Idades e crescimento da cioba, Ocyururs chrysurus, da Costa Central do Brasil

Araújo, Júlio Neves de; Martins, Agnaldo Silva; Costa, Karla Gonçalves da

doi:10.1590/s1679-87592002000100004

Cited by 6 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…material 1) belong to nine families common in several pelagic, reef and estuary regions of the eastern coast of Brazil (e.g. Araújo et al 2002, Costa et al 2003, Tubino et al 2007, Rangely et al 2010, Carneiro and Salles 2011. However, the communities were dominated by few species (n = 20) that were both abundant and frequent as observed in other marine areas (e.g.…”

Section: Discussionmentioning

confidence: 99%

What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages?

Souza¹,

Batista²,

Fabré³

2018

Zoologia

View full text Add to dashboard Cite

ABSTRACT. Seasonal ecological effects caused by temperature and photoperiod are typically considered minimal in the tropics.Nevertheless, annual climate cycles may still influence the distribution and abundance of tropical species. Here, we investigate whether seasonal patterns of precipitation and wind speed influence the structure of coastal fish assemblages and fishing yields in northeast Brazil. Research trips were conducted during the rainy and dry seasons using commercial boats and gear to sample the fish community. Diversity was analyzed using abundance Whittaker curves, diversity profiles and the Shannon index. Principal Component Analysis (PCA) was used to analyze associations between the abundance of species and various environmental variables related to seasonality. A total of 2,373 fish were collected, representing 73 species from 34 families -20 of which were classified as both frequent and abundant. Species richness was greater and more equitable during the rainy season than the dry season -driven by changes in the precipitation rather than to wind speed. Species diversity profiles were slightly greater during the rainy season than the dry season, but this difference was not statistically significant. Using PCA was identified three groups of species: the first associated with wind speed, the second with precipitation, and the third with a wide range of sampling environments. This latter group was the largest and most ecologically heterogeneous. We conclude that tropical coastal fish assemblages are largely influenced by local variables, and seasonally mediated by annual changes related to precipitation intensity and wind speed, which in turn influences fishery yields.

show abstract

Section: Discussionmentioning

confidence: 99%

What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages?

Souza¹,

Batista²,

Fabré³

2018

Zoologia

View full text Add to dashboard Cite

show abstract

“…A first step towards a sustainable exploitation is to provide important basic information such as species growth. Although several studies have been published on Yellowtail Snapper growth using different methods (Dennis, 1991;Mexicano-Cíntora 1999;Allman et al, 2005;Araújo et al, 2002), they rarely provide a measure of parameter uncertainty. Note that the value of a model-based analysis lies not simply in its abi-lity to generate a precise point estimate for a specific outcome, but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome (Briggs et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, consideration of uncertainty is essential when management decisions are made, and it is particularly important when applying the precautionary approach to fisheries management (Maunder et al, 2006). Previous studies (Dennis, 1991;Mexicano-Cíntora, 1999;Allman et al, 2005;Araújo et al, 2002;Torres & Chávez, 1987) assumed by default that the von Bertalanffy model describes Yellowtail Snapper growth adequately; model uncertainty is rarely taken in account; therefore they could be making a modeling error (Punt & Hilborn, 2001) neglecting the understanding of the dynamic studied. In this paper, we try to improve the methodology used previously by providing two methods: maximum likelihood estimates and Bayesian methods for parameter and uncertainty estimation.…”

Section: Discussionmentioning

confidence: 99%

Model uncertainty and Bayesian estimation of growth parameters of Yellowtail Snapper (Ocyurus chrysurus) from Veracruz, Mexico

2018

View full text Add to dashboard Cite

Background. Most growth analyses of Yellowtail Snapper neglect consideration of model and parameter uncertainty. Goals. In this paper, we explore model uncertainty using three models (von Bertalanffy, logistic, and Gompertz) as well as the Akaike criterion for model selection. We also estimate growth parameters and its uncertainty using the maximum likelihood estimation approach (under different assumptions of error variance) and Bayesian methods. Methods. Models were fitted to length-at-age data from organisms caught in Antón Lizardo, Veracruz. Regarding the Bayesian methods, a prior distribution for the asymptotic length was built based on data gathered from literature. We used Monte Carlo Markov Chains (MCMC) methods to fit the logistic model. Results. The Akaike criterion results suggest that the logistic model provided the best fit for the observed data (lowest AIC = 31.4). Parameter estimates included asymptotic length (L ∞ = 64.9 ± 5.43), growth rate (K = 0.49 ± 0.07), and age at the curve inflection point (I = 3.28 ± 0.42). Regarding the Bayesian analysis, MCMC simulations suggest that the most probable value for the asymptotic length was 64.3 cm with an interval of 95% probability (58.7,70.1). The most probable value for the growth rate was 0.48 with an interval of 95% probability (0.42, 0.55). Last, the most probable value for the age at the curve inflection point was 1.7 years with a range of 95% probability (1.31, 2.16). Conclusions. The maximum likelihood estimation (MLE) and the Bayesian framework should be considered basic statistical techniques in the evaluation of individual growth of the species of interest, as they provide a robust analysis of available information of the species and the opportunity to incorporate such analysis to sustainable management practices.

show abstract

“…Observed longevity for L. alexandrei was 22 years; most congeners of lutjanidae that reach larger sizes and also experience ontogenetic migration show better longevity: L. griseus 24 years (Burton, 2001), L. analis 29 years (Burton, 2002), and L. jocu 25 years (Rezende and Ferreira, 2004). However, this pattern was not observed for L. synagris 19 years (Luckhurst et al, 2000), L. griseus in southern Florida 15 years (Burton, 2001), O. chrysurus southern Florida 14 years (Johnson, 1983), and O. chrysurus northeastern Brazil 19 years (Ara ujo et al, 2002), possibly related to fishing pressure. Fisheries have an important impact on longevity: usually when fishing pressure is high, longevity and size tend to diminish among fish populations, especially for target species (Ricker, 1975).…”

Section: Discussionmentioning

confidence: 99%

Age, growth and mortality of Lutjanus alexandrei in estuarine and coastal waters of the tropical south-western Atlantic

Aschenbrenner

Ferreira

2014

J. Appl. Ichthyol.

View full text Add to dashboard Cite

Summary Otolith‐based methods were used to determine life history traits of the endemic Brazilian snapper (Lutjanus alexandrei) in estuarine and coastal environments in the south‐western Atlantic. Fishes were caught as juveniles inside mangrove‐bordered estuaries by traditional corral fisheries whereas adults were captured at sea using motorboats with trap and gill nets. Fish were sampled during landings and 331 otolith pairs were extracted from L. alexandrei. Inshore mangroves comprised individuals of 0–4 years (mean: 2 years), while individuals in deeper reef environments were older (range: 3–22; mean: 8 years), indicating an ontogenetic shift at approximately age 3 or 4. Edge analysis was used to validate the annual deposition in the otoliths, suggesting that opaque growth rings were formed between April and September. Age‐at‐length data were used to predict L. alexandrei growth rates using the von Bertalanffy growth model from where the parameters were calculated: L∞ = 31 cm, k = 0.24, t0 = −1.26, r2 = 0.97. Mortality rates were estimated for coastal habitats, with Z = 0.22 and S = 0.78 year−1, based on ages 7–17. Additionally, evidence of ontogenetic migration is provided by age and size structure.

show abstract

Idades e crescimento da cioba, Ocyururs chrysurus, da Costa Central do Brasil

Cited by 6 publications

References 9 publications

What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages?

What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages?

Model uncertainty and Bayesian estimation of growth parameters of Yellowtail Snapper (Ocyurus chrysurus) from Veracruz, Mexico

Age, growth and mortality of Lutjanus alexandrei in estuarine and coastal waters of the tropical south-western Atlantic

Contact Info

Product

Resources

About