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. Young 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 argue we should reconsider the role of
complexity in the construction of our ecological models when we test
ideas of our understanding of the natural world.