Temporal patterning of environment may determine long term changes in population structure and dynamics as well as trait dynamics. Models that generate realistic sequences of environment over the long term may be particularly useful for scaling up from short term studies. How can we create useful models of long term behavior when we only have short term data? Often historical records of environmental parameters (precipitation, temperature, climatic events [e.g., El Niño years], etc) spanning decades or even centuries are available for particular localities. I show, using two examples, how such data can be combined with detailed data from short term field studies which have been designed to capture (or have fortuitously captured) demographics in the different variants of the environments. One of the examples makes use of monthly rainfall data from 1927-1987 from a weather station in the town of San Andrés Tuxtla available from the Servicio Nacional de Meterología of Mexico. The other example makes use of data on the North Atlantic Oscillation Index measured from 1864-2006 between Iceland and Lisbon.
Results/Conclusions
From monthly rainfall data, I studied the temporal pattern of a variable likely to be important for a perennial understory herb that loses all above-ground biomass every dry season. The parameter was the mean monthly precipitation in mm from Nov-May (range= [9.3, 111.7] μ = 57.1, σ = 25.2). I studied the temporal pattern of the NAO index, which is thought to be important for demography of red deer in the North Block of Rum, Scotland; when negative, winter is colder and when positive, winter is warmer but wetter and stormier (range= [- 4.6, + 4.9], μ = 0.2, σ = 1.9). For each example, I calculated the standardized yearly deviation (deviation from the mean divided by the mean), I classified years in terms of how much higher or lower than the mean they were in standard deviation units and created a Markov chain of environmental dynamics from the historical sequence. I matched years in which demographic data were available to climate category to generate climate-category-specific demography. Combining environment-specific demographic data with environmental dynamics models provides a method to project population and trait dynamics over a temporal mosaic landscape.