Estimating winter losses for managed honey bee (Apis mellifera) colonies is critical for understanding hive productivity and health. This study reports estimates of overwinter colony losses in New Zealand, which has seen exponential growth in the number of managed colonies in recent years. Over 35% of all beekeepers, and 50% of all commercial beekeepers in the country responded to the internet-based 2016 New Zealand Colony Loss Survey, providing detailed information on over 275,000 colonies (over 40% of all registered colonies) that entered winter 2016. Using three different methods, we estimate overall winter losses to be below 10%. However, nearly 29% of beekeepers lost more than 15% of their colonies over winter 2016, and nearly 25% of beekeepers lost more than 20%, indicating considerable skewness. These results are subject to strong regional variation, with the highest losses reported in areas with significant mānuka resources. Similarly, non-commercial beekeepers report substantially higher loss rates than commercial beekeepers. Beekeepers who lost colonies over the winter of 2016 most frequently attributed the cause to colony death, queen problems, or wasps. However, varroa and competition for apiary sites were also identified as important areas of concern. In this analysis, we explore variation in both region and operation size for all five of these challenges. Pé rdidas invernales de colonias de abejas melíferas en Nueva Zelanda en 2016 La estimació n de las pérdidas invernales para las colonias manejadas de abejas melíferas (Apis mellifera) es crítica para comprender la productividad y la salud de las colmenas. Este estudio reporta estimaciones de las pérdidas de colonias de invierno en Nueva Zelanda, en donde se ha visto un crecimiento exponencial en el número de colonias administradas en los últimos años. Más del 35% de todos los apicultores, y el 50% de todos los apicultores comerciales del país respondieron a la Encuesta sobre la Pérdida de Colonias de Nueva Zelanda, realizada por Internet en 2016, la cual proporcionó informació n detallada sobre más de 275,000 colonias (más del 40% de todas las colonias registradas) disponibles en invierno de 2016. Usando tres métodos diferentes, estimamos que las pérdidas totales de invierno están por debajo del 10%. Sin embargo, casi el 29% de los apicultores perdieron más del 15% de sus colonias en el invierno de 2016, y casi el 25% de los apicultores perdieron más del 20%, lo que indica una considerable asimetría. Estos resultados están sujetos a fuertes variaciones regionales, con las mayores pérdidas reportadas en áreas con importantes recursos de manuka. Del mismo modo, los apicultores no comerciales registran pérdidas sustancialmente mayores que los apicultores comerciales. Los apicultores que perdieron colonias durante el invierno de 2016 lo atribuyeron con mayor frecuencia a la muerte de las colonias, a problemas de la reina o las avispas. Sin embargo, la varroa y la competencia por los sitios de colmenares también fueron identificadas como situaciones im...

The feasibility of soya-based diets for pounded American lobster, Homarus americanus, was investigated using diets (40% protein) of low-fat soya-bean meal (SBM) containing various proportions of freeze-dried krill hydrolysate (FDKH) at 0, 12.5, 25, 50, 75, and 100% of dietary protein, in a 60-day trial using stage 5 juveniles. Diets of fresh bluemussel, Mytilus edulis, and a diet of 100% of protein from ®sh meal (FM), approximating the industry diet of ®sh, were included for comparison. Speci®c growth rates (SGR) were not signi®cantly dierent in all krill-containing diets, 100% FM and mussel diets. Juveniles on the mussel diet and the 75 and 100% FDKH diets had signi®cantly higher maximum body weight gains (BWG), longer survivals (days in culture) and shorter moulting cycles. Juveniles deriving 100% of dietary protein from SBM failed to moult and had the lowest SGR and BWG, and the shortest survival period. Carotenoid content (mg kg )1 dry weight), n-3 HUFA pro®les (% of total fatty acids) and arginine (% of total amino acids) in the carcass correlated with dietary pro®les (% of dry diet) and the level of dietary FDKH. Dietary SBM increased carcass 18:2n-6. Soya-bean meal supplemented with FDKH may provide up to 87.5% of dietary protein in arti®cial diets without compromising short-term BWG. KEY WORDS

Core Ideas Procedures were more successful when forage species were distinct hues of green. Overall, predictive accuracy of PowerPoint model was best in Old World bluestem–legume pasture. Only the visual model showed potential in tall wheatgrass–alfalfa pasture. To better manage forage productivity and quality, it is important to accurately and objectively monitor legume content in grass‐legume pastures. Current nondestructive methods are too subjective or labor intensive. Recent research has led to developments in digital image analysis (DIA) and remote sensing techniques useful in pasture research and management. Four nondestructive sampling techniques were compared with traditional botanical hand separations to determine their ability to predict legume content in pastures of tall wheatgrass [Thinopyrum ponticum (Podp.); TW] and alfalfa (Medicago sativa L.): TW‐alfalfa; and in pastures of Old World bluestem [Bothriochloa bladhii (Retz) S.T. Blake; OWB], alfalfa, and yellow sweetclover (Melilotus officinalis L.; YSC): OWB‐legume. Each sampling procedure better estimated and predicted legume content in OWB‐legume pasture (R2cal = 0.40 to 0.78; R2pred = 0.40 to 0.78) than in TW‐alfalfa (R2cal = 0.02 to 0.61; R2pred = 0.08 to 0.61). Overall, the best predictive accuracy was obtained with the PowerPoint model in OWB‐legume pasture. Only the visual model showed potential in TW‐alfalfa because these two species were difficult to distinguish on‐screen by DIA methods or using spectral thresholds. The use of nondestructive sampling techniques to measure legume content is promising for mixed pastures when the grass and legume components are distinct hues of green. Although the need for more research is recognized, the ultimate goal is to apply these findings to automated scanners that offer producers rapid measurements of legume content in mixed pastures.

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