No abstract

Got “Big Data”? Not sure how best to use it? Big Data is becoming an important facet of aquatic ecology, and researchers must learn to harness it to reap the rewards of using it. The benefits of using Big Data are many, and include advancements in scientific understanding at larger scales and higher resolution, applications to improving environmental management and policy, and public engagement. We aim to demystify the use of Big Data for individual scientists, and provide some food for thought for the aquatic ecology community on how to develop this sphere. To achieve this, we highlight six key challenges: (1) how to recognize if you have Big Data, (2) handling Big Data, (3) issues with classical analytical techniques, (4) verification of Big Data, (5) considerations for data sharing, and (6) community development of knowledge infrastructures. We then present approaches and tools which have been successfully applied to these challenges in aquatic ecology and other scientific fields.

Understanding how inbreeding affects endangered species in conservation breeding programs is essential for their recovery. The Hawaiian Crow ('Alalā) (Corvus hawaiiensis) is one of the world's most endangered birds. It went extinct in the wild in 2002, and, until recent release efforts starting in 2016, nearly all of the population remained under human care for conservation breeding. Using pedigree inbreeding coefficients (F), we evaluated the effects of inbreeding on Hawaiian Crow offspring survival and reproductive success. We used regression tree analysis to identify the level of inbreeding (i.e., inbreeding threshold) that explains a substantial decrease in 'Alalā offspring survival to recruitment. Similar to a previous study of inbreeding in 'Alalā, we found that inbreeding had a negative impact on offspring survival but that parental (vs. artificial) egg incubation improved offspring survival to recruitment. Furthermore, we found that inbreeding did not substantially affect offspring reproductive success, based on the assumption that offspring that survive to adulthood breed with distantly related mates. Our novel application of regression tree analysis showed that offspring with inbreeding levels exceeding F = 0.098 were 69% less likely to survive to recruitment than more outbred offspring, providing a specific threshold value for ongoing population management. Our results emphasize the importance of assessing inbreeding depression across all life history stages, confirm the importance of prioritizing parental over artificial egg incubation in avian conservation breeding programs, and demonstrate the utility of regression tree analysis as a tool for identifying inbreeding thresholds, if present, in any pedigree-managed population.