Effects of dopamine D2/D3 receptor antagonism on human planning and spatial working memory

Psychopharmacological studies in humans suggest important roles for dopamine (DA) D2 receptors in human executive functions, such as cognitive planning and spatial working memory (SWM). However, studies that investigate an impairment of such functions using the selective DA D2/3 receptor antagonist sulpiride have yielded inconsistent results, perhaps because relatively low doses were used. We believe we report for the first time, the effects of a higher (800 mg p.o.) single dose of sulpiride as well as of genetic variation in the DA receptor D2 gene (DA receptor D2 Taq1A polymorphism), on planning and working memory. With 78 healthy male volunteers, we apply a between-groups, placebo-controlled design. We measure outcomes in the difficult versions of the Cambridge Neuropsychological Test Automated Battery One-Touch Stockings of Cambridge and the self-ordered SWM task. Volunteers in the sulpiride group showed significant impairments in planning accuracy and, for the more difficult problems, in SWM. Sulpiride administration speeded response latencies in the planning task on the most difficult problems. Volunteers with at least one copy of the minor allele (A1+) of the DA receptor D2 Taq1A polymorphism showed better SWM capacity, regardless of whether they received sulpiride or placebo. There were no effects on blood pressure, heart rate or subjective sedation. In sum, a higher single dose of sulpiride impairs SWM and executive planning functions, in a manner independent of the DA receptor D2 Taq1A polymorphism.This research work was funded by a Core Award from the Medical Research Council and the Wellcome Trust to the Behavioural and Clinical Neuroscience Institute (MRC Ref G1000183; WT Ref 093875/Z/10/Z). Also supported by a Wellcome Trust Senior Investigator Award (104631/Z/14/Z) awarded to TWR. CE was supported by the Swiss National Science Foundation (PA00P1_134135) and the Vienna Science and Technology Fund (WWTF VRG13-007)

Fussy Feeders: Phyllosoma Larvae of the Western Rocklobster (Panulirus cygnus) Demonstrate Prey Preference

The Western Rocklobster (Panulirus cygnus) is the most valuable single species fishery in Australia and the largest single country spiny lobster fishery in the world. In recent years a well-known relationship between oceanographic conditions and lobster recruitment has become uncoupled, with significantly lower recruitment than expected, generating interest in the factors influencing survival and development of the planktonic larval stages. The nutritional requirements and wild prey of the planktotrophic larval stage (phyllosoma) of P. cygnus were previously unknown, hampering both management and aquaculture efforts for this species. Ship-board feeding trials of wild-caught mid-late stage P. cygnus phyllosoma in the eastern Indian Ocean, off the coast of Western Australia, were conducted in July 2010 and August-September 2011. In a series of experiments, phyllosoma were fed single and mixed species diets of relatively abundant potential prey items (chaetognaths, salps, and krill). Chaetognaths were consumed in 2–8 times higher numbers than the other prey, and the rate of consumption of chaetognaths increased with increasing concentration of prey. The highly variable lipid content of the phyllosoma, and the fatty acid profiles of the phyllosoma and chaetognaths, indicated they were from an oligotrophic oceanic food chain where food resources for macrozooplankton were likely to be constrained. Phyllosoma fed chaetognaths over 6 days showed significant changes in some fatty acids and tended to accumulate lipid, indicating an improvement in overall nutritional condition. The discovery of a preferred prey for P. cygnus will provide a basis for future oceanographic, management and aquaculture research for this economically and ecologically valuable species

Models of marine fish biodiversity : assessing predictors from three habitat classification schemes

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modeling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modeling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability