Effect of fishing tactics on the standardization of cardinalfish (Epigonus crassicaudus) catch rates in the demersal multispecies fishery off central Chile

We analyzed the allocation of effective fishing effort and the standardization of cardinalfish (Epigonus crassicaudus) catch rates in the multispecies demersal trawl fishery off central Chile. The period analyzed covered from 1997 to 2004 and included detailed information about fishing hauls. Each haul that contained cardinalfish was assigned into a particular fishing tactic (cluster) by using multivariate analysis of their catch composition. The catch rate standardization was carried out by generalized linear models (GLM). Three fishing tactics were discovered: the first directed effort at cardinalfish, the second at common hake (Merluccius gayi gayi), and the third at Patagonian grenadier (Macruronus magellanicus). Fishing tactic was used as an explanatory variable in the proposed GLM. The fishing tactic effect was one of the most important factors in explaining the variance in the GLM. These results are discussed in the context of how the assignation of a fishing tactic allows unbiased abundance indices to be obtained in this kind of multispecies demersal fishery.

Role of the preferred habitat availability for small shark (

Description of habitat preferences in marine fishes is important in order to understand their spatial distribution and ecology, and are one of the first steps towards conservation. In this paper, we evaluate the influence of environmental conditions (temperature, salinity), location (latitude-longitude, depth), time (year) and availability of preferred habitat on the relative biomass of the narrownose smooth-hound shark (Mustelus schmitti) in El Rincón (~38°–41°S < 50 m), Argentina. We used an extensive database of bottom trawl surveys conducted yearly during southern spring (November–December) between 1994 and 2012, containing 502 sampling stations where relative biomass, environmental variables and location are registered. Relative biomass was modeled using Generalized Additive Models (GAM) in which zeros observations were incorporated using a Tweedie distribution, and model selection was carried out using generalized cross validation values (CGV) and Akaike information criterion (AIC). The best models selected indicate that a combination of location (nearshore areas), depth (<30 m) and salinity (≤ 33.5) was significant in explaining relative biomass across time. In addition, the percentage of preferred habitat by M. schmitti, was also a significant predictor for relative biomass and was correlated to the main freshwater discharge previous to the fisheries survey. Discussion focused on understanding the spatial ecology of this species. We highlighted how environmental variables become a key issue to understand biomass indices derived from fishery-independent surveys

Applying machine learning to predict reproductive condition in fish

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 (&gt;82%) and high proportion of agreement (&gt;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.</p

Applying machine learning to predict reproductive condition in fish

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 (&gt;82%) and high proportion of agreement (&gt;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.</p

Growth estimates of young‐of‐the‐year broadnose sevengill shark, Notorynchus cepedianus , a top predator with poorly calcified vertebrae

The broadnose sevengill shark, Notorynchus cepedianus (Péron, 1807), is a large marine top predator in temperate coastal ecosystems. Some aspects of its life history have been determined, but its growth pattern is yet to be fully understood. The authors used a multi-modelling approach and a sensitivity test to estimate growth parameters from young-of-year (YOY) length data collected off San Antonio Cape (SAC), Argentina, a critical habitat in the Southwest Atlantic Coastal Zone (SACZ). The best selected model, a sex-combined logistic growth model, estimated an asymptotic length (L∞) of 92.58 cm TL (95% C.I.: 86.48–105.89 cm), a growth coefficient (K) of 0.006818 days −1 (95% C.I.: 0.004948–0.008777) and a size at birth (L0) of 40.73 cm. The predicted annual growth (i.e., L1 – L0) was 43.2 cm TL. Males had smaller L0, higher K and achieved larger sizes after 1 year. The YOY in SAC attained a larger L1 and grew faster than their Australian and South African wild counterparts. The consistent year-round presence of YOY in the SAC highlights the importance of this area as a pupping ground and potential nursery for N. cepedianus; this has direct implications for the allocation of research and management effort for the conservation of this species in the Southwest Atlantic

Biphasic growth modelling in elasmobranchs based on asymmetric and heavy-tailed errors

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

A pilot tagging program on southern rays bream (Brama australis):methodology and preliminary recaptures

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 (Biobío 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.</p

A pilot tagging program on southern rays bream (Brama australis):methodology and preliminary recaptures

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 (Biobío 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.</p