How much can we reduce survey effort in a long-term environmental monitoring program based on mark-recapture-recovery (MRR) data, and still get what we want out of it?
In my latest paper (Lahoz-Monfort et al. 2014), which just appeared in early view in Journal of Applied Ecology, we explore this question for the monitoring of juvenile survival of a northern hemisphere seabird, the common guillemot (Uria aalge), at the Isle of May in Scotland. This small island, not far from Edinburgh, is one of the four ‘Key Site’ seabird colonies in the UK’s Seabird Monitoring Programme, and the colonies that cover most of its cliffs and grassy slopes have been intensively monitored for several decades.
We perform data thinning of an existing MRR data set to identify potential field effort reductions, both in terms of ringing and resighting efforts. This way we can explore what would be the effect of spending less time ringing guillemot chicks or looking for ringed adults sitting by the cliffs with a fieldscope, on our ability to estimate environmental influences on juvenile survival.
We also highlight how more aggressive alternative monitoring scenarios (i.e. not ringing chicks at all) can be evaluated with Integrated Population Models (Besbeas et al. 2002), a relatively new modelling approach that combines data collected on different aspects of demography and abundance.
This type of post-study evaluation can help streamline existing long-term environmental monitoring programs, but we’re not advocating such streamlining ourselves: obviously, by reducing effort we always lose something, and some of the most aggressive strategies would deliver much poorer ecological data sets (e.g. we wouldn’t be able to investigate individual-level effects on juvenile survival if no chicks were ringed). But in the current public funding climate, many wildlife monitoring programs are coming under pressure to reduce costs; our approach explores how to adjust field protocols to collect demographic data when the effort reduction is a mandate, incurring the least impact on our ability to achieve monitoring and research objectives.
We used these tools in an exploratory way, but the next step would be to make an explicit link to a decision context (i.e. a fixed budget, explicit objectives for probabilities of detecting effects). Food for thought! (and future papers?)
Lahoz-Monfort, J.J., Harris, M.P., Morgan, B.J.T., Freeman, S.N., Wanless, S. (2014) Exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival. Journal of Applied Ecology. DOI: 10.1111/1365-2664.12214
Besbeas P., Freeman S.N., Morgan B.J.T. & Catchpole E.A. (2002). Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics, 58, 540-547.