Abstract

Scientists have not always had freely accessible high-quality and high-resolution datasets relevant to their study systems. Today, early career researchers routinely confront a deluge of data that is relevant to their research questions. Early-career scientists face the combined challenges of using accessible yet powerful models, under high publication pressure, and with mixed guidance from scientists trained under an earlier era. There exists a temptation to reach for black-box analytical approaches to offer guidance through this wilderness of data. New complex models consisting of artificial intelligence and machine learning tools are poised to be co-opted by large numbers of early career researchers due to their modelling strength and easy, out-of-the-box usage. Just because we can use these new tools, does not mean we always should. I emphasise the role of complexity in the construction of our ecological models and suggest a new model planning process which early career researchers can use when trying to develop our understanding of the natural world.