R.W. Sutherst et al. Agriculture, Ecosystems and Environment 82 2000 303–319 311
can then be used on a geographical scale that is relevant to policy formulation. Luo et al. 1995
used a model of rice blast within a geographical information system GIS to investigate regional
risks under climate change. Basher et al. 1998 illustrate the use of a model of cattle tick popula-
tions and yield losses linked with a GIS to estimate impacts of global change in Australia.
DYMEX is a generic and modular modelling pack- age Maywald et al., 1997, 1999 that addresses the
need to build population models rapidly without the need for a computer programmer. Modules are linked
to provide descriptions of the target system, such as crop, pest and natural enemy, in order to facilitate
the analysis of the system’s behaviour under differ- ent scenarios. DYMEX provides a user-friendly tool
for biologists to build and use mechanistic models of species’ populations and management options. It
is an MS-Windows program, which allows modules to be assembled interactively using icons and dialogs
and it has in-built data formatting, graphics and table generating facilities Maywald et al., 1997, 1999. It
was made possible by the advent of object-oriented programming languages. Models consist of a group
of linked ‘modules’ which are either built using the ‘Builder’ or introduced into the model from a library
of modules that is provided with DYMEX. The com- pleted model is run using a ‘Simulator’, which cur-
rently allows the user to conduct sensitivity analyses and optimisations of timing or number of interven-
tions for example, with associated costings. DYMEX currently lacks the desirable CLIMEX geographical
platform but that is under development at the time of publication.
An illustration of a simplified, but multi-component DYMEX model of a rice-brown plant hopper —
rice-field rat system is shown in Fig. 5. It was pro- duced during a global change training workshop in
Indonesia Sutherst et al., 1999. GMD files are gen- erated by the DYMEX ‘Builder’ and read by the
‘Simulator’. An extract of a DYMEX ‘generic model description’ GMD file illustrating the hidden syn-
tax that describes a function in the rice-field rat mortality module is shown in Table 1. The ‘Builder’
also creates a self-documenting model description in text format, as shown for the same module in Table 2.
A view of the DYMEX simulator dialogue box used to adjust parameter values is shown in Fig. 5.
Table 1 An extract from the RatHopper ‘GMD’ file describing a mortality
function in the rice-field rat’s lifecycle, illustrating the hidden DYMEX syntax used internally to describe models
“Begin Process” “Number” . . .
. . . . . .
“Begin Action”
“Begin Comment” A constant background mortality rate increases in a
quadratic manner above a threshold stress level “End Comment”
“Variable” “Stress” “Function Type” “Quadratic above Threshold”
“Begin Parameter” “Name” “Background Mortality” “”
“Value” “0.0” “0.01” “0.001” “Begin Comment”
Daily “background” mortality rate in mature rats from predators, disease, etc., in the absence of stress due to
food shortage “End Comment”
“End Parameter” “Begin Parameter”
“Name” “Stress Mortality: Threshold” “Value” “0.0” “0.4” “0.25”
“Begin Comment” The value of stress at which mortality of the mature
rats starts to increase above the background level “End Comment”
“End Parameter” “Begin Parameter”
“Name” “Stress Mortality: Multiplier” “Value” “0.1” “0.3” “0.2”
“Begin Comment” The coefficient that determines how rapidly mortality
increases with increasing stress above the stress threshold
“End Comment” “End Parameter”
“End Action” . . .
. . . . . .
“ End Process
”
3. A case study
In Australia DYMEX Modelling workshops have already been conducted by interest groups working on
Queensland fruit fly Bactrocera tryoni, light brown apple moth Epiphyas postvittana, rubbervine Cry-
tostegia grandiflora, bitou bush Chrysanthemoides
312 R.W. Sutherst et al. Agriculture, Ecosystems and Environment 82 2000 303–319
Fig. 5. Hierarchy of dialogue boxes in DYMEX illustrating how model parameter values are changed.
monilifera, wheat stripe rust Puccinia striiformis and diamond-back moth Plutella xylostella. Mod-
elling workshops have also been run on livestock ticks in Africa http:www.ento.csiro.auresearch
pestmgmttickstick.htm, and rice pests in southeast Asia Sutherst et al., 1999. Some outcomes from the
rice-pest workshop are summarized below to illustrate the benefits and constraints of the process.
R.W. Sutherst et al. Agriculture, Ecosystems and Environment 82 2000 303–319 313
Table 2 An extract from the RatHopper text-based model description describing the mortality function in the rice-field rat’s lifecycle that was
illustrated in this table to demonstrate the ability of DYMEX to self-document models Process mortality effects
Combination rule: Complement r1:
Driving variable: Bait Mortality Function shape: Direct
Mortality due to bait use is directly related to the bait effect variable, “Bait” r2:
Driving variable: Stress Function shape: Quadratic above
A constant background mortality rate increases in a quadratic manner above a threshold stress Parameter: Background
Min: 0 Max: 0.01 Default: 0.001 Daily “background” mortality rate in mature rats from predators, disease, etc., in the absence of stress due to food
Parameter: Stress Mortality Min: 0 Max: 0.4 Default: 0.25
The value of stress at which mortality of the mature rats starts to increase above the background Parameter: Stress Mortality
Min: 0.1 Max: 0.3 Default: 0.2 The coefficient that determines how rapidly mortality increases with increasing stress above the stress
r3: Driving variable: Age
Function shape: Quadratic above The mortality rate that is due to the age of the rat increases in a quadratic manner above a threshold
Parameter: Not Min: 0 Max: 0 Default: 0
This parameter of the “Quadratic above Threshold” function is not used for this relationship and is set to zero Parameter: Age Mortality
Min: 100 Max: 150 Default: 120 The age of a mature rat in days at which mortality due to age starts to occur
Parameter: Age Mortality Min: 0 Max: 0.00001 Default: 0.000001
A coefficient that determines how rapidly mortality of mature rats increases with increasing age above the stress ld.
3.1. Integrated impact assessment of rice pests under global change
The first international workshop on modelling global change impacts on pests was a training work-
shop held in Bogor, Indonesia in September 1998, under the auspices of the Biotrop-GCTEImpacts
Centre for southeast Asia IC-SEA Sutherst et al., 1999. The workshop was designed to introduce
participants from seven countries in the region to global change issues and some analytical tools and
approaches that can be used to assess risks and eval- uate adaptation options. Initially, participants used
the CLIMEX model to acquire a regional perspective of the responses of rice pests to climate and climate
change Fig. 3. They then collaborated to develop and apply a simple dynamic ‘RatHopper’ model of
the population dynamics and control of two key pests of rice, the rice-field rat Rattus argentiventer and
the brown plant hopper BPH Nilaparvata lugens in a rice field. Scenarios were then used to explore
likely future directions of IPM in the region, given a number of global change issues.
The RatHopper model simulates rice yield for an area of 1 ha, and the effects of the rice-field rat and
BPH on the rice in the presence of a coccinelid preda-
314 R.W. Sutherst et al. Agriculture, Ecosystems and Environment 82 2000 303–319
tor on BPH. The grossly simplified representation of rice plants acts as a food source for the pests and pro-
vides a means with which to estimate pest effects on yield. The rice dynamics were described using two
life stages: seedlings from planting to flowering and mature plants from flowering to harvest to enable
the different types of pest damage to be estimated. The user selects the planting date, temperature is read
from a data file and green leaf biomass determines the growth rate on the assumption that water is not lim-
iting. Pannicles are produced when the rice reaches a set green leaf biomass, or after a set number of days,
whichever occurs first. Feeding of the BPH reduces the growth rate, green leaf biomass and hence pannicle
weight. Feeding by the rats reduces the pannicle size and hence yield. The rice is harvested a fixed number
of days after seed set. Yield was calculated from the total pannicle mass present at harvesting.
The rat lifecycle was modelled with four life-stages — pups dependent on the mother, immatures inde-
pendent juveniles, mature rats and pregnant females. The growth and mortality of rats were driven primarily
by the availability of food. Scarcity of food increased mortality and inhibited breeding. The available food
consisted of rice seeds and green matter as well as a user-adjustable constant “background” food source.
The rats’ food supply included rice green matter, but only the removal of seeds by rats was actually mod-
elled as damage in the rice crop due to the lack of time in the workshop. The main control measure that was
described in the model was baiting.
The BPH was modelled with four life-stages: eggs, nymphs, and both short- brachypterous and
long-winged macropterous
reproductive adults.
Their development was determined by temperature. When sufficient rice green leaf biomass was avail-
able, the nympal bugs developed into short-winged females. Otherwise, long-winged females were pro-
duced. Short-winged forms are sessile and stayed within the rice crop, while the long-winged forms
migrated out of the crop. Emigration was modelled by using a mortality function. A predator caused
mortality of all stages of the BPH. Control measures available within the model included either chemical
pesticides or inundative releases of predators.
The predator was assumed to be a generalist such as a coccinelid beetle, with predacious larval and adult
stages. It had a considerably longer generation time than that of the BPH. It was assumed that adequate
alternative prey was present at all times when no BPH populations are present. The predator was susceptible
to insecticide treatments aimed at control of BPH.
The required minimum and maximum temperatures and rainfall were obtained from either a DYMEX
“MetManager” module or from a data file. An example of an analysis of the response of the
modelled pests to climate change and of one sug- gested policy response an extra crop per year that was
intended to increase yields is given in Table 3. As shown above, the simulations indicated that with
an increase in temperature of 2
◦
C, which is within the range of most global climate models for the next
century, the rice yield would increase in Jakarta no estimate of grain quality was included. However,
the numbers of BPH would multiply 2.5-fold in the higher temperatures, while rat numbers would de-
crease due to the longer fallow that resulted from the accelerated development of the rice crop. Note that
in this version of the model, no account was taken of adverse effects of high temperatures on the rats, as
inferred from the analysis of the geographical distri- bution using CLIMEX Fig. 3c or on the fertility of
rice plants. The analysis allowed the participants to appreciate the feedback effects on populations from
different components of the system without any pre- tence that the model was sufficiently realistic to be
used as a decision support system.
One proposed policy to increase rice production was reported to be the planting of an extra crop each year.
The result of the model simulations, using existing climatic conditions, was a 4-fold increase in rat num-
bers as the duration of the period when rats had to rely on other sources of food shortened greatly. The
numbers of BPH also increased by 40. Hence the risks were increased and the annual yield of rice, in
the absence of control of the pests, was unchanged be- cause the pests removed almost the entire increment
in yield from the extra crop. These risks were greatly increased under the higher temperatures, which accel-
erated the development of BPH populations without a sufficiently rapid increase in the growth of the rice to
compensate. The potential risks of such a response to a food shortage emphasises the need to conduct risk
assessments of food production under global change.
The established practice of using pest control methods applied when pest numbers exceed a given
R.W. Sutherst et al. Agriculture, Ecosystems and Environment 82 2000 303–319 315
Table 3 Vulnerability analysis of rice in Jakarta to climate change using the RatHopper model
Scenario Rice kg
Rats No. BPH 1000’s
Predators 100’s Bait cost
Insecticide Profit
Current climate 2 crops no pests
731 1095 11
42 19
731 3 crops no pests
740 1658 45
60 19
740 Baits 10 rats
1144 6
60 19
12 1132
Baits 10 rats 1611
5 1
0.4 8
175 1427
Spray 1000 BPH baits 10 rats×10 predators
1521 5
12 22
12 1509
Scenario +2
◦
C 2 crops no pests
505 1229 107
20 505
3 crops no pests 354 1860
36 145
20 354
Baits 10 rats 756
6 145
20 8
748 Baits 10 rats
1809 5
0.5 0.5
12 165
1632 Spray 1000 BPH baits 10
rats×10 predators 1353
6 64
23 16
1337
economic threshold proved to be a relatively robust and effective adaptation to climate change. However,
this relies on an appreciation of the correct cause of the changes in pest numbers and seasonal phenology,
otherwise changes in pest pressure could not be effec- tively interpreted. It also relies on the use of chemical
pesticides, some of which may not be sustainable for environmental reasons or due to the emergence of
resistant strains.
The Internet has the potential to communicate infor- mation on the effects of global change on pests to an
international audience and also to maintain collabora- tive links between members of an interest group. The
report from the rice-pest workshop Sutherst et al., 1999 is available from the IC-SEA Impacts Cen-
tre or the authors, and a summary can be viewed at http:www.icsea.or.idTrsep98.htm. It illustrates how
risks and management strategies can be designed and delivered in socio-economic terms for policymakers,
with communication through the Internet, targeting national laboratories in developing countries where
wider access is unavailable.
The group of participants now constitute a regional ‘interest group’ with the common aim of developing
and applying models to describe the population dy- namics and management of the rice pests. It provides
one possible model for a global pest network on global change research. The group collaborated by pooling
data and expertise to build a model that belongs to the group, while the DYMEX support team provides
archiving and documentation support to manage the software ‘version control’. However, a major con-
straint with the creation of networks is the question of funding the ongoing collaboration, even though it is
perceived by participants to be very cost-effective by adding great value to their current activities. This is
despite the recognition by agencies such as the World Bank that networks are potentially powerful tools in
national development http:www.vita.orgtechnet.
4. DYMEX modelling networks and workshops
