When eggs hatch asynchronously, offspring arising from last-hatched eggs often exhibit a competitive disadvantage compared with their older, larger nestmates. Strong sibling competition might result in a pattern of resource allocation favoring larger nestlings, but active food allocation towards smaller offspring may compensate for the negative effects of asynchronous hatching. We examined patterns of resource allocation by green-rumped parrotlet parents to small and large broods under control and food-supplemented conditions. There was no difference between parents and among brood sizes in visit rate or number of feeds delivered, although females spent marginally more time in the nest than males. Both male and female parents preferentially fed offspring that had a higher begging effort than the remainder of the brood. Mean begging levels did not differ between small and large broods, but smaller offspring begged more than their older nestmates in large broods. Male parents fed small offspring less often in both brood sizes. Female parents fed offspring evenly in small broods, while in large broods they fed smaller offspring more frequently, with the exception of the very last hatched individual. These data suggest male parrotlets exhibit a feeding preference for larger offspring—possibly arising from the outcome of sibling competition—but that females practice active food allocation, particularly in larger brood sizes. These differential patterns of resource allocation between the sexes are consistent with other studies of parrots and may reflect some level of female compensation for the limitations imposed on smaller offspring by hatching asynchrony.

The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and inform- ing resource management through data-intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large-scale efforts that have had mixed success in the individual-driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high-value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists? 

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap.