1. Treating populations and landscapes as signals. A step towards research collaborations? (1)spread of disease, (2)animal welfare vs transport, (3)endangered species, oak landscape (4)climate effects on lichens. Associate Professor Uno Wennergren IFM, Theory and Modelling, Division Theoretical Biology

2. Who? PhD Annie Jonsson: University of Skövde. PhD Lars Westerberg: Linköping University PhD students: –Frida Lögdberg Synchronization between patches Oak landscape –Tom Lindström Dispersal, movements Spread of disease, Foot and Mouth disease –Nina Håkansson Spatial patterns, temporal aspects also Animal transport, Networks, oak landscape –Jenny Lennartssom Spatial patterns Spread of disease, Networks Collaborations: –Kristin Palmqvist Anna Cabrajic Umeå Univ – lichens –Mikael Rönnqvist Matthias Henningsson, Bergen and IEE – transport –Susanne Lewerin Maria Nöremark, National Veterinary Institute (SVA) Ecology 2008, Ecological modelling 2003, 2005, 2008. Oikos 1995, Nature 1995.

3. Outline Conceptual framework –Complexity –Methodological questions –Relate to the scientific field Theoretical Biology Examples by our projects (1)spread of disease, (2)animal welfare vs transport, (3)endangered species, oak landscape (4)climate effects on lichens.

4. Conceptual framework What characteristics of in signal relates to specific characteristics of out signal (increase risk of explosion or extinction)? What impact do the characteristics of the population have on this relation on in and out signal? In signal (time): Temperature Humidity Other population densities etc Population filter: Reproduction Survival Growth Dispersal Out signal (time): Population density

5. Conceptual framework adding complexity In signal Temperature Humidity Other population densities etc Population filter: Reproduction Survival Growth Dispersal Out signal: Population density Spatial domain: Populations exist in a 2 dimensional heterogeneous landscape (or even 3D). Hence the signals are in 2D. Characteristics of 2D signals? Predation and competition between populations: Sets of interacting populations is the filter: Characteristics of sets of out signals? The effect of the characteristics of interactions, feedbacks?

6. Conceptual framework methodological questions, part I In signal Temperature Humidity Other population densities etc Population filter: Reproduction Survival Growth Dispersal Out signal: Population density Spatial domain and sets of population What defines the characteristics of the signals? What characteristics are important (extinction/explosion)? variance mean autocorrelation/aggregation synchronization

7. Conceptual framework methodological questions, part II In signalPopulation filter:Out signal: Spatial domain and sets of populations What defines the characteristics of the signals? What characteristics are important (extinction/explosion)? variance mean autocorrelation-1/f noise-flicker noise, in time and space synchronization between subpopulations How to generate and analyze: variance mean autocorrelation synchronization In 1 dim, 2 dim and….. FFT

8. FFT vs Science in Theoretical Biology Analyzing time series to estimate 1/f noise of densities Testing different in signals and measuring impact on probability of extinction Few studies on the relation between insignal and outsignal measured by change of frequency spectrum Few studies (one or two) on resonance –within system populations –between system and insignal Few studies on how to generate or analyse time series and landscapes by FFT with desired properties No studies made on landscape of resources (in signal) and landscapes of densities (out signal) by FFT –single populations –Sets of populations

9. Our projects as examples (1)spread of disease (2)animal welfare vs transport (3)endangered species, oak landscape (4)climate effects on lichens

10. spread of disease Foot and Mouth disease (recall outbreak in Britain) –Funded by Swedish Emergency Agency What matters to the spead of spread for a given disease? –Connection between farms (Dispersal) –Location of farms (landscape) since assuming that connection is distance dependent –What location pattern is risky? (increase the spread) Describe the actual pattern Test different patterns, virtual farm locations

11. Farms in Skåne (region southern Sweden) Spread of disease Foot and Mouth disease Blue Tongue Etc

12. Farms in Skåne (region southern Sweden) 1/f γ noise Slope gamma= 1.2023 What spread of disease to expect? (replicates) What actions are most effective? For what dispersal/agent? Animal transport strategies and slaughterhouses. Log frequency Log amplitude

13. Generating Coordinates Generate by starting with random (white noise) tilt the line in the frequency plane By inverse Fourier Transform go back to landscape

14. Example on generating Different slopes in the frequency plane Continous or ’binary’ landscapes Different amount of primary habitat

15. Open questions Improve the generating algorithm –Aggregations in different directions, keep the 1/f possible? –Improve how aggregation and density of locations can be generated. Use the same technique for analyzing invasion of new species in new areas (to be expected by climate change) ?

16. Animal welfare vs transport funded by Swedish Animal Welfare agency and Swedish Board of Agriculture Animal transport between farms during routes to slaughterhouses is an animal welfare issue. Can these routes be improved? –From an animal welfare perspective? –Is there a conflict with profit? What is the effect by the production system: –Locations and specificity of slaughterhouses –Locations and specificity of farms –Trends today? Generate production system - landscapes Locations of farms and slaughterhouses

17. Endangered species living on old oaks, the oak landscape What landscape increase the risk of extinction? What environmental noise (growth rates) increase the risk of extinction. –Degree of synchrony between subpopulations Too many oaks of same age –Autocorrelation in time of environmental noise Known variation –Dispersal strategy of organism (not included in this presentation) –Location of subpopulations (oaks)

18. Oaks in Östergötland (region close to Linköping) 30 000 oaks What aggregation – landscape? Oaks Species living on oaks Management plans

19. Oaks in Östergötland (region close to Linköping) 1/f γ noise the slope gamma=1.0125 Can the pattern be improved? For what kind of dispersal/species? What temporal pattern to expect given age structure of todays oaks? Log frequency Log amplitude

20. Autocorrelation in time Synchronization between subpopulations 1/f noise and variance. Different variance in time vs subpopulations. Initial study: no geographic locations of subpopulations. Different models of single population growth. Density dependence.

21. Generated in signal, time series of growth rates for subpopulations time subpopulations Growth rate insignal

22. How we generate – filtering a white noise White noise Variance filter Noise filter Filtered white noise amplitude frequency

23. Questions How do synchronization effect the frequency spectra of single and total population How do synchronization effect the extinction risk of single and total population What is the combined effect of synchronization and autocorrelation (time)? subpopulation growth and dispersal Densitydep growth (neg feedback) ?

24. Coming up.. Time and landscape – 3D. Sets of populations. Resonance.

25. Lichens Platismatia glauca (Näverlav) Both fungi and green algae (and/or ’blue-green algae’) Needs specific combination of –Humidity –Light –Temperature to photosynthesis

26. A day of a lichen in June Northern Sweden Temperature Humidity Light, edge of forest Light, Core of forest Can we describe the different correlation patterns between Humidity, Light and temperature? For different kind of habitats as edge and core? Get rid of 24 h pattern/signal.

27. Photosythesis Light Watercontent Core habitat

28. Summing up Analyze, manipulate and generate time series or landscapes or spatiotemporal patterns. Analyze time series and landscape and spatiotemporal pattern from inherent population level properties (feedback etc) Combine the two to seek for overall system properties (resonances etc)