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Cognitive ability affects connectivity in metapopulation: A simulation approach

Cognitive ability affects connectivity in metapopulation: A simulation approach. Séverine Vuilleumier The University of Queensland. Fragmented landscape. Pop 1. Pop 2. C 12. Patch1. C 21. t ravel path and cost. Patch 2. Context: spatially-explicit metapopulation model. e 1. e 2.

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Cognitive ability affects connectivity in metapopulation: A simulation approach

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  1. Cognitive ability affects connectivity in metapopulation: A simulation approach Séverine Vuilleumier The University of Queensland

  2. Fragmented landscape Pop 1 Pop 2 C12 Patch1 C21 travel path and cost Patch 2 Context: spatially-explicit metapopulation model e1 e2 Landscape heterogeneities and structures / animal behavior

  3. Question What is the influence of cognitive abilities on the connectivity in metapopulation ?

  4. Question What is the influence of cognitive abilities on the connectivity in metapopulation ?

  5. Simulation of interactions between individuals and landscape features during dispersal ? • Therefore, the model must contain …. • the dispersal abilities and the behavioral traits of the animal Animal model • landscape representation with its properties according to animal dispersal (visibility, attractiveness, cost, etc.) Landscape model

  6. Assumptions • Species are moving on the ground • An individual moves across an unfamiliar landscape • Searching behaviour is driven by finding a new habitat patch • Animals are constrained by time, energy and mobility • Animals use their environment to direct searching

  7. Landscape model Frontier Cell Nodes Cell The landscape : an irregular grid in shape and dimension • Allows all spatial representations, roads, habitat patches, etc.

  8. Landscape model: Illustration

  9. Animal Model Animal cognitive abilities • Blind Strategy (B): no knowledge of the environment • Near-Sighted Strategy (N) : use of the neighbouring environment to direct their movements • Far-Sighted Strategy (F): use of the neighbouring environment and visual scanning of the environment to find a new habitat patch

  10. Animal Model Movement strategy algorithms While the individual has enough energyand hasnot reacheda habitat patch, it goes on and chooses with the help of a pseudorandom number a new cell depending on: • the possibility to cross the frontier and the cell, • a probability (computed dynamically)

  11. Probabilities • Blind: depends on the frontier length. • Near-sighted: depends on the attractiveness of neighboring cells and frontiers. • Far-sighted: depends on the attractiveness of cells and frontiers and on the shortest way to a habitat patch that is in the perceptual range

  12. Question What is the influence of cognitive abilities on the connectivity in metapopulation ?

  13. Measure of connectivity • The colonization probability from patch i to patch j (Pij, Pij <>Pji) • The overall exchangeof individuals between two patches i and j , (Pij +Pji) • The balance at a given patch is the difference between flows in and out (Sum Pij – Sum Pji). • The ecological distance(The median value and standard deviation)

  14. Simulations of Dispersal Test area: Rural area in Switzerland Landscape model • Each cell and frontier is characterized by: • the possibility to go through (barrier). • an ecological cost (in terms of distance), • an attractiveness 13 habitat patches From each habitat patches 50’000 individuals are dispersedfor each strategy The starting ecological energy level is “equal to 50 km”

  15. Results: Effect of cognitive strategies on connectivity • The colonization probability from patch i to patch j (Pij, Pij <>Pji) • The overall exchangeof individuals between two patches i and j , (Pij +Pji) • The balance at a given patch is the difference between flows in and out (Sum Pij – Sum Pji). • The ecological distance(The median value and standard deviation)

  16. Overall exchange of individuals: • Average number of connections by patch: • B: 10,6 (89%) • N: 4.1 (33%) • F: 5 (42%) • Average of colonization probability: • B: 37.1% • N: 18.7% • F: 38% Blind strategy Near-sighted strategy Far-sighted strategy In gray, values are between 0% and 1%, and in black, values are larger than 1%.

  17. Results: Effect of cognitive strategies on connectivity • The colonization probability from patch i to patch j (Pij, Pij <>Pji) • The overall exchangeof individuals between two patches i and j , (Pij +Pji) • The balance at a given patch is the difference between flows in and out (Sum Pij – Sum Pji). • The ecological distance(The median value and standard deviation)

  18. Balance at each patch

  19. Results: Effect of cognitive strategies on connectivity • The colonization probability from patch i to patch j (Pij, Pij <>Pji) • The overall exchangeof individuals between two patches i and j , (Pij +Pji) • The balance at a given patch is the difference between flows in and out (Sum Pij – Sum Pji). • The ecological distance(The median value and standard deviation)

  20. Results: Density probability of ecological distance (medians) Blind Strategy Median of ecological distances Near-sighted Strategy Far-sighted Strategy

  21. Results Blind Near Far r2= 0.828 r2= 0.408 r2= 0.419 r2 : Spearman Colonization probability - Median of ecological distances Colonization probability Ecological distance Blind strategy : the smaller the value of ecological distance, the higher the chance to join them Near and far-sighted strategy: high colonization probability can occur at large ecological distances High probability of colonization is not related to shortest distance!

  22. Colonization probability - Standard deviation Blind Near Far r2= 0.105 r2= 0.773 r2= 0.270 Results Colonization probability Standard deviation Blind strategy: high values of colonization probability are related to large variability of ecological distances - number of connections. Near and Far-sighted strategies: High colonization probability can be found for any ecological distances – number of connections Numerous connections do not mean high colonization success!

  23. Discussion • Cognitive abilities seem to act on the spatial structure of populations • lead to the genetic sub-structure of populations • lead to the extinction of marginal populations • Benefits of individual strategy are not linked with benefits for population • It seems not possible to generalize, or even forecast responses of an individual to landscape heterogeneity and fragmentation

  24. Many thanks to Institute of Environmental Science and Technology Swiss Federal Institute of Technology of Lausanne Dept. Ecology & Evolution, University of Lausanne Switzerland

  25. Measure at metapopulation level • The metapopulation capacity of a fragmented landscape wk(Hanski & Ovaskainen, 2000) wk :The leading eigenvalueof the matrix K, which measures the impact of landscape structure for long-term persistence of a species.

  26. Simulated colonization probability curve related to (a) the number of dispersers • (b) the assigned dispersal distances

  27. Frequency of cells being crossed Blind Local Near

  28. Density probability Median value of the distribution of ecological cost grouped by strategies Density probability Random Near Local • Random strategy: the highest values of ecological distance • Random and Local strategy: single peak distribution of the median • This value defines the minimum distance that an individual has to cover in order to join other habitat patches quantification of a landscape to support population. • Local strategy: colonisation can appear at any level of ecological distance.

  29. Density probability Minimum value of the distribution of ecological cost grouped by strategies Density probability Random Near Local All strategies behave the same when patches are close. when the patches are spatially further, the minimum values of ecological cost depends on the strategy.

  30. Equation 1 We modify the colonisation probability by Metapopulation capacity of a fragmented landscape (Hanski & Ovaskainen, 2000) The leading eigenvalueof the matrix K isthe metapopulation capacity of a fragmented landscape that measures the impact of landscape structure for long-term persistence of a species.

  31. Fragmented landscape Pop 1 Pop 2 C12 Patch1 C21 Patch 2 Context: spatially-explicit metapopulation model E1 E2 « Connectivity » Colonization Extinction Hanski and Gyllenberg (1997)

  32. Hanski’s spatially explicit metapopulation model Extinction Colonization Metapopulation capacity of a fragmented landscape (Hanski & Ovaskainen, 2000) The leading eigenvalueof the matrix K isthe metapopulation capacity of a fragmented landscape that measures the impact of landscape structure for long-term persistence of a species.

  33. General conclusions The dispersal model • both local and global aspects of dispersal • allows the simulation of various dispersal strategies, landscape uses, and dispersal cues, • quantification of colonisation probability and ecological distances, • spatial identification of paths, • contributes to a better understanding of factors that may have implications in dispersal processes • offers assistance to planners for management decisions. • metapopulation assumptions • specific movement strategy and cues • the temporal scale • data • the dependency of the results on expert judgment.

  34. F3 Fn F2 Additive probability F1 Fn 1 Pn F2 F1 Random P2 P1 0 P: Probability F: Associated frontier Choosing procedure Cell n Cell 3 Pn P3 ? Cell 2 P2 Cell 1 P1

  35. Dispersal model Dispersal model • Landscape model • Topological properties • Typology • …. • Landscape model • Topological properties • Typology • …. • Animal model • Movement type • Choosing procedure • Dispersal abilities • ….. • Animal model • Movement type • Choosing procedure • Dispersal abilities • ….. Transition loop Habitat patch Habitat patch

  36. Dispersal model • Landscape model • Topological properties • Typology • …. • Animal model • Movement type • Choosing procedure • Dispersal abilities • ….. Transition loop Habitat patch Habitat patch For i=1 1 Active Active entities entities add Path Path [Spatial entity] [Spatial entity] 1 1 Path Path [Spatial entity] [Spatial entity] 1 1 [ [ Spatial entity] Spatial entity] Start [Spatial entity] [Spatial entity] [ [ 1 1 Spatial entity] Spatial entity] Path Path 2 2 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] 1 1 2 2 1 1 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] ….. ….. 1 1 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 ….. ….. i i [Spatial entity] [Spatial entity] ….. ….. i i ….. ….. [Spatial entity] [Spatial entity] [Spatial [Spatial i i [Spatial entity] [Spatial entity] [Spatial [Spatial [Spatial entity] [Spatial entity] i i entity] entity] i i [Spatial entity] [Spatial entity] [Spatial [Spatial i+1 i+1 entity] entity] i i [Spatial [Spatial Recorder Recorder i+1 i+1 [Spatial entity] [Spatial entity] entity] entity] i+1 i+1 [Spatial entity] [Spatial entity] i+1 i+1 entity] entity] i+1 i+1 i+1 i+1

  37. Dispersal model • Landscape model • Topological properties • Typology • …. • Animal model • Movement type • Choosing procedure • Dispersal abilities • ….. Transition loop 2 Habitat patch Topological Habitat patch Relations List of suitable List of suitable entities For i=1 entities Transition 1 loops Active Active entities entities add Path Path [Spatial entity] [Spatial entity] 1 1 Path Path [Spatial entity] [Spatial entity] 1 1 [ [ Spatial entity] Spatial entity] Start [Spatial entity] [Spatial entity] [ [ 1 1 Spatial entity] Spatial entity] Path Path 2 2 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] 1 1 2 2 1 1 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] ….. ….. 1 1 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 ….. ….. i i [Spatial entity] [Spatial entity] ….. ….. i i ….. ….. [Spatial entity] [Spatial entity] [Spatial [Spatial i i [Spatial entity] [Spatial entity] [Spatial [Spatial [Spatial entity] [Spatial entity] i i entity] entity] i i [Spatial entity] [Spatial entity] [Spatial [Spatial i+1 i+1 entity] entity] i i [Spatial [Spatial Recorder Recorder i+1 i+1 [Spatial entity] [Spatial entity] entity] entity] i+1 i+1 [Spatial entity] [Spatial entity] i+1 i+1 entity] entity] i+1 i+1 i+1 i+1

  38. Dispersal model • Landscape model • Topological properties • Typology • …. • Animal model • Movement type • Choosing procedure • Dispersal abilities • ….. Transition loop 2 Habitat patch Topopogical Habitat patch Relations List of suitable List of suitable entities For i=1 entities Transition 1 loops Active Choosing Active entities procedure entities 3 Entitie Entitie add Path Path [Spatial entity] [Spatial entity] 1 1 Path Path [Spatial entity] [Spatial entity] 1 1 [ [ Spatial entity] Spatial entity] Start [Spatial entity] [Spatial entity] [ [ 1 1 Spatial entity] Spatial entity] Path Path 2 2 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] 1 1 2 2 1 1 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] ….. ….. 1 1 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 ….. ….. i i [Spatial entity] [Spatial entity] ….. ….. i i ….. ….. [Spatial entity] [Spatial entity] [Spatial [Spatial i i [Spatial entity] [Spatial entity] [Spatial [Spatial [Spatial entity] [Spatial entity] i i entity] entity] i i [Spatial entity] [Spatial entity] [Spatial [Spatial i+1 i+1 entity] entity] i i [Spatial [Spatial Recorder Recorder i+1 i+1 [Spatial entity] [Spatial entity] entity] entity] i+1 i+1 [Spatial entity] [Spatial entity] i+1 i+1 entity] entity] i+1 i+1 i+1 i+1

  39. Dispersal model • Landscape model • Topological properties • Typology • …. • Animal model • Movement type • Choosing procedure • Dispersal abilities • ….. Transition loop 2 Habitat patch Topological Habitat patch Relations List of suitable List of suitable entities For i=1 entities Transition 1 loops Active Choosing Active entities procedure entities 3 4 Entitie Entitie Limitations tests Limitations tests End add Path Path [Spatial entity] [Spatial entity] 1 1 Path Path [Spatial entity] [Spatial entity] 1 1 [ [ Spatial entity] Spatial entity] Start [Spatial entity] [Spatial entity] [ [ 1 1 Spatial entity] Spatial entity] Path Path 2 2 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] 1 1 2 2 1 1 [Spatial entity] [Spatial entity] [Spatial entity] [Spatial entity] ….. ….. 1 1 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 2 2 [Spatial entity] [Spatial entity] ….. ….. [Spatial entity] [Spatial entity] 2 2 ….. ….. i i [Spatial entity] [Spatial entity] ….. ….. i i ….. ….. [Spatial entity] [Spatial entity] [Spatial [Spatial i i [Spatial entity] [Spatial entity] [Spatial [Spatial [Spatial entity] [Spatial entity] i i entity] entity] i i [Spatial entity] [Spatial entity] [Spatial [Spatial i+1 i+1 entity] entity] i i [Spatial [Spatial Recorder Recorder i+1 i+1 [Spatial entity] [Spatial entity] entity] entity] i+1 i+1 [Spatial entity] [Spatial entity] i+1 i+1 entity] entity] i+1 i+1 i+1 i+1

  40. t8 t9 t10 t7 t12 t6 t11 t5 t4 t10 t9 t3 t8 t7 t2 t6 t1 t5 t4 t1 t3 t2 Distance écologique entre les patches Test:

  41. B A

  42. E1 C12 E2 Patch1 C21 Patch 2

  43. P1 Cell 1 P2 Cell 2 F1 F2 ? Fn F3 Cell n Cell 3 Pn … P3

  44. : Virtual frontier Inhabited area Active cell Forest Hydrological network Cell 1 Cell 2 Cell 3 Road network Cell 4 Hedge Linear features ?

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