APPENDIX A. SHORT DESCRIPTION OF THE ALMASS MODELS FOR
BEMBIDION LAMPROS AND ERIGONE ATRA
The ALMaSS Bembidon lampros model
One of 2 models used in this study, the Bembidion lampros [1] model simulates the species’ behaviour and ecology as individual eggs, larvae, pupae, and adult females
(males are ignored at all stages assuming a sex ratio of 1:1). It is easily capable of handling up to 30 million concurrent agents (beetles) on standard PC architecture. Full documentation for this model can be found at http://www2.dmu.dk/ALMaSS/ODdox/Bembidion/index.html but again a short description is useful here.
B. lampros , is a Palearctic species that has been studied intensively as one of the most common beetles in European agroecosystems, and therefore has a well-described biology and natural history. The parameters are based on field and laboratory data on B. lampros from the literature. When necessary data were not available, parameters were estimated based on published data on other ground beetles with similar life histories.
Model Procedures. Day-degree calculations are driven by actual weather data stored as daily records of mean temperature, mean wind speed and total precipitation.
Reproduction. Only beetles present in preferred habitat (typically open agricultural fields) in spring may initiate reproduction. The rate of egg production f
(egg)
is temperature dependent and was calculated as:
(eqn.1) f
(egg)
(T) = (T - T
0
) * C
T = temperature, T
0
= lower threshold for egg production, C = egg production slope

Development. Temperature dependent development was calculated daily for each developmental stage: egg, 1 st , 2 nd , 3 rd instar and pupa with a transition to the next stage when the ∑f
(dev)
for that stage was greater or equal to 1.0. Development f
(dev) was calculated as:
(eq.2) f
(dev)
(T) = T – T
0
/ L
T = temperature, T
0
= lower threshold for development, L = duration of stage in day-degrees.
Movement. Movement patterns were based on behavioural decisions and preference for habitat. Movement was determined by four parameters: 1) a directional vector that indicates the preferred direction, 2) a weight indicating the strength of the bias towards the directional vector, 3) a maximum allowed distance per time step and 4) the probability of a beetle accepting a sub-optimal habitat, e.g. to cross a road. For each possible direction, the habitat was assessed and the beetle moved to one of the suitable locations. If more than one location of similar quality was available, movement was determined by random choice.
Mortality. Beetles could die from external events, due to density dependent factors
(i.e. competition) or because they reached the end of their life span. External factors causing mortality were either farm operations, mainly soil cultivation operations, or temperature dependent winter mortality. The different cultivation methods were assigned different probabilities of beetle mortality [2], which were implemented when the farm operation in question occurred.
Density dependence was introduced by limiting the number of beetles that could be present in the area surrounding any beetle. Both the size of the area tested and the number

of beetles allowed is a parameter in the system and can be used to control overall population size. Excess beetles were removed from the simulation. Tests indicated that at the densities usually used for beetles altering these values has no impact on the results
[1]. Background mortality was implemented for the egg stage and the three larval instars.
Beetles were assessed for density-dependent and background mortality once each simulation day. In order to avoid bias due to concurrency problems, the order of assessment of beetles was randomised once each time step.
Habitat. Habitat preference depended on life-history requirements. In autumn, beetles migrated to vegetated field boundaries for hibernation. Autumn migration was determined by a probability distribution starting on 1 st
October which is consistent with empirical data, although it is not known what triggers this behaviour. In spring, beetles exhibited directional movement into agricultural fields to reproduce. Spring migration was temperature dependent.
The ALMaSS Spider model
The spider model simulates the species Erigone atra and is fully described by
Thorbek & Topping [3]. The species is well-studied and widely distributed in disturbed and patchy habitats, and is one of the most abundant and ubiquitous agrobiont linyphiids in Northern Europe . Breeding occurs from Spring to Autumn and depending on latitude may result in one to three overlapping generations per year. Eggs are laid in eggsacs which hatch to produce spiderlings which disperse and undergo development through four juvenile instars until reaching the adult stage. Overwintering is a late stage juvenile or adult.

Model Procedures. The spider model considers eggs, juvenile, and adult female spiders (males are assumed not to be limiting). The egg stage has three behavioural states; die after which it is removed from the simulation, develop or hatch and become juveniles in case development is completed. The juvenile stage has four behavioural states; develop, disperse, mature or die. If the habitat of their current location is appropriate, it will develop with a rate according to local prey availability and temperature. Inappropriate habitat or too little prey results in dispersal which entails a mortality risk. Dispersal only occurs under suitable weather conditions. Once the development is completed, they will mature into a female. The females’ states are similar to the juvenile but instead of 'developing' they produce eggsacs. All three stages are affected by crop management activities, which all imply a mortality probability.
Reproduction. Eggsac production rate depends on both temperature and food availability. Since data was not available to parameterise a biophysical model for eggsac production a day-degree model was used. Eggsac production rate was related to food availability in the same way as juvenile development. E. atra females have an upper limit to the number of eggs they can produce during their life time (reproductive potential), and so are moved from the model after the last eggsac is produced.
Reproductive rate and juvenile developmental rate are known to depend on food availability. The spider model uses an underlying ALMaSS landscape property providing a general estimate of insect biomass based on vegetation type and structure. Since detailed data was lacking this was classified into four categories, indicating no food, low, intermediate and high food availability. In the simulations individual crops differ, but as a

general rule recently ploughed fields have no food, a newly harvested field has low food levels, and high food levels are reached when the crop is approaching maximum height.
Development. The biophysical model was used to describe the relationship between eggsac developmental rate and temperature, and was parameterised for Erigone atra based on three years field data of eggsac development and temperature [4].
Juvenile development depends on both temperature and food availability. The biophysical model was also used for juveniles but with the addition of a reduction in growth if food is limiting.
Movement. Dispersal is broken into three components: dispersal motivation, dispersal opportunity and dispersal displacement:

Dispersal motivation - E. atra is among the most common ballooners all year round and motivation for ballooning was incorporated based on a daily background probability for initiating ballooning that increases with starvation together with ballooning directly motivated by external conditions such as crop management activities [5], or being in a nonbreeding habitat. Once this second type of ballooning is triggered, the spider will attempt to balloon until it either succeeds in finding suitable habitat or dies.

Dispersal opportunity - Spiders almost exclusively balloon when wind speed is low and when temperature conditions are suitable. These constraints were incorporated into the model to prevent ballooning when weather conditions were not suitable.


Dispersal displacement - A spider ballooning event consists of many short flights, rather than one long flight and spiders balloon from approximately 2 hours after sunrise until 2 hours after zenith. This information was combined with the probability distribution of distances travelled in 1 hour to calculate how far a ballooning spider would move when ballooning.
Mortality. Three types of mortality are incorporated in the model: i) Mortality caused by crop management e.g. ploughing, pesticides, harvest. This is incorporated as a probability of death if a spider is exposed to the management. The probability is management specific. ii) Density dependent mortality – When the spiders move they check an area for presence of other spiders. Females only check for presence of other females, and juveniles only check for presence of other juveniles. If another individual is already present in the area checked, the newcomer will die. Spider hatchlings often spend some time close together in their mother’s web, but as they grow older their tolerance to conspecifics decrease. This is modelled by increasing the area the spiders check as they grow older. In addition, when eggs in an eggsac hatch, the juveniles move away from it if there are empty grid locations they can occupy within a ‘hatching range’. After 7 days a newly hatched juvenile will make a density check which may result in mortality, subsequently these checks will only be made after each dispersal event. iii) Dispersal mortality - Dispersal induces mortality in several ways; spiders can land in unfavourable habitats or die while in the air. Spiders may desiccate during ballooning or be preyed upon. The model assumes that all these sources of mortality will increase with time spent ballooning, thus mortality chance is proportional to distance covered. iv) Age - An adult spider will die once it has used its reproductive potential or after one year. v) Background

mortality - Each day the spider may be killed by other factors (e.g. predation), thus it has a probability of dying. This mortality risk is different for the three life history stages, and was used to create an age structure based on the life-stages resembling that found under field conditions. vi) Extra mortality in natural habitats, this being an additional consequence of sub-optimal habitat choice.
Habitat. Vegetation types were divided into breeding and non-breeding habitat by assigning categories based on Hanggi et al. [6]. E. atra prefers frequently disturbed habitats for breeding. Non-breeding habitats were typically natural habitats such as forests and semi-natural habitats e.g. natural grassland and shrubs. Breeding habitats were typically agricultural fields, pastures and other frequently disturbed habitats, e.g. coastal marshes.
REFERENCES
1. Bilde T, Topping C. 2004. Life history traits interact with landscape composition to influence population dynamics of a terrestrial arthropod: A simulation study. Ecoscience
11:64–73.
2. Thorbek P, Bilde T. 2004. Reduced numbers of generalist arthropod predators after crop management. J Appl Ecol 41:526–538.
3. Thorbek P, Topping CJ. 2005. The influence of landscape diversity and heterogeneity on spatial dynamics of agrobiont linyphiid spiders: An individual-based model.
BioControl (Dordr) 50:1–33.
4. Thorbek P, Sunderland KD, Topping CJ. 2003. Eggsac development rates and phenology of agrobiont linyphiid spiders in relation to temperature. Entomol Exp Appl
109:89–100.

5. Weyman GS, Sunderland KD, Fenlon JS. 1994. The effect of food-deprivation on aeronautic dispersal behavior (ballooning) in Erigone spp. spiders. Entomol Exp Appl
73:121–126.
6. Hanggi A, Stockli E, Newtwig W. 1995. Habitats of Central European Spiders.
Centre suisse de cartographie de la faune, Neuchâtel, Switzerland.

Table S1: Data points plotted as Figure 4 in the main text
Ap DT Recovery p.
50
Time
Rat e
1
2
1
1
Beetle
% Fields Treated Recovery
25 50 75 10
0
0 0.9 1.7 4.8 10.
0
35 8.1 23.
44.
77.
2 0 1
Time
Spider
% Fields Treated
25 50 75 100
0 2.2 5.7 11.2 19.3
16 18.
38.8 58.7 78.6
9
3 1
4
1
2
3
4
1
10
10
10
10
37 12.
29.
54.
86.
0 3 2 4
37 14.
32.
61.
90.
0 6 3 6
2 1.1 2.4 5.4 9.8
36 10.
26.
48.
81.
5 1 6 2
36 14.
35.
63.
91.
8 0 1 7
37 18.
41.
73.
94.
22 23.
45.6 65.2 82.8
3
24 25.
49.0 68.5 84.3
8
0 2.2 5.9 11.2 19.1
26 28.
55.4 76.0 90.0
8
73 41.
70.3 87.1 95.3
6
85 50.
78.9 92.0 97.5

1 30
2 30
3 30
4 30
1 40
2 40
3 40
4 40
1 20
2 20
3 20
4 20
5 8 9 2
2 1.2 1.9 4.6 9.9
37 12.
30.
55.
86.
1 0 1 3
37 19.
44.
80.
95.
0 0 6 5
214 26.
59.
92.
98.
1 0 8 0
0 0.9 2.2 4.8 9.6
37 14.
34.
64.
90.
3 4 1 5
211 29.
65.
93.
98.
7 1 5 4
211 44.
86.
97.
99.
0 8 5 1
2 0.9 2.1 5.2 9.7
132 18.
44.
79.
94.
2 0 8 2
212 41.
83.
96.
98.
5 0 9 5
214 54.
93.
98.
99.
7 0 5 0
2
0 2.2 5.8 11.1 19.3
74 39.
68.5 86.6 95.3
0
93 57.
85.9 96.7 100.
8 0
101 68.
93.1 100.
100.
3 0 0
0 2.1 5.6 11.1 19.2
84 48.
78.4 93.0 98.9
1
102 69.
93.9 100.
100.
0 0 0
112 78.
98.9 100.
100.
1 0 0
0 2.3 5.7 10.9 19.2
96 55.
85.1 97.5 100.
6 0
111 75.
97.9 100.
100.
0 0 0
122 83.
100.
100.
100.
9 0 0 0

App. Rate = pesticide application rate in multiples of the 80% lethal toxicity threshold.
Recovery time is in days from application for the 2Ha plot experiment.