1. Results Method Local population dynamics followed the Ricker equation, changing from over- to under compensating growth with parameter b, and with environmental noise affecting K: 1.Environmental noise Mass-action mixing process, and landscape implicit. 2. Landscape implicit Mass-action mixing process, and landscape implicit. 3. Landscape explicit Dispersal is distance dependent. Contact Frida Lögdberg Spatiotemporal Biology IFM, Theory and Modelling Linköping University SE-581 83 Linköping, Sweden Email: friwa@ifm.liu.se Extinction risk as a consequence of variation in time and space Frida Lögdberg and Uno Wennergren Introduction and Aim Environmental variation is an important factor for population dynamics, as well as its temporal structure. Autocorrelated noise will decrease the magnitude of population fluctuations compared to a non-atucorrelated environmental noise. This has impacts on extinction risk, but is only valid for a non- structured single-species population. How will autocorrelated environmental noise affect population dynamics in a spatial setting? Conclusions wwåpitåp

2. Extinction risk as a consequence of variation in time and space Frida Lögdberg, Tom Lindström and Uno Wennergren INTRODUCTION 1.Coloured noise on single population - decrease the magnitude of population fluctuations. Is this also true for extinction risk? 2.In a spatial setting noise can be correlated both in time and across space; synchronized environmental variation. Are effects of coloured noise preserved despite synchrony? Regardless of aggregation? Contact Frida Lögdberg Spatiotemporal Biology IFM, Theory and Modelling Linköping University SE-581 83 Linköping, Sweden Email: friwa@ifm.liu.se METHOD Local population Ricker equation Over/under compensatory Parameter b Environmental noise Affecting K Environmental noise New method: 1/|f| γ -noise generated by a spectral synthesis (FFT) in two dimensions (time and space). Noise colour (γ) and synchrony (ρ) between patches can be set with accuracy yet controlling for variance. Landscape, implicit Initial analysis with implicit spatial dimension -Dispersal a mass-action mixing process -All patches equally connected. Landscape, explicit New method: Replicates of landscapes with specified aggregation were generated with a spectral method (FFT). The continuous landscape was transformed to a point pattern. Continuity; a measure of spatial autocorrelation. Contrast; a measure of patch-density dispersion. Dispersal was distance dependent with “fatter tail”-shape of displacement kernel. AIM The importance of the spatial dimension when population dynamics is affected by coloured environmental noise. CONCLUSIONS 1. Overcompensatory dynamics - Yes. Extinction risks decrease with colour. Undercompensatory dynamics – No. Extinction risk at max in medium colour. 2. Yes, the qualitative effect of colour is preserved regardless of synchrony and aggregation. Yet: a. Synchrony has large effect on extinction risks b. Aggregation level of landscape also effect extinction risk Figure 1. Left: overcompensating density regulation. Right: under-compensating density regulation. Extinction risk (A-B), mean of population density (C-D) and population variance (E-F) as a function of environmental noise colour (γ). The curves show different degrees of synchrony (ρ) of environmental noise. Growth rate, r=1.5, number of subpopulations=10, mean K=100, and dispersal=0.1 (mass-action mixing). A C E B D F Figure 2. Landscape is generated with different patch- aggregation; continuity 0,1 and 2 (from left to right), and contrast 2 (upper row) and 5 (lower row). Extinction risk as a function of environmental noise colour (γ). The curves show different degrees of synchrony (ρ) of environmental noise. Growth rate, r=1.5, number of subpopulations=500, mean K=100, and distance dependent dispersal=0.1 Analyzing empirical landscapes with spectral method. Empirical data from tree inventories on old oaks from the Swedish County of Östergötland. Measure of landscape aggregation: continuity=1.02 and contrast=4.7 (compare with middle group of figures in Fig. 2).