Image Credit: Grand Velas Riviera Maya, CC BY-SA 2.0, Image Cropped
In ecological studies, the quality of the data we use is often a concern. For example, individual animals may be cryptic and hard to detect. Certain sites that we should really be sampling might be hard to reach, so we end up sampling more accessible, less relevant ones. Or it could even be something as simple as recording a raven when we’re really seeing a crow (check our #CrowOrNo if you have problems with that last one). Modeling approaches aim to mitigate the effect on our results of these shortcomings in the data collection.
However, even if we had perfect data, when we decide how to model that data, we have to make choices that may not match the reality of the scenario we are trying to understand. Model mis-specification is a generic term for when our model doesn’t match the processes which have generated the data we are trying to understand. It can lead to biased estimates of covariates and incorrect uncertainty quantification.
After the first edition of Ecology for the Masses’ new Stats Corner, many people requested a discussion of p-values. Ask and you shall receive! And as an added bonus, we’ll also talk about confidence intervals. (Image Credit: Patrick Kavanagh, CC BY 2.0, Image Cropped)
Much of ecological research involves making a decision. Does implementing a particular management strategy significantly increase the species diversity of a region? Is the amount of tree cover significantly associated with the number of deer? Do bigger individuals of a species tend to have longer life expectancies?
When animals like these wolves travel in packs, spotting one individual means we’re more likely to spot another soon after. So how do we come up with a reliable population estimate in situations like these? (Image Credit: Eric Kilby, CC BY-SA 2.0, Image Cropped)
The thought of an ecologist may conjure the image of a scientist spending their time out in the field counting birds, looking for moss, studying mushrooms. Yet whilst field ecologists remain an integral part of modern ecology, the reality is that much of the discipline has come to rely on complex models. These are the processes which allow us to estimate figures like the 1 billion animals that have died in the recent Australian bushfires, or the potential spread of species further polewards as climate change warms our planet.
Image Credit: Internet Archive Book Image, Public Domain, Image Cropped
Non-scientists still often think of ecologists as field workers in cargo shorts, running around a grassland with a notebook and a tape measure. Whilst I’d be remiss to say this wasn’t a percentage of us, the last two decades has seen the rise of ecological modelling, which has resulted in a new breed of ecologist. One who is capable of working almost exclusively with data, producing species distribution maps and population fluctuation graphs without leaving the office.
At the forefront of this group is Bob O’Hara, who has long claimed he plans to retire the moment he figures out whether he’s a biologist or statistician. Bob currently works at the Norwegian University of Science and Technology, spending his time with the Centre for Biodiversity Dynamics and the Departments of Mathematical Sciences. I spoke to Bob about the history of ecological modelling, its integration into the wider field, and problems with modern ecological modelling.