Predicting Spread of Invasive Species with Cellular Automa Model

1
Predicting Spread of Invasive Species with
Cellular Automa Model

• Sini Ooperi
• Department of Applied Biology
• University of Helsinki

2
Simulation Machinery
Resource-Contrained Cellular Automata Model RCAM
Spatial Resource Inventory Model SRIM
3
Source Data

• 1) Weather data
• 1.1 Daily min and max temperatures
• 1.2 Daily rainfall data
• 1.3 Day length data
• 2) Habitat data
• 2.1 Corine landcover data, selected categories
• 2.2 Location and area data of commercial potato
  fields (Ministry of Agriculture and Forestry
  Data Service)
• 3) Soiltype data Geological Survey of Finland
• 4) Entomological data Reproduction, flight, and
  overwintering phenology of Colorado Beetle
  various sources

4
Spatial Resource Inventory Model, SRIM

• Outputs
• Growth Index, GI
• Active Flight Index, AFI
• Wind-Aided Flight Index, WAF
• Overwintering Index, OVI
• Habitat Availability Index, HAI
• Habitat Connectivity Index, HCI
• Establishment Index, EI
• Logistic Emigration Index, LOGem
• Logistic Immigration Index,LOGim
• Aerial Emigration Index, AERem
• Aerial Immigration Index, AERim

5
Temperature-based Classification of 30 Summers

• 5 categories quintiles

Hot
Cold
Cool
Moderate
Warm
6
Growth Index
Components
Accumulation of Degree Days (DDs) above thermal
threshold (Temperature Index)
Growth Index (GI) DDs f(CS CWS)
Cold Stress (CS)
sine function
Linear accumulation above the threshold
temperature and rainfall
ColdWetStress (CWS)
7
Active Flight Index

• The flight take off threshold
• 17-20-25 C (Caprio, Grafius 1990)
• 15-20-30 C (Wegorek 1959, Johnson 1969, Termier
  et al. 1988)
• A rapid increase in ambient temperature of 5-7 C
  may trigger takeoffs (Feytaud 1930,Le
  BerrePortier 1950, Le Berre 1952, Termier et al.
  1989)
• Optimal at least 6 h of insolation with temp
  25-27 C (Le Berre 1950,1952,1962 Hurst 1975)

8
Active Flight and Wind-Aided Flight Indices
Dispersal Index
Daily maximum temperature
Active Flight Index f(Number) and
f(Favorability of Flight Take-off Days)

• Favorability Classes
• class t(max)lt15
• class 15ltt(max)lt19
• class 20ltt(max)lt24
• class 25ltt(max)lt29
• class 30ltt(max)lt34

Wind-Aided Flight Index 1)Proportion of Flight
Take-off Days when wind is strong enough for
wind-aided flight
Data of prevailing wind direction in summer months
9
Flight Take-off Rate
sedentary threshold
1.0
N(adults)
0.8
N(sedentaries)
N(flyers)
temperature
15 17 19 21 23 25 27 29 31 34º
10
Temperature-based Classification of 30 Winters

• 3 categories

Cold
Moderate
Mild
11
Overwintering Index
Components
Soiltype
3 classes
Overwintering Index f(Soiltype) x f
(SnowPermanency) x f (SnowDepth) x
f(Frost_Autumn) x f(Frost_Spring)
3 classes
Snow Persistency
Snowdepth
3 classes
Occurence of frost days in autumn
4 classes
Occurence of frost days in spring
Length of Diapause
binary
12
Habitat Availability Index
Components
Candidates from the Corine Landcover categories
Habitat Availability Index HAI Approximated
field area in each cell, 3 categories
Commercial Potato Cultivation Database/ Location
and area data of commercial potato fields
13
Habitat Connectivity Index

• Weights for neighboring landcover types
• W(Forest) -0.3
• W(Urban) -0.6
• W(water) -0.9
• Weights for neighboring locations
• cells sharing common borders W,N,E, and S
• W(vertical,horizontal) 1.0
• diagonal cells NE, NW, SE, and SW
• W(diagonal) 0.75

14
Aerial and Logistic Emigration and Immigration
Indices

• Aerial events are modelled as Poisson processes
  with rate
• Number of potential immigration and emigration
  events per time interval in the area are assumed
  as global constants as follows
• AERim 0.10 (SourceHeikkiläPeltola,2004)
• AERem 0.00
• Logistic events are modelled as the function of
  distance from the major import locations
  (harbors, customs to Russia etc) and as the
  function of proximity to major railroad and
  roads). Thus,
• LOGim
• LOGem
• are not global constants but cell-specific
  values.

15
Establishment Index

• The length of time episode when growth and
  overwintering have been possible in the 30-year
  period of 1971-2000

16
Cell-specific Resource Profiles in Current and
Future Climates

• Dynamic Indices
• Growth Indices, GI
• Active Flight Indices, AFI
• (Wind-Aided Flight Indices, WAF)
• Overwintering Indices, OWI Static Indices
• Habitat Availability Index, HAI
• (Habitat Connectivity Index, HCI)
• (Establishment Index, EI)
• Logistic Emigration Index, LOGem
• Logistic Immigration Index,LOGim
• Aerial Emigration Index, AERem
• Aerial Immigration Index, AERim

17
Outputs of SRIM
Growth Indices GI5, GI4, GI3, GI2, GI1
Active-Flight Indices AFI5, AFI4, AFI3, AFI2,
AFI1
Overwintering Indices OWI3, OWI2, OWI1
Habitat Availability Index HAI
Stochastic Indices
Wind-Aided Flight Indices WAF Logistic
Emigration Index LOGem Logistic Immigration
Index LOGim Aerial Emigration Index
AERem Aerial Immigration Index AERim
Inputs of Resource-contrained Cellular Automata
Model, RCAM
Habitat Connectivity Index HCI
Establishment Index EI
not used in the current version of RCAM
18
Phases of Cellular Automata
Phase 1 Summer
Phase 2 Winter
BeetleBank
Diapause Induction
Emergence
ColdWetMortality
Mortalities
Growth
Dispersal
Migration
reproduction
LOG AER
CS CoM
N8
N16
GI
FrostMortality Autumn
FrostMortality Spring
AFI
WAF
Control Measure Induced Mortality
ColdStress Induced Mortality
OWIf(Soiltype) x f(SnowPermanency) x
f(SnowDepth) x f(Frost_Autumn) x f(Frost_Spring)
19
Occurence Probability of Colorado Beetle
Populations at cell i,j After Summer Step
Occurrence Probability of Colorado Beetle
Populations at cell i,j After Winter Step
20
Running Resource-Constrained Cellular Automa Model

• Building the time sequence for simulation run
• Summer Options

Hot
Cold
Cool
Moderate
Warm
Winter Options
Cold
Moderate
Mild
21
Time Sequence Example
Winters
t1 t2 t3 t4
t5 t6 t7

Summers
Odds are summers and evens are winters.
22
Two-phased automata

• Phase 1 Summer Processes Population
  Pattern
• Growth reproduction density
• Disperal flight
  expansion
• Wind-aided Dispersal flight
  expansion
• Death
  extinction contraction
• Phase 2 Winter Processes
• Survival
  survival maintainance
• Death
  extinction contraction

23
Cell Size based on Annual Dispersal Kernel
5 km
5 km
N16
N16
N8
N8
P(x,y)
24
2 Neighborhood Types
Type (1) Neighborhood, N8 for Active
Flight Type (2) Neighborhood, N16 for Wind-Aided
Flight
25
Model Set Up

• Initialize the vector of summers and winters
• Initialize the selected cells with a selected
  number of Colorado Beetle adults
• Specify whether wind-aided dispersal is in use
• Specify the Poisson frequencies for Logistic and
  Aerial Immigration events
• Specify the spatial extend of immigration and/or
  emmigration events

26
Analyses

• Abundance Analyses of Individual Cells, Clusters,
  or Regions
• Analyses of Time Evolution of Expansion Patterns
  
• Sensitivity Analyses
• factorial analyses on the effects of
• 1) single parameters
• 2) combined effects of parameters
• Error Analyses
• 1) Errors in the source data
• 2) Errors in the model structure

27
Simulation of Different Scenarios Changes in
Adundance and Distribution due to

• Current Climate Current Habitat Network
• Climate Change Current Habitat Network
• Current Climate Habitat Network Change
• Climate Change Habitat Network Change
• Changes in abundance and distribution due to
  Eradication Scheme

28
Thank you for your time