When trying to understand how wildlife, for example a bird species, may react to climate change scientists generally study how species numbers vary in relation to climatic or weather variables (e.g. Renwick et al. 2012, Johnston et al. 2013). The way this tends to be done is by gathering information (data!) about bird numbers as well as the weather variables (for example temperature) in several locations (i.e. in space) and fitting a regression model to these data to detect and illustrate how bird numbers go up or down with temperature.
Data on bird numbers and temperatures in several locations lets researchers see the relationship between the two.
This relationship is then used to forecast how bird numbers may change along with potential temperature changes in the future (i.e. in time), for example due to climate change.
Relationships between bird numbers and temperature in a given location are often used to forecast changes in bird numbers with expected changes in temperatures over time.
It’s 6am on a warm spring morning and I’m about to visit the second of my Breeding Bird Survey1 sites. Like 2,500 other volunteers in the UK, twice a year I get up early to record all the birds I see or hear on the two transects in my randomly selected 1km square. Each year I look forward to these mornings almost as much for the comparisons as the actual sightings. Will there be more or fewer sightings of our summer migrants this year? How will numbers in this rolling Norfolk farmland stack up against those I see in urban, central Norwich?
But simply recording these changes is not enough; we need to understand why they occur if action is to be taken. This requires us to quantify the demographic rates (survival, productivity and movements) that underlie them, which in turn requires samples of marked individuals. Simply counting individuals is not enough. Continue reading →