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.
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.