Biology, Fishery, Conservation and Management of Indian Ocean Tuna Fisheries

The focus of the study is to explore the recent trend of the world tuna fishery with special reference to the Indian Ocean tuna fisheries and its conservation and sustainable management. In the Indian Ocean, tuna catches have increased rapidly from about 179959 t in 1980 to about 832246 t in 1995. They have continued to increase up to 2005; the catch that year was 1201465 t, forming about 26% of the world catch. Since 2006 onwards there has been a decline in the volume of catches and in 2008 the catch was only 913625 t. The Principal species caught in the Indian Ocean are skipjack and yellowfin. Western Indian Ocean contributed 78.2% and eastern Indian Ocean 21.8% of the total tuna production from the Indian Ocean. The Indian Ocean stock is currently overfished and IOTC has made some recommendations for management regulations aimed at sustaining the tuna stock. Fishing operations can cause ecological impacts of different types: by catches, damage of the habitat, mortalities caused by lost or discarded gear, pollution, generation of marine debris, etc. Periodic reassessment of the tuna potential is also required with adequate inputs from exploratory surveys as well as commercial landings and this may prevent any unsustainable trends in the development of the tuna fishing industry in the Indian Ocean

Defining the Effect of the 16p11.2 Duplication on Cognition, Behavior, and Medical Comorbidities.

IMPORTANCE: The 16p11.2 BP4-BP5 duplication is the copy number variant most frequently associated with autism spectrum disorder (ASD), schizophrenia, and comorbidities such as decreased body mass index (BMI). OBJECTIVES: To characterize the effects of the 16p11.2 duplication on cognitive, behavioral, medical, and anthropometric traits and to understand the specificity of these effects by systematically comparing results in duplication carriers and reciprocal deletion carriers, who are also at risk for ASD. DESIGN, SETTING, AND PARTICIPANTS: This international cohort study of 1006 study participants compared 270 duplication carriers with their 102 intrafamilial control individuals, 390 reciprocal deletion carriers, and 244 deletion controls from European and North American cohorts. Data were collected from August 1, 2010, to May 31, 2015 and analyzed from January 1 to August 14, 2015. Linear mixed models were used to estimate the effect of the duplication and deletion on clinical traits by comparison with noncarrier relatives. MAIN OUTCOMES AND MEASURES: Findings on the Full-Scale IQ (FSIQ), Nonverbal IQ, and Verbal IQ; the presence of ASD or other DSM-IV diagnoses; BMI; head circumference; and medical data. RESULTS: Among the 1006 study participants, the duplication was associated with a mean FSIQ score that was lower by 26.3 points between proband carriers and noncarrier relatives and a lower mean FSIQ score (16.2-11.4 points) in nonproband carriers. The mean overall effect of the deletion was similar (-22.1 points; P < .001). However, broad variation in FSIQ was found, with a 19.4- and 2.0-fold increase in the proportion of FSIQ scores that were very low (≤40) and higher than the mean (>100) compared with the deletion group (P < .001). Parental FSIQ predicted part of this variation (approximately 36.0% in hereditary probands). Although the frequency of ASD was similar in deletion and duplication proband carriers (16.0% and 20.0%, respectively), the FSIQ was significantly lower (by 26.3 points) in the duplication probands with ASD. There also were lower head circumference and BMI measurements among duplication carriers, which is consistent with the findings of previous studies. CONCLUSIONS AND RELEVANCE: The mean effect of the duplication on cognition is similar to that of the reciprocal deletion, but the variance in the duplication is significantly higher, with severe and mild subgroups not observed with the deletion. These results suggest that additional genetic and familial factors contribute to this variability. Additional studies will be necessary to characterize the predictors of cognitive deficits

Can trait-based analyses of changes in species distribution be transferred to new geographic areas?

Aim: Anthropogenic environmental change is having a major impact on biodiversity. By identifying traits that correlate with changes in species range, comparative studies can shed light on the mechanisms driving this change; but such studies will be more useful for conservation if they have true predictive power, i.e. if their trait-based models can be transferred to new regions. We aim to examine the ability of trait-based models to predict changes in plant distribution across seven geographic regions that varied in terms of land cover and species composition. Location: Britain and Flanders (Belgium). Methods: We estimated distribution change for more than 1000 species for over 70 years of data (1930s to 2004), using data from published plant atlases. We identified regional trait-based models of plant distribution change. Traits included morphological characteristics, Ellenberg values and distribution-based traits. The trait models were then used to predict change in all other regions, with the level of linear correlation between predicted and observed changes in range used as a measure of transferability. We then related transferability to land cover and species similarity between regions. Results: We found that trait correlates of range change varied regionally, highlighting the regional variation in the drivers of range change in plants. These trait models also varied in the amount of variation explained, with r2 values ranging from 0.05 to 0.17. A key cross-regional difference was the variation in the relationship between soil nutrient association (Ellenberg N) and distribution change, which was strongly positive in Flanders and southern England but significantly negative in northern Scotland. We found that transferability between regions was significantly correlated with the level of similarity in land cover. Main conclusions: We conclude that trait-based models can predict broad-scale changes in species distributions in regions that share similar land-cover composition; however, predictions between regions with differing land-cover cover tend to be poor