on krill, and potentially increased catch limits by 24% compared to the baseline model. Additionally, the stock assessment model allowed a higher catch limit when fishing was during peak summer months than autumn. Number of days with active fishing was negatively related to precautionary catch limits. Future stock assessments should incorporate contemporary spatiotemporal fishing trends and consider implementing additional ecosystem components into the model.