Developing ALMaSS, a landscape-scale IBM simulation for wildlife

management in Denmark

Chris Topping,

NERI, Department of Landscape Ecology,Kalø, DK-8410 Rønde, Denmark

Developing ALMaSS, a landscape-scale ABM simulation for wildlife

management in Denmark

What will the impact on

wildlife be of changing to

organic farming?

Will the new motorway

have important

implications for wildlife?

How can we optimise

ground-water protection schemes to maximise

wildlife benefit?

What happens if we start planting

hedgerows or removing

hedgerows on a large scale?

What will the influence of

altering pesticide usage

be on agricultural

wildlife?

Questions?

Fragmentation?

Conservation Genetics?

Population viability?

Optimal placement of wildlife road tunnels?

Open field and marginal habitat birds

Polyphagous predator 1

Polyphagous predator 2

Small mammalherbivore, grassland specialist

Large mammal, mosaic specialist

We have adopted an individual-based model approach usingindicator species

Large mammal, woodland mosaic specialist

ALMaSS (Animal, Landscape and Man Simulation System)

Landscape Model Animal Models

Landscape Modelling

Building

River

Road

Forest

Field-boundary

Grass

ScrubField

Landscape Structure

Managed by the same farmer

The collection of fields managed forms the farm unit.

Each farm is given a type and a crop rotation which it applies to its fields

Landscape Animation

Spring barley

Day degreesV

eget

atio

n H

eigh

t

The red, green and yellow lines show three potential paths through this decision tree.

Autumn Plough

Slurry

Stock FarmerArable Farmer

Manure

NPK Liquid NH3 PK

Sow

Start

Spring Barley Crop Management PlanThis diagram shows the events leading up to sowing in the spring barley management plan.

First the decision is whether to plough in Autumn.If we plough in autumn then the next step is application of fertiliser, otherwise it will depend on whether the farmer is a stock or arable farmer, and what he has available.

NPK

Stock FarmerHarrow

Now if we have not ploughed already, do it in Spring.Next Harrow and if a stock farmer correct the fertiliser using NPK (it is possible to reach here and not to have applied any fertiliser). If he is an arable farmer then fertilise.

Finally we can sow.

Spring Plough

All events can have time periods, probabilities, dependencies and soil/weather conditions attached.

Other Landscape Sub-Models

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Veh

icle

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JanFebMarAprMayJunJulAugSepOctNovDec• Seasonal and daily variation in

traffic load on all roads

• Soil type, slope and aspect of all areas

• Further subdivision of forested areas by means of remote sensing techniques

• Weather data

Types Modelling

Complexity Gradient

Population Models IBMs

Q. Why use the more complex models?

A. Because we believe they are more accurate at giving answers under certain conditions.

In particular when dealing with practical questions, often related to a specific location or structural type of landscape then local interactions between individuals and between individuals and their environments become potentially important.

When does an individual becomes an agent?

.....When that individual starts to make decisions based on information it gathers, in order to carry out its own agenda (in our case survival and reproduction).

Agent-based modelling

Animal Modelling

?

Exploring

Establish A Territory

Disperse

Die

Mate

Habitat is very low quality

A transition

Modelling using states and transitions

Communication Other Organisms

Farming Events

Reacting to Events

EnvironmentalConditions‘!’

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The results can be used to generate:• Individual-based information - e.g. developmental rates

• Spatially related information - e.g. where the animals live, breed or forage

• Population information - sum of the individuals to give population descriptors

A selection of results related to agricultural

management

Some Example Applications

1. Practical - Environmental Impact Assessment – Pesticides

2. Ecological – Life-History Strategy Analyses

3. Theoretical – Population Genetics

Pesticides and Skylarks - The Perceived Wisdom:

Energy Loss Energy Loss

Energy Loss Energy Loss

The ALMaSS approach:

Two factors were investigated:

• With and Without pesticides

• Field Size – 1x & 2x real size

Using the same basic energetic model, but translating the model to a landscape scale and from a population-based approach to an agent-based one.

SmallNo Pesticide

SmallWith Pesticide

LargeNo Pesticide

LargeWith Pesticide

Skylark Population Numbersunder two simulated pesticide regimes

and with large and small fields

Simulation Year

Mea

n P

opul

atio

n S

ize

Would we be better off reducing pesticide usage or altering field size conditions?

• Pesticides cause a mean of 4% reduction in population size

• Large fields cause 37% reduction

• There is an interaction between weather and pesticide usage

• The interaction is also affected by other factors (time-lags & other mortalities)

(Topping & Odderskær in prep)

Analyses of life-history strategies

For example: Polyphagous predator studies

How do LHSs interact with man’s management of the landscape?

• Differential sensitivity to pesticides

How do LHSs interact with landscapes?

• We know some species do particularly well in agricultural landscapes. This probably has something to do with their LHS – can we quantify this?

Minimising Pesticide Impacts

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pu

lati

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og

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Adults 1st gen.

Eggs

Larvae

Pupae

Adults 2nd gen.

Damaging time to spray

DispersalSafer time

to spray

How does area and timing of pesticide applications effect the dynamics of non-target organisms?

UncertainEffects

Here the effect depends upon the area and timing of pesticides. the more

synchronised in time and space the worse the impact.

Simulated carabid population size with three different movement rates

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ulat

ion

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e (L

og

scal

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Big-M20Big-M5Big-M3

By altering life-history parameters we can simulate a range of different species:

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Field Boundary% and Size Categories

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an

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Simulations of carabid movement rates, proportion of fields with

grassy boundaries and field size

General increase in population size with

more field boundaries

Steep increase in population size with

increasing movement rate

Smaller populations

in large fields

a ba b

The ratio a/b is smaller in small

fields indicating an interaction between

field size and movement - lower

movement rate has a smaller penalty in

small fields

SOURCESOURCE DFDF SS SS F-ratioF-ratio P P

field size 1 12.1 161.0 < 0.0001

dispersal ability 3 9282.641529.5 < 0.0001

boundary condition 2 44.1 292.6 < 0.0001

field × dispersal 3 6.9 30.5 < 0.0001

field × boundary 2 1.0 6.8 0.0011

dispersal × boundary 6 36.8 81.4 < 0.0001

time 1 0.4 5.7 0.0168

run 4 0.3 1.0 0.4188

(Bilde & Topping in prep)

Population Genetics

Important because it can show past population events, current population structure and predict future population viability

Genetic Modelling in ALMaSSThis is made possible by the fact that we can track matings between individuals, therefore we can track gene-flow.

The prototype for this is the field vole model. Model field voles have a simple genetic code which is made up of a single chromosome, with 16 loci, and four alleles at each locus.

Each chromosome is made of two strands of DNA, therefore each vole carries 32 alleles in 16 pairs

e.g. a c a d b b a c a a d a b a d c

c a a a d b c a a b a b a a a a

The combination of the agent-based model and genetics opens the way for a range of interesting questions.

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For example:Investigating fragmentation effects

Population viability and the risk of extinction vortices

But there is the even more tantalizing possibility of linking the genotype with the phenotype (in this case the model parameters).

• Evolution of dispersal• Optimal life-history strategies• Theoretical genetics e.g. creation of hybrid zones

Time

Po

pu

lati

on

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e (

N)

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ecte

d H

eter

ozy

go

sity

(H

e)

P- P+

But..a reduction of 24.6% in expected heterozygosity (He ). This indicates a change of the genetic structure of the population due to the fragmentation of the landscape by the perturbation.

Implications of annual local population perturbations on the genetic diversity of voles

The population perturbation produced a reduction of 0.8% of the landscape carrying capacity measured by N.

Main Contributors to ALMaSS:

Chris ToppingPeter OdderskærJane Uhd JepsenFrank NikolajsenCino Pertoldi Peter LangePoul Nygaard AndersenGeoff GroomTrine BildePernille ThorbekSiri ØstergaardTine Sussi Hansen

Practical Session

In 2 parts:

1)Some technical information about the building of ALMaSS

- What kinds of things do we have to deal with technically - some examples of landcape and animal modelling.

2)Hands on use of a simulation

1. Model is programmed in C++ - an object-oriented language with good code re-use features and very efficient execution code.

2. When running the basic model will occupy a lot of RAM. A typical 10 x 10km simulation between 1 and 1.5 GB RAM, but some require 2.0 GB

3. Simulation runs can last up to two weeks for numerous species with complex behaviours

Technical Information:

Landscape Modelling:

1) Mapping• All available electronic data sources are

utilised, together with aerial photos and ground truthing

• Data is initially collected together in a GIS where the structure and habitat information can be combined.

• The GIS exports a raster map together with a polygon reference. The raster map is then used to represent the landscape in the computer – a technique called ’fly-weight’ is used to maximise computation efficiency.

1) Mapping

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5 6 7 8

One copy of the information for each polygon

Fly-weight (Gamma et al, 1994)

Uses sharing to support large numbers of objects efficiently

9 x 9 x 10 = 810 pieces of info.

12 x 10 = 120 pieces of info.

If each polygon is described using 10 pieces of information

T h ir d T h r eadE v en t 1

T h ir d T h r eadE v en t 2

T h ir d T h r eadE v en t 3

M ain T h r eadE v en t 1

M ain T h r eadE v en t 2

M ain T h r eadE v en t 3

M ain T h r eadE v en t 4

S ec o n d T h r eadE v en t 1

S ec o n d T h r eadE v en t 2

S ec o n d T h r eadE v en t 3

Q u eu e u pn ex t ev en t

C o n d it io n a lly q u eu eu p n ex t ev en t

Q u eu e u p n ex tev en t d ep en d en t u p o nth is ev en t o c c u r in g

F o u r th T h r eadE v en t 1

F o u r th T h r eadE v en t 2

Farm Management Event Threads

Mod ify vege ta tionb iomass asapp rop r ia te

Q ueue upthe next

even t

R eco rd even ttype fo r th is

po lygon

Are anyp re -requ is ite

even ts comp le te?

Are wea the rcond itionssu itab le?

Even tqueued

T rigge r Even t

Is the even tdue?

D e te rm in e if th ee ve n t w ill o ccu r

b a s e d o np ro b a b il itie s (if a n y)

Y

Y

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W ait O ne D ay

Is the even tO ve rdue o rtoo La te?

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Farm Event Flow Diagram

Female Skylark Behavioural Diagram

Arrive In Sim. Area

Emigrating

Temp. Leave Area

Initiation

Follow MateStopping BreedingCare for Young

Finding TerritoryFlocking

Floating

Die

Building Up resources

Immigration

New Brood

Attract Mate

Prepare For Breeding

Give Up Territory

Make NestLay EggsIncubating

Egg Hatch

Adult Foraging

Assess home range for insect resources

Calculate food accessibility for each

habitat

Use time to feed

• Calculate the area of each habitat polygon in home range

• Interrogate the polygon for the insect biomass

• Determine the total available resource by multiplication• Using a matrix based on the weather, vegetation height and biomass, calculate the feeding hindrance factor for each habitat polygon• Based on the resource present an accessibility allocate the feeding time available to maximise the insect resource collected, or calculate the time required to obtain a target amount of resource

Egg DevelopmentDevelopment is a function of temperature experienced by the eggs. This in turn is related to the environmental temperature, and the time the female spends incubating.• Get the time required for the female to feed herself (get enough food to maintain her EM .• Assume that the time is spent evenly through the day so that 20% of the day feeding is assumed to be 20% of each hour therefore 12 mins off the nest in each hour.• The cooling effect can then be calculated using the ambient temperature and a cooling rate for skylark sized eggs.• It is assumed that the cooling rate and warming rate are identical, so the time spent cooling is the time spent to raise the egg to the females body temperature.• The day degrees experienced by the egg are therefore twice the time spent off the nest multiplied by the mean cooling/warming temperature, plus the rest of time at incubating temperature.• Egg mortality can result if the female spends too long from the nest. data sources: Kendeigh et al., Avian Energetics &

O’Conner 1985 The growth and development of birds

Nestling Growth

Growth is given by:

(Insects Ingested * Insect Assimilation Rate * Conversion EfficiencyAGE) - EM

Data source Pinowski & Kendeigh Granivorous Birds in Ecosystems

Each day each parent feed insects to the young after they have obtained enough to cover their own EM. They are fed preferentially based on the size of the young, if two or more are equal sized then food allocation is random.

Putting this together with field data:

• There is one main factor that we have to base our energetics on. This is the extraction efficiency of the skylarks.

• The problem is this cannot be measured.

• However, by treating it as a fitting parameter it is possible to vary this figure and evaluate the result.

• By altering extraction efficiency it was possible to iterate to a value which resulted in simulated hatching and development rates being equal to those observed in the field:

Skylark Hatching Day - Observed and Predicted

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Observed and Predicted Nest Leaving Day

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